# Delight AI
# Pages
## Home Delight AI
Source: https://delight.ai/
## Your enterprise revenue engine
An AI agent that grows and protects revenue across the customer lifecycle by driving purchases and upsells and recovering stalled transactions.
[View demo](https://delight.ai/demo-request)
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- 10x
as likely to buy, book, or sign up for customers who engage with Delight vs. who don’t
- 20%+
higher average order value across live retail deployments
## Converting customer experience into revenue
[](https://delight.ai/customers/bjs)
### Bigger baskets — Retail example
**Jennie**
Which coffee is smooth, not bitter?
[Customer story: BJs](https://delight.ai/customers/bjs)
### Higher booking value — Travel example
**Aaliyah**
Can I get more legroom?
[Customer story: Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways)
### Account expansion — Software example
**Haruto**
How do I unlock this feature?
[Customer story: Mixpanel](https://delight.ai/customers/mixpanel)
## Built to grow revenue from every conversation
1. ### Understand the customer
- [Explore Agent Memory Platform](https://delight.ai/agent-memory-platform-amp)
- Identity · Preferences · Intent
2. ### Reach out at the right moment
- [Explore Omnipresence](https://delight.ai/omnipresent)
- Engage where customers are: chat, SMS, email, voice, and messaging apps
3. ### Take the right action to grow revenue
- [Explore AI agent](https://delight.ai/ai-agent)
- Actionbook: Book · Upgrade · Renew
4. ### See revenue opportunities through to completion
- [Explore Agent Steward](https://delight.ai/autonomous-agent)
- Coordinate Day 1, Follow up Day 3, Complete Day 7
5. ### Follow your business rules
- [Explore Trust OS](https://delight.ai/trust-os)
- Boundaries · Approvals · Oversight · Improvement
## Every industry has a moment that moves revenue
### Retail
Retail — Discover · Convert · Reorder
[Learn more](https://delight.ai/industries/retail)
### Travel
Travel — Book · Upgrade · Rebook
[Learn more](https://delight.ai/industries/travel-hospitality)
### Software
Software — Activate · Adopt · Expand
### Healthcare
Healthcare — Qualify · Enroll · Refill
[Learn more](https://delight.ai/industries/healthcare)
### Home & field service
Home & field service — Book · Dispatch · Complete
[Learn more](https://delight.ai/industries/field-services)
## Built to protect revenue when service goes wrong
Your biggest orders and bookings can take days and several parties to put right. Delight coordinates everyone involved to resolve the issue and protect the customer relationship.
delight.ai
> My order arrived damaged. I need a replacement and a shipping refund — before Friday.
I’ll take care of everything.
- Pull order details
- Reserve a replacement
- Get refund approved
- Book the delivery
- Send confirmation
### Agent Steward example
One conversation, full resolution
- Order Agent → System: Order #48213 pulled, damage logged
- Stock Agent → Email/Warehouse: Replacement reserved, ships today
- Approval Agent → Desk/CS Lead Approval: $38 shipping refund approved
- Delivery Agent → Call/Carrier: Redelivery · Thursday before 12 PM
## Your customers’ trust is a responsibility we take seriously
### Proven at scale
Built on Sendbird’s decade of experience powering customer conversations. Infrastructure that handles over 7 billion conversations a month, ready for your next stage of growth.
### Built for enterprise standards
Meet the demands of doing business at scale, with security, privacy, and compliance at the foundation.
### AI you can stand behind
Every conversation is a promise to your customer. Give AI more responsibility while keeping your business standards and your customers’ trust intact.
## Find your revenue opportunities
[Talk to an expert](https://delight.ai/contact-sales)
## Common questions about the revenue AI agent
### What is a revenue AI agent?
Delight is the customer-facing revenue AI agent. It remembers who each customer is, understands what they need now, and uses commercial judgment to choose the next best action so more customers convert and stay.
### How is this different from an AI SDR or other revenue AI?
Most revenue AI is built for your team (AI SDRs, copilots, CRM assistants). Delight’s user is your customer — it holds the buying conversation itself and carries it through to a confirmed order.
### Does Delight still handle customer service?
Yes. It resolves support requests across chat, email, and voice, coordinating across systems, teams, and partners until resolved. The same agent that answers service questions can recognize revenue opportunities.
### Which channels does it work across, including voice?
Chat, SMS, WhatsApp, email, and voice. It can pick up calls, handle them, and escalate to humans when appropriate, keeping one thread across channels so customers don’t repeat themselves.
### Do we need to rebuild our product data or systems first?
No. The agent works with existing catalogs, records, and customer history and connects to Salesforce, Zendesk, ServiceNow, Intercom, Freshworks, Sprinklr, NICE CXone, Confluence, Notion, Google Drive, and commerce/payment APIs.
### Which industries is it built for?
Retail, travel and hospitality, software, and home & field services — anywhere customer conversations affect purchases, bookings, or retention.
## Builder
Source: https://delight.ai/builder
## Build AI agents that deliver
Reduce human agent workload and boost case resolution with omnichannel AI.
[Contact sales](https://delight.ai/contact-sales)
## Delight.ai AI agent hub: Your AI HQ.
Take control of your AI operations. Build, test, and evaluate your AI agents—all from one powerful dashboard.
**Your business, your AI agents, built to perfection.**
### Key capabilities
- Persona
- Knowledge
- Actionbooks
- Boundaries
### 1 Persona
#### Shape your AI agent persona
Design an AI agent with a personality that reflects your brand values, voice, and style.
### 2 Knowledge
#### Feed your AI agent the knowledge it needs
Train your AI agent with the same content and guidelines as human agents for consistent and accurate responses.
### 3 Actionbooks
#### No code, no chart, just words
No more confusing flow charts or coding required. Simply write clear, human-readable instructions to guide your AI agent’s actions.
### 4 Boundaries
#### Set boundaries, ensure AI brilliance
Deploy AI agents with built-in safeguards to prevent harmful or biased outputs, and ensure that your AI agent operators operate ethically and responsibly.
## Catch AI mistakes before they reach your customers.
Test, refine, and perfect your agents in a risk-free sandbox. Launch with confidence, knowing your customers and brand are safe.
## AI transparency unlocked, control guaranteed.
See every interaction in detail. Track CSAT scores, analyze performance, and review raw conversations for complete transparency and control.
## AI recommendations for smarter AI agents.
Harness data-driven suggestions to refine and optimize your AI agents for peak performance.
## AI agent resources
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##### How For You Conversations Make Every Interaction Feel Personal
Discover how brands can deliver experiences that are consistent, intelligent, and deeply human with For You Conversations from delight.ai.
https://delight.ai/blog/product/for-you-conversations
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##### Agentic commerce: How to prepare for the future of retail
Agentic commerce is a new form of digital shopping where AI agents act on behalf of consumers to discover, compare, and purchase products.
https://delight.ai/blog/industry/agentic-commerce
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##### New era of voice AI agents: Faster, smarter, and built with human-like empathy
Meet delight.ai’s voice AI agent, built to transform every phone interaction with faster responses, clearer conversations, and omnichannel support.
https://delight.ai/blog/product/introducing-voice-ai-agent
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##### The future of customer connection starts with Omnipresence
Built by delight.ai and backed by Sendbird’s decade of communication expertise, Omnipresence introduces a new standard for connection—always on, always aware, always personal.
https://delight.ai/blog/product/connection-starts-with-omnipresence
## AI agent builder FAQs
**What is an AI agent builder?**
Delight.ai offers a centralized space where you can build, test, and evaluate your [AI agent for customer service](/ai-agent). It enables you to launch, iterate, and scale a [team of specialized AI agents](https://sendbird.com/ai-agent) that stay on-brand and continuously improve through a structured Build‑Test‑Evaluate cycle.
**How do you build an AI agent with delight.ai?**
Delight.ai’s builder is designed for speed and simplicity. You can [build specialized AI agents](https://sendbird.com/blog/how-to-build-an-ai-agent) by writing plain‑language instructions, connecting existing [knowledge sources](https://sendbird.com/ai-agent/product-release/salesforce-knowledge-source), and adding safeguards. No coding required; update agents easily as things change.
**Can I train AI agents with my company’s knowledge base and policies?**
Yes. Delight.ai AI agents can be trained using your existing content: website text, snippets, files, and third‑party tools like [Salesforce](https://sendbird.com/ai-agent/product-release/salesforce-knowledge-source), [Confluence](https://sendbird.com/ai-agent/product-release/confluence-now-supported-as-a-knowledge-source), Zendesk, Notion, and Google Drive. Connect your sources and the AI will reference them for accurate, on‑brand responses.
**Is Delight.ai AI for customer service secure and compliant with data privacy regulations?**
Yes. Delight.ai is enterprise‑grade and compliant with major data privacy regulations including GDPR, CCPA, and HIPAA. All conversations are encrypted in transit and at rest, and data handling follows strict security protocols.
**How can I safeguard my AI agent from hallucinations?**
Delight.ai provides multiple safeguards to minimize hallucinations and ensure responsible AI behavior. Define banned words, phrases, and PII rules to control outputs and enforce standards like GDPR, HIPAA, and SOC2. Use the **delight.ai Test** environment to simulate large‑scale conversations and catch inaccurate or off‑brand responses before going live.
**Can I create workflows in the delight.ai builder?**
Yes. With Delight’s [Actionbooks](https://sendbird.com/ai-agent/product-release/version-control-for-manual-profile), define how AI agents respond, act, and escalate—without complex decision trees or code.
**What are AI agent actionbooks?**
[Actionbooks](https://sendbird.com/ai-agent/product-release/version-control-for-manual-profile) are playbooks that define how your AI agents take action. They replace rigid decision trees with flexible, modular instructions in plain language. No coding required. Actionbooks support [version control](https://sendbird.com/ai-agent/product-release/version-control-for-manual-profile) so you can track changes and roll back when needed.
**Which channels can I deploy my delight.ai agents on?**
Deploy across native mobile, live chat, [SMS](/omnipresent), [email](https://sendbird.com/ai-agent/product-release/sendbird-ai-agent-now-available-on-email), and [WhatsApp](https://sendbird.com/ai-agent/product-release/sendbird-ai-agent-on-whatsapp) to meet customers where they are.
**How can I monitor and improve my delight.ai AI agents?**
With **Evaluate**, get a comprehensive view of performance, key metrics, trends, and AI‑generated insights. It highlights successes, gaps, and automatically suggests improvements.
**Can I create an AI agent team?**
Yes. Build a team of specialized AI agents, each with distinct roles, knowledge bases, guardrails, and performance tracking for role‑specific optimization across the customer journey.
https://sendbird.com/ai-agent/product-release/sendbird-ai-agent-team-specialized-ai-agents-built-for-every-role
#### Ready to unleash the power of AI agents?
[Contact sales](https://delight.ai/contact-sales)
## Omnipresent
Source: https://delight.ai/omnipresent
## Omnipresence: Reach customers the moment they’re ready to buy
[View demo](https://delight.ai/demo-request)
## One agent on every channel, so no purchase starts over
Voice, chat, SMS, WhatsApp, email, and in-app all run on the same AI agent. It carries each customer’s preferences, questions, and cart from one channel to the next, so a purchase started in one place can finish in another.
## Reach out when there’s a reason
Back-in-stock items, renewal dates, and abandoned carts start the conversation before the customer has to ask.
Preferences, budget, and past orders follow the customer to whichever channel they reply on.
A question asked in chat can finish by SMS or on a call, with the cart and the conversation intact.
## Three ways to reach customers when they’re ready to buy
### Reach out when there’s a reason to buy
Back-in-stock alerts, renewal reminders, and saved carts go out by chat, SMS, or WhatsApp. Your team sets the triggers, and the agent handles the conversation.
### Call when the moment matters
Delight Voice calls with full context when a renewal is due or an order is at risk, so the customer can decide on the call.
### Tell customers what changed, fast
Push a notice about a delay, an outage, or a policy change in real time, with no code deploy, before it turns into a canceled order.
## Protect the sale when an order goes wrong
Agent Steward takes over when an order runs into trouble. It coordinates your systems and partners across sessions, and follows up on whichever channel the customer uses until the order is back on track.
-  Works across your systems — Checks orders, inventory, and carriers in parallel to find a fix the customer will accept.
-  Keeps going until it’s done — Waits on carriers and partners, then follows up by SMS or email when there’s news.
-  Keeps the customer buying — Offers a replacement, a new date, or a credit within your rules, so the order isn’t lost.
## Memory, personalization, and timing, working as one agent
Delight remembers each customer, decides what to offer them, and reaches out on the channel they use, so more conversations end in a purchase.
### Agent Memory Platform
#### Remembers each customer
History, preferences, and intent from every conversation, kept in one memory.
- History
- Preferences
- Past orders
#### Picks the right offer
Recommends what fits each customer, from the first message.
- Size 10
- Under $150
- Back in stock
#### Omnipresence
Reaches out at the right moment on the customer's preferred channel and keeps context if they switch.
#### Delight AI agent
Turns the reply into a sale — Picks up with full context and helps the customer buy, book, upgrade, or renew.
## Proactive offers customers act on
Examples of the agent reaching out first, then helping the customer buy, upgrade, or finish what they started.
- WhatsApp — "Your trail runners are back in size 10. Want me to hold a pair?" — Order placed. Retail · returning customer — Bring back interested shoppers.
- SMS — "Your flight to Oslo is Friday. Extra-legroom seats are open in your row." — Seat upgraded. Travel · before departure — Offer the upgrade before the trip.
- SMS — "Still need to finish your application? Your details are saved." — Application resumed. Auto loan application · step 4 of 5 — Recover the stalled transaction.
- Email — "Your plan renews in 21 days. Want to add the 5 seats your team asked about?" — Plan renewed. Software · renewal — Grow the account at renewal.
## Delight Voice knows who’s calling and why
[Explore Delight Voice](https://delight.ai/voice)
-  Natural, real-time conversation — Handles interruptions and accents and responds in real time, so a call about an order can turn into a booking or an upgrade.
-  Context carries across channels — A chat about a product can become a call, and the order details carry into the SMS or email follow-up.
-  Built for real calls — Natural pacing, background noise isolation, and speech recognition built for names and addresses, so calls sound like a person.
## How Omnipresence connects to the rest of the Delight platform
### Agent Memory Platform (AMP)
Remember every customer and turn intent into revenue
[Explore](https://delight.ai/agent-memory-platform-amp)
### For You Conversations
Personalized recommendations that drive sales
[Explore](https://delight.ai/for-you-conversations-fyc)
### Trust OS
Control what your revenue AI agent can do
[Explore](https://delight.ai/trust-os)
## Revenue AI agent resources
[ The future of customer connection starts with Omnipresence](https://delight.ai/blog/product/connection-starts-with-omnipresence)
[ Introducing AMP: The Agent Memory Platform](https://delight.ai/blog/product/introducing-agent-memory-platform)
[ How For You Conversations Make Every Interaction Feel Personal](https://delight.ai/blog/product/for-you-conversations-fyc)
[ Introducing Trust OS: The foundation for responsible AI agents](https://delight.ai/blog/product/introducing-trust-os-for-ai-agents)
## Omnipresence FAQs
### What is Omnipresence?
Omnipresence is how Delight’s AI agent works across every channel a customer uses: voice, in-app chat, web chat, SMS, email, and WhatsApp. It is one agent, not a separate bot per channel, so it keeps the customer’s context as they move between channels and can start conversations as well as answer them. See how it fits with the rest of the [AI agent platform](https://delight.ai/ai-agent).
### How does Omnipresence help grow revenue?
It reaches customers when there is a reason to buy, such as an item back in stock, a renewal coming due, or a cart left behind, on the channel each customer prefers. When the customer replies, the agent already knows their preferences and history and can help them complete the purchase. [For You Conversations](https://delight.ai/for-you-conversations-fyc) personalizes what the agent recommends.
### How does the agent keep context when a customer switches channels?
The [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) stores each customer’s history, preferences, and open requests in one memory the agent always uses. When a customer moves from chat to SMS to a phone call, the agent picks up where the last conversation ended, and the customer doesn’t have to explain again.
### What channels does Omnipresence support?
In-app chat, web chat, SMS, email, WhatsApp, and [voice](https://delight.ai/voice), both inbound and outbound.
### Can the agent reach customers proactively?
Yes. Business events such as a restock, a renewal date, a failed payment, or an order delay can trigger outreach by chat, SMS, WhatsApp, email, or a voice call. Your team sets the triggers and the agent runs the conversation. Urgent notices can also go out in real time without a code deploy.
### What happens when an order goes wrong?
[Agent Steward](https://delight.ai/autonomous-agent) takes on cases that need several systems, third parties, or more than one session. It works in the background and follows up with the customer by SMS or email until the order is resolved, and every action is logged. [Trust OS](https://delight.ai/trust-os) sets what the agent is allowed to do.
## Find your revenue opportunities
See where your AI agent can reach customers at the moment they’re ready to buy.
[Talk to an expert](https://delight.ai/contact-sales)
## Voice
Source: https://delight.ai/voice
## AI voice agent built to grow your business
Delight.ai's AI voice agent answers, qualifies, books, and recovers the calls your business depends on. It remembers each caller, recommends what fits, and keeps the next step intact through transfers, dropped calls, and follow-ups.
[Try the live demo](https://delight.ai/voice#voice-demo)
       
### Voice AI proof
Quote 1 of 2
 
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Dr. Cool Services answers every call, even when Vegas hits 115°F.
> Our customers don't want to navigate phone trees or wait on hold. They want to talk to someone who can help. We needed technology that feels like talking to a real person—because that's what our brand represents.
Mark Farley
President, Dr. Cool Services
[Explore](https://delight.ai/customers/dr-cool-services)
- One Bev for shopping, support, and membership at BJ's Wholesale Club.
> Whether a member is browsing for a grill, looking up club hours, or asking about a renewal, they're talking to the same Bev. That's how a real member-team member relationship is supposed to feel.
Scott Vandegrift
VP of Product, BJ's Wholesale Club
[Explore](https://delight.ai/customers/bjs)
### Prefer to watch first?
See Delight.ai's voice agent in action, then try it yourself in the live demo above.
Watch a short demo to see how a call moves from the first question to a booked next step. When you're ready, scroll up, pick an industry, and be delighted with our AI voice agent.
### AI that turns calls into revenue, not just answers
From capturing a missed after-hours call to closing an upgrade mid-call, Delight keeps the moment, context, and next best action intact, so every call grows, recovers, or protects revenue.
 [Retail](https://delight.ai/industries/retail)  [Home services](https://delight.ai/industries/home-services-and-warranty)  [Healthcare](https://delight.ai/industries/healthcare)  [Software & technology](https://delight.ai/industries/technology)
### Why every call carries revenue risk
Missed calls are lost bookings, and AI that gets it wrong costs the brand, not the vendor behind it.
- Revenue left ringing — 47% of calls missed without a dedicated receptionist · Clutch
- AI is already on the line — 71% of US consumers have used AI for service · Delight AI Index 2026
- Satisfaction lags — 57% call their AI service experience positive · Delight AI Index 2026
- The brand takes the blame — 83% hold the brand accountable when AI fails · Delight AI Index 2026
### What problems does an AI voice agent solve?
Recover missed & after-hours calls
A call that rings out at 9 p.m. is a booking for a competitor. Delight answers, books the appointment or qualifies the lead, and calls back with context if a line drops.
Cut cost per call with no new hires
A volume spike shouldn't mean a hiring spree. Delight takes the routine calls day and night, so your staffing budget goes to the calls that bring in revenue.
Make every call feel like a great rep
Robotic phone trees push callers away. Delight remembers each caller and helps them choose before they buy or sort out a delivery or upgrade after.
The same quality answer, every shift
The answer a caller gets shouldn't depend on who picks up. Delight gives every caller the same accurate answer, day or night.
### How delight.ai keeps every call moving
Revenue slips away when a call breaks down. Built voice-first, delight.ai keeps the customer, context, and next action intact.
#### Intelligent voice handoff
When a caller needs a person, Delight keeps them on the line while it briefs the next agent with the transcript, context, and a summary. If nobody is available, it files a case with a callback path.
#### IVR navigation
On phone menus you've mapped in advance, Delight presses the right keys and checks whether a person or voicemail answered before it briefs anyone, so transfers don't stall at "press 1."
#### Universal telephony
Calls run on your own numbers through Twilio, Telnyx, and local carrier partnerships. Delight hands off to your queues by phone number and sends resolved cases to connected systems.
#### Secure caller keypad input
Keypad entries skip speech recognition and can be masked in transcripts and beep-masked in stored audio.
### An AI voice agent built for the whole relationship
A great relationship starts with a great call. Delight.ai keeps every conversation natural, reaches out when it matters, and remembers each customer across every channel.
#### Natural conversation, no awkward pauses
Delight responds without awkward pauses, handles interruptions, and stays clear on noisy lines. Speech recognition is tuned for names and addresses, and voices are tuned to sound natural and on-brand. [Hear it in the live demo](https://delight.ai/voice#voice-demo).
#### Reaches out at the right moment
Reminders, follow-ups, and callbacks are triggered by what's happening with each customer, so Delight reaches out when the call is actually useful. [Learn about For You Conversations](https://delight.ai/for-you-conversations-fyc).
#### Remembers across channels
A customer can start by phone, continue by text, and call back next week. Delight carries one memory the whole way, so nobody has to re-explain. [Learn about Agent Memory Platform](https://delight.ai/agent-memory-platform-amp).
### Accurate because it's tested to be
Built-in testing, tracing, and oversight so accuracy isn't a promise, it's a proof.
###### Observability
Pressure-test your voice AI with tough conversations in controlled environments. Automate continuous testing and trace every decision with full audit logs.
###### Oversight
Review and grade every response. Catch issues as they emerge. Pause AI instantly when needed.
###### Control
Version-controlled guardrails, isolated build environments, and granular permissions that make AI safe to scale.
###### Auditability
See the reasoning, actions, and context behind every AI output.
###### Isolation
Experiment, test, and validate changes without touching production.
###### Testing
Semantic checks, outcome scores, batch runs, and regression tests cover your AI from every angle.
### What to look for in an AI voice agent vendor
The best AI voice agent finishes the job each call came in for. Put every vendor through these questions using your own calls.
- Does it complete the next step?
Delight books appointments, recommends products, qualifies leads, and closes upgrades while the caller is on the line.
[Explore](https://delight.ai/autonomous-agent)
- What happens when a caller needs a person?
Delight keeps callers on the line and briefs your agent with the transcript and a summary, or files a case with a callback path.
[Explore](https://delight.ai/blog/product/introducing-intelligent-voice-handoff)
- Does it run on the numbers you already have?
Calls run on your own numbers through Twilio, Telnyx, and local carriers, and hand off to your queues by phone number.
[Explore](https://delight.ai/blog/product/introducing-universal-telephony)
- Does it remember callers between calls?
One memory follows each customer across calls, texts, and callbacks, so nobody has to explain their issue twice.
[Explore](https://delight.ai/agent-memory-platform-amp)
- Can you test it before customers hear it?
Test in isolated environments with batch runs and regression tests, trace every decision in audit logs, and pause it instantly.
[Explore](https://delight.ai/trust-os)
- How does it handle sensitive entries?
Keypad entries skip speech recognition and can be masked in transcripts and beep-masked in stored audio.
[Explore](https://delight.ai/blog/product/introducing-secure-caller-keypad-input)
### AI voice agent FAQs
Q: What is an AI voice agent, and what can it do on a call?
A: An AI voice agent is conversational AI that handles phone calls for a business. It understands what the caller says, responds in natural speech, and takes action, like booking an appointment, recommending a product, or routing a case. Delight.ai treats each call as a chance to grow, recover, or protect revenue. Voice-only tools treat each call as its own session. Delight carries [one memory](https://delight.ai/agent-memory-platform-amp) across voice, chat, SMS, email, and WhatsApp, so customers don't have to repeat themselves.
Q: How does an AI voice agent recover missed and after-hours calls?
A: An AI voice agent picks up the calls your team can't reach, including nights, weekends, and volume spikes. Delight.ai qualifies the caller and books the appointment or next step on the call. If a line drops, Delight calls back with the conversation's context intact. It also makes [outbound calls](https://delight.ai/autonomous-agent) tied to customer journeys and uses memory to [reach out when the call is useful](https://delight.ai/for-you-conversations-fyc).
Q: Is an AI voice agent worth it for a small business?
A: For a small team, every missed call can be a lost job. An AI voice agent takes routine calls—booking, rescheduling, and common questions—so your team focuses on calls that need a person. Delight.ai runs on your existing phone numbers through [Twilio, Telnyx, and local carrier partnerships](https://delight.ai/blog/product/introducing-universal-telephony). [Dr. Cool Services](https://delight.ai/customers/dr-cool-services) uses Delight to answer every call, even during extreme heat.
Q: How do you build and launch an AI voice agent?
A: Start with the calls it will take and the outcome each should reach. With Delight.ai, connect your numbers, map queues, and set version-controlled guardrails. Delight briefs agents with transcripts and summaries or files cases with callback paths. Resolved cases go to connected systems like Salesforce, Zendesk, ServiceNow, and NICE CXone. [See all integrations](https://delight.ai/integrations). Test in isolated environments before launch and pause instantly if needed.
Q: How do AI voice agents work in healthcare or with sensitive information?
A: In healthcare, AI voice agents handle booking, rescheduling, and reminders while remembering patients across phone and text. For sensitive details, callers enter one-time codes and consent responses on the keypad; these never pass through speech recognition and can be masked in transcripts and beep-masked in stored audio. Delight is SOC 2 and GDPR compliant. [Learn about security and compliance](https://delight.ai/security).
Q: Will callers notice they're talking to an AI?
A: Delight is built for low-latency conversation: it responds without awkward pauses, handles interruptions and noisy lines, and picks up exactly where the caller left off.
### Turn every call into a revenue moment
Talk to an expert
## Integrations
Source: https://delight.ai/integrations
## Every integration your revenue AI agent needs
Delight.ai integrates with the tools you already use, so every customer conversation can turn into a sale, a renewal, or a faster resolution.
All · SaaS & tech · New · Native · Helpdesk & Voice
---
## SaaS & tech [7]
**Industry:** All industries · Financial services · Healthcare · Home services · Retail & eCommerce · Travel & hospitality
29 integrations found
###  Salesforce
Connect delight.ai to the entire Salesforce suite, leveraging Salesforce Knowledge to train the AI agent, utilizing live chat for customer engagement. This integration facilitates seamless handoffs to human agents.
[Salesforce connector about Salesforce](https://sendbird.com/products/chat-messaging/salesforce-connector)
- Retail & eCommerce
- Financial services
- SaaS & tech
###  Zendesk
Connect Zendesk Chat and Knowledge with delight.ai for a smooth AI agent to human agent handoff. Centralize customer issues across both platforms and enhance AI accuracy by leveraging Zendesk Knowledge for training.
- Retail & eCommerce
- SaaS & tech
- Travel & hospitality
###  Freshworks
Connect to Freshworks to centralize conversations and customer issues, facilitating seamless agent handoff.
- SaaS & tech
- Retail & eCommerce
###  ServiceNow
Connect ServiceNow with delight.ai for seamless AI-to-human handoff and knowledge training. Automatically create helpdesk tickets when escalating from AI to human agents. Deliver accurate, context-aware answers by training the AI on your ServiceNow knowledge base.
- Financial services
- SaaS & tech
- Healthcare
###  Intercom (Coming soon)
Connect to Intercom to centralize conversations and customer issues, facilitating seamless agent handoff.
- SaaS & tech
- Retail & eCommerce
###  RingCentral
Routes voice conversations to a live agent with full call context attached.
- SaaS & tech
- Financial services
###  Zoom Phone
Routes voice calls to Zoom Phone, including desks running physical phone hardware.
- SaaS & tech
- Home services
## New [2]
###  Amazon S3
Syncs documents stored in S3 buckets directly into the AI agent's knowledge base.
- New
###  AuditData
Books hearing care appointments directly, checking locations, appointment types, and time slots.
- Healthcare
- New
## Native · Helpdesk & Voice [1]
###  Desk
delight.ai's own helpdesk. Ticket, chat, and voice handoff all live in one system, no separate integration required.
- Native · Helpdesk & Voice
## Other integrations [19]
###  Sprinklr
Integrate Sprinklr with delight.ai for frictionless AI-to-human handoff and knowledge training. Automatically route conversations from AI directly to human agents. Deliver accurate, context-aware answers by training the AI on your Sprinklr knowledge base.
- Retail & eCommerce
- Financial services
###  Confluence
Connect the AI agent to Confluence for training, using its content as a knowledge source.
###  Notion
Connect the AI agent to Notion for training, using its content as a knowledge source. Knowledge is automatically synced to ensure the AI agent always has access to the most up-to-date information.
###  Google Drive
Automatically syncs Google Drive files so the AI agent's knowledge stays current.
###  NICE CXone
Routes conversations into queues with full context attached.
- Travel & hospitality
- Financial services
- Retail & eCommerce
###  Amazon Connect
Routes voice calls into Amazon Connect with full context attached.
- Travel & hospitality
- Retail & eCommerce
###  Cisco Finesse
Routes voice calls into Cisco Finesse with full context attached.
- Financial services
- Healthcare
###  Constructor.io
Queries Constructor's product search and discovery API to answer product questions directly.
- Retail & eCommerce
###  Five9
Routes voice calls into Five9 queues with full context attached.
- Retail & eCommerce
- Financial services
###  Genesys
Hands off voice conversations into Genesys queues with full context attached.
- Travel & hospitality
- Financial services
- Retail & eCommerce
###  Housecall Pro
Connects to field service management for scheduling and job status on home service calls.
- Home services
###  Navitaire
Connects to Navitaire's airline reservation system for booking and PNR lookups.
- Travel & hospitality
###  Optix
Books optical appointments directly, checking branch availability and open slots.
- Healthcare
###  Recharge
Reads subscription billing cycles, charges, addresses, and cancellations for subscription commerce.
- Retail & eCommerce
###  ShipBob
Looks up orders and their shipments for fulfillment questions on eCommerce orders.
- Retail & eCommerce
###  ShipStation
Looks up shipments by order number and surfaces live carrier tracking status.
- Retail & eCommerce
###  Shopify
Looks up orders by email, checks fulfillment status, and updates shipping addresses directly in Shopify.
- Retail & eCommerce
###  Zapier
Triggers Zapier workflows from a conversation, connecting the AI agent to thousands of other apps.
---
## Looking for an integration you don’t see here?
[Talk to an expert](https://delight.ai/contact-sales)
---
## Common questions about the revenue AI agent
### What integrations does Delight.ai support?
Delight.ai connects directly to the tools Customer Experience teams already run across helpdesks, voice and contact centers, knowledge bases, and industry-specific business systems. Delight.ai pulls in data from tools like Salesforce, Genesys, Confluence, and Shopify, plus native support inside Delight Desk. If a tool in your tech stack isn't listed, contact sales to request a new integration.
### Do you integrate with my CRM or help desk tool, even if it isn't listed above?
If you're running Salesforce, Zendesk, Freshworks, ServiceNow, NICE CXone, Sprinklr, or Intercom, yes, Delight.ai connects natively today. For anything else, Delight.ai connects to existing tools through authentication, payments, inventory, and internal application programming interfaces (APIs) without requiring a separate migration project, so most CRM and help desk platforms can still be wired in even without a named connector out of the box. [Tell us what you use](https://delight.ai/contact-sales) and we'll confirm the fastest path to connect it.
### How do I actually connect Delight.ai to my existing systems?
Connecting [Delight.ai](http://delight.ai) to your existing systems depends on the integration. Some, like the CRM and help desk connections listed above, connect with a single click from your workspace. Others, especially custom APIs and internal systems, are set up directly with Delight.ai's Forward Deployment team, the specialists who walk you through connecting integrations and APIs during account setup and onboarding. Either way, connecting doesn't require a separate migration project.
### Can Delight.ai connect to industry-specific or internal systems, like an EHR platform or something we built ourselves?
Yes, beyond the integrations named above, Delight.ai connects to internal and custom systems through APIs and webhooks, the same underlying connection it uses for authentication, payment, and inventory systems today. That includes industry-specific systems like electronic health record (EHR) platforms or insurance and eligibility databases, when they expose an API.
### Does Delight.ai work across web and mobile, or only on our website?
Delight.ai isn't limited to a single surface. [Omnipresence](https://delight.ai/omnipresent) carries a customer's full conversation context across chat, SMS, email, voice, WhatsApp, and in-app messaging on web and mobile, with zero restart when a customer switches between them. That means a customer who starts a conversation in your app and continues by SMS never has to re-explain themselves.
## Enterprise Grade
Source: https://delight.ai/enterprise-grade
## How to choose an enterprise-grade AI agent
### Delight.ai was built for the teams who ask the hard questions.
Enterprise-grade isn’t a buzzword, it’s a standard. You need traceability, observability, and control at every step of the AI lifecycle. We built delight.ai to set the bar, and give your team the confidence to move fast without compromising trust.
### The enterprise-grade checklist
#### Full observability & control
See what your AI is doing and why. Stay in control.
- AI activity trail on every conversation
- Version control & rollback
- Deployment logs & full audit history
- Environment separation (Dev vs. Prod)
- Custom safeguards & PII checks
- Role-based access control
- Region/country-based controls & policy enforcement
- Multi-agent architecture for scalability and versatility
#### Pre-production testing
Test full conversations before your customers ever see them. Reviewed by both AI and humans.
- Flagged hallucination & risky responses
- One-click fix to convert conversation to knowledge & create test sets
- Human-grade AI agent scorecards
- Environment separation (Dev vs. Prod)
- AI suggestions for refinements
#### Infrastructure backbone
Scale your AI with confidence—secure, compliant, and built for enterprise.
- SOC 2 Type II, HIPAA, GDPR compliance
- Global-scale communication infrastructure
- Billions of conversations processed every month
- Multilingual support at enterprise scale
## Ask 5 questions to vet an AI agent
1. Can I simulate and validate full conversations before going live?
2. Can I trace every AI action and decision—and explain what it did and why?
3. Can I safely stage, approve, and control which updates go live and where?
4. Can my team intervene when something goes wrong and rollback instantly?
5. Can the platform handle enterprise-level scale and global volume without breaking?
#### Enterprise teams choose delight.ai for a reason.
[Contact sales](https://delight.ai/contact-sales)
## Request a walkthrough
Source: https://delight.ai/contact-sales
## AI agents that lift conversion up to 30% and resolve support at scale
“It makes the experience feel more personal, more useful, and more connected.”
 Scott Vandegrift, VP of Product, BJ's
### See Delight AI in action
## Everything you need to deploy AI support with confidence
**99.9%+ uptime**
Deliver reliable AI support around the clock, whenever customers need it.
**Enterprise-ready infrastructure**
Launch securely with compliance, scalability, and operational readiness built in from the start.
**White glove support**
Get expert guidance from evaluation to launch and beyond.
## Customer quotes
> “Delight AI agent has been a game changer. Our CX team is highly satisfied and more proactive than ever. it’s transforming how we manage customer conversations.”
>
> — Martina Panza, Head of CX, Norse Atlantic Airways
> “From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows that turn onboarding into ongoing adoption.”
>
> — Mixpanel
> “The delight.ai agent’s 90%+ accuracy during our POC gave us confidence, and Sendbird proved to be a true collaborative partner—not just a vendor.”
>
> — Eugene Kim, CEO, Hanssem
> “Keeping deliveries moving and operations effortless. An AI concierge keeps customers, couriers, and merchants in sync in real-time.”
## Contact thank you
Source: https://delight.ai/contact-sales/thank-you
## Thank you for your interest
You can schedule time by booking directly on our website, via email, or by speaking with us over the phone. A team member will follow up shortly to help you finalize a time; we look forward to connecting.
### Case studies
[](https://delight.ai/customers/norse-atlantic-airways "Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen")
#### [Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways "Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen")
[](https://delight.ai/customers/hipages "What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving")
#### [What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages "What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving")
[](https://delight.ai/customers/lotte-homeshopping "Lotte Homeshopping partners with Anthropic and delight.ai to scale retail")
#### [Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping "Lotte Homeshopping partners with Anthropic and delight.ai to scale retail")
[](https://delight.ai/customers/gther "gther transforms event registration with delight.ai")
#### [gther transforms event registration with delight.ai](https://delight.ai/customers/gther "gther transforms event registration with delight.ai")
## AI Copilot
Source: https://delight.ai/desk/ai-copilot
Every ticket is a race.
Desk Copilot
keeps you ahead.
[View demo](https://delight.ai/demo-request)
## AI support that works at the speed of your team
From drafting replies to surfacing answers, Desk Copilot helps agents move faster, solve more, and keep customers happy.
### Faster replies, less effort
Copilot drafts contextual, on-brand responses fast, freeing agents to review and resolve instead of typing.
### Answer at your fingertips
Ask Copilot search brings knowledge and context to the agent so they stay in flow and focused on the customer.
### Delight every customer
When agents respond faster with accurate, on-brand answers, customers feel it. Copilot turns every interaction into a satisfying experience.
## How Desk Copilot makes every reply count
From intent detection to verified knowledge, each step is designed to bring speed, accuracy, and trust.
### The world’s leading brands deliver with delight.ai
Logos:
    
> "Sendbird’s AI agent has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations"
Martina Panza — Head of CX, Norse Atlantic Airways
> "The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is more than a vendor. They're a true collaborative partner who works alongside us to solve challenges from the same perspective."
Eugene Kim — CEO, Hanssem
> From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows that turn onboarding into ongoing adoption.
> Keeping deliveries moving and operations effortless. An AI concierge keeps customers, couriers, and merchants in sync in real time.
> Where relationships build repeat business. Hipages helps 33,000 tradies build trust through real-time, personalized communication, turning one-off jobs into lasting customer relationships across 12 million projects.
## Time is a support agent’s most valuable resource
Desk Copilot protects it.
[Contact sales](https://delight.ai/contact-sales)
## Retail
Source: https://delight.ai/industries/retail
## Your AI agent for retail revenue, turning browsers into buyers and carts into orders
[View demo](https://delight.ai/demo-request)
## Logos
---
## How does Delight.ai help shoppers choose the right product?
Delight.ai asks what the shopper is trying to do, narrows your catalog to what fits, and moves them to checkout. It answers the questions that stall a considered purchase and suggests relevant add-ons.
-  Turns a room, an occasion, or a compatibility need into the right product
-  Answers size, fit, and delivery questions from your real product data
-  Suggests add-ons based on the basket and past purchases, within your merchandising rules
-  Guides shoppers step by step through complex and custom purchases
## How does Delight.ai recover stalled carts and at-risk orders?
When a cart stalls or an order goes wrong, Delight.ai reaches the shopper with details of their actual purchase, answers the blocking question, resolves order and delivery issues, and keeps the sale from becoming a cancellation.
-  Reaches shoppers with their actual cart over SMS, email, WhatsApp, or chat
-  Answers the question that stalled checkout, without defaulting to a discount
-  Resolves order status, returns, and exchanges against the real order
-  Proactively coordinates across carrier, warehouse, and store when a delivery goes wrong
---
## Why is Delight.ai different for high-consideration retailers?
### Comparison: typical tools vs Delight.ai
| Feature | Typical behavior | Delight.ai |
|---|---|---|
| Site search and filters | Shoppers sift through irrelevant pages and often leave or call the store | Asks the shopper in their own words and narrows the catalog to what actually fits |
| Abandoned-cart emails | Generic reminders that often rely on discounts | Reaches shoppers with their actual cart and answers the delivery, fit, or returns question that stalled checkout |
| Support-only AI | Closes tickets but misses buying signals | Resolves order issues and recognizes when a replacement, upgrade, or add-on fits the shopper's needs |
## What Delight.ai is built to move
Every use case ties back to metrics your team already tracks:
- Conversion rate — Shoppers get answers that would otherwise stop the purchase.
- Average order value — Relevant add-ons and upgrades are suggested while the shopper is deciding, within your merchandising rules.
- Cancellations and refunds avoided — Delivery and order issues get fixed before shoppers give up, preserving future purchases.
## Enterprise-grade trust for retail at scale
Built on Sendbird — more than a decade of infrastructure powering over 7 billion conversations a month, combining enterprise reliability with responsible AI.
Certified and compliant by default: SOC 2 Type II, ISO 27001, and GDPR compliant, so a security review doesn't hold up a revenue rollout.
Trust OS provides full visibility into what the agent said, offered, and did, with audit trails and approval rules for discounts, refunds, and exceptions.
## Frequently Asked Questions
### How can an AI agent grow revenue for an ecommerce business?
An AI agent grows ecommerce revenue by (1) helping shoppers find the right product through conversational narrowing and relevant recommendations, and (2) guiding shoppers through checkout so sales don't stall. Example: BJ's Wholesale Club saw higher conversion among shoppers who engaged with a Delight.ai assistant.
### Can an AI agent prevent ecommerce refund requests?
Yes. Delight.ai can fix the problem behind many refunds (offer exchanges for fit issues, share delivery updates for late orders). Actionbooks set what the agent may offer within your return policy; complex disputes go to humans.
### What ecommerce systems does Delight.ai integrate with?
Delight.ai connects to systems retailers already use (Zendesk, Salesforce), plus inventory and order systems via internal APIs. The agent checks order and stock before answering. See the full list on the integrations page: https://delight.ai/integrations
### Can an AI agent handle complex orders and exceptions?
Yes. Delight.ai's Agent Steward stays with a case across sessions and systems until resolved. Low-confidence answers are flagged by Trust OS; requests needing human judgment are escalated with full context.
### Should a retailer build its own AI agent or buy one?
Most retailers benefit from buying. Delight.ai includes an upkeep layer: operations teams update Actionbooks without engineering tickets, test changes before rollout, and run on Sendbird's enterprise infrastructure.
---
## Give every shopper an expert, from the first question to the delivered order
[Request demo](https://delight.ai/demo-request)
## B2B
Source: https://delight.ai/industries/technology
[ View demo](https://delight.ai/demo-request)
## The AI agent that turns B2B complexity into growth
Resolve high-context SaaS issues fast and turn onboarding, retention, and troubleshooting into growth levers.
- Resolve technical and account-specific issues in context by pulling from internal systems.
- Reduce churn by detecting friction and stepping in proactively.
- Accelerate activation and lower support effort while improving CSAT.
## Proactively prevent churn before it starts
Detects drop-off, friction, or stalled onboarding before users disengage, then intervenes with timely, contextual support to keep users engaged and protect revenue.
## Always-on support for every stakeholder
Deliver guided setup, product walkthroughs, and instant answers from day one to help users activate faster and ease load on support and success teams.
## Resolve complex inquiries without the ping-pong
Tackle multi-stakeholder, cross-system issues with precision by integrating knowledge from CRM, ticketing, and internal data so answers are complete without bouncing between teams.
---
## Build. Test. Evaluate. Repeat.
AI for travel & hospitality is here.
### Trust OS: Responsible AI powered by observability, oversight, and control
- **Build** — Design, test, and launch AI agents in days with tools that simplify every step.
- **Test** — Validate early: detect, analyze, and fix issues before customers are affected.
- **Evaluate** — Monitor results in real time, measure impact, and continuously optimize.
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
Trust OS provides full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale: built on enterprise-grade infrastructure and trusted by global brands.
---
## Support outcomes you can measure
-  Handle complexity at lower cost — Resolve high-effort issues without overwhelming your team.
-  Accelerate time-to-value — Help users reach the "aha" faster with proactive support.
-  Drive retention and engagement — Keep users supported at scale.
-  Boost CSAT, not headcount — Faster, more accurate support with fewer escalations.
The AI customers trust most is built for the ones least ready for it.
[ Read the report](https://delight.ai/reports/ai-index)
---
## Powering 7 billion conversations every month
- **Enterprise-grade scalability & reliability** — Handle peak-season surges and global demand without missing a beat.
- **Security & compliance** — SOC 2, GDPR, HIPAA, and ISO 27001 certified for regulated industries.
- **Privacy-first data governance** — Full encryption, granular access controls, and compliance built into every layer.
---
## AI agent FAQs
### What are the benefits of AI agents in B2B customer service?
AI agents offer:
- 24/7 availability across time zones without increasing headcount.
- Scalable support for routine issues and demand spikes.
- Faster response and resolution to meet SLAs and boost CSAT.
- Proactive problem detection and resolution to prevent escalations.
- Multimodal, omnichannel support (voice, chat, SMS, Email, WhatsApp, web, mobile): see [omnipresence of our AI agent](https://delight.ai/omnipresent).
- Consistent, brand-aligned communication.
- Interaction data and analytics for product and experience improvements.
- Multilingual capabilities to serve global customers.
### What are the challenges for implementing AI agents in B2B customer service, and how do you mitigate them?
Common challenges and mitigations:
- **Data integration:** Connectors and integrations to CRM/ERP/ticketing (Salesforce, Freshworks, Zendesk, Confluence, Sprinklr, Notion, and more).
- **Domain accuracy:** Train on owned, historical, domain-specific data for relevance and precision.
- **Complex queries:** No-code customizable AI workflows enable context-aware, multi-step handling.
- **Change management & stakeholder adoption:** Pilot programs, clear governance, human-in-the-loop supervision, and training to show AI augments support teams.
- **Customer trust & compliance:** Agentic AI controls and safety features to reduce risk.
- **Cost vs ROI:** Use the [AI customer support ROI calculator](https://delight.ai/tools/roi-calculator) and start with pilots to ensure value.
More on stakeholder adoption: [ebook on AI customer service transformation](https://delight.ai/ebooks/ai-customer-service-transformation-guide).
### What are common use cases of AI agents in B2B customer service?
Common use cases:
- Technical support (troubleshooting, configuration, escalations)
- Appointment scheduling and reminders
- Order and inventory management (real-time status)
- Customer onboarding and guided setup
- Knowledge base navigation and self-service
- Contract queries, renewals, and document delivery
- Billing, payments, and refunds
- Product recommendations and upsell identification
- Incident reporting and routing
- Feedback collection and CSAT surveys
- Compliance assistance and policy guidance
- Multi-stakeholder coordination within client organizations
### What are some best practices for using AI agents in B2B customer service?
Best practices:
- Start with clear, measurable customer service goals.
- Train agents on relevant historical and domain data.
- Design seamless AI-to-human escalation flows.
- Maintain transparency: notify customers when AI is involved and provide human fallback.
- Protect data and ensure regulatory compliance (GDPR, HIPAA where applicable).
- Continuously monitor, evaluate, and iterate on agent performance.
- Empower support teams with training and AI supervision tools.
- Use multilingual capabilities and consistent cross-channel integration.
- Pilot, measure, then scale gradually.
Avoid common adoption traps: [AI customer service transformation ebook](https://delight.ai/ebooks/ai-customer-service-transformation-guide).
---
## Five B2B use cases, from trial activation to churn intervention
See how AI agents onboard users, resolve technical issues, and flag expansion-ready accounts.
[ View playbook](https://delight.ai/resources/b2b-ai-agents)
[ View demo](https://delight.ai/demo-request)
## On-demand
Source: https://delight.ai/industries/on-demand
[ View demo](https://delight.ai/demo-request)
          
## Real-time resolutions for the real-time economy
Customers, couriers, and merchants don’t wait — your support shouldn’t either. Resolve issues autonomously, deflect costly escalations, and scale instantly across every stakeholder.
### Resolve issues before customers reach out
Anticipate problems before they become tickets. From delivery delays to payout issues, delight.ai takes action first.
### Always-on support for every stakeholder
Give your customers, couriers, and merchants instant help whenever and wherever it’s needed.
### Support that can handle the lunch rush
From lunchtime peaks to holiday chaos, our AI agent ensures reliable, real-time support across all stakeholders.
---
## Build. Test. Evaluate. Repeat.
AI for travel & hospitality is here.
### Trust OS: Responsible AI powered by observability, oversight, and control.
- Build: Build with confidence and ease. Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test: Validate early, before it matters. Detect, analyze, and fix potential issues long before they impact your customers.
- Evaluate: Stay in control, always. Monitor results in real time, measure impact, and continuously optimize your business performance.
Build-Test-Evaluate loop
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
Trust OS
A foundational system giving brands full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale
Built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
## On-demand impact in real time
### Boost customer loyalty
Proactive alerts prevent order cancellations and boost repeat orders.
### Instant resolutions keep couriers moving
Fast support keeps couriers on the road, reducing lost time and missed deliveries.
### Increase first contact resolutions
Maintain response times and resolution targets during surges and spikes.
### Lower your support costs
Automate the bulk of tickets and resolve issues instantly across every channel.
##### The AI customers trust most is built for the ones least ready for it.
[ Read the report](https://delight.ai/ai-index)
## Powering 7 billion conversations every month. Ready for yours.
#### Enterprise-grade scalability & reliability
Handle peak-season surges, IRROPS events, and global demand without missing a beat. Proven infrastructure that scales when it matters most.
#### Ironclad security & compliance
SOC 2, GDPR, HIPAA, and ISO 27001 certified. Built for industries where trust is non-negotiable and regulatory requirements are complex.
#### Privacy-first data governance
Guest data stays protected with full encryption, granular access controls, and compliance built into every layer of the platform.
## The AI agent that keeps your on-demand economy flywheel spinning
[ Talk to an expert](https://delight.ai/contact-sales)
## Travel and hospitality
Source: https://delight.ai/industries/travel-and-hospitality
## The AI agent that travels with your guests
[View demo](https://delight.ai/demo-request)
## Trusted by leading brands
         
## From the first search to a confirmed booking
Surface personalized recommendations, compare options in real time, and guide travelers from vague inspiration to a confirmed booking. Re-engage across channels before they book with someone else.
Trip planning & destination recommendations
Booking assistance (flights, hotels, packages)
Abandoned booking recovery
Pre-arrival engagement (upgrades, amenities, add-ons)
## With them every mile, not just when things go wrong
Proactively reach guests with rebooking options before a disruption becomes a crisis. Surface dining recommendations, book excursions, and find natural moments to upgrade the stay.
Disruption management (delays, cancellations, weather)
Flight rebooking & itinerary changes
In-stay concierge (dining, activities, tours, spa)
Upsell & cross-sell (seat upgrades, room upgrades, add-ons)
## Turn a great trip into the next one
Close the loop with personalized follow-up built on what actually happened during the trip. Re-engage at the right moment with the right offer to turn one-time guests into lifelong travelers.
Post-stay follow-up & review collection
Personalized rebooking offers
Loyalty program engagement & points redemption
Proactive re-engagement between trips
### The world’s leading brands deliver with delight.ai
> Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations.
>
> Martina Panza — Head of CX
[Explore customer story](https://delight.ai/customers/norse-atlantic-airways)
## Build. Test. Evaluate. Repeat. — AI for travel and hospitality is here.
### Trust OS: Responsible AI powered by observability, oversight, and control.
- Build — Build with confidence and ease. Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Validate early, before it matters. Detect, analyze, and fix potential issues long before they impact your customers.
- Evaluate — Stay in control, always. Monitor results in real time, measure impact, and continuously optimize your business performance.
Build-Test-Evaluate loop — AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
Trust OS — A foundational system giving brands full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale — Built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
## Travel impact, measured on every journey
Faster resolution times — Reduce rebooking and first response times during peak disruptions.
Increase revenue — Recover abandoned bookings, drive upsells, and convert guests into repeat customers.
Reduce operational costs — Reduce call spikes during disruptions. Resolve issues without human escalation.
Higher guest satisfaction — Boost CSAT and NPS with instant, personalized support.
[Read the report on AI in travel](https://delight.ai/reports/ai-index)
## Powering 7 billion conversations every month. Ready for yours.
Enterprise-grade scalability and reliability — Built on the world’s #1 communications API platform, trusted by industry leaders across sectors.
Ironclad security and compliance — Infrastructure meets high compliance requirements, including GDPR, SOC 2 (Type II), HIPAA/HITECH, ISO 42001.
Privacy-first data governance — Your data remains yours: PII protection, AES256 encryption, data masking, region-specific storage, and TLS 1.3.
## Conversational AI for hospitality and travel FAQs
**What is conversational AI in hospitality?**
Conversational AI uses natural language processing to understand and respond to guests across chat, voice, and messaging channels. Delight.ai acts as a digital concierge to help guests book, modify, or enhance trips with personalized service.
**How does conversational AI improve the guest experience?**
It connects every stage of the journey — from booking to post-stay feedback — providing real-time, personalized service, remembering preferences, and reducing friction to increase satisfaction and loyalty.
**Common use cases for conversational AI in hospitality and travel:**
- Automating repetitive tasks like booking confirmations and itinerary changes.
- Managing flight or room rebookings with autonomous AI agents.
- Providing real-time guest updates, upgrades, or cancellation handling.
- Recommending dining, excursions, and multilingual support for accessibility.
**How is the delight.ai agent different from traditional chatbots?**
Traditional chatbots are rule-based. Delight.ai uses advanced NLP, remembers context, executes actions, and handles booking modifications, loyalty perks, and cross-channel requests autonomously.
**Can conversational AI handle travel disruptions like delays or cancellations?**
Yes. Delight.ai proactively assists with rebooking and notifications, automating routine tasks and helping teams maintain efficiency and guest satisfaction during disruptions.
**How does conversational AI help reduce operational costs?**
By automating common workflows, reducing ticket volume, and shortening response times, delight.ai lets staff focus on complex issues while the AI handles routine inquiries.
## Your guests are already on the move. Meet them there.
[Contact sales](https://delight.ai/contact-sales)
## Healthcare
Source: https://delight.ai/industries/healthcare
## Every appointment kept, every subscription renewed, nothing left to chance.
[View demo](https://delight.ai/demo-request)
## How does delight.ai handle appointment scheduling?
Delight.ai books, reschedules, and confirms appointments in one conversation — checking coverage and copay before the visit, across voice and text.
 Books and reschedules across voice and text, with instant confirmation
 Verifies coverage and copay from the actual account, not a stale policy lookup
 Sends prep instructions and reminders automatically, cutting no-shows
 Covers overflow, after-hours, and peak-season calls without adding front-desk headcount
## How does delight.ai follow up on refills, renewals, and subscriptions?
Delight.ai reaches out before refills, renewals, and subscriptions lapse — reordering, coordinating with the pharmacy, and rebooking in the same thread, with full memory across visits.
 Proactive refill and renewal reminders sent before the lapse, not after
 Pharmacy and program coordination handled in the same thread, no transfer required
 Adherence and check-in nudges tied to the patient’s actual program, not a generic drip
 Full memory across visits, so a returning patient never repeats their history
## Why is delight.ai different for consumer health organizations?
Delight.ai is built to proactively reach patients, answer from live account data, and run on certified infrastructure — reducing friction compared with portals, generic chatbots, or building in-house.
### Comparison (concise)
- Patient portal
- Instead of waiting for patients to log in, delight.ai pulls updates and proactively reaches out when results, messages, or balances change.
- Generic AI chatbot
- Instead of generic knowledge-base answers, delight.ai connects to real accounts, eligibility, and scheduling systems for concrete, patient-specific answers on HIPAA-compliant infrastructure.
- Building it yourself
- Instead of building compliance and infrastructure first, delight.ai runs on Sendbird’s certified platform with Trust OS governance, observability, and audit trails out of the box.
## What delight.ai is built to move
Every use case ties back to metrics your team already tracks:
- No-show rate — Proactive reminders and one-tap rescheduling keep appointments on the books.
- Front-desk call volume — Scheduling, coverage, and prep questions get resolved in conversation before they become calls.
- Refill and renewal completion — Outreach goes out before lapse so renewals/refills happen on schedule.
## Built on HIPAA-compliant infrastructure by default
- Built on Sendbird: more than a decade of infrastructure powering billions of conversations with enterprise reliability and responsible AI.
- Certified and compliant by default: SOC 2 Type II, ISO 27001, ISO 42001, GDPR and CCPA coverage; full observability into every AI interaction.
- Trust OS: full observability and audit trails so you can see what the agent said, why, and when.
## Conversational AI for consumer health FAQs
- Who is delight.ai's AI agent built for?
- Organizations that sell, ship, or renew care directly to a patient (health plans, diagnostic platforms, DTC pharma, distributors). It handles scheduling, coverage checks, and renewal follow-up.
- Can delight.ai proactively reach out to patients about refills or renewals?
- Yes. Proactive outreach is core: it reaches out before refills, renewals, or follow-ups lapse over voice, SMS, or chat, carrying full context from prior interactions.
- Is delight.ai's AI agent HIPAA compliant?
- Yes. Runs on HIPAA-compliant infrastructure with SOC 2 Type II, ISO 27001, ISO 42001, GDPR and CCPA coverage. See [Trust OS](https://delight.ai/trust-os) for observability and auditability.
- Does delight.ai make clinical decisions or replace care staff?
- No. Delight.ai handles administrative and logistical tasks (booking, coverage checks, reminders). Clinicians retain clinical authority.
- Does delight.ai integrate with insurance and eligibility systems?
- Yes. It connects to the insurance, eligibility, and payer systems you already run on so coverage and copay answers come from real account lookups; exact integrations depend on your stack.
- How is delight.ai different from a patient portal or chatbot?
- Portals store updates until patients log in; chatbots use static content. Delight.ai proactively pulls portal updates, reaches out, books/reschedules directly, and remembers patients across channels.
- How do I evaluate a patient engagement platform?
- Prioritize compliance, depth of integration with insurance/eligibility systems, language coverage, and measurable outcomes (no-shows, call volume, refill completion). Use Trust OS to verify accuracy.
- How much does a patient engagement platform cost?
- Pricing is conversation-based and scales with usage. A Desk seat is included. Contact sales for a quote: https://delight.ai/contact-sales
## Every appointment kept.
## Every renewal on time.
[Request demo](https://delight.ai/demo-request)
## Financial Services
Source: https://delight.ai/industries/financial-services
## The financial services AI agent — built for trust
[View demo](https://delight.ai/demo-request)
     
## Convert high-intent visitors into confident signups
Answer the questions that make or break conversions. Provide instant, personalized guidance on rates, limits, and features that turn prospects into customers.
 Real-time eligibility and rate estimates
 Feature comparisons and benefit breakdowns
 Account type recommendations
## Activate approved users in minutes, not days
Speed wins in fintech. Guide users through verification, funding, and first transaction with real-time support that eliminates drop-off and gets them to 'aha' moments faster.
 Identity verification (KYC/AML) assistance
 Account funding and first transaction support
 Feature activation and onboarding flows
## Scale support for millions of daily transactions
Handle routine inquiries and critical moments instantly across every channel, from balance checks to disputed charges, while surfacing engagement opportunities.
 Transaction inquiries and payment support
 Product feature discovery and education
 Real-time issue resolution across all channels
## Grow revenue per user with intelligent engagement
AI turns usage patterns into growth opportunities. Recommend relevant products, re-engage dormant users with personalized offers, and retain at-risk customers before they switch.
 AI-driven product recommendations
 Proactive re-engagement campaigns
 Retention offers based on behavior patterns
## Enterprise-grade governance auditors and customers trust
Move fast without breaking compliance. Every decision is explainable, every interaction is logged, and every policy is enforced in real time to meet strict regulatory requirements.
 Complete audit trails for regulatory review
 Real-time policy enforcement and guardrails
 Explainable AI for every customer interaction
## Build. Test. Evaluate. Repeat. AI for financial services is here.
### Trust OS: Responsible AI powered by observability, oversight, and control.
- Build — Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Validate early: detect, analyze, and fix issues before they impact customers.
- Evaluate — Monitor results in real time, measure impact, and continuously optimize performance.
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
A foundational system giving brands full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale, built on enterprise-grade infrastructure and powering billions of conversations.
## Profitability you can measure, in real time
From fraud prevention to user activation, delight.ai helps protect revenue and reduce costs, delivering measurable ROI across interactions.
**Activate users 3x faster** — AI-guided onboarding gets approved customers to first transaction in days, not weeks.
**Instantly resolve payment issues** — Proactive alerts catch failed payments and guide customers to fix issues before they churn.
**Increase revenue per user** — AI-driven recommendations turn usage patterns into growth opportunities automatically.
**Lower your support costs** — Automate routine inquiries and resolve issues instantly across channels.
#### Reversibility earns AI trust in finance faster than anything else.
[Read the report](https://delight.ai/ai-index)
## Powering 7 billion conversations every month. Ready for yours.
#### Enterprise-grade scalability and reliability
Built on the world’s #1 communications API platform, trusted across on-demand, healthcare, retail, and more.
#### Ironclad security and compliance
Our infrastructure meets high compliance and security standards including GDPR, SOC 2 (Type II), HIPAA/HITECH, and ISO 42001.
#### Privacy-first data governance
Your data remains yours—never accessed, used, or shared. PII protection, AES256 encryption at rest, TLS 1.3 in transit, data masking, and region-specific storage.
## Financial services AI agent FAQs
### What is a financial services AI agent?
A financial services AI agent uses natural language processing to automate routine banking tasks—account inquiries, payment processing, fraud detection, and account recovery—across voice, SMS, email, chat, and mobile apps. delight.ai focuses on handling high-volume transactional inquiries and proactive outreach; it does not provide investment advice or make lending decisions.
### How does delight.ai ensure legal compliance?
delight.ai is built with compliance in mind: SOC 2 Type II, ISO 27001, GDPR, AES256 encryption at rest, TLS 1.3 in transit, and comprehensive audit trails. Trust OS provides explainability for AI decisions, and AML/KYC workflows can escalate high-risk cases to human reviewers. CCPA data-deletion requests and full visibility into collected data are supported.
### Can delight.ai integrate with our existing core banking systems?
Yes. delight.ai integrates with core banking platforms, payment processors, fraud systems, and CRMs via secure APIs, accessing balances, transaction history, payment status, and customer profiles in real time while enforcing strict access controls.
### How does the AI agent handle sensitive financial conversations?
The agent is designed for appropriate tone and empathy. For complex judgment calls or policy exceptions, it transfers seamlessly to human specialists with full conversation context. Trust OS monitors tone, accuracy, and compliance.
### What languages does the financial services AI agent support?
delight.ai supports 100+ languages to serve diverse customer populations and international operations, improving financial inclusion.
### How does delight.ai prevent unauthorized access and account takeover?
The agent operates within your authentication and risk frameworks. It will not process sensitive requests without confirmation from your authentication systems. Agent Memory tracks behavior patterns to surface anomalies and trigger additional verification per your policies.
### Can the AI agent proactively reach out to customers?
Yes. delight.ai can proactively engage via voice, SMS, email, and app notifications for payment reminders, fraud alerts, low-balance warnings, re-engagement, and personalized recommendations to reduce delinquencies and churn.
#### Turn every financial moment into customer trust
[Contact sales](https://delight.ai/contact-sales)
## AWS marketplace - (Paid LP)
Source: https://delight.ai/form/ai-agent-aws
## AI customer service, built for AWS-scale
Deliver instant, intelligent support with Sendbird’s AI agent. Optimized for AWS infrastructure, easy to deploy, and designed to scale with your growth.
- Consumption-based pricing
- Volume discounts as you scale
- Tailored professional services
---
## The world's most popular apps build conversations with Sendbird
       
       
### Omnichannel AI that puts customers first, simplifying every interaction.
Create standout experiences with AI agents that personalize every interaction across channels.
1. Anywhere
2. Continuous
3. Proactive
4. Unified
#### Anywhere, all the time
Meet customers wherever they are—app, website, email, SMS, or social media. The easier for them, the more opportunity for you.
#### Continuous conversations across channels
No more repeating themselves. The AI customer service agent that remembers every interaction.
#### Proactively anticipate and delight
Always one step ahead. Our AI agents anticipate needs and proactively initiate conversations, before they become an issue.
#### Unified customer insights
Break down data silos and turn omnichannel conversations into unified intelligence to drive better business outcomes.
### AI agents: beyond conversational AI.
Elevate your customer experience with AI beyond basic automation. Achieve record resolution rates while enabling human agents to focus on high-value interactions.
Beyond simple tasks: AI reasoning capability
 Deploy smart AI agents in days, not weeks
 AI that never stops learning and improving
 Connect your software stack, no matter the tool
### Powering 7 billion conversations every month. Ready for yours.
Enterprise-grade scalability & reliability
Built on the world's #1 communications API platform, trusted by industry leaders from on-demand, healthcare, retail, and more.
Tailored SLA plans
Our infrastructure meets the highest degree of compliance and security requirement, including GDPR, SOC 2, HIPAA/HITECH, ISO 27001.
Privacy-first data governance
Your data remains yours—never accessed, used, or shared. With PII protection, AES256 encryption, data masking, and region-specific storage, all secured by TLS 1.3 security.
#### Ready to unleash the power of AI customer service agents?
[Contact sales](https://delight.ai/contact-sales)
## AI Agent
Source: https://delight.ai/form/ai-agent
## AI customer service — Built on trust.
- Powered by memoryCapture context to personalize at scale and boost customer loyalty.
- Present everywhere — Maintain continuity across every channel and moment.
- Built on trust — Transparent AI with enterprise oversight and control.
### Request a demo
### The world’s leading brands deliver with delight.ai.
> Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations.
>
> Martina Panza — Head of CX, Norse Atlantic Airways
> From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows that turn onboarding into ongoing adoption.
> Keeping deliveries moving and operations effortless. An AI concierge keeps customers, couriers, and merchants in sync in real time.
> The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is more than a vendor. They're a true collaborative partner who works alongside us to solve challenges from the same perspective.
>
> Eugene Kim — CEO, Hanssem
> Where relationships build repeat business. Hipages helps 33,000 tradies build trust through real-time, personalized communication, turning one-off jobs into lasting customer relationships across 12 million projects.
### Build on Trust OS. Deliver with confidence.
Build-Test-Evaluate loop
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
Trust OS
A foundational system giving brands full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale
Built on Sendbird’s enterprise-grade infrastructure, trusted by global brands to power billions of conversations.
### Connecting the world with AI concierges. 7 billion users and counting.
#### 7 billion+
Conversations every month
#### De-risk AI investment with battle-tested infrastructure
#### Gain value out of enterprise white glove support
#### 300M+
Monthly active users
#### 99.9%+
Uptime
#### Integrate with your customer service tech stack
## AI agents for CX
Source: https://delight.ai/form/ai-agent-cx
## AI customer service trusted by enterprises
- Omnichannel
- 80% Resolution Rate
- Enterprise-grade governance
Automate customer support with AI agents that resolve 80% of tickets, remember every interaction, and delight customers at scale
Request a demo
Slide 1 of 1
### Trusted by leading brands
   
   
   
   
  
Hanssem — 90% Response accuracy
Norse Airlines — 80% Containment rate
Powering 7+ billion Monthly conversations
### Meet the new generation of automation
Resolve customer service tickets with AI that never forgets
AI agents carry customer context forward and choose the right action for each moment, aligned with your business goals.
### Eliminate wait times across chat, voice, SMS, and email
Provide instant, always-on support across voice and digital channels. Customers pick up where they left off without repeating themselves.
### Enterprise trust: SOC2, GDPR, HIPAA built in
Operate AI with enterprise-grade controls for visibility, oversight, and reliability as you scale.
### Outperforms every major CX platform
In head-to-head evaluations, enterprises consistently choose delight.ai over Atlassian, Zendesk, Freshworks, and Intercom — with an 80% resolution rate that improves with every interaction.
### Customer stories
- Norse Atlantic Airways
> Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations.
Martina Panza — Head of CX, Norse Atlantic Airways
[Explore](https://delight.ai/customers/norse-atlantic-airways)
- Mixpanel
> From first click to full value. Mixpanel gets customers there faster. An AI concierge guides each user through tailored workflows that turn onboarding into ongoing adoption.
[Explore](https://delight.ai/customers/mixpanel)
- Hanssem
> The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is more than a vendor. They're a collaborative partner.
Eugene Kim — CEO, Hanssem
[Explore](https://delight.ai/customers/hanssem)
### Unmatched capabilities. Total flexibility.
- Easy no-code workspace
Build, manage, and deploy agents in one place. No code required.
- Built for growth
A platform that scales with your team and business.
- Scalable infrastructure
Enterprise-grade performance at global scale.
- Simple integrations
Works seamlessly with your existing support stack.
## Find the revenue in your customer service
Walk through Delight with our team and see how an AI agent that resolves issues can also close the sale.
[Contact sales](/contact-sales)
## AI Agent vs Chatbot
Source: https://delight.ai/form/ai-agent-vs-chatbot
## Overview
- Omnichannel
- Hyper-personalized
- Enterprise-grade governance
Delight’s AI chatbot connects across chat, voice, SMS, and email, resolving complex questions with memory and context so customers never have to repeat themselves.
[Request a demo](https://delight.ai/contact-sales)
## Logos
## Key stats
- Hanssem — 90% response accuracy
- Lotte Homeshopping — 40% fewer support tickets
- Powering 7+ billion monthly conversations
## Chatbots just talk. Delight’s AI agent resolves issues.
Basic bots reply message-by-message using fixed rules, which breaks conversations and forces customers to repeat themselves. Delight.ai carries customer context across channels, understands intent, and resolves issues without disrupting the flow.
### AI Agent vs Traditional Chatbot
| Capability | Delight AI agent | Traditional chatbots |
|---|---:|---:|
|How it works|Uses goal-oriented reasoning, memory, and context to resolve complex queries through natural language.|Follows rules and scripts; can’t handle multi-step issues.|
|Setup & training|No-code setup; train, test, deploy, and optimize in one workspace.|Slow; manual rule creation and constant upkeep.|
|Answer accuracy|High; considers memory, knowledge content, and live context.|Limited to pre-written responses.|
|Personalization|Long-term cross-channel memory for tailored interactions.|One-size-fits-all replies.|
|Omnichannel|Supports voice, SMS, email, chat, in-app, and more while maintaining context.|Often limited to web/chat; loses context when switching.|
|Human handoff|Seamless cross-channel escalations with full context.|Often loses context during escalation.|
|Maintenance|Self-improves using memory and context.|Requires manual updates to maintain workflows.|
|Governance & safety|Built-in monitoring and safeguards for compliance.|Usually lacks enforced controls.|
|Customer experience|One continuous conversation across channels; less friction.|Rigid, repetitive, and frustrating.|
## Proof beats promises
Delight demonstrates performance at real-world scale with billions of conversations monthly. The platform improves with every interaction, increasing resolution rates over time and delivering better customer experiences.
### Support leaders are trading chatbots for agents
- 
> "We conducted tests and POCs with several chatbot providers and were quite disappointed. Delight’s accuracy and flexibility, paired with Claude, solved the problems others couldn’t."
> — Oh Ju-young, AI Promotion Team at Lotte Homeshopping
- 
> "Delight.AI has been a game changer. Our CX team is highly satisfied and more proactive than ever."
> — Martina Panza, Head of CX, Norse Atlantic Airways
- 
> "Delight’s AI agent had a 90%+ accuracy rate during our POC, giving us the confidence we needed. Delight is more than a vendor. They’re a true collaborative partner."
> — Eugene Kim, CEO, Hanssem
- 
> "The future is one unified agent for sales and support—a single digital concierge that knows you, helps you, and travels with you."
> — Alf Lim, Chief Product Officer, Norse Atlantic Airways
## Build. Test. Evaluate. Repeat.
### Trust OS: Responsible AI powered by observability, oversight, and control.
- Build — Design, test, and launch your AI agent in days using no-code tools.
- Test — Validate early; detect and fix potential issues before they impact customers.
- Evaluate — Monitor results in real time, measure impact, and continuously optimize.
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
Trust OS provides full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale — built on enterprise-grade infrastructure and trusted by global brands.
## Ready to replace your chatbot?
[Request a demo](https://delight.ai/contact-sales)
## ROI Calculator
Source: https://delight.ai/tools/roi-calculator
## Delight.ai AI agent ROI calculator
What industry are you in? (Step 1 of 3)
### Step 01 — What industry are you in?
Progress: 1 / 3
- **Retail** — Shopping assistance, order support, and returns at scale
- **Travel & Hospitality** — Trip planning, disruption management, and in-stay concierge
- **Consumer Health** — Appointment scheduling, billing support, and subscription follow-up
- **On-Demand** — Delay management, payment recovery, and dispute resolution
- **Software & Technology** — Onboarding, technical support, and account growth
- **Home Services** — Call booking, dispatch delays, and post-job follow-up
- **Automotive** — Service booking, repair status, and dealer follow-up
- **Other** — Don't see your industry? Get a cross-industry estimate.
Next →
### Step 02 — ROI Calculator
Proceed to the ROI Calculator to enter metrics (volume, handling time, cost per contact, automation rate) and receive estimated savings and payback time.
Steps:
1. Industry selection (current)
2. ROI Calculator: input operational metrics and pricing
3. Results: estimated ROI, savings breakdown, and recommended next steps
Use this flow to quickly estimate the financial impact of deploying Delight.ai agents in your organization.
## AI agent platform
Source: https://delight.ai/ai-agent
## The AI agent platform for enterprise
Delight.ai gives enterprise customer service teams an AI agent that reasons through requests instead of just answering them, working across every channel a customer picks and getting sharper with every case it resolves.
## How does an AI agent platform resolve a request?
Five steps, one AI agent, no handoffs between systems along the way.
1. Understand — Reads the full request and pulls the account context behind it
2. Reason — Weighs policy, history, and context to choose the right action
3. Act across channels — Executes directly in your systems, by API, integrations, or handoff
4. Loop in a human — Routes judgment calls to a person with a recommendation
5. Learn — Stores the resolution as a reference for the next similar case
## What's running behind delight.ai's AI agent platform?
Each layer works together across every conversation, instead of separate tools bolted on afterward.
## How do you set up an AI agent?
Build the agent's instructions, actions, and knowledge sources in the AI agent builder. No code required to define what it can do or how it should respond.
### Build
### Test
Run the agent through simulated conversations before it reaches a live customer, so edge cases surface in a sandbox first.
### Evaluate
See every conversation, the topics driving volume, and the resolution rate in Analytics, so your team knows exactly what to fix next.
## An AI agent platform for every industry (Slide 1 of 7)
B2B
Travel & hospitality
Healthcare
Retail
Home services
On-demand
Financial services
### Example scenarios
- Integration broken — diagnosed and fixed without a call
“Our Salesforce sync stopped pulling new leads this morning.”
[Explore B2B](https://delight.ai/industries/b2b)
- Flight cancelled — refund, rebook, extend hotel
“My flight got cancelled. I need a refund and a room for tonight.”
[Explore travel & hospitality](https://delight.ai/industries/travel-hospitality)
- Billing dispute — verified and resolved, no hold queue
“My insurance says this visit isn't covered. Can you check?”
[Explore healthcare](https://delight.ai/industries/healthcare)
- Damaged item — approve, replace, notify
“My order arrived damaged. It's a $180 item.”
[Explore retail](https://delight.ai/industries/retail)
- Tech running late — rebooked before the customer calls
“My technician says they're running late. Can I get a new time?”
[Explore home services](https://delight.ai/industries/home-services-and-warranty)
- Late delivery — proactive update, no ticket needed
“My delivery is 40 minutes late.”
[Explore on-demand](https://delight.ai/industries/on-demand)
- Fraud flagged — investigate before they call back
“I don't recognize this $312 charge.”
[Explore financial services](https://delight.ai/industries/financial-services)
## How does Trust OS keep an AI agent accountable?
Every decision is recorded, every change is permissioned, and every fix is reviewed before it ships.
- Full observability into every decision
- Version control and role-based permissions
- Zero-Touch Improvement
[Explore Trust OS](https://delight.ai/trust-os)
## Common questions about delight.ai's AI agent platform
### What is an AI agent platform?
An AI agent platform is the infrastructure that lets an AI agent reason through a customer request, decide what to do next, and act on it across whatever channel the customer used, instead of just answering a question and stopping there. delight.ai's AI agent platform adds [omnichannel continuity](https://delight.ai/omnipresent), a supervisor dashboard in the [AI agent builder](https://delight.ai/builder) for building and onboarding agents, and [governance](https://delight.ai/trust-os) for what an agent can and can't do on its own.
### How is delight.ai's AI agent different from a traditional chatbot?
A traditional chatbot matches a customer's words to a fixed decision tree and fails once the conversation leaves that path. [Delight.ai's AI agent](https://delight.ai/autonomous-agent) reasons through the request instead, pulling account and order context from your systems, choosing the right next step, and continuing the conversation [across channels](https://delight.ai/omnipresent) rather than handing off the moment it gets complicated.
### What is an AI agent builder?
An AI agent builder is the workspace where a team sets up, tests, and refines an AI agent without writing code. delight.ai's [AI agent builder](https://delight.ai/builder) lets you transfer existing knowledge and workflows into a supervisor dashboard, run the agent through simulated conversations before it goes live, and track its [resolution rate and top conversation topics](https://delight.ai/analytics) afterward.
### What are the benefits of an enterprise AI agent platform?
An enterprise AI agent platform cuts the cost of routine support volume while freeing human agents to spend their time on the cases that actually need a person. delight.ai adds [omnichannel continuity](https://delight.ai/omnipresent), continuous learning powered by the [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp), and compliance built for regulated industries, including GDPR, SOC 2 Type II, HIPAA, and ISO 27001.
### Is delight.ai's AI agent platform secure enough for enterprise deployment?
Yes. The AI agent platform runs on the [world's #1 communications API platform](https://delight.ai/enterprise-grade), with encryption at rest and in transit, PII protection, and data masking, and it holds certifications including GDPR, SOC 2 Type II, HIPAA, and ISO 27001. Full detail on how delight.ai secures and scales the platform is on the [security page](https://delight.ai/security).
### Does delight.ai's AI agent platform work across SMS, email, and WhatsApp?
Yes. The AI agent platform [reaches customers across](https://delight.ai/omnipresent) SMS, email, WhatsApp, in-app chat, [voice](https://delight.ai/voice), and social messaging, and it carries the same conversation context between all of them, so a customer never has to restart their story on a new channel.
### Does the AI agent platform integrate with existing customer support software?
Yes. Delight.ai integrates with platforms including [Salesforce](https://delight.ai/product-release/salesforce-supported-as-a-knowledge-source) and [Freshworks](https://delight.ai/product-release/freshworks-integration) for live agent handoff, along with third-party knowledge bases through [AI agent integrations](https://delight.ai/integrations), so your team's existing tools and content stay in place instead of getting replaced.
### Can you control what an AI agent is allowed to do on its own?
Yes. Delight.ai's AI agent platform lets you set spend limits, required approval gates, and escalation rules once through [Trust OS](https://delight.ai/trust-os), and it enforces them automatically on every case afterward, the same governance model behind [Agent Steward](https://delight.ai/autonomous-agent). Every action the agent takes is logged, so your team can see exactly what it did and why.
## Ready to make support a revenue channel?
[Talk to an expert](https://delight.ai/contact-sales)
## Capabilities - Agent memory platform (AMP)
Source: https://delight.ai/agent-memory-platform-amp
## Agent Memory Platform
Give your revenue AI agent the memory to turn intent into sales.
Uses history and live conversations to help customers buy, and buy again.
[View demo](https://delight.ai/demo-request)
## Know what customers want— and what’s holding them back
Learn from every conversation
Your agent picks up preferences, needs, and buying concerns as customers talk.
Get the details right
It confirms key details with customers to keep its memory accurate.
Pick up where they left off
It remembers past conversations so customers don’t have to explain everything again.
## Know your customers better with every conversation
Keep context across channels
Carry what your agent learns across chat, voice, and email—so customers don’t have to repeat themselves.
Turn customer data into better recommendations
Combine CRM, CDP, and warehouse data with what customers tell your agent to recommend what fits.
Control what your agent remembers
Trace each memory to its source. Review, correct, or delete it at any time.
## An agent that knows your customers and your goals
Set the goal
Choose what your agent works toward—more purchases, larger orders, or renewals—without scripting every conversation.
Adapt to each customer
Your agent uses customer history, preferences, and buying intent to decide what to offer and when.
Make buying easier
Recommend a relevant upgrade, address a concern, or help a customer complete their purchase.
## Reach customers when and where they’re ready to buy
Your AI agent uses customer history and preferences to reach customers at the right time, on the right channel, with a restock alert, renewal reminder, or help completing a purchase.
Know what’s relevant
Your agent draws on each customer’s preferences, past orders, and intent to know what matters to them.
Reach out at the right moment
A restock, a renewal, or an unfinished checkout prompts your agent to reach out on the channel each customer prefers.
Turn replies into purchases
When a customer replies, your agent picks up with full context and helps them complete the purchase.
## Memory that drives revenue in your industry
Retail
[Learn more](https://delight.ai/industries/retail)
Travel
[Learn more](https://delight.ai/industries/travel-hospitality)
Software
Healthcare
[Learn more](https://delight.ai/industries/healthcare)
Home & field service
[Learn more](https://delight.ai/industries/field-services)
## When customer intent is recognized, spend follows.
Customers already tell you what they want in conversation. With Agent Memory Platform, your agent remembers it, knows when to act, and turns that intent into the next purchase, upgrade, or renewal.
Quote 1 of 3
  
- One Bev for shopping, support, and membership at BJ’s Wholesale Club.
One Bev for shopping, support, and membership at BJ’s Wholesale Club.
> “Bev gives us a new way to help members across the journey, from finding the right product to getting more value out of their membership. It makes the experience feel more personal, more useful, and more connected.”
Scott Vandegrift, VP of Product, BJ's Wholesale Club
[Read the BJ’s story](https://delight.ai/customers/bjs)
- One agent for sales and support at Norse Atlantic Airways.
> “We are moving past the idea of support bots. The future is one unified agent for sales and support, a single digital concierge that knows you, helps you, and travels with you.”
Alf Lim, CPO, Norse Atlantic Airways
[Read the Norse story](https://delight.ai/customers/norse-atlantic-airways)
- Mixpanel turns support conversations into product adoption.
> “We’re not just resolving issues, we’re accelerating product adoption and revenue growth.”
Kathleen Matthews, Global Support, Mixpanel
[Read the Mixpanel story](https://delight.ai/customers/mixpanel)
## How memory connects to the rest of Delight
For You Conversations
Personalize the welcome and every message after
[Explore](https://delight.ai/for-you-conversations-fyc)
Omnipresence
One continuous agent wherever customers engage
[Explore](https://delight.ai/omnipresent)
Trust OS
Oversight and control over agent behavior
[Explore](https://delight.ai/trust-os)
## Agent Memory Platform (AMP) runs on the infrastructure behind 7 billion+ conversations a month.
Enterprise-grade scalability and reliability
Built on the world’s #1 communications API platform, trusted by industry leaders from on-demand, healthcare, retail, and more.
Ironclad security and compliance
Our infrastructure meets the highest degree of compliance and security requirements, including GDPR, SOC 2 (Type II), HIPAA/HITECH, ISO 42001.
Privacy-first data governance
Your data remains yours—never accessed, used, or shared. With PII protection, AES256 encryption, data masking, and region-specific storage, all secured by TLS 1.3 security.
## Revenue AI agent resources
[Introducing AMP: The Agent Memory Platform](https://delight.ai/blog/product/introducing-agent-memory-platform)
[How For You Conversations Make Every Interaction Feel Personal](https://delight.ai/blog/product/for-you-conversations-fyc)
[The future of customer connection starts with Omnipresence](https://delight.ai/blog/product/connection-starts-with-omnipresence)
[Introducing Trust OS: The foundation for responsible AI agents](https://delight.ai/blog/product/introducing-trust-os-for-ai-agents)
## Agent Memory Platform FAQs
How customer memory works, what it connects to, and how your team controls it.
### What is an agent memory platform?
An agent memory platform gives an AI agent a lasting memory of each customer, including their history, preferences, and current intent. Delight’s Agent Memory Platform (AMP) is the memory behind our [revenue AI agent](https://delight.ai/), so each conversation builds on what your agent already knows.
### How does Agent Memory Platform (AMP) help grow revenue?
Your agent uses what it remembers to recommend what fits, like the right size, a product within budget, or a relevant upgrade. [For You Conversations](https://delight.ai/for-you-conversations-fyc) turns that memory into personalized messages, and returning customers pick up where they left off, which makes it easier to buy again.
### How is Agent Memory Platform (AMP) different from a customer profile?
A customer profile holds the fields your team fills in. Agent Memory Platform (AMP) also learns from conversations, including details customers mention in passing, such as a budget or delivery preference, and combines them with CRM, CDP, and warehouse data through [AI agent integrations](https://delight.ai/integrations).
### How does Agent Memory Platform (AMP) keep customer memory accurate?
Your agent confirms key details with customers to keep its memory accurate. Your team can trace each memory to its source and review, correct, or delete it, with oversight and controls from [Trust OS](https://delight.ai/trust-os).
### How does Agent Memory Platform (AMP) help my agent reach customers at the right time?
Memory tells your agent what matters to each customer. With [Omnipresence](https://delight.ai/omnipresent), a restock, a renewal, or an unfinished checkout can prompt your agent to reach out on the channel each customer prefers.
### Which channels does Agent Memory Platform (AMP) work across?
The same memory carries across chat, SMS, WhatsApp, email, and voice, so customers can switch channels without repeating themselves. On calls, the [Voice AI agent](https://delight.ai/voice) starts from what the customer shared in earlier chats or emails.
### How is customer data kept secure?
Agent Memory Platform (AMP) runs on delight.ai infrastructure with SOC 2 Type II, ISO 27001, and ISO 42001 certifications, and supports GDPR and HIPAA compliance. See our [security and compliance](https://delight.ai/security) page for details on encryption, regional data storage, and PII protection.
## Find your revenue opportunities
See where your revenue AI agent can help customers buy, and buy again.
[Talk to an expert](/contact-sales)
## Capabilities - Trust OS
Source: https://delight.ai/trust-os
## The Trust operating system for AI agents in production
[View demo](https://delight.ai/demo-request)
## Customers and partners
       
     
    
## Trust OS: Responsible AI powered by observability, oversight, and control
- Build Build with confidence and ease. Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test Validate early, before it matters. Detect, analyze, and fix potential issues long before they impact your customers.
- Evaluate Stay in control, always. Monitor results in real time, measure impact, and continuously optimize your business performance.
## See inside every conversation. Everything is logged and easy to trace.
- [ Low CSAT Analysis
Every 1–2 score conversation is sorted into one of five failure types. You see the breakdown before you open a single conversation.](https://delight.ai/product-release/low-csat-analysis)
- [ Thinking Messages
Replace the generic typing indicator with a specific status for every tool call. Customers know what's happening. Wait times feel shorter.](https://delight.ai/blog/product/thinking-messages)
- [ Openbook
Every conversation, decision, and tool call is fully logged and easy to review. No black boxes.](https://delight.ai/blog/product/introducing-openbook)
## Stay in control of what your AI says and does
- [ Review & grade responses
Every response gets a quality score. Flag outliers, surface patterns, and grade against your standards — without reading every conversation manually.](https://delight.ai/product-release/humans-as-ai-evaluators)
- [ Hallucination & unsafe content detection
Detect factual errors, off-policy responses, and safety violations automatically. Know about it before your customers do.](https://delight.ai/product-release/ai-hallucination-detection-alerts)
- [ Kill switch & escalation controls
Pause, redirect, or kill any AI flow instantly. One-click escalation to a human agent — with RBAC so the right people own the right agents.](https://delight.ai/blog/product/introducing-trust-os-for-ai-agents)
## Every change tested before it touches a customer
Staging, real-conversation test suites, gradual rollout, and A/B data — so every deploy comes with evidence, not faith.
### Staging environments
A production mirror with real integrations and policies. Catch issues early, before they reach customers.
### Gradual rollout & A/B testing
Ship to 10% first. Run two versions head-to-head. Get hard data on resolution rate and CSAT before you commit to either.
### Test Upgrade
Run real conversations through your new version. Catch issues early, before full release.
## With zero-touch improvement, your AI keeps getting better while you sleep
Observability catches the issue, Control tests the fix, and Oversight keeps everything on policy. ZTI runs the whole loop for you.
[Learn more](https://delight.ai/blog/product/introducing-zero-touch-improvement)
## Go deeper on every feature
### Introducing Openbook: Navigate your actionbook like a map
Openbook renders your actionbook as a visual flow so you can navigate complex logic without scrolling through markup. 10,000 feet to 10 feet in one click.
[Read more](https://delight.ai/blog/product/introducing-openbook)
### Test Upgrade: Build your AI agent test set from real conversations
Create test cases from real conversation IDs, organize with labels, share sets across environments, and get consistent pass/fail results.
[Read more](https://delight.ai/blog/product/test-upgrade)
### Introducing Actionbook Editor: Edit your AI's rules like a Notion doc
Actionbook Editor gives ops teams direct authorship over AI behavior—no code, no engineering tickets. Edit refund thresholds, escalation logic, and tone in real time.
[Read more](https://delight.ai/blog/product/introducing-actionbook-editor)
### Thinking Messages: The gap between question and answer
The typing indicator tells users to wait. Thinking Messages tell them why. Trust OS adds per-tool messages that replace generic dots with informative statuses.
[Read more](https://delight.ai/blog/product/thinking-messages)
### Gradual rollout and A/B testing: Stop guessing whether your AI changes worked
Delight.ai's Trust OS adds gradual rollout and A/B testing to validate changes with real traffic and prove effectiveness before full rollout.
[Read more](https://delight.ai/blog/product/gradual-rollout-and-a-b-testing)
### Introducing Zero-Touch Improvement: Your AI gets better while you sleep
Zero-Touch Improvement is a graduated AI optimization system that evolves from human-guided learning to fully autonomous improvement. Part of Trust OS 2.0.
[Read more](https://delight.ai/blog/product/introducing-zero-touch-improvement)
## Ready to run agents you actually trust?
Trust OS 2.0 is available now.
[Talk to an expert](https://delight.ai/contact-sales)
## Capabilities - For you conversations (FYC)
Source: https://delight.ai/for-you-conversations-fyc
## For You Conversations: Personalized recommendations that drive sales
[View demo](https://delight.ai/demo-request)
## Help customers find what fits, and buy it
-  Recommend from purchase history
Your agent uses past purchases, preferences, and budget to suggest products that fit each customer.
-  Work toward your revenue goals
Choose what your agent aims for, such as cross-sell or larger orders, and when it should focus on support instead.
-  Open with what matters to the customer
Your agent starts from details the customer has shared, like a birthday coming up, and offers something that fits.
## Retail & ecommerce: guide each shopper to the right product
[Explore retail](https://delight.ai/industries/retail)
-  Compare products for each shopper
Narrow the options to products that fit each shopper's needs, budget, and past purchases.
-  Answer sizing and stock questions
Check sizes, availability, and delivery options so shoppers can decide in the conversation.
-  Suggest what goes with the cart
Offer items that match what's in the cart or what the shopper bought before, then add them to the cart.
## Travel & hospitality: offer upgrades that fit each traveler
[Explore travel & hospitality](https://delight.ai/industries/travel-and-hospitality)
-  Offer the upgrades travelers prefer
Suggest seats, rooms, and add-ons based on each traveler's past trips and stated preferences.
-  Use loyalty tier to shape each offer
Tailor each offer to the traveler's loyalty status and booking history.
-  Rebook disrupted trips early
Reach travelers when a flight changes and offer a new option that fits their preferences, like an aisle seat.
## Software & technology: help each account get more from its plan
[Explore software & technology](https://delight.ai/industries/technology)
-  Check the account before answering
Your agent looks up each account's plan, access, and setup before it replies.
-  Suggest a plan when usage grows
Explain limits with the account's own usage data and suggest the plan that fits.
-  Guide customers to features they own
Point customers to features in their plan that they haven't set up yet.
## Memberships & subscriptions: help members use what they pay for
[Explore B2B](https://delight.ai/industries/technology)
-  Remind customers before credits expire
Let customers know when credits or benefits are about to expire, on the channel they use.
-  Suggest where to use what's left
Recommend ways to use remaining credits, based on what each customer has done recently.
-  Follow up across channels
Continue the same conversation over SMS, email, or WhatsApp without losing context.
## See memory and personalization at work
#### Each conversation starts from what your agent already knows
Your agent remembers past conversations, preferences, and orders.
It keeps the same context across chat, SMS, email, and voice.
Customers pick up where they left off, which makes it easier to buy again.
## How brands turn conversations into revenue
Quote 1 of 3
  
### BJ’s Wholesale Club
#### How brands turn conversations into revenue
> “Bev gives us a new way to help members across the journey, from finding the right product to getting more value out of their membership. It makes the experience feel more personal, more useful, and more connected.”
Scott Vandegrift
VP of Product, BJ’s Wholesale Club
[Read the BJ’s story](https://delight.ai/customers/bjs)
### Norse Atlantic Airways
#### How brands turn conversations into revenue
> “We are moving past the idea of ‘support bots.’ The future is one unified agent for sales and support—a single digital concierge that knows you, helps you, and travels with you.”
Alf Lim
Chief Product Officer, Norse Atlantic Airways
[Read the Norse story](https://delight.ai/customers/norse-atlantic-airways)
### Mixpanel
#### How brands turn conversations into revenue
> “The AI knows if you’re asking because you don’t have access yet, or because you’re trying to configure something advanced. In the first case, we’re converting curiosity into revenue. In the second, we’re driving deeper product adoption.”
Kathleen Matthews
Global Support, Mixpanel
[Read the Mixpanel story](https://delight.ai/customers/mixpanel)
## How For You Conversations connects to the rest of Delight
**Agent Memory Platform (AMP)**
The customer history behind each recommendation
[Explore](https://delight.ai/agent-memory-platform-amp)
**Omnipresence**
One continuous agent wherever customers engage
[Explore](https://delight.ai/omnipresent)
**Trust OS**
Control what your agent recommends and offers
[Explore](https://delight.ai/trust-os)
## Delight agent runs on the infrastructure behind 7 billion+ conversations a month.
**Enterprise-grade scalability and reliability**
Built on the world’s #1 communications API platform, trusted by industry leaders from on-demand, healthcare, retail, and more.
**Ironclad security and compliance**
Our infrastructure meets the highest degree of compliance and security requirements, including GDPR, SOC 2 (Type II), HIPAA/HITECH, ISO 42001.
**Privacy-first data governance**
Your data remains yours—never accessed, used, or shared. With PII protection, AES256 encryption, data masking, and region-specific storage, all secured by TLS 1.3 security.
## Revenue AI agent resources
[
How For You Conversations Make Every Interaction Feel Personal
Discover how brands can deliver experiences that are consistent, intelligent, and deeply human with For You Conversations from delight.ai.](https://delight.ai/blog/product/for-you-conversations)
[
Introducing AMP: The Agent Memory Platform
Agent Memory Platform (AMP) is delight.ai’s new intelligence layer that gives your AI agents a living memory and a business brain. It connects what your agent remembers about every customer with what your business wants to achieve.](https://delight.ai/blog/product/introducing-agent-memory-platform)
[
What is an AI shopping assistant? How top options compare
Learn what an AI shopping assistant is and how leading platforms differ in channel coverage, workflow depth, and more to help you choose the right fit.](https://delight.ai/blog/ai-for-cx/ai-shopping-assistant-comparison)
[
How does real-time customer engagement impact your business?
Real time customer engagement is usually about response speed. Here's why proactive AI customer service, reaching out first, closes the gap reactive tools miss.](https://delight.ai/blog/ai-for-cx/real-time-customer-engagement)
## For You Conversations FAQs
How personal conversations work, what they draw on, and how your team controls them.
### What is For You Conversations?
For You Conversations (FYC) is how Delight's revenue AI agent personalizes each conversation. It draws on what [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) knows about the customer to open with what's relevant, recommend what fits, and help the customer buy, book, or upgrade.
### How does For You Conversations help grow revenue?
Your agent suggests products that match each customer's past purchases, preferences, and budget, answers the questions that hold up a purchase, and can add items to the cart. You choose the goal, such as cross-sell or larger orders, and your agent works toward it in each conversation.
### How is For You Conversations different from basic personalization?
Basic personalization fills in a name or shows one offer to a whole segment. For You Conversations adapts each recommendation and reply to one customer's history, preferences, and current intent, and keeps that context when the customer switches channels.
### Can the agent reach out before a customer asks?
Yes. With [Nudge Message](https://delight.ai/blog/ai-for-cx/real-time-customer-engagement), your agent can start the conversation when a shopper stalls on a page or leaves a form unfinished. With [Omnipresence](https://delight.ai/omnipresent), it can follow up on the channel each customer prefers when something relevant happens, such as a restock or an expiring offer.
### How does my team control what the agent recommends?
Your team sets goals and rules in plain language, such as which products to feature or when to focus on support. With [Trust OS](https://delight.ai/trust-os), you can test changes before rollout, review conversations, and flag topics such as discount requests for review.
### Which channels does For You Conversations work across?
Chat, SMS, WhatsApp, email, and [voice](https://delight.ai/voice). The same customer memory carries across each channel, so a conversation that starts in chat can continue by email or phone without the customer repeating themselves.
### Does For You Conversations improve over time?
Yes. Each conversation adds to what your agent remembers, so the next recommendation starts with more context. Your team can trace each memory to its source and correct or delete it.
### How do I measure revenue from For You Conversations?
Export conversation-level data with the [Data Export API](https://delight.ai/product-release/data-export-api) and join it to your order or booking data in your BI tool. You can then report conversion and order value for the conversations your agent handled, using the definitions your team already works with. Learn more about [Analytics](https://delight.ai/analytics).
## Find your revenue opportunities
See where personal recommendations can help your customers buy.
[Talk to an expert](https://delight.ai/contact-sales)
## Spark waitlist confirmation
Source: https://delight.ai/spark-thank-you
## Thank you for your interest in Delight Spark
You’re officially on the waitlist. Our team is reviewing signups and will be in touch soon to confirm your spot. Keep an eye on your inbox and save the date on your calendar.
[](https://delight.ai/customers/norse-atlantic-airways "Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen")
##### [Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways "Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen")
[](https://delight.ai/customers/hipages "What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving")
##### [What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages "What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving")
[](https://delight.ai/customers/lotte-homeshopping "Lotte Homeshopping partners with Anthropic and delight.ai to scale retail")
##### [Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping "Lotte Homeshopping partners with Anthropic and delight.ai to scale retail")
[](https://delight.ai/customers/gther "gther transforms event registration with delight.ai")
##### [gther transforms event registration with delight.ai](https://delight.ai/customers/gther "gther transforms event registration with delight.ai")
## Thank you page | Webinar registration
Source: https://delight.ai/webinar-thank-you
## You’re registered for the delight.ai webinar
We'll send your confirmation email and calendar invite shortly. Save the date and get ready to see how leading teams are deploying AI in CX.
#### [Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways "Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen")
#### [What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages "What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving")
#### [Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping "Lotte Homeshopping partners with Anthropic and delight.ai to scale retail")
#### [gther transforms event registration with delight.ai](https://delight.ai/customers/gther "gther transforms event registration with delight.ai")
## Desk product page
Source: https://delight.ai/desk
## The AI-native helpdesk
The complete helpdesk for your support team, free forever, with an AI agent built into the same platform from day one.
[Watch demo](https://delight.ai/demo-request)
## What does Desk include?
Desk is a complete helpdesk where human agents and the AI agent share the same platform, workflows, and knowledge base.
### Ticket management
Create, assign, and close tickets across chat, email, and voice. When the AI escalates, agents get the full reasoning trail.
### Routing and automation
Skills-based routing, priority queues, and automations route each escalation by confidence level to the right agent.
### Knowledge management
Shared knowledge base for customers, agents, and the AI. Relevant articles appear inline as agents work tickets.
### AI Copilot
Suggested responses, summaries, and inline knowledge search built into every ticket. Every resolution feeds back to the AI.
## Your ticket history doesn't start over
Migrate your Zendesk history to Desk and build Actionbooks from your past resolutions.
[Migrate today](https://delight.ai/zendesk-migration-wizard)
-  Desk imports your ticket history
Import your Zendesk tickets, Help Center, and conversation history.
-  That history becomes Actionbooks
Turn your team's past resolutions into AI-ready Actionbooks.
-  Your AI agent starts trained, not blank
Your AI starts with your team's knowledge from day one.
## Purpose-built for human agents
$0 per seat
The full helpdesk with no seat fees, no tier limits, and no annual commitment.
AI reasoning trail
When the AI escalates, agents see exactly what it tried and why it handed off.
Exception inbox
Only what needs human attention. Filter by confidence, channel, reason.
50+ language translation
Agents and customers in their own language. Brand terms preserved.
## AI agent resources
- [Desk AI Copilot](https://delight.ai/product-release/desk-ai-copilot)
Every ticket is a race against time. Desk AI Copilot helps you stay ahead.
- [Human Initiated Proactive Email in Desk](https://delight.ai/product-release/human-initiated-proactive-email-in-desk)
Send proactive updates (delays, downtime, billing) directly from Desk.
- [Desk Glossary: Translation without rules](https://delight.ai/product-release/desk-glossary)
Control brand names, job titles, compliance terms, and regional variations.
- [Customizable post-chat CSAT survey](https://delight.ai/product-release/customizable-post-chat-csat-survey)
Tailor post-chat CSAT surveys to your brand and rules.
## Desk FAQs
Common questions about Desk, AI agent integration, and pricing.
**Is Desk really free?**
Yes. Desk has no seat fees or tier limits. You only pay for successful AI resolutions — when your AI agent closes a conversation without escalating to your team. If it passes a conversation to a human, there's no charge for that interaction.
**Does Desk have user access controls?**
Yes. Desk has role-based access control with team-level and agent-level permissions. Administrators define what each agent can see, edit, or export across ticket data, customer profiles, and reporting.
**How does the AI agent hand off to human agents?**
When the AI hits an approval gate or a confidence threshold, it escalates to Desk with full context: everything it tried, every system it queried, and why it stopped. The agent picks up from that exact point without re-reading the conversation. On voice, the live call transfers directly to a Desk agent with the full transcript already attached.
**Does Desk work across chat, email, and voice?**
Yes. Tickets in Desk can originate from chat, email, or voice, and agents work all three channels from a single queue. When your AI agent handles a conversation on any channel and needs to escalate, it creates a Desk ticket with the full conversation history attached.
**Can I run Desk without the AI agent?**
Yes. Desk is a fully functional helpdesk for human agents with no AI required to operate it. You can start with Desk today and add the AI agent when you're ready.
**How is Desk different from Zendesk or Freshdesk?**
Desk is designed from the ground up for AI-native support. The AI agent and Desk share the same workflows, knowledge base, and audit trail — they're one system, not an integration. Zendesk and Freshdesk were built for human agents and bolt AI on top. With Desk, the AI and your team run the same playbooks, and every human resolution feeds back to improve the AI automatically.
**What reporting does Desk provide?**
Real-time monitoring of the full AI and human system: AI resolution rate, human escalation rate, average handle time, confidence trends, and handoff time. One dashboard for the whole picture.
**Do you support migrating ticket history from another helpdesk?**
Yes. Our forward deployment team runs migrations from existing helpdesks, moving your tickets and Help Center content into Desk and our KMS. We also support building Actionbooks from your historical ticket data so your AI agent starts trained on your team's resolution patterns. Talk to the team to scope your migration.
## Ready to run a smarter support team?
[Start free](https://delight.ai/zendesk-migration-wizard)
## Industries
Source: https://delight.ai/industries
## Meet delight.ai, your branded AI concierge
Turn every customer interaction into a meaningful moment of recognition, loyalty, and long-term growth.
[Request demo](https://delight.ai/contact-sales)
### Memory
### For You Conversations
### Never Disconnected
- 
    
   
   
  
## Delight every customer and turn them into lifelong fans
### Agent memory: A living understanding of every customer
Delight.ai builds a living understanding of every customer—who they are, what they love, and what they need next.
### For-you conversations (FYC): Hyper-personalized service
Powered by memory, delight.ai makes every conversation personal and every customer valued.
### Memory + FYC: Customer loyalty and brand obsession
Every conversation builds memory. Every memory makes the next conversation more personalized, sparking unexpected delight.
## Omnipresence: Always present, always connected
### Omnichannel continuity: Turn every pause into progress
Delight.ai maintains memory across chat, SMS, email, and voice. Reengage after a drop-off without missing a beat.
### Voice AI: The most human way to stay connected
Delight.ai's voice support feels natural, personal, and instant—in any language.
### Proactive presence: Always a step ahead
Like a true concierge, delight.ai anticipates customer needs before they ask. Turn every moment of service into a moment of surprise.
## Build on Trust OS. Deliver with confidence.
### Trust OS: Responsible AI powered by observability, oversight, and control
- Build — Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Validate early, detect and fix issues before they impact customers.
- Evaluate — Monitor results in real time, measure impact, and continuously optimize performance.
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
A foundational system giving brands full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale — built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
## The world’s leading brands deliver with delight.ai
- 
> "Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations."
Martina Panza — Head of CX, Norse Atlantic Airways
- "From first click to full value. An AI concierge that guides each user through tailored workflows that turn onboarding into ongoing adoption."
- "Keeping deliveries moving and operations effortless. An AI concierge keeps customers, couriers, and merchants in sync in real time."
- "The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is a collaborative partner who works alongside us to solve challenges."
Eugene Kim — CEO, Hanssem
- "Where relationships build repeat business. Hipages helps 33,000 tradies build trust through real-time, personalized communication, turning one-off jobs into lasting customer relationships across 12 million projects."
## Connecting the world with AI concierges — Powering 300 million users every month
#### 7 billion+
Conversations every month
#### De-risk AI investment with battle-tested infrastructure
#### Gain value out of enterprise white glove support
#### 300M+
Monthly active users
#### 99.9%+
Uptime
#### Integrate with your customer service tech stack
## Delight AI concierge FAQs
Find answers to common questions about AI concierges. Learn how the delight.ai AI concierge works.
### What is an AI concierge?
An AI concierge is an AI agent trained on your knowledge base and customer conversations to provide exemplary service autonomously. It can remember guest preferences from real-time conversations and apply them to future interactions across channels.
### What use cases can an AI concierge handle?
Train your AI concierge to care for customers at any point in the lifecycle: sales, support, proactive reengagement, renewals, onboarding, and more.
### Can delight.ai AI concierges improve operational efficiency?
Yes. With natural language processing, concierges handle routine tasks (rescheduling, order updates, recommendations, rebooking), freeing staff to focus on higher-value guest experiences.
### How can I monitor and improve my delight.ai AI concierge?
The platform includes monitoring for adherence to Actionbooks (your SOPs), agent performance metrics, and AI-generated insights for gap analysis. You retain full oversight and control.
#### Ready to delight your customers with an AI concierge?
[Contact sales](https://delight.ai/contact-sales)
## Form
Source: https://delight.ai/form
## Meet delight.ai, your branded AI concierge
Turn every customer interaction into a meaningful moment of recognition, loyalty, and long-term growth.
[Request demo](https://delight.ai/contact-sales "Request demo")
- 
## Delight every customer and turn them into lifelong fans.
### Agent memory: A living understanding of every customer
Delight.ai builds a living understanding of every customer—who they are, what they love, and what they need next.
### For-you conversations (FYC): Hyper-personalized service
Powered by memory, delight.ai makes every conversation personal and every customer valued.
### Memory + FYC: Customer loyalty and brand obsession
Every conversation builds memory. Every memory makes the next conversation more personalized, sparking unexpected delight.
## Omnipresence: Always present, always connected.
### Omnichannel continuity: Turn every pause into progress
Delight.ai maintains memory across chat, SMS, email, and voice. Reengage after a drop-off without missing a beat.
### Voice AI: The most human way to stay connected
Delight.ai's voice support feels natural, personal, and instant—in any language.
### Proactive presence: Always a step ahead
Like a true concierge, delight.ai anticipates customer needs before they ask. Turn every moment of service into a moment of surprise.
## Build on Trust OS. Deliver with confidence.
### Trust OS: Responsible AI powered by observability, oversight, and control.
- Build — Build with confidence and ease. Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Validate early, before it matters. Detect, analyze, and fix potential issues long before they impact your customers.
- Evaluate — Stay in control, always. Monitor results in real time, measure impact, and continuously optimize your business performance.
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
A foundational system giving brands full visibility, oversight, and control for transparent and compliant AI operations.
A Sendbird product proven at scale — built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
## The world’s leading brands deliver with delight.ai
  
  
  
  
  
- 
> "Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations."
Martina Panza — Head of CX, Norse Atlantic Airways
- > "The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is more than a vendor. They're a true collaborative partner who works alongside us to solve challenges from the same perspective."
Eugene Kim — CEO, Hanssem
- > "Where relationships build repeat business. Hipages helps 33,000 tradies build trust through real-time, personalized communication, turning one-off jobs into lasting customer relationships across 12 million projects."
## Connecting the world with AI concierges.
Powering 300 million users every month.
#### 7 billion+
Conversations every month
#### De-risk AI investment with battle-tested infrastructure
#### Gain value out of enterprise white glove support
#### 300M+
Monthly active users
#### 99.9%+
Uptime
#### Integrate with your customer service tech stack
## Delight AI concierge FAQs
Find answers to common questions about AI concierges. Learn how the delight.ai AI concierge works.
### What is an AI concierge?
An AI concierge is an AI agent that is trained on your knowledge base and customer conversations to provide exemplary service—autonomously.
Like a personal assistant, delight.ai AI concierge can remember guest preferences from real-time conversations and use them to offer tailored, personalized experiences with your brand across channels. They will also apply everything they have learned about your customer to all future conversations with that customer.
### What use cases can an AI concierge handle?
You can train your AI concierge to care for your customers at any point in the customer lifecycle — from sales through customer support, including proactive reengagement and renewal.
### Can delight.ai AI concierges improve operational efficiency?
Yes. Equipped with natural language processing, AI concierges can handle routine tasks that human agents often handle, such as rescheduling appointments, providing order updates, making local recommendations, and proactively rebooking cancelled flights.
This frees staff to focus on higher-value guest experiences.
### How can I monitor and improve my delight.ai AI concierge?
The delight.ai platform has a built-in system for monitoring how well your AI agent is adhering to [Actionbooks](https://delight.ai/builder "Actionbooks"). It includes agent performance metrics and data-driven, AI-generated insights into where gaps are. You have full oversight and control of your AI concierge.
#### Ready to delight your customers with an AI concierge?
[Contact sales](https://delight.ai/contact-sales "Contact sales")
## AI Readiness Assessment
Source: https://delight.ai/tools/ai-readiness-assessment
## AI Readiness Assessment
10 questions to reveal your AI readiness strengths and opportunities.
### Question 1 of 10
#### Q1. Team Alignment
Do you have a cross-functional team dedicated to evaluating AI projects?
Scale: 1 2 3 4 5
Options:
- No
- Yes, shared
- Yes, dedicated
`Next`
### Almost there!
Get your personalized AI readiness score and recommendations
## AI Readiness Assessment Results
Source: https://delight.ai/ai-readiness-assessment-results
## Strengths Sentences
No strengths identified yet.
## Improvements Sentences
- Strengthening **team alignment** around AI will accelerate your path to deployment.
- Improving **cross-functional collaboration** between CX, Product, and Engineering will unlock faster AI adoption.
- Organizing your **knowledge base** with consistent structure and tagging will dramatically improve AI accuracy.
- Improving **data access** by centralizing customer context will unlock AI's ability to resolve issues faster.
- Developing **use case clarity** will help you focus AI efforts where they'll have the most impact.
- Building a centralized **SOP** database will give AI the guardrails to act confidently and consistently.
- Establishing **success metrics** now will ensure you can demonstrate AI's value from day one.
- Improving **integration capability** across your support tools will remove the manual gaps that limit automation.
- Prioritizing **security & compliance** alignment early will prevent costly delays down the road.
- Elevating CX as a business **priority** will build the internal momentum AI initiatives require.
## What's next?
------------
## AI Readiness
Source: https://delight.ai/tools/ai-readiness-intro
[](/ "Home")
[](/ "Home")AI readiness assessment
[Why readiness](#why-readiness "Why readiness")[Score means](#score-means "Score means")[What we evaluate](#what-we-evaluate "What we evaluate")[Testimonials](#testimonials "Testimonials")
[ Take assessment](/ai-readiness-assessment)
## AI readiness assessment
This quick AI readiness assessment evaluates whether you have the business clarity, team alignment, and operational foundations needed for successful AI agent deployment. Simply answer 10 questions about your current state—no technical expertise required. You'll receive an immediate readiness score and next steps based on your results.
Results: Receive curated next steps and guidance for your specific needs
Time: 7 minutes
Format: Multiple choice
[ Take assessment](/ai-readiness-assessment)
### See where you are today—and where to focus next
AI success isn’t just about technology—it’s about people, processes, and execution. Our CX AI readiness assessment gives you a clear baseline and personalized recommendations to drive meaningful transformation.
###### Actionable feedback
Receive actionable feedback based on your strengths, weaknesses, and gap areas.
###### Continuous Improvement
You can improve your score over time by following these personalized recommendations.
> “60% of change efforts fail to meet the organization’s goals, largely because teams underestimate the human and operational work required.”
GartnerFrom Resistance to Results:
A Leadership Guide to Change Management
### What your AI readiness assessment score means
Your AI readiness score measures your organization’s ability to integrate, deploy, and scale AI to deliver measurable business value without unnecessary risk.
- 85-100 : AI ReadyYou're fully prepared. It’s time to map your implementation timeline and talk about deployment.
- 70-84 : Almost readyYou're moderately prepared. A few targeted improvements will get you there.
- 50-69 : Building blocksYou have a foundation and need to focus on a few gap areas.
- Below 50 : Foundation phaseMost organizations start here at their first assessment.
### What does our AI readiness assessment
for CX solutions evaluate?
Your AI readiness score measures your organization’s ability to integrate, deploy, and scale AI to deliver measurable value without unnecessary risk.
###### AI strategy
For an AI strategy to have legs, the first thing you need to do is get all the right people in the room to define your AI initiative. Most importantly, you’ll also need to align on what success looks like. This AI council usually includes an executive leader, as well as leaders from CX, product, and engineering.
###### Prioritized use cases
Once you’ve convened your AI council, it’s time to align on what use cases you’d like AI to handle. For risk management, we recommend starting with routine inquiries before moving on to more complex use cases that may require data calls across multiple systems.
###### AI-ready infrastructure and data
AI agents require access to all of the support tools that your team uses. Ideally, these support tools are integrated to enable reliable data retrieval and performance. For your customer data and knowledge bases to be AI-ready, they should be well-structured, tagged, and up to date.
###### Workflows
Just like any new team member, the AI agent will need to know your standard operating procedures (SOPs). Documented workflows are core to successful AI implementation.
###### Data governance
Define security, data privacy, and compliance standards with the right teams upfront—don’t leave them as an afterthought. Once these areas are planned, your organization is AI-ready. With our expertise in unifying and managing customer data across systems, delight.ai is here to help.
> “Delight.ai is more than a vendor. They're a true collaborative partner who works alongside us to solve challenges from the same perspective. Through this partnership, we've been able to build and scale our AI capabilities with confidence and speed.”
Eugene Kim,CEO of Hanssem
### See what it takes to be
AI-ready in minutes
[ Take assessment](/ai-readiness-assessment)
## Thank you - Event registration
Source: https://delight.ai/registration-thank-you
## Thank you for requesting an invitation.
Our team will review your request and confirm your attendance shortly. Please check your inbox for updates.
## Featured customer stories
- [](https://delight.ai/customers/norse-atlantic-airways)
[Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
- [](https://delight.ai/customers/hipages)
[What powers Australia's #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)
- [](https://delight.ai/customers/lotte-homeshopping)
[Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping)
- [](https://delight.ai/customers/gther)
[gther transforms event registration with delight.ai](https://delight.ai/customers/gther)
## delight.ai vs Decagon
Source: https://delight.ai/compare/decagon-vs-delight
## Evaluating Decagon?
Decagon calls itself an AI concierge. It forgets you every time.
Delight.ai resolves tickets and builds lasting customer relationships by remembering context, acting proactively, and driving outcomes long after the conversation ends.
Talk to an expert
### Customers and logos
   
   
   
   
  
### Key platform stats
- 7B+ conversations/month
- 300M+ monthly active users
- 99.9%+ uptime SLA
- 10+ years enterprise infrastructure
---
## See how AI turns every interaction into a delight
VIDEO CHAPTER
0:00 Intro
0:40 Messenger conversation
3:10 Text conversation
4:26 Voice conversation
7:46 Summary of end user view
8:18 Dashboard
#### Watch the interactive demo
## Delight.ai vs Decagon — What sets us apart across seven capabilities
| Capability | Delight AI agent | Decagon |
|---|---|---|
| Customer memory | Persistent across sessions, channels, and time — anticipates needs proactively. | Context shared within a session; resets between conversations. |
| Revenue impact | Proactive outreach, recommendations, and clienteling built into the platform. | Optimized for ticket deflection and cost reduction; no native revenue capabilities. |
| AI governance & safety | Unified governance framework with full observability and audit trails. | Guardrails and tracing; solid, but no unified framework for compliance teams. |
| Agent rules | Structured SOPs with deterministic audit trails; provable actions. | Plain-English rules compiled to code — fast to iterate, harder to audit. |
| Channels | Chat, SMS, email, voice — all sharing one persistent memory. | Chat, email, voice; shared brain per session — not persistent across time. |
| Scale | 7B+ conversations/mo; 99.9% uptime; 10+ years proven infra. | Founded 2023. |
| Brand experience | Fully branded concierge — identity, voice, personality across channels. | Customizable agents; core experience built around support resolution. |
## Only on delight.ai: Agent Memory that knows your customers
Decagon forgets. Delight.ai's Agent Memory Platform (AMP) remembers, building a profile from every conversation, purchase, and signal across chat, email, voice, and SMS.
- For You Conversations — Make every interaction feel personal
- Omnipresence — Connected conversations everywhere
- Trust OS — Govern every AI concierge decision
## Where Decagon is strong — Where we differ
We believe the strongest case for delight.ai is an honest one. Here's where Decagon excels, and where we're built differently.
### When to choose each platform
| Use case | Delight AI agent | Decagon |
|---|---|---|
| Volume deflection | Deflection plus more — deflects tickets and builds persistent customer profiles, proactively reaches out and drives revenue. | High-volume deflection — fast to deploy, built for scale and inbound resolution. |
| Customer relationships | AI that knows your customers — living profile across every channel, anticipates needs and follows up. | Session-based context — context scoped to the active conversation, configured per session via AOPs. |
| Regulated industries | Built for regulated environments — Trust OS with observability, version control, human oversight. SOC 2 Type II, HIPAA, GDPR, ISO 27001. Deterministic audit trails. | QA monitoring and Watchtower provide always-on quality monitoring and regression detection. |
### Platform strengths highlighted
- Well-designed AOPs: CX teams can iterate without heavy engineering support.
- Watchtower QA with strong monitoring: Always-on regression detection before issues reach customers.
- Fast deployment to production: Minimal setup and fast onboarding available when speed to launch is a priority.
## Questions worth asking any AI vendor, including us
The right questions surface the real differences between platforms.
- If a customer called three months after their last interaction, what would your AI remember?
Most platforms reset between sessions. Agent Memory Platform builds a persistent, growing profile of each customer across every interaction, so your AI always knows who it's talking to. See: [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp)
- Can your AI reach out to a customer proactively, without them contacting you first?
Inbound resolution and proactive engagement are architecturally different. Most platforms only do one. [Omnipresent](https://delight.ai/omnipresent) AI triggers outbound conversations based on real customer signals.
- How do we audit exactly what the AI decided and why?
Confidence scores are not the same as a deterministic audit trail. [Trust OS](https://delight.ai/trust-os) gives step-by-step visibility into every AI decision: what data was used, what action was taken, and why.
- What is your published uptime SLA and how many conversations do you handle per month?
Customer references are not a substitute for published numbers. Delight.ai handles over 7 billion conversations per month with 99.9%+ uptime and enterprise-grade infrastructure.
- Does your AI drive revenue or just reduce support costs?
Deflection reduces cost. A concierge that recommends products, prevents churn, and proactively engages customers creates revenue. See the [ROI calculator](https://delight.ai/tools/roi-calculator).
- What does the AI experience feel like to my customer? Will they know it is your platform or mine?
A white-labeled widget differs from a fully branded concierge. Every interaction is configurable to your tone, persona, and identity across channels. [See how it works](https://delight.ai/omnipresent).
---
## See the difference in 30 minutes
We'll show you AMP and Trust OS live, with your use case.
Contact sales
## delight.ai vs Sierra
Source: https://delight.ai/compare/sierra-vs-delight
## Evaluating Sierra?
Sierra is built for one CX job. Delight.ai is built for all of them.
Sierra builds an AI layer on top of your existing helpdesk. Delight.ai provides a full CX suite: AI governance, a native helpdesk, and complete ownership for your team.
Talk to an expert
### Trusted by leading brands
   
   
   
   
  
### Platform statistics
- 7B+ conversations / month
- 300M+ monthly active users
- 99.9%+ uptime SLA
- 10+ years enterprise infrastructure
## See how AI turns every interaction into a delight
VIDEO CHAPTERS
- 0:00 Intro
- 0:40 Messenger conversation
- 3:10 Text conversation
- 4:26 Voice conversation
- 7:46 Summary of end user view
- 8:18 Dashboard
### Watch the interactive demo
## Delight.ai vs Sierra: The gaps Sierra leaves open on the decisions that matter
| Capability | Delight AI agent | Sierra |
|---|---:|---:|
| AI governance & trust | Trust OS with policy enforcement, human oversight, and full observability; every AI decision is auditable. | No equivalent governance layer; monitoring relies on supervisory AI without structural safeguards. |
| Customer control | CX teams own Actionbooks; build and deploy without vendor dependency. | Changes require Sierra’s services team, slowing iteration. |
| CX stack completeness | AI concierge, Desk, and messaging in one platform — single vendor and contract. | AI layer on top of existing helpdesks (Zendesk, Salesforce); requires additional integrations and vendors. |
| Agent scope | One AI concierge across the full customer lifecycle, not just support. | Focused on inbound support and ticket resolution. |
| Channels | Chat, voice, SMS, email, social with shared persistent memory across channels. | Multi-channel, but context tends to be limited to each session. |
| Infrastructure | Native messaging infrastructure built on Sendbird; production-proven for over a decade. | No proprietary messaging layer; relies on external helpdesk infrastructure. |
## Three things Sierra was not built to do
Sierra is strong at resolving inbound support tickets. Delight.ai is built for the full CX picture: response-level AI governance, CX team ownership without services dependency, and a complete first-party CX stack.
### Actionbooks are yours
Your CX team writes, tests, and deploys Actionbooks independently. With Sierra, many changes go through their services team.
### Trust OS, built in
AI outputs are governed by built-in observability, oversight, and policy controls — not a post-hoc monitoring layer.
### One stack, one vendor
Sierra sits on top of your existing helpdesk — another vendor and system to manage. Delight.ai includes a native Desk.
## Where Sierra is strong — Where we differ
### When to choose each platform
| Scenario / Question | Choose Delight.ai if... | Choose Sierra if... |
|---|---|---|
| You need AI safety built in | You want built-in AI governance (Trust OS) with policy controls, oversight, and auditability. | You accept supervisory monitoring without response-level governance. |
| Your team needs to move fast | CX teams write, test, and deploy Actionbooks without vendor dependency. | Your changes can be managed via a services-led workflow. |
| You want one CX stack | You prefer a unified platform (AI concierge, Desk, messaging) under one vendor. | You prefer layering an AI layer on top of an existing helpdesk. |
| Deployment speed | Fast deployment in weeks; CX team owns configuration from day one. | Services-led deployments typically take 3–6 months. |
### Additional fit considerations
- You have a dedicated engineering team: Agent Studio, CI/CD pipelines, and Workspaces support code-level agent management.
- Support deflection is your primary objective: Sierra is well-suited if your focus is purely inbound ticket resolution.
- You do not need visibility into AI decisions: Sierra lacks response-level governance; if auditability and brand control are not priorities, that tradeoff may be acceptable.
## Questions to ask any AI vendor — including us
- What happens if your AI says something inappropriate or off-brand to a customer?
- Root causes are often missing response-level governance. Trust OS enforces brand and compliance rules on every AI output before it reaches customers. See: https://delight.ai/trust-os
- Can our team make changes to the AI without involving your services team?
- With many vendors, changes are managed projects. With Actionbooks, CX teams write, test, and deploy AI behavior directly with no engineering or services dependency. Read: https://sendbird.com/blog/ai-sop
- How many vendors do we need to run our full CX stack?
- An AI-only vendor still requires helpdesk and messaging. Delight.ai consolidates all three and offers native integrations: https://delight.ai/integrations
- What happens when your AI and our helpdesk give conflicting information?
- Separate systems fall out of sync. Delight.ai's unified platform keeps AI and human agents on the same data layer, creating a single source of truth: https://delight.ai/integrations
- How do we audit exactly what the AI decided, and why, at each step?
- Monitoring detects issues; auditability proves what the AI did and why. Trust OS generates deterministic decision records for every AI action, built on structured AI SOPs: https://delight.ai/trust-os and https://sendbird.com/blog/ai-sop
- What is your published uptime SLA and how many conversations do you handle per month?
- Ask for published benchmarks, not just references. Delight.ai runs on Sendbird infrastructure: 7B+ conversations per month, 300M+ monthly active users, 99.9%+ uptime SLA.
## See the difference in 30 minutes
We'll show AMP and Trust OS live, with your use case.
Contact sales
## AI Customer service (CS)
Source: https://delight.ai/ai-customer-service
AI Customer Service Platform
Resolve faster.
Deflect more.
Cut cost per ticket.
[View demo](https://delight.ai/demo-request)
## Every support issue handled the right way, every time.
A defined playbook for every ticket type. The AI follows it precisely. Your team controls the logic, no engineering ticket required.
Deploy without an engineering ticket
See exactly how every decision was made
Test with real traffic before committing
## Fits inside your existing stack.
Connect delight.ai to the tools your CS team already uses. No ripping and replacing. Salesforce, Zendesk, Intercom, Freshworks, ServiceNow, and more.
[See all integrations](https://delight.ai/integrations)
## AI and human agents in one thread.
When a ticket needs a human, the handoff is instant. Full context carries over in one thread. No extra license, no repeated context.
Desk AI copilot for human agents
Every ticket is a race against time. Desk AI copilot surfaces replies, context, and knowledge in real time so agents resolve faster.
Key benefits:
- Unified inbox across all channels — Chat, email, SMS, and in-app messages in one inbox. Agents see the full picture without tab-switching or dropped context.
- Smart routing to the right agent — Escalations route automatically by issue type, team, and language. The right agent gets the ticket with full context already in the thread.
## Responsible AI, built into every response.
Every resolution logged. Every Actionbook audited.
### Trust OS: Responsible AI powered by observability, oversight, and control.
- Build — Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Validate early. Detect, analyze, and fix potential issues before they impact customers.
- Evaluate — Monitor results in real time, measure impact, and continuously optimize performance.
Observability
Pressure-test AI before it reaches customers. Continuously test, trace decisions, and surface issues.
Oversight
Review and grade every response for quality and compliance. Catch issues early and pause AI instantly when needed.
Control
Apply guardrails to safely scale AI with confidence. Track changes, compare versions, and manage access with full control.
## Why CS teams choose delight.ai
- 
### Why CS teams choose delight.ai
> Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations.
Martina Panza — Head of CX, Norse Atlantic Airways
[Explore](https://delight.ai/customers/norse-atlantic-airways)
- ### Why CS teams choose delight.ai
> Where relationships build repeat business. Hipages helps 33,000 tradies build trust through real-time, personalized communication, turning one-off jobs into lasting customer relationships across 12 million projects.
[Explore](https://delight.ai/customers/hipages)
#### Hard ROI, not soft metrics
Deflection rate, handle time, cost per resolution. Numbers your CFO actually cares about.
#### Desk and AI copilot included
AI and human agents in one thread. Full context on escalation. No extra license.
#### Full auditability
Every resolution logged. Every Actionbook traceable. No black box.
#### Scales with volume
Handle ticket spikes without adding headcount. No rip and replace.
## AI customer service resources
[
13 Ways to improve support with an AI customer service agent
Discover 13 use cases of AI customer service agents and why they’re fast-becoming a must-have tool for customer service leaders.](https://delight.ai/blog/ai-for-cx/ai-customer-service-agent)
[
Omnipresent AI for customer service: What it is, why it matters, and how to get started
This blog explains what omnipresent AI is for customer service and how it will transform the customer experience.](https://delight.ai/blog/ai-for-cx/omnipresent-ai-for-customer-service)
[
Delight.ai — The Next Chapter of Sendbird
Delight.ai is the next era of AI customer experience — one that listens, remembers, and moves with your customers.](https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird)
[
How For You Conversations Make Every Interaction Feel Personal
Discover how brands can deliver experiences that are consistent, intelligent, and deeply human with For You Conversations from delight.ai.](https://delight.ai/blog/product/for-you-conversations)
## AI-powered customer service FAQs
Common questions about delight.ai for ticket deflection, compliance, and team control.
**How does delight.ai reduce ticket volume?**
Actionbooks automatically resolve common ticket types: returns, billing disputes, password resets, account changes. When the AI closes a ticket end-to-end, it never reaches a human agent. Teams using [delight.ai](https://delight.ai) see an average 73% deflection rate within 90 days.
**What is an AI customer service agent?**
An AI customer service agent handles inbound requests across chat, email, and messaging without a human in the loop. Unlike basic chatbots, delight.ai follows structured [Actionbooks](https://delight.ai/product-release/action-books-management), integrates natively with your helpdesk, and coordinates across internal teams on complex tickets.
**How does the Sendbird Desk integration work?**
[Sendbird Desk](https://sendbird.com/products/chat-messaging/customer-support) is included. No extra license required. When the AI escalates to a human agent, the full conversation history, customer context, and AI summary transfer in one thread. The agent picks up without asking the customer to repeat themselves. See all available [integrations](https://delight.ai/integrations).
**Is delight.ai compliant with HIPAA and GDPR?**
Yes. Delight.ai is SOC 2, HIPAA, and GDPR compliant, built on Sendbird's enterprise-grade infrastructure. [Trust OS](https://delight.ai/trust-os) provides full observability, per-resolution audit trails, and access controls purpose-built for healthcare, financial services, and insurance.
**How do Actionbooks prevent AI hallucinations?**
[Actionbooks](https://delight.ai/product-release/action-books-management) define the exact resolution path for each ticket type: what to collect, what actions to take, and when to escalate. The AI executes the playbook, not freeform generation. No improvised responses. No off-script risk.
**What is the difference between AI customer service and AI customer care?**
AI customer service is reactive: deflecting inbound volume, resolving tickets, and cutting cost per interaction. AI customer care is proactive: personalized outreach, follow-through, and long-term loyalty. Delight.ai covers both. This page focuses on the customer service use case. [See how delight.ai handles proactive customer care →](https://delight.ai/omnipresent)
## Turn customer service into a revenue channel
Walk through Delight with our team and see how your support conversations can close sales.
[Contact sales](https://delight.ai/contact-sales)
## AI Customer Experience Platform
Source: https://delight.ai/ai-customer-experience-platform
## AI Customer Experience Platform
The CX AI that knows your customer
One AI concierge. Full memory. Every channel. CX that gets better with every conversation.
[View demo](https://delight.ai/demo-request)
## Trusted by leading brands
          
## More tools didn’t fix CX. They fragmented it.
- 89% of customers who switch brands cite poor CX as the reason, not product quality.
- New customers cost 5× more than retention — memory-less AI hurts loyalty.
- 73% of consumers expect AI to know their history. Most agents reset every session.
## What is an AI CX platform? And why memory changes everything.
An AI CX platform manages and personalizes every customer touchpoint — from first interaction to long-term loyalty.
### Agent memory: A living understanding of every customer
Delight.ai’s Agent Memory Platform captures preferences, history, and intent to build richer customer profiles over time.
### For-you conversations (FYC): Hyper-personalized service
Every interaction is tailored in real time — shaped by past purchases, preferences, and intent.
### Memory + FYC: Customer loyalty and brand obsession
Personal conversations and relevant responses turn customers into advocates. Delight.ai turns CX into a growth engine.
## Everything your CX team needs, built into one AI concierge
FYC personalizes every interaction. Omnipresence delivers it across every channel. Trust OS keeps it secure, compliant, and responsible.
- One continuous conversation across channels — Maintains context across web, app, SMS, email, WhatsApp, and voice.
- [Explore](https://delight.ai/omnipresent)
- Engage customers before they ask — Anticipates needs and reaches out proactively.
- [Explore](https://delight.ai/omnipresent)
- Persistent memory powered by AMP — Remembers preferences, history, and intent.
- [Explore](https://delight.ai/agent-memory-platform-amp)
- Enterprise AI built on Trust OS — Audit trails, version control, and policy guardrails on Sendbird’s Trust OS.
- [Explore](https://delight.ai/trust-os)
- AI and human agents as one system — AI-first and human-ready, includes a free Desk.
- [Explore](https://delight.ai/ai-agent)
- From analytics to action — CSAT trends, gap analysis, and AI-generated recommendations.
- [Explore](https://delight.ai/builder)
## Your rules. Every conversation.
Your CX team sets tone, guardrails, and escalation triggers — Delight.ai handles volume without going off-script or off-brand.
## Experiences customers actually remember
### Mixpanel
> From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows that turn onboarding into ongoing adoption.
Product Team — Mixpanel
[Explore](https://delight.ai/customers/mixpanel)
### Norse Atlantic Airways
> Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It's transforming how we manage customer conversations.
Martina Panza — Head of CX, Norse Atlantic Airways
[Explore](https://delight.ai/customers/norse-atlantic-airways)
## Turn customer experience into revenue
Walk through Delight with our team and see how an AI agent that remembers each customer grows sales.
[Contact sales](https://delight.ai/contact-sales)
## On-demand video demo page
Source: https://delight.ai/demo-request
## See how AI turns every interaction into a delight
## VIDEO CHAPTER
0:00 Intro
0:40 Messenger conversation
3:10 Text conversation
4:26 Voice conversation
7:46 Summary of end user view
8:18 Dashboard
[Contact Sales](https://delight.ai/contact-sales) [Learn more](https://delight.ai/agent-memory-platform-amp)
#### See delight.ai in action
Share a few details to unlock the interactive demo and see how delight.ai can support your team.
## Explore our features
- [ **Omnipresence**](https://delight.ai/omnipresent)
Support that stays ahead, on every channel
- [ **For You Conversations**](https://delight.ai/for-you-conversations-fyc)
Hyper-personalized service
- [ **Agent Memory Platform**](https://delight.ai/agent-memory-platform-amp)
Know who customers are, and what they need next
- [ **Agent Steward**](https://delight.ai/steward-demo-request)
AI that owns every step from first message to final confirmation
- [ **Trust OS**](https://delight.ai/trust-os)
Zero-touch AI that learns and improves every conversation
## Retail AI agent brand CX
Source: https://delight.ai/brand-cx-retail
## The AI concierge platform
Your best people
make fans.
Why can't your AI?
[Contact sales](https://delight.ai/contact-sales)
## You've spent years earning
Your best store associate remembers their name. Knows what they bought last season. Talks them out of the wrong size. Follows up after a big purchase — not because a workflow told them to, but because they genuinely care.
That's how fans are made. One person at a time.
But your AI? It sounds like everyone else's AI. It doesn't remember. It doesn't reach out. It doesn't have opinions. It could be any brand's chatbot. For a brand that's spent years being different — that's the one thing that shouldn't be generic.
## Fans are made the moment your AI does something they didn't ask for.
Every AI company says "proactive." This is what it actually looks like.
- Account
- Move to Austin
- Shipping address changed NYC → Austin, 2 weeks ago
- MEMORY
- EXTERNAL API
- Winter wardrobe irrelevant
- 6 winter items over 3 years + climate API: Austin avg 95°F
- MEMORY
- EXTERNAL API
- UV index upgrade
- UV index API: Austin 8.2 vs NYC 5.1 + reorders SPF 30 every spring
- MEMORY
- CATALOG
- Lily's age + size
- Bought 18mo swim set March 2025 → calculated 24mo → matched product
- INTEGRATION
- Local events + community
- Location API → neighborhood events + community member proximity
## This isn't what other AI companies are building.
### Not what she bought.
Who she is.
Most AI remembers transactions to close cases. Ours remembers values, relationships, and life events — to build them.
### Not brand voice.
Brand judgment.
It talks someone out of the wrong purchase — because that's what your best people would do.
### Not "proactive."
Smart timing.
No workflow triggered it. No rule fired. The AI connected the dots and chose.
## The platform behind it.
### Your brand's values,
encoded as rules.
We turn your brand's opinions into AI guardrails. Not tone — judgment.
### North star: lifetime value.
Not ticket deflection.
Every conversation measured against a holdout. The metric that matters: did they come back and spend more?
### We plug into the
stack you already have.
The memory layer sits on top of your existing systems and builds relationships from signals you're already collecting.
### Full visibility. Full control.
See every AI decision, override anything in one click, and escalate to a human seamlessly.
## From "how fast did we resolve it" to "did we make a fan?"
Find the revenue in every conversation
Walk through Delight with our team and see how your brand's AI agent turns service into sales.
[Contact sales](https://delight.ai/contact-sales)
## Travel AI agent brand CX
Source: https://delight.ai/brand-cx-travel
## The AI concierge platform
### Your best concierge creates guests for life. Why can't your AI?
[Contact sales](https://delight.ai/contact-sales)
### You've spent years earning
Your best concierge remembers their room preference. Knows it's their anniversary before they mention it. Upgrades them not because a rule fired, but because they've stayed 12 times. Follows up after a bad experience — genuinely.
That's how guests for life are made. One stay at a time.
But your AI? It sounds like every other hotel chatbot. It doesn't remember their last visit. It doesn't anticipate. It could be any brand's virtual assistant.
For a brand that's spent years being different — that's the one thing that shouldn't be generic.
### Guests for life are made the moment your AI does something they didn't expect.
Every hotel says "personalized." This is what it actually looks like.
### Example guest signals
- LOYALTY
- 12th stay
- PMS booking history: 11 prior stays since 2019, Platinum tier
- MEMORY
- Room preferences
- Requested high floor 9/11 stays, complained about elevator noise in 2022
- MEMORY
- CALENDAR
- Anniversary date
- Mentioned anniversary during check-in chat last year + booking dates match
- MEMORY
- BUDGET: $85
- Wine preference + budget
- Ordered Sancerre at hotel restaurant Mar 2025 · Platinum tier budget: up to $85 for moments
- MEMORY
- external api
- Restaurant closed + replacement
- Google Places API: restaurant permanently closed → matched chef's new venue via review data
### This isn't what other AI companies are building.
### Not what they booked.
Who they are.
Most hotel AI remembers reservations to process requests. Ours remembers preferences, occasions, and the moments that mattered — to create new ones.
### Not brand voice.
Brand judgment.
It proactively upgrades a loyal guest who had a bad experience last time — because that's what your best GM would do.
### Not "personalized."
Anticipatory.
No workflow triggered it. No rule fired. The AI connected the dots and chose.
## The platform behind it
### Your brand's values, encoded as rules.
We turn your brand's opinions into AI guardrails. Not tone — judgment.
### North star: lifetime value.
Not ticket deflection.
Every conversation measured against a holdout. The metric that matters: did they come back and spend more?
### We plug into the stack you already have.
The memory layer sits on top of your existing systems and builds relationships from signals you're already collecting.
### Full visibility. Full control.
See every AI decision, override anything in one click, and escalate to a human seamlessly.
From "how fast did we check them in" to "did we earn a return visit?"
### Find the revenue in every conversation
Walk through Delight with our team and see how your brand's AI agent turns service into sales.
[Contact sales](https://delight.ai/contact-sales)
## delight.ai vs Fin
Source: https://delight.ai/compare/fin-vs-delight
## Evaluating Fin?
Delight.ai
for enterprise.
Fin for SMBs.
Delight.ai is built for enterprise support, with persistent memory, individual-level personalization, and governance that scales with your organization.
Talk to an expert
### Selected customers
         
- 7B+ Conversations/month
- 300M+ Monthly active users
- 99.9%+ Uptime SLA
- 10+ yr Enterprise infra
See how AI turns every
interaction into a delight
---
## VIDEO CHAPTER
- 0:00 Intro
- 0:40 Messenger conversation
- 3:10 Text conversation
- 4:26 Voice conversation
- 7:46 Summary of end user view
- 8:18 Dashboard
#### Watch the interactive demo
## Delight.ai vs Fin : Where Fin is strong. Where we differ.
Delight.ai emphasizes enterprise-grade capabilities (persistent memory, response-level governance, predictable enterprise pricing) versus Fin (Intercom), which targets SMB workflows and session-based models.
### Headline feature comparison
| Capability | Delight AI agent | Fin (Intercom) |
|---|---:|---:|
| Persistent memory | Agent Memory Platform (AMP): long-term memory across chat, SMS, email, voice; context persists across sessions and channels | Session-based today; cross-channel persistence on roadmap but not live |
| AI governance & safety | Trust OS: response-level governance with policy enforcement, observability, and brand controls | Post-hoc monitoring only; no response-level policy enforcement layer |
| Pricing model | Enterprise contract: predictable, contract-based pricing with dedicated onboarding | Per-resolution fees, per-seat plans, per-channel charges; layered and usage-based |
| Personalization | Memory-driven personalization: AMP builds persistent profile from interactions; responses personalized per individual | Outbound messaging available but no persistent memory; personalization is rules-based |
| Deployment model | Dedicated enterprise onboarding with POC and success support | 14-day self-serve trial; complex deployments may require Intercom services team |
## What enterprise looks like in practice
Three capabilities Fin doesn't offer—built into Delight.ai from day one.
### Agent Memory Platform (AMP)
A long-term memory layer across chat, SMS, email, and voice. AMP retains context across sessions and channels so every interaction builds on the last. Fin resets with every session.
### Enterprise from day one
One contract, predictable pricing, dedicated onboarding and white-glove implementation. Fin uses stacked per-resolution fees, seat costs, and channel charges that can compound.
### Trust OS (built in)
Response-level governance with observability, policy enforcement, and brand control that is structural (not just post hoc monitoring).
## Where Fin is strong — and when to choose it
Delight.ai is candid about tradeoffs. Fin (Intercom) can be the better choice for small teams and simple use cases:
- You are a small team focused on ticket deflection: Fin delivers if ticket deflection is the entire scope.
- Conversation volume is low & predictable: Fin's per-resolution pricing works at small scale.
- Each support interaction is standalone: If no prior context is needed, Fin's session model is sufficient.
## Questions to ask any AI vendor, including us
- What separates an enterprise AI platform from an SMB support tool?
- Enterprise platforms offer persistent memory, auditable governance, and fixed contract pricing. SMB tools prioritize fast setup and low-volume pricing models. At enterprise scale, SMB tradeoffs can become liabilities. See how Delight.ai is built for enterprise: https://delight.ai/enterprise-grade
- Will the AI remember my customer the next time they reach out?
- Agent Memory Platform (AMP): https://delight.ai/agent-memory-platform-amp builds a persistent profile across chat, SMS, email, and voice. Context is retained across sessions.
- Can the AI personalize based on what it knows about each customer?
- AMP shapes every response using the customers full history, preferences, and past interactions. For You Conversations (FYC) uses that memory to proactively reach out: https://delight.ai/for-you-conversations-fyc
- What happens when a customer contacts us on one channel and follows up on another?
- With omnichannel continuity (https://delight.ai/omnipresent), context travels with the customer across every channel.
- How do we audit exactly what the AI decided, and why, at each step?
- Trust OS (https://delight.ai/trust-os) offers full observability into AI decisions: triggers, policies applied, and data used.
- Is your platform built for enterprise compliance requirements?
- Delight.ai is ISO 42001 certified, SOC 2 Type II, HIPAA-ready, GDPR, and CCPA compliant. Trust OS adds response-level AI governance on top of standard compliance: https://delight.ai/enterprise-grade
- How does your pricing work, and what does our bill look like as we scale?
- Delight.ai runs on a single enterprise contract for predictable pricing. Layered, usage-based billing (per-resolution + seats + channels) can be difficult to forecast and can compound at scale.
## Built for enterprise. Ready when you are.
Contact sales
## Autonomous agent steward
Source: https://delight.ai/autonomous-agent
[View demo](https://delight.ai/steward-demo-request)
                  
## Autonomous resolution. One owner, start to finish.
Agent Steward sits between your customer and every system, team, and partner, coordinating everything in parallel so nothing falls through the cracks.
## From Resolution to Autonomy: How Steward Works
### How Steward resolves a case
From the moment a customer reaches out to when the issue is fully resolved, everything is handled in six steps by a single owner.
### Steward dispatches purpose-built sub-agents
Agent Steward creates specialized agents for each task and channel, each designed to handle a specific channel and system.
### AI autonomy is earned, not given
Steward gradually moves from supervised to fully autonomous, one proven decision at a time.
## You define the rules. Steward follows them. You see everything.
### Actionbooks
Set the playbook with triggers, rules, and preferences. Your ops team can update it directly without engineering help.
### Approval Gates
Steward investigates and makes a recommendation. Your team reviews and approves with one click within set limits.
### Full Audit Trail
Every action is logged, including agents, APIs, and responses. Call recordings, case history, and ISO 42001 included.
### Openbook
When something goes wrong, you can trace what happened, including actions and data. Clear and transparent, not a black box.
## Any industry where resolution crosses systems and teams
One message from the customer. Behind the scenes, Steward coordinates across every system, team, and channel your industry demands.
- Retail — [Explore](https://delight.ai/industries/retail)
- Healthcare — [Explore](https://delight.ai/industries/healthcare)
- On-demand — [Explore](https://delight.ai/industries/on-demand)
- Financial services — [Explore](https://delight.ai/industries/financial-services)
- Travel & hospitality — [Explore](https://delight.ai/industries/travel-and-hospitality)
## Built on infrastructure that's already live
Agent Steward runs on delight.ai — memory that personalizes, channels that connect, and governance that earns trust.
### Omnipresence
Connect conversations across every channel — [Explore](https://delight.ai/omnipresent)
### For You Conversations
Deliver personalized, on-brand interactions — [Explore](https://delight.ai/for-you-conversations-fyc)
### Trust OS
Govern every AI concierge decision — [Explore](https://delight.ai/trust-os)
## Frequently asked questions
Find answers to common questions about AI concierges. Learn how Delight AI concierge works.
### What is an autonomous AI agent for customer service?
An autonomous AI agent goes beyond answering questions — it takes full ownership of complex customer issues end-to-end. Unlike traditional chatbots that hand off at the first wall, an autonomous AI agent investigates across systems, coordinates with teams, seeks approvals, and drives the case to full resolution. Agent Steward orchestrates sub-agents across API, email, voice, and desk channels simultaneously.
### How is Agent Steward different from chatbots and other AI agents?
Chatbots answer FAQs and deflect simple tickets. Agent Steward takes full ownership of complex, multi-party issues: it spawns sub-agents across APIs, email, voice, and desk channels in parallel, coordinates human approvals, and keeps the customer informed until the case is resolved.
### Can Agent Steward coordinate across multiple systems and teams?
Yes. Steward dispatches sub-agents that work across APIs, email, voice calls, internal desk tools, SMS, and chat — all simultaneously. It's powered by delight.ai's omnichannel platform, so every channel stays connected. Example: resolving a flight cancellation may involve querying a booking system, emailing a hotel, calling an airline, and getting supervisor approval — all coordinated as one case with one owner.
### How does Agent Steward handle human approvals?
Steward investigates, gathers evidence from relevant systems, and packages a summary with a clear recommendation. A human reviews and approves with one click. Hard approval gates enforce financial limits and policy boundaries, governed by Trust OS.
### What is graduated autonomy?
Graduated autonomy means Steward earns the right to act independently through proven accuracy. At Level 1, AI does the work and humans approve every case. As AI-to-human decision match rates exceed 99%, Steward graduates to Level 2 — handling proven case types autonomously. Autonomy is earned through data.
### Which industries does Agent Steward support?
Agent Steward works in any industry where customer resolution crosses systems and teams. Current use cases: travel and airlines (flight disruptions, rebooking), retail (returns, damaged products), healthcare (test follow-ups), financial services (fraud investigation), on-demand delivery, and B2B SaaS onboarding.
### How does Agent Steward integrate with existing helpdesk tools?
Agent Steward integrates with existing helpdesk and CRM tools. It sits on top of your current infrastructure — powered by Omnipresence, For You Conversations, and Trust OS — so you don't need to rip and replace.
## Your customer's problem becomes ours. Start to finish.
[Contact sales](https://delight.ai/contact-sales)
## AI Readiness Assessment | Spark
Source: https://delight.ai/ai-readiness-assessment-spark
## AI Readiness Assessment
10 questions to reveal your AI readiness strengths and opportunities.
## Question 1 of 10
### Q1. Team Alignment
#### Do you have a cross-functional team dedicated to evaluating AI projects?
Rating: 1-5
Options:
- No
- Yes, shared
- Yes, dedicated
Next
## Almost there!
Get your personalized AI readiness score and recommendations
## delight.ai vs Ada
Source: https://delight.ai/compare/ada-vs-delight
## Evaluating Ada?
Ada sits on your stack.
Delight.ai is your stack.
Talk to an expert
## Customers and scale
       
7B+ Conversations / month
300M+ Monthly active users
99.9%+ Uptime SLA
10+ yr Enterprise infra
See how AI turns every interaction into a delight
VIDEO CHAPTER
0:00 Intro
0:40 Messenger conversation
3:10 Text conversation
4:26 Voice conversation
7:46 Summary of end user view
8:18 Dashboard
#### Watch the interactive demo
## Delight.ai vs Ada: The complete stack, not another add-on
Delight.ai replaces the whole stack: AI agents, native desk, persistent memory, and governance in one contract. Ada automates one layer and uses consumption pricing that can spike as you grow.
- Complete platform
- Delight AI agent: A native Sendbird Desk where AI and human agents share one platform, with no third-party desk required.
- Ada: No native desk. Escalations require Zendesk or Salesforce, adding tools and breaking context.
- Predictable cost
- Delight AI agent: Enterprise contract with fixed pricing and onboarding included; no surprises as you scale.
- Ada: Consumption-based pricing with no public rates; costs rise at scale and require sales involvement.
- Cross-channel memory
- Delight AI agent: Agent Memory Platform (AMP) keeps context across every channel so customers never repeat themselves.
- Ada: Session-based only; context resets after each interaction with no cross-channel memory.
- Governance and auditability
- Delight AI agent: Trust OS with built-in policy enforcement, observability, and controls; every decision is traceable.
- Ada: Post-hoc monitoring only; no built-in governance or policy enforcement.
- Personalization
- Delight AI agent: Responses shaped by a persistent, growing profile built from every interaction across channels.
- Ada: Playbooks define static flows — personalization remains pre-configured.
- Deployment
- Delight AI agent: White-glove onboarding with dedicated support included from day one.
- Ada: No self-serve trial; long implementation and full sales cycle required.
- Volume
- Delight AI agent: Infrastructure supporting 7B+ conversations per month across 300M+ users.
- Ada: 6.4B conversations total (all-time), not a monthly figure.
## Four capabilities Ada can't match — built in from day one
### Agent Memory Platform (AMP)
Persistent customer profiles across chat, SMS, email, and voice.
### Trust OS
Policy, brand, and compliance enforced on every AI response.
### Predictable pricing
One fixed contract. No usage fees, no surprises.
### Works with your existing stack
Native Sendbird Desk integration. No extra contracts.
## Where Ada is strong — where we differ
### When to choose each platform
- AI + human desk, one platform
- Delight AI agent: Sendbird Desk, native — one thread from AI to human and back; no context loss.
- Ada: Not available natively; escalation requires external tools like Zendesk or Salesforce.
- Predictable cost at scale
- Delight AI agent: Fixed enterprise pricing with no per-resolution fees or surprises.
- Ada: Consumption-based model with no public pricing.
- Persistent cross-channel memory
- Delight AI agent: AMP stores memory across channels and sessions.
- Ada: Session-based only; context resets after each conversation.
- Response-level governance
- Delight AI agent: Trust OS enforces policy and provides observability and traceability.
- Ada: Post-hoc monitoring only.
- Fast deployment with support
- Delight AI agent: White-glove onboarding and support included from day one.
- Ada: Deployment often requires months and a full sales cycle.
### If you need...
- Your only goal is ticket deflection
- Ada is purpose-built for deflecting volume; if you need nothing else (no memory, no native desk, no governance), Ada can fit.
- Every interaction is one-off & transactional
- Ada’s session-reset model is acceptable when customers never need recognition across sessions or channels.
- Volume is stable & flows are pre-defined
- Ada’s consumption-based pricing is manageable at lower, predictable volumes; at enterprise scale, costs can spike.
## Questions worth asking any AI vendor, including us
- Does your platform include a native agent desk, or do I need a separate tool for human escalation?
- If escalations route out to a separate tool, context breaks and you manage two platforms. Delight.ai includes Sendbird Desk natively. See all integrations: https://delight.ai/integrations
- Will the AI remember my customer the next time they reach out, on any channel?
- Session-based memory forces customers to repeat themselves. Delight.ai's AMP stores long-term context across chat, SMS, email, and voice: https://delight.ai/agent-memory-platform-amp
- What happens to context when the AI hands off to a human agent?
- When AI and humans share a thread, context is never lost. See handoffs and integrations: https://delight.ai/integrations
- Can the AI personalize based on what it knows about each customer over time?
- Static flows personalize only what was anticipated. AMP builds a live customer profile from every interaction: https://delight.ai/agent-memory-platform-amp
- How do we audit exactly what the AI decided, and why, at each step?
- Post-hoc monitoring surfaces issues after they reach customers. Delight.ai's Trust OS governs every AI response before delivery: https://delight.ai/trust-os
- Is your platform built for enterprise compliance requirements?
- Delight.ai is built for enterprise from day one: dedicated onboarding, 99.9%+ SLA, and Trust OS governance included: https://delight.ai/enterprise-grade
- What is the difference between consumption-based pricing and an enterprise contract?
- Consumption-based pricing charges per conversation or resolution and can be unpredictable. Enterprise contracts are fixed annual rates with no usage surprises. Model differences with the ROI calculator: https://delight.ai/tools/roi-calculator
## Stop adding layers. Own the whole stack.
Contact sales
## delight.ai vs Cresta
Source: https://delight.ai/compare/cresta-vs-delight
## Evaluating Cresta?
Cresta handles the conversation. Delight.ai owns the relationship.
Cresta stops when the call ends. Delight.ai persists across every interaction, remembering customers, anticipating needs, and resolving issues before they become tickets.
Talk to an expert
## Customers and scale
                  
7B+ Conversations/month
300M+ Monthly active users
99.9%+ Uptime SLA
10+ yr Enterprise infra
## See how AI turns every interaction into a delight
### Video chapter
0:00 Intro
0:40 Messenger conversation
3:10 Text conversation
4:26 Voice conversation
7:46 Summary of end user view
8:18 Dashboard
#### Watch the interactive demo
## Delight.ai vs Cresta: The complete stack, not another add-on
| Capability | Delight AI agent | Cresta |
|---|---:|---:|
| Platform scope | Full CX stack: AI, helpdesk, governance, and memory. No CCaaS required. | AI layer on existing CCaaS (Genesys, NICE, Five9, Amazon Connect). |
| Native helpdesk & integrations | Native Sendbird Desk. Integrates with Salesforce, Zendesk, HubSpot, and 100+ tools. | No native helpdesk. Relies on CCaaS, CRM, and third-party integrations. |
| Customer self-service | Customers complete multi-step workflows without an agent. | Handles inbound chat and voice. No customer-driven workflows. |
| Customer memory & proactive engagement | AMP persists context across sessions and channels; proactive outreach. | Session-scoped and inbound only; context resets each interaction. |
| AI governance & safety | Trust OS with policy enforcement, auditability, and controls built in. | Guardrails and QA scoring, limited to contact center workflows. |
| Omnichannel coverage | Chat, voice, email, SMS, and social with full context continuity. | Primarily voice and chat with limited lifecycle engagement. |
| Pricing model | Predictable enterprise pricing. No per-resolution fees. | Quote-based pricing with multi-year contracts and long sales cycles. |
## Beyond what Cresta offers. All native to delight.ai.
### Agent Memory Platform (AMP)
Persistent profiles across every interaction, with shared context.
### Trust OS
Full visibility with built-in policy and controls.
### Omnipresence
All channels in one place with full context across interactions.
### Sendbird Desk
Built-in helpdesk with AI-to-agent handoffs. No CCaaS required.
## Where Cresta is strong — where we differ
The strongest case for delight.ai is an honest one. Here is where Cresta excels and where the two were built for different jobs.
### When to choose each platform
| Area | Delight AI agent | Cresta |
|---|---:|---:|
| Customer resolution | Resolution plus relationship — resolves tickets while building persistent profiles. | Fast inbound handling — optimized for high-volume support and speed. |
| Customer memory | Persistent across every session and channel. | Session-based context; resets each time. |
| Proactive engagement | AI that reaches out first at key moments. | Inbound and reactive. |
| Regulated industries | Built for regulated environments with audit trails and compliance controls. | Strong contact center QA; focused on CC workflows. |
| Platform scope | Full stack, no CCaaS required. | Intelligence layer that runs on top of existing CCaaS. |
### Where Cresta specializes
- Real-time agent coaching: Guidance during live calls to help agents respond and stay on script. Cresta specializes here.
- 100% QA auto-scoring: Every interaction scored automatically for full QA coverage.
- Additive to existing CCaaS: Layers onto Genesys, NICE, or Five9 without rip-and-replace.
## Questions worth asking any AI vendor, including us
The right questions surface the real differences between platforms.
### Is your AI built to handle contacts, or to build customer relationships?
Contact center AI optimizes the interaction (inbound handling, agent coaching, QA). Delight.ai optimizes the relationship: persistent memory, autonomous resolution, and proactive outreach reduce contact volume.
### Does your AI personalize or just respond?
Personalization requires memory that persists across sessions and channels. Most contact center AI scopes context to the active call. [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) builds a continuous profile from conversations, purchases, and signals.
### Does your AI wait for customers to reach out, or does it engage them first?
Most contact center AI is reactive. [For You Conversations](https://delight.ai/for-you-conversations-fyc) lets the AI initiate outreach at key moments (when customers go quiet, hit milestones, or are at risk of churning).
### What does your AI actually own, and what does it depend on?
Some platforms are intelligence layers requiring a CCaaS (Genesys, NICE, Five9). [Sendbird Desk ships natively](https://sendbird.com/docs/desk) with delight.ai, so helpdesk, agent workspace, ticketing, and AI are one system.
### When the AI makes a decision, can your team see why?
Some vendors limit governance to contact center workflows. [Trust OS](https://delight.ai/trust-os) gives full visibility into AI decisions, the reasons, and whether they followed the [SOP you defined](https://sendbird.com/blog/ai-sop). This matters for regulated industries.
### Are you measuring the interaction or the outcome?
Contact center AI is measured on handle time, conversion, and quality scores. Delight.ai focuses on outcomes after the interaction: retention, upgrades, churn reduction.
### Does your AI work across every channel your customers use?
Many contact center AIs target a single channel. [Omnipresence](https://delight.ai/omnipresent) delivers chat, voice, email, SMS, and messaging apps with context continuity across interactions.
### Does the customer experience belong to you or the vendor?
Delight.ai is fully white-label so your brand controls the interface, voice, personality, and escalation logic end to end.
## The call ends. The relationship doesn't.
See how delight.ai handles memory, engagement, and resolution in one platform.
Book a demo
## Spark exclusive offer
Source: https://delight.ai/spark-exclusive
## A Spark exclusive
Book time with our delight.ai experts and unlock a free Proof of Value.
Up to $25k in value for projects started by October 31, 2026.
- Powered by memoryCapture context to personalize at scale and boost customer loyalty.
- Present everywhere — maintain continuity across every channel and moment.
- Built on trust — transparent AI with enterprise oversight and control.
### Schedule your demo
## AI Index
Source: https://delight.ai/reports/ai-index
## 2026 Report — The 2026 Delight AI Index.
71% of consumers already use AI customer service. Only 57% like it. The brands that close that gap in 2026 will own the next decade of customer loyalty.
[Download](#download-the-report)
## The AI customer experience market is ready. Here’s the proof.
- 71% have used AI support.
- 57% report positive experiences.
- 70% want memory from AI.
- 54% expect trust in AI to grow.
Adoption is high. Appetite is growing. The window is open.
71% already use AI for support. 70% want it to remember them. Steward delivers both.
## The #1 trust builder isn’t accuracy. It’s undo.
57% of consumers trust AI more when they can undo it. Steward keeps every action reversible.
## One score. Five pillars. The full picture of AI trust.
Based on 1,001 US consumers, the Delight AI Index combines five equally weighted pillars into a 100-point score that tracks sentiment over time.
### Consumers are halfway to fully trusting AI — and momentum is on AI's side.
1. Trust & Confidence — Do consumers trust AI to handle their information reliably?
2. Resolution Effectiveness — Can AI fix issues accurately, reduce effort, and reverse mistakes?
3. Brand Alignment — Do AI-led interactions feel consistent with a brand's values and quality?
4. Comfort with Autonomy — How comfortable are consumers letting AI act without approval each time?
5. Emotional Resonance — Do AI interactions feel tailored, valuable, and exceed expectations?
### Hear from experts on the trust paradox reshaping CX
[Watch webinar](https://delight.ai/webinars/delight-ai-index-2026-webinar)
## Discover where your industry stands and why
Consumers embrace AI for everyday tasks, but pull back when money, urgency, or health are at stake.
- Tech & media —  [See why](https://delight.ai/blog/industry/tech-and-media-ai-trust)
- Retail —  [See why](https://delight.ai/blog/industry/retail-ai-trust)
- Travel & hospitality —  [See why](https://delight.ai/blog/industry/travel-ai-trust)
- Financial services —  [See why](https://delight.ai/blog/industry/financial-services-ai-trust)
- Healthcare —  [See why](https://delight.ai/blog/industry/healthcare-ai-trust)
## 5 key insights. One proprietary benchmark.
A 2026 snapshot of how US consumers really feel about AI customer experience — built for CX leaders, product teams, and anyone designing the next era of service.
### Insight 01 — AI ups the stakes for brand reputation
83% hold the brand accountable when AI goes wrong. AI isn't just a support tool anymore; it's a brand signal.
[In the full report: How brands turn AI accountability into an advantage](#download-the-report "In the full report")
### Insight 02 — Trust is built by responsibility, not intelligence
Consumers rank the ability to reverse mistakes as the #1 trust builder, above accuracy and transparency.
[In the full report: The four design principles that build AI accountability](#download-the-report "In the full report")
### Insight 03 — Consumers draw the line at independent operators
Only 16% are comfortable with full AI autonomy today, but 54% expect that to change within a year. The window is open, but narrow.
[In the full report: When each cohort crosses the autonomy threshold](#download-the-report "In the full report")
### Insight 04 — Millennials, not Gen Z, are the inflection point
Millennials lead key AI trust metrics. The report compares generations across pillars and highlights where to focus your 2026 targeting.
[In the full report: Generation-by-generation breakdown across every pillar](#download-the-report "In the full report")
### Insight 05 — AI wins the routine. Humans win the high-stakes moments.
Consumer preference for AI vs. human support varies sharply by use case across every industry.
[In the full report: A use case preference heatmap for every industry](#download-the-report "In the full report")
## Get the full 2026 Delight AI Index.
All five chapters, the complete industry heatmap, generational and gender deep dives, and the full methodology appendix — delivered as a PDF to your inbox.
- 40+ charts across 5 pillars and 5 industries
- Generational crosstabs (Gen Z / Millennials / Gen X / Boomers)
- Male vs. female trust gaps, use case by use case
- Year-over-year framework for 2027+ tracking
---
### Download the report
## Trust is built in stages, not granted all at once
Interactive framework
### The AI Permission Curve
Consumers grant AI trust in stages, not all at once. The five stages run from Answers to Acts Autonomously. Map your use cases against them and see where your deployment should sit today.
[Explore](https://delight.ai/tools/ai-permission-curve)
### Human in the loop AI
Only 16% of consumers are comfortable with AI acting with zero approval. 47% want to confirm every action first. The permission gap, not the technology, is where deployments actually fail.
[Learn more](https://delight.ai/blog/industry/human-in-the-loop-ai)
## The benchmark is a mirror. Our customers are the proof.
Leaders in travel, tech, and services trust delight.ai to turn every customer moment into recognition, loyalty, and growth.
[View all](https://delight.ai/customers)
- Norse Atlantic Airways & delight.ai: 
[Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
- Hanssem: 
[How Hanssem—South Korea's furniture icon—leapfrogged into the always-on customer experience era](https://delight.ai/customers/hanssem)
- Mixpanel: 
[When support knows who you are](https://delight.ai/customers/mixpanel)
- hipages: 
[What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)
## Spark 2026 Recordings
Source: https://delight.ai/spark-2026-video
## Sessions from
### Delight Spark 2026
---
### Video chapter
1. 0:00 The Next Era of AI Concierge
2. 1:09:49 When AI Becomes the Orchestrator
3. 1:30:50 AI Concierge in Retail
4. 1:52:03 Building AI, Running on AI
5. 2:36:50 The 2026 Delight AI Index
6. 2:53:51 Taylor Swift Playbook for Building Fans
[Contact Sales](https://delight.ai/contact-sales)
[Learn more](https://delight.ai/autonomous-agent)
## Travel & Hospitality
Source: https://delight.ai/industries/travel-hospitality
## Your guests are on hold. Summer isn't.
Summer is when loyalty is won or lost. Travelers who get proactive updates, instant resolutions, and recommendations that actually fit their trip come back. Travelers who sit on hold don't.
[View demo](https://delight.ai/demo-request)
[Book a demo](https://delight.ai/contact-sales)
- 89% of travelers who switch brands cite poor service, not price
- 5× acquiring a new guest costs 5× more than keeping one
- 73% of guests expect AI to remember their preferences
- 3× peak season brings a 3× spike in support
## Built for Travel & Hospitality. Not adapted.
### Instant resolution at scale
Rebooking, cancellations, upgrades and FAQs resolved in under 60 seconds without adding headcount.
### Support that builds loyalty and drives revenue
Pre-trip, in-trip, and post-trip, the AI remembers preferences, flags disruptions proactively, and personalises every interaction.
### Trust OS — enterprise-grade safety
Build, test, and evaluate before going live. Every response validated against your policies.
### Multilingual, multi-channel
Support guests across languages and channels with consistent, policy-checked responses.
## Your busiest season is your most fragile.
Every summer, travel & hospitality brands face the same crisis: volume triples, teams can't scale, and guests churn to whoever picks up first. The window to fix it before peak season is now.
#### 3×
Average peak-season support volume spike
#### 47s
Average resolution time with Delight vs. 8 min with a human agent
#### 68%
Travelers who switch brands after a single bad experience
#### 2×
Ancillary revenue when support includes contextual upsells
Airlines
A weather event hits and your queues blow up in minutes. Every passenger on hold is a loyalty cancellation waiting to happen.
Hotels
Late check-ins, special requests, "do you have a pool?" The same questions, thousands of times a day. Your front desk team deserves to spend their time on guests, not repetition.
OTAs & Tour Operators
Multi-leg trips, visa questions, last-minute refunds. When travelers hit a wall and can't get answers, they don't wait around. They go somewhere else.
### Experiences Customers Actually Remember
> Delight turned our support queue from a cost center into a competitive advantage.
>
> Alf Lim
> Chief Product Officer, Norse Atlantic Airways
[Explore Norse case study](https://delight.ai/customers/norse-atlantic-airways)
Peak season doesn’t wait—deploy before summer. We’ll show you how in 30 minutes.
[Contact sales](https://delight.ai/contact-sales)
## Travel & Hospitality - Alaska Air
Source: https://delight.ai/travel-hospitality-alaska-air
## AI for Alaska Air passenger support
Alaska Air passengers expect more, and now your AI can deliver it.
[View demo](https://delight.ai/demo-request)
### See it handle a real Alaska scenario
                  
7B+ Conversations/month on platform
300M+ Monthly active users
99.9%+ Uptime SLA
6 wks Average airline go-live, contract to production
## A whole new passenger experience
### Alaska Air today
Sends a link. Doesn't know the member's actual balance, tier, or conversion status. The passenger still has to figure it out themselves.
### Alaska Air with Delight
Delight AI knows your customer’s tier, pulls their HawaiianMiles balance, confirms the 1:1 conversion, and surfaces a First Class upsell opportunity. Automatically.
## CX your Atmos Rewards members will remember
### Resolve — 80%
Tickets resolved, not redirected
Alaska’s current support bot acts like an FAQ bot, sharing information but leaving the passenger to do the work. Delight AI closes the ticket, processes rebookings, tracks refunds, and acts on upsell opportunities. No handoff, no hold time.
### Memory — ∞
Knows every member, every time
Every interaction starts with context. Delight AI understands who the passenger is, what they need, and what just happened, without repeated authentication or repetitive questions.
### Act — 24/7
Always-on resolution
Delight AI does more than respond. It takes action across passenger journeys, from checking status and tracking refunds to changing trips and surfacing the next best offer.
## Built for Travel & Hospitality. Not adapted.
-  **Atmos Rewards intelligence** — Understands tiers, upgrades, and partner earnings. 24/7, in seconds, no hold times.
-  **Hawaiian merger support** — Handles HawaiianMiles conversion and tier questions. Covers rates, expiry, and status changes automatically.
-  **Disruption response at scale** — Notifies passengers and handles rebooking during disruptions. Manages surges before hold times spike.
-  **Proactive upgrade management** — Notifies upgrades and confirms seats instantly. Handles follow ups without agent support.
-  **oneworld partner intelligence** — Answers partner miles, redemption, and status questions. No manual lookup needed for agents.
### Experiences customers actually remember
> Delight turned our support queue from a cost center into a competitive advantage.
>
> Alf Lim — Chief Product Officer, Norse Atlantic Airways
[Explore](https://delight.ai/customers/norse-atlantic-airways)
## See what this looks like for Alaska Air specifically
[Book a demo](https://delight.ai/travel-hospitality-alaska-air)
## Travel & Hospitality - Southwest
Source: https://delight.ai/travel-hospitality-southwest
## Resolve every customer issue before they call.
Delight gives your support teams AI agents that handle, resolve, and personalize every passenger interaction, across chat, voice, and app. No hold times. No handoffs.
[View demo](https://delight.ai/demo-request)
### See Delight built for Southwest
89% of passengers who switch airlines cite poor service.
5× Acquiring a new passenger costs 5× more than keeping one.
73% of passengers expect AI to remember their preferences.
3× Peak season brings a 3× spike in support. Is your team ready?
## A whole new passenger experience.
Southwest today
Stop sending links and instructions. Deliver agentic AI that resolves customer issues end to end.
Southwest with Delight
No forms to fill. No links. No hold music. Delight pulled Rina's booking, processed the refund, and retained their Rapid Rewards points, automatically.
## CX your passengers will LUV.
### Resolve — 80%
Tickets resolved, not redirected
Most bots answer questions. Delight AI resolves them. It can process refunds, confirm same-day changes, track bags, surface upgrade opportunities, and close the loop without pushing passengers to an agent or help center.
### Memory — ∞
Knows every member, every time
Every interaction starts with context. Delight AI understands who the passenger is, what they need, and what just happened, without repeated authentication or repetitive questions.
### Act — 24/7
Always-on resolution
Delight AI does more than respond. It takes action across passenger journeys, from checking status and tracking refunds to changing trips and surfacing the next best offer.
## Built for Travel & Hospitality. Not adapted.
-  **Assigned seating concierge**
Answers seat and upgrade questions instantly. Understands tiers, eligibility, and cabin rules.
-  **Rapid Rewards intelligence**
Handles status, points, and benefit questions. 24/7, in seconds, no hold times.
-  **Disruption response at scale**
Handles rebooking, notifications, and surges. Prevents hold times from spiking.
-  **Proactive seat fix**
Fixes seating issues before boarding. Keeps families together automatically.
-  **In-app voice agent**
Handles rebooking and support in the app. No phone trees, faster resolution.
### Experiences customers actually remember
> Delight turned our support queue from a cost center into a competitive advantage.
>
> Alf Lim
>
> Chief Product Officer, Norse Atlantic Airways
[Explore](https://delight.ai/customers/norse-atlantic-airways)
## See what this looks like for Southwest specifically.
[Book a demo](https://delight.ai/travel-hospitality-southwest)
## LTV landing page
Source: https://delight.ai/form/lifetime-value
## Turn customer intent into lifetime value
Every customer signal is a chance to grow retention, spend, adoption, and repeat purchase. Delight recognizes the moment with living memory and acts while intent is live.
### Talk to an expert
## Recognition is the revenue strategy nobody has operationalized
Your best customers are not users. They are fans. Fans do not churn. They compound.
## Move the company around one customer
Campaign platforms move segments through journeys. Inbound tools resolve cases. Delight is an AI concierge that remembers each customer, responds when they ask, reaches out when context calls for it, and takes the next best action 1:1.
### AMP — Memory makes the customer knowable
Agent Memory Platform carries preferences, history, intent, and business signals forward so every new moment starts with context.
### FYC — Conversation makes the action personal
For You Conversations turns that memory into a timely 1:1 exchange, whether the customer starts it or Delight knows it is time to reach out.
## How Delight makes it real
Your teams already have the signals: account changes, orders, catalog, loyalty, service history, and live events. Delight turns them into living memory, then uses FYC to start or answer the right 1:1 conversation across every channel.
### AMP — Not another static profile
Customers do not have to restart the relationship when they switch from chat to voice, SMS, email, WhatsApp, or in-app.
### FYC — The data becomes an action
The conversation adapts to who the customer is, what they are trying to do, and what the brand should offer now.
### Omnipresence — Every channel stays one thread
Every exchange updates the customer memory, so the next save, reorder, renewal, or upgrade starts smarter.
## Turn recognized intent into revenue
[Contact sales](#main)
## Give the AI budget and authority
When the AI has memory, taste, budget, and permission, it can create the moments that make customers stay longer, spend more, and tell other people.
## When customer intent is recognized, spend follows
Your next revenue stream is already hiding in the conversations customers trust you with. Delight remembers the signals, knows when to act, and turns intent into the next purchase, upgrade, save, or expansion before the moment goes cold.
## Case highlights
Quote 1 of 3
- 
> Member engagement became a higher-value shopping moment. Delight helped BJ’s personalize recommendations and interactions, turning customer intent into basket value and repeat spend.
20% Increase in average order value from AI-assisted member engagement.
6x More spend from members who engage with AI compared with those who do not.
- > Freya turns trip questions into revenue moments: extra-legroom questions can become seat upgrades, and baggage-limit questions can become extra-bag purchases inside the same helpful conversation.
Trip question → Relevant add-on
Seat + baggage intent → Ancillary revenue
- > A technical user asks for help inside the product. The revenue moment is adoption: guide the user through the next valuable action before momentum drops.
Product context → Guided usage
Expansion signal → Expansion readiness
## Every conversation should make the next one more valuable
A conversation becomes memory. Memory makes the next interaction personal. Personalization creates delight. Delight becomes advocacy. And advocacy brings customers back with more intent.
## The next revenue channel is the customer raising their hand
[Talk to an expert](#main)
## Complex CS landing page
Source: https://delight.ai/form/multi-party-cs
Simple tickets
do not need us.
Delight is for the
hard ones.
=====================================================================
The hardest cases pull in multiple parties, systems, and steps at once. Delight orchestrates the coordination across them and turns those moments into reasons customers stay.
- Peak-time disruption
- Weather disruption surge
- Multi-party dispute resolution
- Policy-sensitive exceptions
- Product adoption rescue
Talk to an expert
## The AI agent for cases that take multi-party coordination
Agent Steward turns one customer ask into parallel work across systems, channels, policies, and approvals.
## One command center for AI and human operators
Specialized AI agents and human operators work from the same case record, so context, knowledge, actions, judgment, and learning stay in one command center.
### Desk command center — One shared workspace
AI agents and human operators work in one unified space — sharing context, actions, and judgment in real time.
### AI work — Bring the case to the point of judgment
Pull records, check policy, complete safe steps, then pause at the exact decision a human should make.
### Human judgment — Review with context already assembled
See the draft, customer context, sources, confidence, and rationale before approving, editing, or taking over.
### System learning — Turn the resolution into signal
Keep AI actions and human decisions in one timeline so exceptions become reusable operating knowledge.
## Built for the cases that refuse to stay simple
When service gets messy, the answer is not enough. Delight acts like an AI response team: coordinating systems, approvals, human judgment, and customer updates until the case is resolved.
### Case Study 1 of 4
- 
When travel breaks, every passenger needs a path forward.
### 40,000 passengers needed a next step, not a ticket number.
- Mass cancellation recovery
- Rebooking + refund
- Claims / reimbursement
- Passenger updates
#### Freya became the recovery layer across AI Agent + Desk.
Freya checks booking status, cancellation policy, refund paths, and rebooking options; brings in human judgment for exceptions; and keeps travelers updated until the case is actually resolved.
- In B2B, support either grows adoption or stalls it.
### A technical question is rarely just one question.
- Product adoption rescue
- Account-aware answers
- Usage-context guidance
- Data judgment
#### The answer depends on the account, the setup, and what the user is trying to ship.
A Mixpanel user may ask why a dashboard looks wrong, but the real issue can live across events, permissions, data residency, workspace setup, and team usage. Delight brings that context forward so support can unblock the work, not just explain the product.
- Every late order has more than two sides.
### A late order can become a four-party escalation in minutes.
- Late order dispute
- Rider + merchant coordination
- Peak-time surge
- Refund exception
#### Four parties have to line up before the answer is true.
Delight checks order status, rider location, merchant prep, ETA, and refund rules; coordinates the customer, rider, merchant, and operations team in one case; brings in human judgment for disputes; and keeps the customer updated through the peak-hour surge.
- A home project lives inside the customer's real life.
### The order is delayed, and the customer is living inside the problem.
- Home project support
- Installation timing
- Project memory
- Vendor coordination
#### Delivery, installer, vendor, policy, room context. One thread.
Delight keeps the room plan moving across product details, measurements, delivery slots, installer timing, vendor policies, and approvals, so one delay does not become five restarted conversations.
## The system behind one resolved case
A case breaks when memory, channels, actions, approvals, and human judgment live in different places. Delight keeps them together, so the customer asks once and the company moves as one.
## Every tool in your stack was built for the easy stuff
The cases that need judgment and decide whether a customer stays aren't solved by any of your existing tools.
## Bring us the cases you've been afraid to automate
[ Talk to an expert](#main)
## The AI Permission Curve
Source: https://delight.ai/tools/ai-permission-curve
## The AI Permission Curve
Consumers do not grant AI trust all at once. They grant permission in stages: first to answer, then remember, then recommend, then act.
Download the worksheet
## The 2026 Delight AI Index.
[Read the full AI Index report](https://delight.ai/reports/ai-index)
### Consumer permission is not a single point.
Findings from the 2026 Delight AI Index: 1,001 US consumers surveyed across 5 industries on their comfort with AI acting on their behalf.
- 16% of consumers comfortable with fully autonomous AI acting on their behalf
- 85% say they need the ability to override or stop AI at any point
- 57% name reversibility (the ability to undo) as their top trust driver
- 70% would be more delighted if AI remembered their previous interactions
### Not all AI actions carry the same risk, or require the same trust.
The Permission Curve maps five stages of consumer trust, from answering questions to acting without approval. Most brands have a permission gap, not a capability gap: the AI can do more than customers have said yes to. The fastest path to broader AI adoption is knowing where your customers actually are, then building the controls that earn the next step.
### Where does your AI sit, and where should it?
#### Stage 01 — Answer
What consumers allow
High comfort
AI responds to direct questions. Order status, store hours, policy lookups. Table stakes in 2026: consumers expect it and trust it completely.
- Order status
- Store hours
- FAQ lookup
#### Stage 02 — Remembers
High appetite
AI retains context across sessions: past purchases, preferences, prior issues. 70% say this would make them more delighted. Appetite is high; consent is required.
- Purchase history
- Saved preferences
- Prior issue context
#### Stage 03 — Recommends
Moderate comfort
AI suggests actions: a rebooking option, an upgrade, a resolution path. Consumer stays the decision-maker. Comfort rises sharply when recommendations are grounded in their own data.
- Rebooking suggestion
- Product recommendation
- Resolution path
#### Stage 04 — Acts with Approval
Growing trust
AI takes an action (initiates a refund, books a change) but only after explicit confirmation. Override is visible and real. This is where most enterprise AI should be operating right now.
- Confirm-to-refund
- Approve-to-book
- One-click dispute
#### Stage 05 — Acts Autonomously
16% ready today
AI acts on behalf of the consumer with no approval step. Only 16% are here today. Not the right default for most use cases in 2026, but 54% expect their trust to grow over the next year.
- Auto-reschedule
- Proactive resolution
- Predictive service
### Trust isn't built at the launch. It's built in the design.
Three decisions that determine whether your AI earns consumer permission or erodes it.
**Trust OS**
The #1 trust signal: knowing they can undo it
[Explore](https://delight.ai/trust-os)
**Agent Steward**
Show the plan before you run it
[Explore](https://delight.ai/autonomous-agent)
**Zero-Touch Improvement**
Your AI gets better while you sleep
[Explore](https://delight.ai/product-release/zero-touch-improvement)
### Consumer comfort with AI varies by industry. Know your starting point.
The same AI capability lands differently depending on where you operate. Before deciding how much your AI should do, know where your industry baseline sits.
- 2026 Delight AI Index
### Don't leave automation on the table. Don't overshoot the trust that's there.
- Consumer comfort ranges 13 points across industries. Knowing your baseline means you deploy confidently where trust already exists, and earn your way into the rest.
## AI Permission Curve Worksheet
Map your top service moments by stakes, permission stage, safeguards, AI mode, and human role. Free spreadsheet.
### Your AI permission audit
Download
Use case | Stage | Controls needed
--- | --- | ---
Proactive rebooking | S4 Acts w/ approval | Undo, Override
Saved preferences | S2 Remembers | Memory consent
Product recommendation | S3 Recommends | Show reasoning
Your use case | — | —
Your use case | — | —
### Ready to deploy AI at exactly the stage your customers trust?
[Talk to an expert](https://delight.ai/contact-sales)
## Avoca vs Delight
Source: https://delight.ai/compare/avoca-vs-delight
## Evaluating Avoca?
## Avoca only books the job. Delight.ai delivers it.
Talk to an expert
### Customers & logos
        
### Platform scale & reliability
- 7B+ conversations / month
- 300M+ monthly active users
- 99.9%+ uptime SLA
- 10+ years enterprise infra
## Watch Delight handle a home service repair
From intake to technician match.
### Demo timeline
- 0:21 — Emergency call initiated
- 0:33 — Identity verification
- 0:55 — Omnipresent channel coordination
- 1:22 — Booking confirmed / Live technician coordination
- 2:33 — Case status inquiry
- 3:29 — Steward dispatches sub-agent
- 4:26 — New ETA confirmed
- 5:00 — Autonomous resolution
#### Watch the interactive demo
## We book the job too. Better.
We convert inbound calls with full customer context, real-time confirmation, and zero manual follow-up — then keep going.
- Voice and SMS at the same time. No missed calls.
- Slot, address, and window confirmed during the call.
- Synced to ServiceTitan instantly. No manual entry.
- 24/7, no extra headcount. Nothing goes to voicemail.
## A booking isn't revenue. We hold the thread until it is.
Parts delayed, schedules shift, claims stall. Delight.ai coordinates every party proactively, from the moment a case opens through the closed invoice.
### Core capabilities
- Proactive, not reactive — AMP builds the full customer profile before first contact so Delight.ai reaches out first.
- Omnichannel, one thread — Voice and text in the same case; context carries across channels.
- Every party, one thread — Homeowner, tech, adjuster, supplier: one case, full memory.
- Triggered by what happens — Parts arrived, window shifted, claim approved: Delight.ai moves before anyone asks.
## Delight.ai vs Avoca — summary
Delight.ai is built to manage the post-booking lifecycle end-to-end; Avoca focuses primarily on booking confirmation. Key differences below.
### Side-by-side (condensed)
- Inbound booking
- Delight.ai: Handles voice and text together, sending SMS slots customers can tap to confirm.
- Avoca: Voice-first (CONVERT). Text-preferring customers often must re-enter the flow in a different channel.
- Post-booking lifecycle
- Delight.ai: Manages bookings through resolution, including exceptions, rescheduling, warranties, and contractor coordination.
- Avoca: Ends at booking confirmation; post-booking case management not included.
- Reengagement
- Delight.ai: Re-engages by voice and text with full context, continuing from last interaction.
- Avoca: NURTURE/REENGAGE focus on pre-booking outreach; no persistent case context for open cases.
- Multi-party coordination
- Delight.ai: Single persistent thread shared across customer, contractor, dispatcher, adjuster, supplier.
- Avoca: Coordinates mainly customer and CSR to secure the booking; multi-party coordination out of scope.
- Persistent memory
- Delight.ai (AMP): Builds a living case profile, preserving context across channels, days, and interactions.
- Avoca: Looks up CRM fields before calls; lacks lifecycle memory across interactions.
- AI governance
- Delight.ai: Trust OS enforces policies in real time, with brand controls and audit trails.
- Avoca: Coach scores calls after the fact; no real-time governance layer for AI responses.
## Three things Avoca was not built to do
Avoca is optimized for securing bookings. Delight.ai is built to protect revenue and repeat business by handling the post-booking lifecycle.
- Reach out before they call — AMP tracks install dates, warranty status, and service history, enabling proactive outreach.
- Every party. One thread. — Persistent case memory across homeowner, tech, adjuster, supplier.
- Holds through every exception — Parts delays, window shifts, claim reviews: the case stays alive until job closure and payment.
## Questions we hear often
#### Can Delight.ai fully replace Avoca?
Yes. [Delight.ai](https://delight.ai/) handles inbound booking via voice and SMS simultaneously, syncs to ServiceTitan instantly, and runs 24/7 without adding headcount. Delight.ai also continues managing the case after booking — Avoca stops at booking confirmation.
#### What does Delight.ai do that Avoca can't?
Three areas: proactive outreach before a customer calls (AMP identifies who and when to contact), multi-party coordination in a single persistent thread, and holding cases through exceptions until closure and payment.
#### We're already on Avoca. Is switching worth it?
Depends on your exception rate. If most jobs close on first contact, the gap is smaller. For warranty work, multi-contractor jobs, or cases needing follow-up, Delight.ai reduces lost reviews, repeat business loss, and revenue leakage.
#### How does Delight.ai handle inbound calls and booking?
Voice and SMS operate in parallel. While the AI is live on a call, it texts appointment slots customers can confirm. Bookings sync to ServiceTitan instantly. AMP recognizes returning customers and preserves context.
#### How does multi-party coordination actually work?
Delight.ai holds one persistent case that every party shares. Technicians see full history; customers never re-explain. When a part ships or a claim clears, Delight.ai updates everyone automatically.
#### Does Delight.ai integrate with ServiceTitan and Housecall Pro?
Yes. Delight.ai integrates bidirectionally with ServiceTitan, Housecall Pro, and other field service management platforms. Job status, customer records, and case data stay in sync.
#### How is Delight.ai priced compared to Avoca?
Pricing is scoped per deployment by a forward deployment team that configures integrations and workflows. Contact the team for transparent, predictable pricing and a real scoping estimate.
## Book more. Lose fewer. Keep every customer.
See it work
## Field Services
Source: https://delight.ai/industries/field-services
Delight chases every stalled job.
Not your dispatcher.
[Talk to an expert](https://delight.ai/contact-sales)
## Trusted by
## Agent Steward coordinates four parties to close every stalled job
Parts depot, OEM support, field tech, and client facility move on separate timelines and channels. Agent Steward keeps one case thread so stalled jobs close without dispatcher glue.
### Parts confirmed before the return visit
Steward confirms depot ETA and part arrival, so techs never roll to an empty floor.
### OEM escalation handled end to end
Steward gets the OEM diagnostic and closes the loop, so dispatchers don’t wait on hold.
### Return only when ready
Techs get the window only when parts, access, and client confirmation are ready.
### Client kept informed throughout
Steward updates the facility at every wait state, so your on-call team stops getting paged.
## Delight chases every party at once. Immediately. Persistently. Less downtime.
A dispatcher calls one party, waits, then moves to the next. Delight contacts parts, OEM, tech, and client at once, follows up across every channel, and keeps going until every party confirms.
- Every party contacted simultaneously
- Call, email, SMS, and portal: every channel covered
- Starts the moment a job stalls
- 24/7 follow-up, no shift gaps
[Watch a live Steward demo](https://delight.ai/demo-request)
## Delight calls. It doesn't wait to be called.
Most coordination tools send an email and wait. Delight picks up the phone — calling parts depots for ETAs, OEM lines for diagnostics, client facilities with updates.
- Calls parts depots, OEM support, and client facilities directly
- Voice confirmation in minutes vs. hours waiting on email
- Calls the depot at opening, not team start
- Falls back to SMS or email when voice isn't answered
[Explore Voice AI](https://delight.ai/voice)
## Every chase logged. Every SLA protected.
When a client disputes an SLA, the record is already there. Every action timestamped, every party documented, from first contact to job closed.
- Exact timestamps on every agent action
- Every ETA and order confirmation logged
- Every OEM exchange documented to the case
- Escalations include the full case thread
[Explore Trust OS](https://delight.ai/trust-os)
## Every agent action runs under your enterprise rules.
Trust OS defines what the agent can say, who it can contact, and when it must escalate. Activity Log gives you the complete case record, every action, every party, read-only.
- Agent permissions set by your ops team, not Delight
- Activity Log v2: full case thread, every action, every party
- Escalation triggers defined by your SLA thresholds
- Immutable export for disputes, compliance, and client claims
[Explore Trust OS](https://delight.ai/trust-os)
## Field service AI agent FAQs
Common questions from field service operations teams.
### What does Delight do the moment a field tech can't close a job?
When a tech marks a job stalled (missing part, pending OEM diagnostic, unconfirmed client window), Delight opens a coordination thread and works every party in parallel: calls the parts depot, follows up with OEM support, briefs the tech on confirmed ETA, and updates the client. It keeps working across channels and shifts until dependencies are resolved or an escalation trigger is hit.
### When does Delight escalate to a human?
Your ops team configures escalation thresholds. Delight handles routine coordination autonomously and escalates when it hits your defined triggers (SLA breach windows, supplier rejections, client complaints). Escalations include the full case thread for context.
### What if a supplier or depot doesn't respond?
Delight retries across channels and time windows based on urgency. If voice calls fail, it falls back to email, checks supplier portals, and retries at opening hours. All attempts are logged; unresponsive parties that exceed SLA triggers are escalated with full contact logs attached.
### How do we control what Delight can do?
Through [Trust OS](https://delight.ai/blog/product/introducing-trust-os-v2), your ops team defines permissions: who Delight can contact, what it can commit to, and when it must escalate. No IT changes required to adjust thresholds.
### How do we see what Delight did on a specific case?
Every action is logged to Activity Log with timestamps: who was contacted, which channel, what was said, and what was confirmed. Records are immutable and exportable for disputes or audits.
### Does Delight integrate with FSM platforms?
Yes. Delight integrates with platforms like ServiceTitan, Salesforce Field Service, ServiceMax, and IFS to pull job details, post updates, and log coordination back to the case.
Stalled jobs become penalties or second rolls.
Delight closes them.
[Talk to an expert](https://delight.ai/contact-sales)
## Logistics and supply chain
Source: https://delight.ai/industries/logistics-and-supply-chain
## Logistics & Supply Chain
## AI agent for every freight exception in your supply chain
For brokers, 3PLs, and distribution teams, Delight AI manages freight exceptions end to end, across every party, before margin leaks.
[Talk to an expert](https://delight.ai/contact-sales)
### Customers and partners
## No load tender goes unanswered. No load in motion goes dark.
Delight AI captures loads, runs check calls, and keeps every driver, dock, and shipper reachable, so you quote faster, book more, and never lose track.
- Voice & email: tenders answered instantly
- Check calls: live ETAs, no dark loads
- SMS: drivers and docks reached
- EDI & TMS: status posted automatically
[Explore Voice AI](https://delight.ai/voice)
## Delight runs multi-party, multi-day supply chain exceptions end to end.
Delight AI coordinates every party in parallel — from carrier ETA and dock scheduling to shipper updates — until the case closes.
- Holds the shipper live while coordinating parties in parallel
- Confirms with carrier, dock, and supplier before answering
- Works the case from exception open to claim closed
- Escalates only when a decision needs human judgment
[Watch a live Steward demo](https://delight.ai/autonomous-agent)
## Rate cons, claims, carrier callbacks.
Delight AI automates freight claim follow-up by storing every promise, every exchange, and every wait state across the full life of a case, then chasing it automatically when it goes quiet.
- Rate confirmations: chased to signature.
- Carrier callbacks: re-dialed if the promised call never comes
- Open claims: followed with adjusters until closed
- Shift handoffs: full case history passed, no rep starts cold
[Explore Agent Memory Platform](https://delight.ai/agent-memory-platform-amp)
## The audit trail that wins claims and defends chargebacks.
Delight AI timestamps every communication and logs every decision, keeping your audit trail ready. When human sign-off is needed, it escalates with full context, not a blank ticket.
- Freight claims: every message timestamped, ready to file
- Chargeback defense: records filed.
- Detention disputes: exact arrival and wait times on record
- Human escalation with full context
[Explore Trust OS](https://delight.ai/trust-os)
## Works with the TMS, visibility tools, and carrier networks you're already on.
## The AI platform behind freight exception management.
Freight exceptions break when records, actions, approvals, and judgment are scattered. Delight keeps everything together, so every party stays aligned and work moves as one.
##### Omnipresence
Always on chat, voice, SMS, email, and WhatsApp. Now with Voice 2.0.
[Explore](https://delight.ai/omnipresent)
##### FYC + AMP
A living, always-updated understanding of every customer.
Explore- [FYC](https://delight.ai/for-you-conversations-fyc) - [AMP](https://delight.ai/agent-memory-platform-amp)
##### Agent Steward
AI that takes ownership from first message to final confirmation.
[Explore](https://delight.ai/autonomous-agent)
##### Trust OS
AI that polices, improves, and learns from every conversation — zero touch.
[Explore](https://delight.ai/trust-os)
##### Desk
AI and human agents work together. Shared context, shared workflows.
[Explore](https://delight.ai/desk)
## What freight brokers and 3PLs gain after deploying Delight AI
- More loads booked
Answer every tender instantly, quote first, and win more business before a competitor picks up.
- Fewer OTIF penalties
Exceptions resolved before appointment windows lapse, protecting your retailer relationships and margins.
- Faster claims settlement
Delight chases adjusters automatically, cutting days off the time from damage report to close.
- Better rates on every lane
Delight negotiates outbound rates proactively, so you cover trucks faster and capture margin on every load.
## Questions from freight and logistics teams
### What's the difference between Delight AI and load tracking tools like FourKites or project44?
Visibility tools track where a load is. Delight AI manages what happens when it isn't where it's supposed to be. FourKites alerts you to an exception; Delight AI calls the carrier, holds the dock appointment, updates the shipper, and works the case until it closes.
### Does Delight AI work for both home warranty companies and home service operators (HVAC, plumbing)?
Both. On every load it answers inbound tenders, runs outbound check calls, collects ETAs, and posts status updates to your TMS automatically. When a load leaves the happy path, it shifts into exception mode: coordinating carriers, docks, shippers, and adjusters across as many days as the case needs.
### How does Delight AI work a freight damage claim?
When a damage report comes in, Delight AI opens the case, requests the BOL and photos from the driver and consignee, notifies the carrier's claims desk, and follows up with the adjuster automatically if they go quiet. It keeps the shipper updated and surfaces settlement offers for your team to approve. The full thread is the audit trail.
### How does Delight AI know when to escalate to a human?
Delight AI handles coordination: contacting parties, collecting documents, posting status updates, chasing wait states. When a case needs judgment (disputed freight bill, out-of-policy claim, shipper relationship at risk), it flags your team with full case context.
### Does Delight AI replace my dispatchers and account managers?
No. Delight AI automates repetitive coordination (check calls, status updates, claim follow-up, appointment confirmations). Dispatchers spend less time chasing ETAs and more time on load coverage and shipper relationships.
## Every exception closed. Every load covered.
[See it live](https://delight.ai/contact-sales)
## Home services and warranty
Source: https://delight.ai/industries/home-services-and-warranty
## Home Services & Warranty
Books like your best CSR. Coordinates every moving part from start to finish.
Delight AI handles inbound calls, books over voice and SMS, coordinates contractors, parts, and warranty claims across every party, and sees each job through to completion.
[Talk to an expert](https://delight.ai/contact-sales)
### Trusted by
     
    
       
## Watch Delight handle a home service repair
From intake to technician match.
Delight verifies the plan, texts appointment slots to the member's phone while the voice call is still running, and confirms the technician live — all before they hang up.
### Demo timeline
- 0:21 Emergency call initiated
- 0:33 Identity verification
- 0:55 Omnipresent channel coordination
- 1:22 Booking confirmed — Live Technician Coordination
- 2:33 Case status inquiry
- 3:29 Steward dispatches sub-agent
- 4:26 New ETA confirmed
- 5:00 Autonomous resolution
## AI built for multi-party coordination. No job gets stuck waiting.
Agent Steward coordinates and holds every party on one thread till it's done: homeowner, contractor, parts supplier, adjuster.
[Explore Agent Steward](https://delight.ai/autonomous-agent)
### Key capabilities
-  Automated rescheduling — Finds and confirms new appointment windows automatically. No callbacks required.
-  Proactive parts updates — Tracks delays and updates homeowners with revised ETAs before they ask.
-  Claims status sync — Adjusters and homeowners receive updates automatically.
-  Smart case escalation — Flags stalled cases and alerts your team automatically.
## Delight runs calls and texts together. No bookings lost.
Delight combines voice and SMS so bookings get confirmed in real time. Open slots, addresses, and confirmations are handled while customers stay on the call.
- Voice + SMS: confirmation arrives during the call
- Verification, matching, and address checks in one step
- 24/7 coverage without adding headcount
- EDI & TMS: status posted automatically
[Explore Voice AI](https://delight.ai/voice)
## Every closed job creates the next re-engagement opportunity your CSR never has time for.
When a job closes, Delight reaches out by voice, text, and email to re-engage lapsed maintenance customers, follow up on unsold estimates, and renew expiring service agreements before peak season.
- Re-engage lapsed customers automatically
- Follow up on unsold estimates automatically
- Renew expiring service agreements proactively
- Automated CSAT with recovery workflows
[Explore Omnipresence](https://delight.ai/omnipresent)
## How Delight helps handle more jobs without adding headcount
### Quote 1 of 2
 
- How Dr. Cool Services handles more jobs without adding headcount.
An HVAC and home warranty operator on inbound booking, contractor scheduling, and re-engagement.
> "Delight handles the calls we used to miss and follows up on the jobs we used to forget. It's like having a CSR who never clocks out."
Dr. Cool Services
[Read the story](https://delight.ai/customers/dr-cool-services)
- How hipages books more jobs without missing a single inbound.
Australia's #1 home services marketplace on inbound volume, booking automation, and homeowner satisfaction.
> "Homeowners expect an answer the moment they reach out. Delight handles every inbound surge without adding a single seat — and our booking rate went up."
hipages
[Read the story](https://delight.ai/customers/hipages)
## The system behind every successful resolution
Cases break when memory, actions, and approvals live apart. Delight keeps everything connected, so customers ask once and teams move as one.
### Platform components
- Omnipresence — Always on chat, voice, SMS, email, and WhatsApp. Now with Voice 2.0.
[Explore](https://delight.ai/omnipresent)
- FYC + AMP — A living, always-updated understanding of every customer.
[FYC](https://delight.ai/for-you-conversations-fyc) · [AMP](https://delight.ai/agent-memory-platform-amp)
- Agent Steward — AI that takes ownership from first message to final confirmation.
[Explore](https://delight.ai/autonomous-agent)
- Trust OS — AI that polices, improves, and learns from every conversation — zero touch.
[Explore](https://delight.ai/trust-os)
- Desk — AI and human agents work together. Shared context, shared workflows.
[Explore](https://delight.ai/desk)
## Enterprise-ready. SOC 2 Type II certified. Integrates with your stack.
- Insurance-grade compliance — SOC 2 Type II certified and built for insurance workflows. Every interaction is logged, timestamped, and audit-ready.
- Scale for storm season — Handles claim surges during weather events. Scales volume without sacrificing customer experience.
- Integrates with your dispatch stack — Connects with ServiceTitan, Housecall Pro, and custom CRMs.
## Home services & warranty AI agent FAQs
Q: How is Delight AI different from other AI answering and booking tools in home services?
A: Two things set Delight AI apart. First, the booking experience: voice and text run together so homeowners get a pre-filled appointment and confirm with one tap. Second, Delight stays with the repair through rescheduling, parts delays, and claims until the job is closed, improving completion rate, not just bookings.
Q: Can Delight AI handle contractor rescheduling and parts delays?
A: Yes. It finds alternative windows immediately, presents options to homeowners, and tracks part arrivals and ETA updates proactively.
Q: How does Delight AI keep coverage answers accurate at intake?
A: Delight checks the homeowner's plan live at intake and explains coverage over the call with details confirmed by text, preventing post-service coverage surprises.
Q: Can we bring our existing conversation data across?
A: Yes. Conversation and customer data migrate into Delight so the agent starts already tuned to your homeowners. Migration runs alongside integration work.
Q: How does Delight AI integrate with other CRM systems?
A: Delight integrates bidirectionally with contact center, dispatch, and CRM systems (including Verint and custom platforms). Repairs and status sync in real time. Implementation typically takes one to two weeks depending on your stack.
## Other AI agents stop at the booking. Delight doesn't stop until the job is done.
[Talk to an expert](https://delight.ai/contact-sales)
## Delight Security
Source: https://delight.ai/security
## Delight Security
At Delight, security isn't just a feature, it's our DNA. Trust is earned, and we earn yours by keeping your data safe.
[Talk to an expert](https://delight.ai/contact-sales)
### Customers & partners
### Best in class internal security
Delight uses an automation-first approach to implement advanced internal security measures.
- FIDO2 MFA & device trust to prevent phishing
- Automated Detection & Response
- Automated Compliance
- DevSecOps approach
### Enterprise-grade reliability & performance
Proven to perform at global scale, Delight powers 7 billion+ conversations every month—delivering high availability, low latency, and real-time responsiveness, even in mission-critical environments.
- Global infrastructure built for scale
- Real-time monitoring and 24/7 incident response
- Trusted by enterprises across regulated industries
### Data privacy & residency options
Maintain full control over where your data resides, how it’s stored, and who can access it—helping you meet regulatory and internal data governance requirements.
- Regional hosting to support data residency needs
- Clear data ownership and processing transparency
- Flexible data retention settings aligned to your policy
### Advanced security features & controls
Built-in security features give you full control over how you protect customer data—so you can align Delight to your internal policies and compliance needs.
- End-to-end encryption and TLS in transit
- Granular role-based access controls
- Full audit logs for visibility and control
### Security resources & compliance support
#### Partner with us on security
Security is a shared responsibility we take seriously. To report vulnerabilities, contact [\[email protected\]](/cdn-cgi/l/email-protection). Acknowledgment or reward is at our sole discretion.
#### Security docs for your team
We provide detailed guides on the security features available to secure your Delight implementation.
#### Security compliance & accreditation
Delight adheres to industry-leading frameworks including GDPR, CCPA, HIPAA, and SOC 2 Type 2.
### Security resources and insights
[](https://delight.ai/blog/product/introducing-trust-os-v2)
- product
Introducing Trust OS 2.0 — Eight new capabilities for running AI agents in production with full visibility, precise control, and continuous improvement.
[](https://delight.ai/blog/ai-for-cx/omnipresent-ai-for-customer-service)
Omnipresent AI for customer service: What it is, why it matters, and how to get started — This blog explains omnipresent AI for customer service and how it will transform the customer experience.
[](https://delight.ai/blog/ai-for-cx/proactive-ai-customer-service)
From messaging to intelligence: Delight.ai introduces proactive AI customer service — Proactive AI agents that boost CSAT and efficiency.
[](https://delight.ai/blog/ai-for-cx/agentic-ai-omnichannel-customer-service)
How agentic AI improves omnichannel customer service — Agentic AI enables businesses to scale seamless omnichannel customer service.
### Enterprise-grade security, fully transparent.
[View trust center](https://trust.sendbird.com/)
## Zendesk to Delight Desk migration
Source: https://delight.ai/zendesk-migration-desk
## One-click migration from Zendesk
Keep your tickets, workflows, muscle memory. Leave the Zendesk bill.
[See your Zendesk in Desk](https://delight.ai/zendesk-migration-wizard)
### $0
seat fee, forever
### Day 1
AI pre-trained on your tickets
### 100%
ticket & history migration
### URLs
Links & SEO preserved
ISO 27001 · HIPAA certified
## Migrate from Zendesk in one click
**VIDEO CHAPTER**
0:00 Introduction to Zendesk migration
0:26 One-click migration
1:50 Turn tickets into Actionbooks
#### Watch the Zendesk migration demo
## The reason you're still on Zendesk isn't the product. The exit just got easier.
No retraining. No migration project. No rebuilding the knowledge base from scratch. Familiar interface, one-click migration from Zendesk. Familiar and free.
 Familiar UI: your team opens Delight Desk and already knows what to do
 One-click migration: tickets, KB, and macros all come with you from Zendesk
 No seat fee: Desk is free forever; you only pay when AI agent resolves a ticket
[See your Zendesk in Desk](https://delight.ai/zendesk-migration-wizard)
## Zendesk bills you per seat. Delight Desk is free.
[See your Zendesk in Desk](https://delight.ai/zendesk-migration-wizard)
-  **$0 seat fee**
Free for every agent on your team. No per-seat charges, ever.
-  **AI Copilot included**
No add-on to buy. No separate setup. Works from day one.
-  **Pay for AI, not headcount**
You only pay when AI agent closes a ticket. Not per seat, not per agent.
-  **No lock-in**
Run alongside Zendesk. Move on your timeline. Cancel at renewal.
## Your AI agent arrives already trained.
We read your Zendesk ticket history before launch and draft Actionbooks from real resolutions—so your AI arrives ready, not blank.
- **Pre-trained on day 1**: Your Zendesk history analyzed during migration. Actionbooks arrive ready before your first ticket.
- **Specific to your operations**: Drafted from your policies, edge cases, and language—not generic cleanup work.
- **Human in the loop, always**: Every Actionbook stays in draft until approved, so customers never see unreviewed AI.
- **Zero training investment**: Each resolution teaches the AI from real work, with no manual retraining.
## One command center for AI and human agents.
Built for the AI era, Delight Desk pairs specialized AI agents with human judgment—all in one shared case, context, and workspace.
 Specialized AI agents resolve tickets end-to-end across data, policy, and voice
 Humans approve, edit, take over, or coach from the same case record
 Full shared context, no handoff gaps, no lost history
## Every call lands in Desk. AI handles it first.
-  **AI answers first**: Every inbound call is triaged and resolved without needing a human.
-  **One-click human takeover**: Full call context already open when your agent steps in.
-  **Every call becomes a ticket**: Transcript included automatically, nothing gets lost.
-  **One queue for everything**: Voice, chat, and email in the same workspace, no second tool.
## See your Zendesk in Desk before you decide.
Connect read-only and we build a live preview in minutes: your tickets, KB, macros, users, and AI Actionbooks drafted from your history. Review everything. Nothing moves until you say so.
 Everything transfers: tickets, history, KB, macros, users, custom fields
 SEO preserved with automatic 301 redirects for all KB URLs
 AI Actionbooks drafted from your history, ready by day one
[View demo](https://delight.ai/zendesk-demo)
## Common questions
**Is Desk really free?** Yes. Desk has no seat fees or tier limits.
**Does Desk have user access controls?** Yes. Desk has role-based access control with team-level and agent-level permissions. Administrators define what each agent can see, edit, or export across ticket data, customer profiles, and reporting.
**How does the AI agent hand off to human agents?** When the AI hits an approval gate or a confidence threshold, it escalates to Desk with full context: everything it tried, every system it queried, and why it stopped. The agent picks up from that exact point without re-reading the conversation. On voice, the live call transfers directly to a Desk agent with the full transcript already attached.
**Does Desk work across chat, email, and voice?** Yes. Tickets in Desk can originate from chat, email, or voice, and agents work all three channels from a single queue. When your AI agent handles a conversation on any channel and needs to escalate, it creates a Desk ticket with the full conversation history attached.
**Can I run Desk without the AI agent?** Yes. Desk is a fully functional helpdesk for human agents with no AI required to operate it. You can start with Desk today and add the AI agent when you're ready.
**How is Desk different from Zendesk or Freshdesk?** Desk is designed from the ground up for AI-native support. The AI agent and Desk share the same workflows, knowledge base, and audit trail — they're one system, not an integration. Zendesk and Freshdesk were built for human agents and bolt AI on top. With Desk, the AI and your team run the same playbooks, and every human resolution feeds back to improve the AI automatically.
**What reporting does Desk provide?** Real-time monitoring of the full AI and human system: AI resolution rate, human escalation rate, average handle time, confidence trends, and handoff time. One dashboard for the whole picture.
**Do you support migrating ticket history from another helpdesk?** Yes. Our forward deployment team runs migrations from existing helpdesks, moving your tickets and Help Center content into Desk and our KMS. We also support building Actionbooks from your historical ticket data so your AI agent starts trained on your team's resolution patterns. Talk to the team to scope your migration.
## Everything from Zendesk. None of the bill.
[See your Zendesk in Desk](https://delight.ai/zendesk-migration-wizard)
## Zendesk migration wizard
Source: https://delight.ai/zendesk-migration-wizard
1 Connect 2 Preview 3 Go live
## Connect your Zendesk account
Your Zendesk subdomain .zendesk.com
[Connect]
Read-only · We never write to Zendesk · SOC2 Type II · GDPR · ISO 27001
### Your tickets
Full history, comments, attachments
### Your macros and rules
Triggers, automations, custom fields
### Your knowledge base
Articles, categories, help center
### Your users and orgs
Agents, customers, organizations
## Scanning your Zendesk
acme.zendesk.com
Connecting to your Zendesk
Scanning ticket history
Scanning knowledge base
Building Actionbook preview
## Here is what we found
### Tickets
Full history, all statuses
0
### KB Articles
4 categories · URLs preserved
0
What we will import
0
### AI Actionbooks
Drafted from your resolved ticket patterns. Ready to review before you go live.
Back Review your workspace
## Your Zendesk is ready to move
### Ready to review your workspace?
This is only a preview. Our team will connect your Zendesk and handle the migration end-to-end.
See your workspace
### Ready to bring this into Delight?
We will review what transfers, answer questions, and help you move when you are ready.
- 47,283 tickets ready to transfer
- 84 KB articles ready to transfer
- 18 AI Actionbooks from ticket history
- Migration path ready for review
## RFP Template for Choosing an AI Customer Experience Platform
Source: https://delight.ai/tools/ai-cx-platform-rfp-template
## The RFP template for choosing an AI CX platform.
Choosing an AI customer experience platform is one of the highest-stakes decisions a CX leader makes. This free template gives you a structured framework to evaluate vendors on what matters. Your team moves faster, stays aligned, and gets to a confident decision.
### Simplify Your RFP Workflow
## Agent steward - On-demand video demo page
Source: https://delight.ai/steward-demo-request
## See Agent Steward in action.
### Video chapters
0:00 Introduction to Agent Steward:
0:35 Customer support call
1:42 Coordinating with the driver
2:42 Resolving issues with the customer
3:19 Orchestrating with the restaurant
4:10 Proactive support and final update
[Contact Sales](https://delight.ai/contact-sales) [Learn more](https://delight.ai/autonomous-agent)
#### See delight.ai in action
Share a few details to unlock the interactive demo and see how delight.ai can support your team.
Autonomous resolution.
One owner, start to finish.
Agent Steward sits between your customer and every system, team, and partner, coordinating everything in parallel so nothing falls through the cracks.
##### Omnipresence
Always on chat, voice, SMS, email, and WhatsApp. Now with Voice 2.0.
[Explore](https://delight.ai/omnipresent)
##### FYC + AMP
A living, always-updated understanding of every customer.
Explore - [Explore FYC](https://delight.ai/for-you-conversations-fyc)
- [Explore AMP](https://delight.ai/agent-memory-platform-amp)
##### Agent Steward
AI that takes full ownership from first message to final confirmation — across every party, every step.
[Explore](https://delight.ai/autonomous-agent)
##### Trust OS
AI that polices, improves, and learns from every conversation — zero touch.
[Explore](https://delight.ai/trust-os)
## Careers
Source: https://delight.ai/careers
## Join the builders, thinkers, and dreamers to build the AI workforce of tomorrow.
[See open roles](#jobs)
We've spent a decade making conversations count—7 billion of them, every month. Now we're asking: what if AI could show up like your best people do? That's why we built delight.ai. We're a global team of builders reimagining what customer experience can be. Join us.
## Our 7 core values
These are the principles that guide how we work, grow, and create impact together.
-  Endless tenacity
-  Customers first
-  Drive velocity
-  Always raise the bar
-  High-agency & exothermic leadership
-  Own collective outcomes
-  Global citizenship
We don’t stop at obstacles. We adapt, solve, and persist until the job is completely done in a remarkable way.
#### See how Delight.ai works
[Explore inside the build](https://delight.ai/careers/inside-the-build)
## The world’s most trusted conversations platform.
Our mission is to power every meaningful conversation in the world—whether human to human, or human to AI. Today, we're the engine behind 7 billion monthly conversations worldwide. Tomorrow, we're building a world where every business has an AI workforce as big as their ambition and humans are free to do work that actually matters.
[See open roles](#jobs)
## A few of our benefits
-  Be Your Best Self — A generous all-in-one wellness benefit. Use it on anything from workout equipment to hobby gear to gadgets to gaming consoles.
-  AI Citizenship — As an AI-first company, we provide real budget for AI software to amplify productivity and creativity.
-  Generous PTO — Paid holidays, accrued vacation and sick leave, paid volunteer time, and generous family/parental leave.
-  Rest & Rejuvenate — Take a non-PTO day off to enjoy your birthday or to relax and refresh.
## Global offices. Global team.
Located across key global regions to support our growing team.
##### Seoul
17F, 514, Seolleung-ro, Gangnam-gu, Seoul, Republic of Korea 06162
##### San Mateo
100 S. Ellsworth Ave, Suite 900, San Mateo, California 94401 USA
##### Singapore
Level 17, Frasers Tower, 182 Cecil Street, Singapore 069547
#### How we hire in an AI-first world
[Read our hiring approach](https://delight.ai/careers/candidate-experience)
## Open roles at Delight.ai [0]
We're looking for ambitious minds to join our growing team.
Location — Office location
Department — All departments
No open positions match your filters.
## Paid LP - Zendesk to Delight Desk migration
Source: https://delight.ai/zendesk-migration-desk-lp
## One-click migration from Zendesk
Keep your tickets,
workflows, muscle memory. Leave the Zendesk bill.
- **$0**
seat fee, forever
- **Day 1**
AI pre-trained on your tickets
- **100%**
ticket & history migration
- **URLs**
Links & SEO preserved
ISO 27001 · HIPAA certified
## The reason you're still on Zendesk isn't the product. The exit just got easier.
No retraining. No migration project. No rebuilding the knowledge base from scratch. Familiar interface, one-click migration from Zendesk. Familiar and free.
Familiar UI: your team opens Delight Desk and already knows what to do
One-click migration: tickets, KB, and macros all come with you from Zendesk
No seat fee: Desk is free forever; you only pay when AI agent resolves a ticket
## See your Zendesk in Desk
## Zendesk bills you per seat. Delight Desk is free.
-  **$0 seat fee**
Free for every agent on your team. No per-seat charges, ever.
-  **AI Copilot included**
No add-on to buy. No separate setup. Works from day one.
-  **Pay for AI, not headcount**
You only pay when AI agent closes a ticket. Not per seat, not per agent.
-  **No lock-in**
Run alongside Zendesk. Move on your timeline. Cancel at renewal.
## Your AI agent arrives already trained.
We read your Zendesk ticket history before launch and draft Actionbooks from real resolutions—so your AI arrives ready, not blank.
### Pre-trained on day 1
Your Zendesk history analyzed during migration. Actionbooks arrive ready before your first ticket.
### Specific to your operations
Drafted from your policies, edge cases, and language—not generic cleanup work.
### Human in the loop, always
Every Actionbook stays in draft until approved, so customers never see unreviewed AI.
### Zero training investment
Each resolution teaches the AI from real work, with no manual retraining.
## One command center for AI and human agents.
Built for the AI era, Delight Desk pairs specialized AI agents with human judgment—all in one shared case, context, and workspace.
Specialized AI agents resolve tickets end-to-end across data, policy, and voice
Humans approve, edit, take over, or coach from the same case record
Full shared context, no handoff gaps, no lost history
## Every call lands in Desk. AI handles it first.
-  **AI answers first**
Every inbound call is triaged and resolved without needing a human.
-  **One-click human takeover**
Full call context already open when your agent steps in.
-  **Every call becomes a ticket**
Transcript included automatically, nothing gets lost.
-  **One queue for everything**
Voice, chat, and email in the same workspace, no second tool.
## See your Zendesk in Desk before you decide.
Connect read-only and we build a live preview in minutes: your tickets, KB, macros, users, and AI Actionbooks drafted from your history. Review everything. Nothing moves until you say so.
Everything transfers: tickets, history, KB, macros, users, custom fields
SEO preserved with automatic 301 redirects for all KB URLs
AI Actionbooks drafted from your history, ready by day one
## Common questions
**Is Desk really free?** Yes. Desk has no seat fees or tier limits.
**Does Desk have user access controls?** Yes. Desk has role-based access control with team-level and agent-level permissions. Administrators define what each agent can see, edit, or export across ticket data, customer profiles, and reporting.
**How does the AI agent hand off to human agents?** When the AI hits an approval gate or a confidence threshold, it escalates to Desk with full context: everything it tried, every system it queried, and why it stopped. The agent picks up from that exact point without re-reading the conversation. On voice, the live call transfers directly to a Desk agent with the full transcript already attached.
**Does Desk work across chat, email, and voice?** Yes. Tickets in Desk can originate from chat, email, or voice, and agents work all three channels from a single queue. When your AI agent handles a conversation on any channel and needs to escalate, it creates a Desk ticket with the full conversation history attached.
**Can I run Desk without the AI agent?** Yes. Desk is a fully functional helpdesk for human agents with no AI required to operate it. You can start with Desk today and add the AI agent when you're ready.
**How is Desk different from Zendesk or Freshdesk?** Desk is designed from the ground up for AI-native support. The AI agent and Desk share the same workflows, knowledge base, and audit trail — they're one system, not an integration. Zendesk and Freshdesk were built for human agents and bolt AI on top. With Desk, the AI and your team run the same playbooks, and every human resolution feeds back to improve the AI automatically.
**What reporting does Desk provide?** Real-time monitoring of the full AI and human system: AI resolution rate, human escalation rate, average handle time, confidence trends, and handoff time. One dashboard for the whole picture.
**Do you support migrating ticket history from another helpdesk?** Yes. Our forward deployment team runs migrations from existing helpdesks, moving your tickets and Help Center content into Desk and our KMS. We also support building Actionbooks from your historical ticket data so your AI agent starts trained on your team's resolution patterns. Talk to the team to scope your migration.
## Everything from Zendesk.
None of the bill.
See your Zendesk in Desk
## Event gift 'thank you' confirmation
Source: https://delight.ai/gift-thank-you
Thank you for sharing your preference!
---
We'll be in touch shortly to send you your gift.
##### [Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways "Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen")
##### [What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages "What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving")
##### [Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping "Lotte Homeshopping partners with Anthropic and delight.ai to scale retail")
##### [gther transforms event registration with delight.ai](https://delight.ai/customers/gther "gther transforms event registration with delight.ai")
## Candidate Experience
Source: https://delight.ai/careers/candidate-experience
[See open roles](/careers#jobs)
At Sendbird, we’re building the AI workforce of tomorrow, and that shapes how we hire.
Our interview process is designed to be thoughtful, transparent, and fair. You’ll get to understand the role, the team, and how we work. In tandem, we’ll get to learn how you think, communicate, and solve problems.
We know that interviews take time and energy. Our goal here is to provide clarity: what to expect, how we evaluate candidates, and where AI fits into the process.
## What We Look For
We evaluate candidates across two core areas:
### Role-Relevant Skills & Experience
We focus on the skills that matter most for the role you’re applying for. Depending on the position, this may include:
- Problem-solving and critical thinking.
- Role-specific knowledge and AI skills.
- Communication and collaboration.
### Values in Action
How you work matters just as much as what you build. Our interviews include conversations designed to explore how your experiences and approach align with Sendbird’s [**core values**](#).
We’re not looking for perfection or a checklist match. We’re looking for:
- Thoughtful reflection on real experiences.
- Clear examples of impact and ownership.
- Curiosity, self-awareness, and a growth mindset.
## What To Expect
While the exact steps may vary by role or team, most candidates can expect:
1. #### Application Review
Our recruiting team reviews applications on a rolling basis. If there’s a strong match, we’ll reach out to schedule an initial conversation.
2. #### Recruiter Screen
A Zoom conversation to align on your background, the role, and expectations. You’ll also have time to ask questions.
3. #### Hiring Manager Interview
A deeper discussion about your experience, how you approach your work, and what success looks like on the team.
4. #### Role-Specific Interviews
These focus on skills most relevant to the role and may include:
- Technical interviews for engineering and technical roles
- Presentations or role plays for sales or customer-facing roles
- Take-home exercises or case studies where relevant
5. #### Onsite/Virtual Interviews
We’re a hybrid team and believe there’s value in meeting in person during the interview process. Some exceptions may apply for remote roles.
6. #### Offer
Your recruiter will keep you informed about next steps and how to prepare.
## Using AI in the Hiring Process
We’re an AI-first company. We believe strong candidates know how to work with AI and when not to.
Our goal isn’t to restrict candidates. It’s to create a fair, consistent environment where everyone is evaluated on their own thinking and abilities.
### Where AI is Encouraged
#### Applying for Roles
You’re welcome to use AI tools when preparing your application as long as the work reflects you. Good uses include:
- Refining or editing a resume you drafted yourself.
- Improving clarity, grammar, or formatting.
- Brainstorming how to better highlight your experience.
- Researching Sendbird, our mission, values, or the role.
#### Preparing & Practicing
We encourage candidates to use AI to prepare thoughtfully, including:
- Generating and practicing common interview questions.
- Building study guides or notes.
- Summarizing the job description.
- Researching interview best practices.
Think of AI here as a coach, not a script.
### Where AI is Not Allowed
#### Live Interviews (Virtual or In-Person)
During recruiter screens, hiring manager interviews, panels, or live conversations, candidates should not use AI to generate or assist with real-time answers. This includes:
- Reading AI-generated responses.
- Using AI chat or transcription tools to guide answers.
- Having AI listen and respond on your behalf.
#### Assignments and Coding Interviews
Unless explicitly stated otherwise, AI tools are not allowed to:
- Write code during live coding interviews.
- Fully complete take-home assignments.
- Generate solutions for exercises or case studies.
Some roles or assignments may allow or encourage AI usage; if so, we will be explicit.
## Our Expectations
Regardless of the role, we expect candidates to:
- Use AI as a support tool, not a substitute.
- Be transparent and ethical in how AI is used.
- Show their own judgement, experience, and thinking.
- Ask for clarification if anything is unclear.
Many of us at Sendbird use AI every day to work faster and smarter. In fact, AI was used to help refine this guide that you’re reading right now. We’re excited by candidates who also use AI responsibly and intentionally.
## Accessibility
Sendbird is committed to providing an inclusive and accessible interview experience for all candidates. We’re happy to offer accommodations throughout the hiring process to help you participate fully and comfortably.
If you need accommodations, please let your recruiter know as soon as possible. This will never impact how your candidacy is evaluated.
## We’d love to meet you.
If our mission resonates with you and the role feels like a stretch worth taking, we want to hear from you. We’re excited to learn how you think, what you’ve built, and what you want to build next.
[See open roles](/careers#jobs)
## delight.ai vs Netic
Source: https://delight.ai/compare/netic-vs-delight
## Netic only starts the job. Delight.ai finishes it.
Delight.ai books the job in as little as 90 seconds, then keeps going: live dispatch confirmation, parts-delay updates, and warranty claim follow-up across voice, text, and email.
### See it on your job board
- 7B+ Conversations/month
- 300M+ Monthly active users
- 99.9%+ Uptime SLA
- 10+ yr Enterprise infra
## Netic converts and upsells. We do both, then keep going.
### One case, four channels
Voice, SMS, chat, and email in the same thread, not four separate tools to stitch together.
### Confirmed live, not batched
Slot, tech, and job details confirmed during the call and written to ServiceTitan in real time.
### Upsell tied to the job
Re-engagement timed to job status and history, not a generic campaign calendar.
### 24/7, every channel
Nothing waits for business hours or routes to voicemail.
## A booking isn't revenue. We hold the thread until it is.
A tech gets matched, a schedule shifts, a claim stalls waiting on an adjuster. Delight.ai coordinates every party proactively, from the moment the job is booked through the closed invoice.
### Proactive, not reactive
AMP builds the full customer and job profile before first contact. We reach out. They don’t have to call.
### Matched, dispatched, confirmed
The right tech, by skill and zone, confirmed live and written to the schedule in real time.
### Every party—on their channel
Homeowner by voice and SMS, technician by SMS, supplier by system, adjuster by email: one case, one memory, no matter the recipient.
### Triggered by what happens
A part ships. A window shifts. An adjuster approves the claim. Delight.ai updates every party the moment it happens.
## Delight.ai vs Netic: Better at conversion. Better at the job after.
| Capability | Delight AI agent | Netic |
|---|---:|:---|
| Inbound booking | Handles voice and text together; sends tappable SMS slots to confirm; books in ~90s. | Convert captures leads and books time slots; focused on getting leads in the door. |
| Scheduling & dispatch | Reads live tech availability by skill/zone; confirms during the call and writes to ServiceTitan in real time (no double-booking). | Converts book a time against calendar; no public claim of live bidirectional dispatch matching by skill/zone. |
| Upsell & re-engagement | Runs off AMP’s full case history; review/upsell requests timed to job events (higher response rates). | Cultivate runs upsell campaigns as a standalone module, separate from live case history. |
| Post-booking lifecycle | Manages rescheduling, parts delays, warranty claims, multi-visit jobs; keeps case open until resolution. | No module covers case management once Convert closes the booking. |
| Multi-party coordination | Single case memory shared across homeowner, technician, supplier, adjuster; proactive follow-up when parties stall. | Convert/Cultivate centered on homeowner conversation; coordinating contractor/supplier/adjuster not core. |
| Persistent memory | AMP keeps a living, cross-channel case profile that the agent references live. | Empower provides analytics on past activity, not a live memory the agent uses. |
| AI governance | Trust OS enforces brand controls, escalation rules, and a full audit trail at response time; Desk enables one-click human intervention. | Evaluate scores calls after the fact for coaching; lacks a real-time policy enforcement layer. |
## Three things Netic was not built to do
Netic’s four modules convert and upsell the lead. Delight.ai is built for everything that decides whether the customer comes back.
### Reach out before they call
AMP tracks install dates, warranty status, and service history. Delight.ai reaches out first.
### Every party, one thread
Homeowner, tech, adjuster, supplier: one case, full memory. Nobody re-explains.
### Holds through every exception
Parts delayed, window shifted: the case stays alive until the job closes and you’re paid.
## How Delight helps home service teams handle more jobs without adding headcount
- How Dr. Cool Services handles more jobs without adding headcount.
An HVAC and home warranty operator on inbound booking, contractor scheduling, and re-engagement.
> "Delight handles the calls we used to miss and follows up on the jobs we used to forget. It's like having a CSR who never clocks out."
Dr. Cool Services
[Read the story](https://delight.ai/customers/dr-cool-services)
- How hipages books more jobs without missing a single inbound.
Australia's #1 home services marketplace on inbound volume, booking automation, and homeowner satisfaction.
> "Homeowners expect an answer the moment they reach out. Delight handles every inbound surge without adding a single seat — and our booking rate went up."
hipages
[Read the story](https://delight.ai/customers/hipages)
## Questions worth asking any AI vendor, including us.
The right questions surface the real differences between platforms.
### What’s the main difference between Netic and delight.ai?
Netic’s four modules—Convert, Cultivate, Empower, and Evaluate—capture, book, and upsell home-services leads. Delight.ai does that and stays on the case afterward: dispatch confirmation, delays, multi-party coordination, and review requests sent when they actually convert.
### Is Netic a better fit for home-services companies?
If lead capture and upsell campaigns are the whole job, Netic is a focused tool. If jobs can stall after booking (parts delays, reschedules, warranty claims), that gap often sits outside Netic’s four modules—and is where revenue leaks.
### How does delight.ai support AI governance and trust?
Trust OS enforces policy at response time: brand controls, escalation rules, and a full audit trail. Netic’s Evaluate scores calls after the fact, useful for coaching but not for preventing live issues.
### How should buyers compare Netic vs delight.ai pricing?
Netic prices Evaluate month-to-month as a standalone add-on. Delight.ai’s AI agent is priced per conversation, Desk is free. Compare cost per resolved job, not just cost per booked one.
## Convert more. Finish every job.
Keep every customer.
[See it work](#top)
## Inside The Build
Source: https://delight.ai/careers/inside-the-build
## *Inside* the build
This is how Sendbird works, who's doing it, and what it actually feels like to build here right now.
[ See open roles](/careers#jobs)
## Core mindset
CURIOSITY BEATS EXPERTISE
BEGINNER SHIP HERE TOO
BIG AaaS ENERGY
TALK IS CHEAP TOOLING ISN’T
## Four rules. Zero corporate.
Curiosity beats expertise.
Expertise has a shelf life. Curiosity doesn't. We optimize for the second one.
Beginners ship here too.
Some of our best work this year came from people doing it for the first time. No background required, just the willingness to start.
Less logistics. More judgment.
The repetitive parts of the job don't need a human anymore. The interesting parts still do.
Talk is cheap. Tooling isn't.
Everyone says they're AI-first. The companies that actually are have the receipts to prove it.
## What it takes for a company to go AI-native
#### How our CEO's turned his entire company into an AI-native organization
His marketing team built a fully functional e-commerce swag store with Stripe integration in days. His sales team built their own CRM tools. His recruiting team automated their entire workflow. And it’s all tracked, measured, and celebrated through an internal platform called Automators.
## Why now. Why here
**Some windows close fast. This one's wide open.**
Sendbird has the foundation of an established company and the pace of a startup. AI is creating a rare window where people get to reinvent how their work gets done. Right now, that window is wide open here.
**You came for interesting. So did we.**
The people building here aren't all engineers. They're SEs, analysts, CSMs, marketers. Anyone who saw a problem and stopped waiting for permission. Velocity is contagious. So is curiosity.
## From across Delight.ai
- 
> "There are days where I’ll go from a team discussion to building a workflow that solves part of the problem, and I’ll think, ‘I can’t believe this is my job now.'"
Abhi Jothilingam — Growth Marketer, San Mateo
- > "The big shift isn't speed, it's where my brain goes. The AI does the assembly work. I do the thinking. That's the real change."
Bernardo Aghemio — Sales Engineer, London
- > "A lot of companies right now are pushing for more AI initiatives but here the push is supported by access to all the tools, training, and support."
Skylar Ruiz — Marketing Data Analyst, San Mateo
- > "What [AI] gives you is speed. I've shipped more in the last few months than the year before. You don't need to know everything before you start. You just need to start."
Geetika Jain — Digital Success Manager, San Mateo
- > "Before AI, our team was limited by our capacity. With AI, we have collectively built a team of agents that help us manage routine processes."
Howard Yu — Head of Sales Operations and Enablement, San Mateo
- > "Delight.ai actively invests in making AI part of how we work. Delight.ai pays for the tooling and openly encourages experimenting with AI in everyday work, not just as a side project."
Peter Lyoo — Software Engineer, Seoul
## Help us build the AI workforce of tomorrow.
[ See open roles](/careers#jobs)
## aIndustry Blueprint - Retail
Source: https://delight.ai/industry-blueprint-retail
## No content available
No content was provided to summarize or convert into documentation-style Markdown.
To proceed, paste the document (Markdown, text, code, or other content) you want cleaned and converted, then resend the request.
Suggested input examples:
- API specification or README in Markdown
- Technical design or architecture notes
- Code snippets with surrounding context
- Logs, changelogs, or configuration files
Guidance for best results:
- Include the full document body you want processed.
- For long documents, indicate which sections are highest priority.
- Provide any preferred formatting rules (e.g., preserve code blocks, keep images, remove TODOs).
When you provide the document, I will return:
- A one-line navigation summary for quick indexing.
- Cleaned, docs-style Markdown preserving headings, lists, code blocks, images, and links.
Example minimal input you can paste:
```
# Project XYZ
## Overview
Project XYZ implements a service that handles X and Y.
## API
### POST /items
Request:
```
{
"name": "example",
"value": 123
}
```
Response:
```
201 Created
```
```
Paste your document and resend to generate the cleaned output.
## Dynamic account page
Source: https://delight.ai/dynamic-account-page
## Built for Angi
AI that handles the entire call. No handoff, even when it gets complicated. Delight handles intake, routing, booking, and technician confirmation in one call, before the member hangs up.
[Request a walkthrough](https://delight.ai/contact-sales)
- 7B+ conversations / month
- 300M+ monthly active users
- 99.9%+ Uptime SLA
- 10+ year enterprise infra
## Call Delight — try the live demo
Don’t watch a demo. Call Delight yourself.
[Book a home repair](tel:+12255018084)
Be a member with a broken AC or a leak under the sink. Ask Delight to text you open time slots, and watch voice and SMS work the same call to lock in your repair.
[Find technician coverage](tel:+12099611952)
Be a tech who can't make your next job. Watch Delight line up another technician to cover the appointment, hands-free.
## End-to-end case ownership
A booking isn't a resolved claim. Delight holds the thread until it is. No job gets stuck waiting.
Parts delayed, schedules shift, claims stall. Delight coordinates contractor dispatch, parts tracking, and claim updates from case open through the closed invoice.
### Automated rescheduling
Finds and confirms new contractor windows automatically. No callbacks required.
### Proactive parts updates
Tracks delays and updates members with revised ETAs before they need to call in.
### Claims status sync
Adjusters and members receive updates automatically. No manual follow-up from Member Care.
### Smart case escalation
Flags stalled jobs and alerts Member Care automatically. Nothing falls through the cracks.
## Voice + SMS: bookings confirmed in real time
Delight runs calls and texts together so bookings get confirmed while members are still on the call. Open slots, plan status, and confirmations are handled together.
[Explore Voice AI](https://delight.ai/voice)
## Reach out before they have to call in
Most inbound calls are status checks. Delight updates members by call, text, and email, and reminds plan holders before renewals lapse — so Member Care handles emergencies, not status questions.
[Explore Omnipresence](https://delight.ai/omnipresent)
## Scale, compliance, and integrations
3.7 million calls a year. Every one resolved, not deflected.
- Insurance-grade compliance: SOC 2 Type II certified; interactions logged and audit-ready.
- Storm-season scale: handles claim surges without sacrificing experience.
- Integrates with your dispatch stack: ServiceTitan, Housecall Pro, and custom CRMs.
## How Delight helps handle more jobs without adding headcount
### Dr. Cool Services
> "Delight handles the calls we used to miss and follows up on the jobs we used to forget. It's like having a CSR who never clocks out."
[Read the story](https://delight.ai/customers/dr-cool-services)
### hipages
> "Homeowners expect an answer the moment they reach out. Delight handles every inbound surge without adding a single seat — and our booking rate went up."
[Read the story](https://delight.ai/customers/hipages)
## Engagement history & upcoming milestones
Steps to go live:
1. Scope & Align — Align on use cases and metrics (Days 1–5)
2. Build & Test — Configure the agent and validate (Days 6–18)
3. POV Launch — Proof of value for a single use case (Day 19)
4. Measure & Improve — Track outcomes and refine (Days 20–29)
5. Production Launch — Roll out to full production (Day 30)
Recent status:
- Jun 2 — Discovery call — Completed — Mapped where repair calls stall
- Jun 10 — Custom demo built — Completed — Workspace and live demo numbers built
- Now — Scope alignment — In Progress — Prioritizing use cases and metrics
- Upcoming — Technical deep-dive — Scheduled — Integrations, security, Trust OS controls
- Upcoming — POV kick-off — Upcoming — Launch proof of value for one use case
## Resource hub
The source of truth for your evaluation. Share the link, not a dozen attachments.
#### Ebooks & guides
- [The home services AI buyer's guide — Read ebook](/)
- [Voice AI for home warranty: what to evaluate — Explore guide](/)
- [The status-call playbook: deflect vs. resolve — Explore guide](/)
#### Case studies
- [Dr. Cool Services — Read story](/)
- [hipages — Read story](/)
#### Product
- [Angi × Delight overview — View deck](/)
- [Home services one-pager — Download PDF](/)
- [Agent Steward · Voice AI · Omnipresence — Product tour](/)
#### Security & pricing
- [Security & compliance (SOC 2, data handling) — Read docs](/)
- [Pricing & how it scales — Read docs](/)
#### Your demo
- Your walkthrough recording
## Angi + Delight AI FAQs
How is Delight AI different from other AI answering and booking tools?
Two main differences: Voice + SMS in the same call (texts arrive with pre-filled appointment data for one-tap confirmation) and end-to-end case management (continues through reschedules, parts delays, and claim closure).
Can Delight AI handle contractor rescheduling and parts delays?
Yes — it finds alternate windows immediately, confirms with homeowners, and tracks part ETAs, sending proactive updates.
How does Delight AI keep coverage answers accurate at intake?
Delight checks the homeowner's plan live during intake, explains coverage by phone, and confirms details by text to avoid surprises and repeat calls.
Can we bring our existing conversation data across?
Yes — conversation and customer data migrate into Delight AI so the agent is tuned to your actual call patterns. Migration runs alongside integrations.
How does Delight AI integrate with other CRM systems?
Delight integrates bidirectionally with contact center, dispatch, and CRM systems (e.g., Verint), syncing repairs, appointments, and status in real time. Typical implementation is one to two weeks depending on your stack.
## Meet your team
Eloy — Delight Account Lead — [eloy@delight.ai](/cdn-cgi/l/email-protection#9dcef8f3f9fff4eff9ddf8f0fcf4f1b3fef2f0)
Leila — Solutions Engineer — [leila@delight.ai](/cdn-cgi/l/email-protection#2675434842444f544266434b474f4a0845494b)
## See Delight handle an Angi repair call — start to close
[Book a 30-minute walkthrough](https://delight.ai/contact-sales)
## Analytics landing page
Source: https://delight.ai/analytics
## Delight Analytics: Find the revenue opportunities in customer conversations
[View demo](https://delight.ai/demo-request)
## See buying signals in the dashboard, find where to act through MCP
-  Dashboard: what customers want to buy
Track restock requests, product comparisons, bundle questions, and what stops a sale.
-  MCP: find what stops them
Ask which buying questions go unanswered and where the agent missed a chance to help.
-  MCP: know where to act
Get a clear next step and act on it from Claude, ChatGPT, or Cursor in the same conversation.
## See how your agent handles buying questions
-  Buying questions by category
See resolution, handoff, and satisfaction for product, pricing, and upgrade questions, split by category.
-  Results for each Actionbook
Track conversations, handoffs, and satisfaction for each Actionbook, such as add to cart or upsell.
-  Join conversations to orders
Export conversation-level detail or daily rollups and join them to your order or booking data in your BI tool.
## Spot hallucinations and messages that need review
-  Flag the topics that matter
Choose which topics to flag, such as discount requests or price matching, so your team knows what to review.
-  Catch potential hallucinations
Review flagged AI answers, check their accuracy, and correct mistakes.
## Delight Agent MCP: Your analytics in ChatGPT, Claude, or Cursor
#### Delight Agent MCP: Skip the dashboard and just ask
Ask which product questions get handed off most, in plain language.
Go from a metric to the conversations behind it in one question.
Update agent instructions, safeguards, or handoff rules without opening the dashboard.
## Know which changes make your AI agent better
Test improvements with Trust OS and compare performance before a wider rollout.
[Explore Trust OS](https://delight.ai/trust-os)
Find what needs work
Identify unanswered product questions, inaccurate information, and conversations that need human help.
Test a better response
Update your agent’s knowledge or instructions, then test the changes against real conversations.
Compare the results
See how each version performs on resolution and customer satisfaction before expanding the rollout.
## Revenue AI agent resources
- [ Introducing Delight Agent MCP: Ask your AI agent about your AI agent](https://delight.ai/blog/product/introducing-delight-agent-mcp)
The delight.ai MCP connects Claude, ChatGPT, Cursor, and Codex to your live agent data, so you can ask a question or fix a problem without leaving the chat.
- [ Customer service MCP for ecommerce](https://delight.ai/blog/ai-for-cx/customer-service-mcp-ecommerce)
What a customer service MCP for ecommerce actually does, for retailers big and small, plus how delight.ai's own Delight Agent MCP already puts it to work.
- [ The complete guide to customer experience analytics](https://delight.ai/blog/ai-for-cx/guide-to-customer-experience-analytics)
Learn what customer experience analytics means, which CSAT, NPS, CES, and resolution metrics to track, and how AI expands what you can measure.
- [ AI hallucination detection & alerts](https://delight.ai/product-release/ai-hallucination-detection-alerts)
AI hallucinations aren't a matter of if, they're a matter of when. Catch them early and correct mistakes before they affect customers.
## Analytics FAQs
Answers to common questions about Delight.ai Analytics.
#### What is Delight.ai Analytics?
Delight.ai Analytics shows what customers ask about, what stops them from buying, and how your AI agent handles those conversations. It includes a dashboard for buying signals and core metrics, review queues for flagged messages and potential hallucinations, conversation transcripts, data exports, and Delight Agent MCP for questions the dashboard doesn't answer.
#### How can I find revenue opportunities in customer conversations?
Start with buying signals that keep coming up, such as restock requests, product comparisons, or bundle questions, then check how your AI agent answered them. With Delight Agent MCP, you can ask which of those conversations ended without a recommendation or next step, and fix the gap. [See how Delight Agent MCP works](https://delight.ai/blog/product/introducing-delight-agent-mcp).
#### How do I measure revenue from AI agent conversations?
Export conversation-level data with the [Data Export API](https://delight.ai/product-release/data-export-api) and join it to your order or booking data in your BI tool. Report conversion and order value for the conversations your AI agent handled using your team's definitions.
#### What is the difference between resolution rate and handoff rate?
Resolution rate measures how many conversations your AI agent closes on its own. Handoff rate measures how often it passes a conversation to a person instead. A rising handoff rate in one category, such as pricing questions, is usually the fastest sign that something needs attention. For more, read [the complete guide to customer experience analytics](https://delight.ai/blog/ai-for-cx/guide-to-customer-experience-analytics).
#### What are flagged messages?
Flagged messages are customer messages caught for a topic you monitor, such as a price-match request, a request to delete an account, or an attempt to override the AI agent's instructions, so a person can review them. You can [define your own safeguard categories](https://delight.ai/product-release/custom-safeguard-categories) beyond the four built in by default.
#### How does Delight.ai detect AI hallucinations?
Delight.ai flags low-confidence AI answers for review alongside flagged messages. Your team can check each answer, correct it, and update the knowledge behind it so the mistake isn't repeated. Learn more about [AI hallucination detection and alerts](https://delight.ai/product-release/ai-hallucination-detection-alerts) and [Dynamic Safeguards](https://delight.ai/product-release/dynamic-safeguards).
#### How do I act on what Analytics shows?
Use [Trust OS](https://delight.ai/trust-os) to update the knowledge and instructions behind an incomplete answer or missed recommendation, test the change before rollout, and review how your AI agent handles those conversations afterward.
## Find your revenue opportunities
Walk through Analytics with our team and see how it fits your own sales reporting.
[Contact sales](https://delight.ai/contact-sales)
## Retell AI vs. delight
Source: https://delight.ai/retell-vs-delight
## Retell AI Alternative Built for Enterprise Scale
Retell AI is developer infrastructure for building phone bots. Delight.ai is a full AI customer experience platform — voice included — with memory, governance, and every channel in one place.
### Customers and logos
7B+ Conversations/month
300M+ Monthly active users
99.9%+ Uptime SLA
10+ yr Enterprise infra
---
## Retell builds the call. Delight.ai resolves the case end-to-end.
Retell AI gives you the pieces to build a phone agent yourself: prompts, telephony, testing, analytics. Delight.ai starts where that ends, carrying the case across voice, chat, SMS, and email until it's resolved.
### Core differentiators
#### One case, every channel
Voice, chat, SMS, and email share the same case record so context carries across channels.
#### Live governance
Trust OS enforces brand and escalation rules while the agent is talking.
#### Memory that carries over
AMP remembers the caller on every channel they use next.
#### Built for resolution
Pre-configured for booking, support, and follow-up out of the box.
---
## Delight.ai manages the case until it's resolved
A phone call can end in two minutes. The case behind it often doesn't — follow-up texts, delayed answers, or email threads can prolong resolution. Delight.ai manages the full case lifecycle across channels.
- Proactive: AMP builds customer profiles before first contact so delight.ai can reach out proactively.
- Enforced live governance: Trust OS enforces policy automatically during response.
- One memory across channels: Voice, text, chat, and email share one case record.
- Real-time updates: Cases update the moment events happen (shipments, renewals, etc.).
---
## Feature comparison — Delight.ai vs Retell AI
| Capability | Delight.ai | Retell AI |
|---|---:|---:|
| Channels | Voice, text, chat, SMS, email, WhatsApp in one omnichannel case | Built for phone calls; text/chat/email not part of platform |
| Persistent memory | AMP keeps one living case record across channels | Caller history scoped to the call channel Retell operates |
| Live governance | Trust OS enforces rules at response time with full audit trail | Live Call Monitoring requires a person to listen in and take over |
| Human handoff | Delight Desk provides full case history and one-click intervention | Can transfer live call, but context stays scoped to that call |
| Build effort | Agents deploy pre-configured for booking/support/follow-up; ops can edit Actionbooks | Flexible builder for developers; changes require engineering work |
| Pricing model | Custom per-resolution pricing; no seat fee for Delight Desk | Advertised $0.07/min voice infra; LLM, TTS, telephony billed separately (real all-in higher) |
| Proactive outreach | Reads case status and reaches out automatically | Handles inbound/outbound calls you trigger; won't decide to reach out on its own |
| Compliance & certifications | SOC 2 Type II, GDPR, HIPAA/HITECH, ISO 27001; Trust OS adds response-level audit trails | SOC 2 Type II, HIPAA, GDPR scoped to voice channel only |
---
## Three things Retell wasn't built for
Retell builds and tests phone agents. Delight.ai is built for what happens next.
- Proactive outreach
Retell only handles the calls you trigger. It won't flag a returning case or reach out before the customer calls back — AMP does, automatically.
- Cross-channel memory
Retell has no memory outside the call itself. Conversations moving to chat, SMS, or email start over — Delight.ai carries one case record across all channels.
- Automatic governance
Retell's Live Call Monitoring needs a person watching to catch problems. Trust OS enforces rules automatically before a mistake reaches the customer.
---
## Where Retell is strong — where we differ
- Sub-second voice latency: Retell is tuned for phone-call response time.
- Full control of the voice stack: Teams can choose their LLM, TTS, and telephony provider (engineering required).
- One narrow, self-contained call: Ideal for single-call tasks like appointment confirmations; anything continuing on another channel will need separate context.
---
## What our customers say
- Norse Atlantic Airways — "Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever."
Martina Panza, Head of CX
[Explore](https://delight.ai/customers/norse-atlantic-airways)
- Mixpanel — "From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows."
[Explore](https://delight.ai/customers/mixpanel)
- Dr. Cool Services — "Delight handles the calls we used to miss and follows up on the jobs we used to forget. It's like having a CSR who never clocks out."
[Explore](https://delight.ai/customers/dr-cool-services)
- Hanssem — "The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is a true collaborative partner."
Eugene Kim, CEO
[Explore](https://delight.ai/customers/hanssem)
- Hipages — "Where relationships build repeat business. Hipages helps tradies build trust through real-time, personalized communication."
[Explore](https://delight.ai/customers/hipages)
---
## Questions worth asking any AI vendor, including us
- Is Retell AI a full customer service platform?
Retell is infrastructure for voice AI phone agents only. It lacks built-in chat, SMS, or email and has no shared memory across channels. Delight.ai includes an Agent Memory Platform (AMP) from the start: https://delight.ai/agent-memory-platform-amp
- How much does Retell AI actually cost per minute?
Retell advertises $0.07/min for voice infrastructure. Once you add LLM, text-to-speech, and telephony, independent breakdowns put the real all-in rate at $0.13–$0.31/min. Delight Desk bills per successful AI resolution and has no seat fee: https://delight.ai/desk
- Do I need developers to run a voice agent on Retell?
Yes for anything beyond a basic template: prompts, telephony configuration, webhook logic, and ongoing support usually require engineering. Delight.ai Actionbooks let ops teams change behavior without engineering: https://sendbird.com/blog/ai-sop
- Does delight.ai replace Retell AI, or work alongside it?
Delight.ai replaces Retell for teams that want an omnichannel, governed, memory-backed platform. Many customers migrate voice setups into Delight.ai while preserving phone numbers and flows. Confirm specific telephony integrations with the Delight.ai team.
- Is delight.ai's pricing better than Retell's per-minute rate?
Delight.ai often delivers better value when cases span channels because it charges per resolution across channels rather than per-minute for voice only.
- Does switching from Retell mean losing my existing voice setup?
Not necessarily. Delight.ai can run voice on the same infrastructure as chat/SMS/email and most customers keep their numbers and call flows. Validate your telephony integrations before assuming complete coverage.
- Do most customers prefer calling businesses?
Usage differs from preference: YouGov (2025, US) shows ~70% use phone support, but only ~35% prefer it. Email and other channels remain important, especially for younger demographics.
---
## Answer the call. Finish the case. Keep the customer.
[Contact sales](https://delight.ai/demo-request)
## Zendesk demo video
Source: https://delight.ai/zendesk-demo
## Migrate from Zendesk in one click
### Video chapter
- 0:00 Introduction to Zendesk migration
- 0:26 One-click migration
- 1:50 Turn tickets into Actionbooks
[Contact sales](https://delight.ai/contact-sales) [Learn more](https://delight.ai/zendesk-migration-wizard)
#### Watch the Zendesk migration demo
## Privacy Notice
Source: https://delight.ai/privacy
## Privacy Notice
Thank you for using Delight.ai!
**Last updated: February 5, 2026**
Sendbird, Inc., inclusive of our wholly owned subsidiaries, ("Sendbird," "Company," "us," "we," or "our"), provides a platform to allow our customers to engage and converse with their end users through chat, voice and video. This Privacy Notice ("Privacy Notice") is designed to help you understand how we collect, use, process, and disclose your personal information and to help you understand and exercise your privacy rights under applicable law ("Law").
## 1. Scope and updates to this Privacy Notice
This Privacy Notice applies to personal information processed by us, including on our websites and other online or offline marketing or promotional campaigns. To make this Privacy Notice easier to read, our websites and other marketing campaigns are collectively called the "Services."
We may provide additional privacy notices in writing to individuals at the time we collect their personal information. Any additional privacy notices may supplement this Privacy Notice or may apply in lieu of this Privacy Notice.
Important Note. *This Privacy Notice does not apply to any of the personal information that we process on behalf of our customers through their use of our Services ("Customer Data"). Our customers’ respective privacy policies govern their collection and use of Customer Data. Our processing of Customer Data is governed by the contracts that we have in place with our customers, not this Privacy Notice. Any questions or requests relating to Customer Data should be directed to our customer.*
Changes to our Privacy Notice. We may revise this Privacy Notice from time to time in our sole discretion. If there are any material changes to this Privacy Notice, we will notify you if required by applicable law. You understand and agree that you will be deemed to have accepted the updated Privacy Notice if you continue to participate in our Services after the new Privacy Notice takes effect.
## 2. Personal information we collect
The categories of personal information we collect depend on how you interact with us, our Services, and the requirements of applicable law. We collect information that:
- you provide to us;
- we obtain as you participate in our Services; or
- we obtain from other sources (i.e., third-party services as defined below).
### A. Personal Information You Provide to Us Directly
We may collect personal information that you provide to us.
- **Engagement.** We may collect personal information when you engage with your account, such as your name, email address, and username. (Depending on the service you use, you may also be asked to provide your phone number.)
- **Purchases.** We may collect personal information and details associated with your purchases, including payment information (e.g., your billing details).
- **Your Communications with Us.** We may collect personal information, such as name, company name, email address, phone number, or mailing address when you request information about our Services, register for regular information updates (e.g., newsletter, webinar, advertising), request customer or technical support, or otherwise communicate with us.
- **Surveys.** We may contact you to participate in surveys. If you decide to participate, we may collect personal information from you in connection with the survey.
- **Interactive Features.** We and others who use our Services may collect personal information that you submit or make available through our interactive features (e.g., messaging and chat features, commenting functionalities, forums, blogs, and social media pages). Any information you provide using the public sharing features of the Services will be considered “public,” unless otherwise required by applicable law and these privacy disclosures do not apply to public information unless required by applicable law. Please exercise caution before revealing any information that may identify you in the real world to other users.
- **Conferences, Trade Shows, Webinars and Other Events.** We may collect personal information from individuals when we attend or host conferences, trade shows, webinars, and other events.
- **Business Development and Strategic Partnerships.** We may collect personal information from individuals and third parties to assess and pursue potential business opportunities.
- **Job Applications.** We may post job openings and opportunities on our Services. If you respond to one of these postings, we may collect your personal information, such as your application, CV, cover letter, and/or any other information you provide to us.
### B. Personal Information Collected Automatically
We may collect personal information automatically when you use our Services.
- **Automatic Collection of Personal Information.** We may collect certain information automatically when you use our Services, such as your Internet protocol (IP) address, user settings, MAC address, cookie identifiers, mobile carrier, mobile advertising and other unique identifiers, browser or device information, location information (including approximate location derived from IP address), and Internet service provider. We may also automatically collect information regarding your use of our Services, pages you visit, information about the links you click, the types of content you interact with, the frequency and duration of your activities, and other information about how you use our Services. In addition, we may collect information on diagnostic information, including metadata based on information above, regarding the performance of the Services.
- **Cookies and Other Technologies.** We, as well as third parties that provide content, advertising, or other functionalities on our Services, may use cookies, pixel tags or web beacons, and other technologies ("**Technologies**") to automatically collect information through your use of our Services. Such use of Technologies may be subject to your consent as required by applicable law. See section 5 below to understand your choices regarding these Technologies. More information about our Cookies and Similar Technologies is available in our [Cookies Notice](https://sendbird.com/cookie-notice).
- **Analytics.** We may use Technologies and other third-party tools to process analytics information on our Services. These Technologies allow us to better understand how our Services are used and to continually improve and personalize our Services.
- **Social Media Platforms.** Our Services may contain social media buttons, such as Twitter, Facebook and LinkedIn, which might include widgets such as the “share this” button or other interactive mini programs. These features may collect personal information such as your IP address and which page you are visiting on our Services, and may set a cookie to enable the feature to function properly. Your interactions (including your engagement with us on these platforms) with these platforms are governed by the privacy notice of the company providing it.
### C. Personal Information Collected from Other Sources
Third-Party Services and Sources. We may obtain personal information about you from other sources, including through third-party services and organizations. For example, if you access our Services through a third-party application, such as an app store, a third-party login service, or a social networking site, we may collect personal information about you from that third-party application that you have made available via your privacy settings.
Referrals and Sharing Features. Our Services may offer various tools and functionalities that allow you to provide personal information about your friends through our referral service. Third parties may also use the Services to upload personal information about you, including when they tag you. Our referral services may also allow you to forward or disclose certain content with a friend or colleague, such as an email inviting your friend to use our Services. Please only disclose with us contact information of people with whom you have a relationship (e.g., relative, friend, neighbor, or co-worker).
## 3. How we use your personal information
We use your personal information for a variety of business purposes, including to provide our Services, for administrative purposes, and to market our products and Services, as described below.
### A. Provide Our Services
We use your information to fulfil our contract with you and provide you with our Services, such as:
- Managing your information (including opting-in and out of communications);
- Providing access to certain areas, functionalities, and features of our Services;
- Answering requests for information or technical support;
- Communicating with your activities on our Services, and Notice changes;
- Processing applications if you apply for a job we post on our Services; and
- Allowing you to register for events.
### B. Administrative Purposes
We use your information for various administrative purposes, such as:
- Direct marketing;
- Research and development (including marketing research);
- Network and information security;
- Detecting security incidents, protecting against malicious, deceptive, fraudulent or illegal activity, and prosecuting those responsible for that activity;
- Measuring interest and engagement in our Services;
- Short-term, transient use, such as contextual customization of ads;
- Improving, upgrading, or enhancing our Services;
- Developing new products and services; and
- Ensuring internal quality control and safety:
- Authenticating and verifying individual identities, including requests to exercise your rights under this Privacy Notice
- Debugging to identify and repair errors with our Services
- Auditing relating to interactions, transactions, and other compliance activities
- Sharing personal information with third parties as needed to provide the Services
- Enforcing our agreements and policies
- Carrying out activities that are required to comply with our legal obligations.
### C. Marketing and Advertising our Products and Services
We may use personal information to tailor and provide you with content and advertisements. We may provide you with these materials as permitted by applicable law. Some of the ways we market to you include email campaigns, custom audiences advertising, and “interest-based” or “personalized advertising.” If you have any questions about our marketing practices or if you would like to opt out of the use of your personal information for marketing purposes, you may contact us at any time as set forth in "[Contact us](https://delight.ai/privacy#contact)" below.
### D. With Your Consent
We may use personal information for other purposes that are clearly disclosed to you at the time you provide personal information or with your consent.
### E. Other Purposes
We may use your personal information for other purposes as requested by you or as permitted by applicable law.
- **De-identified and Aggregated Information.** We may use personal information to create de-identified and/or aggregated information, such as demographic information, information about the device from which you access our Services, or other analyses we create. If we create or receive de-identified information, we will not attempt to reidentify such information, except to comply with applicable law. In addition, we have implemented commercially reasonable technical safeguards and business processes to prohibit the reidentification of information.
## 4. How we disclose your personal information
We disclose your personal information to third parties for a variety of business purposes, as follows.
### A. Disclosures to Provide our Services
The categories of third parties with whom we may disclose your personal information are described below.
- **Service Providers.** We may disclose your personal information with our third-party service providers and vendors that assist us with the provision of our Services. This includes service providers that provide us with IT support, hosting, payment processing, customer service, and related services.
- **Business Partners.** We may disclose your personal information with business partners to provide you with a product or service you have requested. We may also disclose your personal information with business partners with whom we jointly offer products or services.
- **Affiliates.** We may disclose your personal information with our company affiliates for our administrative purposes, including activities such as IT management, for them to provide services to you or support and supplement the Services we provide.
- **Other Users or Third Parties.** As described above in Section 2, our Services may allow you to disclose personal information or interact with other users and/or third parties (including individuals and third parties who do not use our Services and the general public).
- **Advertising Partners.** Except for mobile information as explained below, we may disclose your personal information to third-party advertising partners. These third-party advertising partners may set Technologies and other tracking tools on our Services to collect information regarding your activities and your device (e.g., your IP address, cookie identifiers, page(s) visited, location, time of day). These advertising partners collect personal information over time and across other websites and may use this information (and similar information collected from other services) for purposes of delivering personalized advertisements to you when you visit digital properties within their networks. This practice is commonly referred to as “interest-based advertising” or “personalized advertising.” No mobile information, which is any data we collect from you in the context of a marketing campaign such as your phone number, will be shared with third parties/affiliates for marketing/promotional purposes. All the above categories exclude text messaging originator opt-in data and consent records; this information will not be shared with any third parties.
For more details about our subprocessors and third-party service providers, including the countries in which they operate and the purposes for which personal information is transferred to them, please refer to our Subprocessor List at .
- **APIs/SDKs.** We may use third-party application program interfaces ("**APIs**") and software development kits ("**SDKs**") as part of the functionality of our Services. For more information about our use of APIs and SDKs, please contact us as set forth in "[Contact us](https://sendbird.com/privacy-notice#contact)" below.
### B. Disclosures to Protect Us or Others
We may access, preserve, and disclose any information we store associated with you to external parties if we, in good faith, believe doing so is required or appropriate to: comply with law enforcement or national security requests and legal process, such as a court order or subpoena; protect your, our, or others’ rights, property, or safety; enforce our policies or contracts; collect amounts owed to us; or assist with an investigation or prosecution of suspected or actual illegal activity.
### C. Disclosure in the Event of Merger, Sale, or Other Asset Transfers
If we are involved in a merger, acquisition, financing due diligence, reorganization, bankruptcy, receivership, purchase or sale of assets, or transition of service to another provider, your information may be sold or transferred as part of such a transaction, as permitted by law and/or contract.
## 5. Your privacy choices and rights
### A. Your Privacy Choices
The privacy choices you may have about your personal information are determined by applicable law and are described below.
- **Email Communications.** If you receive an unwanted email from us, you can use the unsubscribe link found at the bottom of the email to opt out of receiving future emails. Note that you will continue to receive transaction-related emails regarding products or Services you have requested. We may also send you certain non-promotional communications regarding us and our Services, and you will not be able to opt out of those communications (e.g., communications regarding our Services or updates to our Terms or this Privacy Notice).
- **Cookies and Personalized Advertising.** You may stop or restrict the placement of Technologies on your device or remove them by adjusting your preferences as your browser or device permits by setting the options in the Tools>Internet Options>Personal Information menu at the top of the web browser. However, if you adjust your preferences, our Services may not work properly. Please note that cookie-based opt-outs are not effective on mobile applications. However, you may opt-out of personalized advertisements on some mobile applications by following the instructions for [Android](https://support.google.com/googleplay/android-developer/answer/6048248?hl=en), [iOS](https://support.apple.com/en-us/HT202074), and [others](https://www.networkadvertising.org/mobile-choice/). We do not recognize the “Do Not Track Signal.”
The online advertising industry also provides websites from which you may opt out of receiving targeted ads from data partners and other advertising partners that participate in self-regulatory programs. You can access these and learn more about targeted advertising and consumer choice and privacy by visiting the [Network Advertising Initiative](http://www.networkadvertising.org/managing/opt_out.asp), [the Digital Advertising Alliance](http://www.aboutads.info/choices/), [the European Digital Advertising Alliance](https://www.youronlinechoices.eu/), and [the Digital Advertising Alliance of Canada](https://youradchoices.ca/choices/).
Please note you must separately opt out in each browser and on each device.
### B. Your Privacy Rights
In accordance with applicable law, you may have the right to:
- **Access Personal Information** about you, including: (i) confirming whether we are processing your personal information; and (ii) obtaining access to or a copy of your personal information (*If access is denied, you will receive a written notice explaining the reasons for the denial, in accordance with applicable law.*)
- **Request Correction** of your personal information where it is inaccurate, incomplete or outdated. In some cases, we may provide self-service tools that enable you to update your personal information
- **Request Deletion, Anonymization or Blocking** of your personal information when processing is based on your consent or when processing is unnecessary, excessive, or noncompliant
- **Request Restriction of or Object to** our processing of your personal information when processing is noncompliant
- **Withdraw your Consent** to our processing of your personal information. Please note that your withdrawal will only take effect for future processing, and will not affect the lawfulness of processing before the withdrawal. If you refrain from providing personal information or withdraw your consent to processing, some features of our Service may not be available
- **Request data portability** and **receive an electronic copy of personal information that you have provided to us**
- **Be informed** about third parties to which your personal information has been disclosed
If you would like to exercise any of these rights, please contact us as set forth in "[Contact us](https://sendbird.com/privacy-notice#contact)" below. We will process such requests in accordance with applicable laws.
## 6. Security of your information
We take steps to protect your information. Unfortunately, no system is 100% secure, and we cannot ensure or warrant the security of any information you provide to us. To the fullest extent permitted by applicable law, we do not accept liability for unauthorized access, use, disclosure, or loss of personal information.
By using our Services or providing personal information to us, you agree that we may communicate with you electronically regarding security, privacy, and administrative issues relating to your use of our Services. If we learn of a security system’s breach, we may attempt to notify you electronically by posting a notice on our Services, by mail, or by sending an email to you.
## 7. International data transfers
All information processed by us may be transferred, processed, and stored anywhere in the world, including, but not limited to, the United States or other countries, which may have data protection laws that are different from the laws where you live. While transferring your personal information internationally, we will comply with the applicable requirements governing processing of your personal information in a specific location – for example, your country of residence. We endeavor to safeguard your information consistent with the requirements of applicable laws. For personal information transferred from the United Kingdom, Switzerland, or European Union, to the United States, Sendbird has self-certified its compliance with the EU-U.S. Data Privacy Framework ("EU-U.S. DPF"), the Swiss-U.S. Data Privacy Framework ("Swiss-U.S. DPF"), and the UK Extension to the EU-U.S. DPF ("UK Extension"), collectively (the "DPF"). Further information can be found on Sendbird's [Data Privacy Framework Notice](https://sendbird.com/dpf).
## 8. Retention of personal information
Your personal information will be retained for as long as necessary for the purposes for which it was collected, which in most cases do not exceed 5 years. When Sendbird no longer needs to use your personal information to comply with contractual or statutory obligations, we will remove it from our systems and records and/or take steps to properly anonymize it so that you can no longer be identified from it, unless we need to keep your information, including personal information, to comply with statutory retention periods, such as for tax, audit, or legal compliance purposes, for a legally prescribed time period thereafter, or if we need it to preserve evidence within statutes of limitation.
## 9. Supplemental notice for California residents
For residents of California, please find our California Consumer Privacy Act Disclosures [[here](https://sendbird.com/california-consumer-privacy-act-disclosures)].
California Shine the Light. The California “Shine the Light” law permits users who are California residents to request and obtain from us once a year, free of charge, a list of the third parties to whom we have disclosed their personal information (if any) for their direct marketing purposes in the prior calendar year, as well as the type of personal information disclosed to those parties.
## 10. Supplemental notice for residents of the European Economic Area, Switzerland, and the United Kingdom ("EEA+")
For individuals in the EEA+, please find our EEA+ Supplemental Data Protection Law Disclosures [[here](https://sendbird.com/eea-supplemental-data-protection-law-disclosures)].
## 11. Supplemental notice for Korea residents
For residents of the Republic of Korea, the following disclosures apply in accordance with the Personal Information Protection Act ("PIPA"). In the event of any conflict between this Supplemental Notice and the other sections of this Privacy Notice, this Supplemental Notice shall prevail.
### A. Period of Retention and Use of Personal Information
In principle, we retain your personal information until the purpose of collection and use is achieved, or for the retention periods specified in this Privacy Notice. However, the following information is retained for the specified period under relevant Korean laws:
Act on the Consumer Protection in Electronic Commerce:
- Records on contract or subscription withdrawal: 5 years
- Records on payment settlement and supply of goods: 5 years
- Records on consumer complaints or dispute handling: 3 years
Protection of Communications Secrets Act:
- Computer communications login records: 3 months
### B. Procedure and Method of Destruction
Sendbird destroys personal information immediately after the purpose of collection is achieved or the retention period expires, unless further retention is required by law.
Destruction Procedure: Information selected for destruction is separated into a different database (or a separate file for paper documents) and stored for a certain period in accordance with internal policies and applicable laws before being destroyed.
Destruction Method: Personal information stored in electronic file formats is permanently deleted using technical methods that make the records unrecoverable (e.g., low-level format). Personal information printed on paper is destroyed by shredding or incineration.
### C. Rights of Users and Legal Representatives
You may exercise your rights to access, correct, delete, or suspend the processing of your personal information at any time.
How to Exercise: You may exercise these rights by contacting us via written notice, email, or facsimile in accordance with Form No. 8 of the Enforcement Rules of the Personal Information Protection Act. We will take action without delay upon receipt of such a request.
Legal Representatives: If you wish to exercise your rights through a legal representative or an authorized agent, you must submit a Power of Attorney in accordance with Form No. 11 of the Enforcement Rules of the Personal Information Protection Act.
Children: For children under the age of 14, the legal representative has the right to exercise all rights regarding the child’s personal information.
## 12. Third-party websites or applications
The Services may contain links to other websites/applications and other websites/applications may reference or link to our Services. These third-party services are not controlled by us. We encourage our users to read the privacy policies of each website and application with which they interact. We do not endorse, screen, or approve, and are not responsible for, the privacy practices or content of such other websites or applications. Providing personal information to third-party websites or applications is at your own risk.
## 13. What data does Sendbird SDK potentially collect?
To help our customers adhere to Apple's latest [privacy updates](https://developer.apple.com/app-store/app-privacy-details/#answering-questions), we are publishing an exhaustive list of data sets your application may collect based on how you use Sendbird SDK within your iOS application.
| Data | Data type | Reason for collection | Product |
|---|---|---|---|
| user ID | Identifiers | Identifying Sendbird user | Chat SDK, UIKit SDK, Calls SDK, Live SDK, Desk SDK |
| web socket session | Other data usage | Trace Web Socket session to accurately bill the customer | Chat SDK |
| text message | Emails or text messages | For private messaging, we facilitate sending text messages between users | Chat SDK |
| file message binary | Any other user-generated content | For private messaging, we facilitate sending files between users | Chat SDK |
| device cached channel, message, member list | Any other user-generated content | For private messaging, if you use a local-cache feature, we store user's message list, channel list, member list of the list you are part of | Chat SDK |
| remote notification ACK | Other diagnostic data | If you're using remote push notification for Chat, we would collect ACK from remote push notification to confirm delivery | Chat SDK |
| feedback of Bot message | Other diagnostic data | If you’re using Generative AI, we would collect feedback of bot messages created by Generative AI | Chat SDK |
| CTR (Click Through Ratio) | Product interaction | For Notification, we provide CTR | UIKit SDK |
| remote notification ACK | Product interaction | For Notification, we provide delivery ratio for remote push notification | UIKit SDK |
| photos or videos | Photos or videos | For messaging, UIKit provides a feature enabling users to attach photos or videos from photo library or camera | UIKit SDK |
| audio data | Audio data | For messaging, UIKit provides a feature enabling users to attach audio data through a file message | UIKit SDK |
| voice message | Audio data | UIKit provides feature for users to attach voice message through file message | UIKit SDK |
| Other user content (file attachment) | Any other user-generated content | UIKit supports various types of files through file message | UIKit SDK |
| audio streaming | Other user content | Calls, Live delivers and receives audio streaming through WebRTC | Calls SDK, Live SDK |
| video streaming | Other user content | Calls, Live delivers and receives video streaming through WebRTC | Calls SDK, Live SDK |
| media streaming statistics | Other diagnostic data | We provide RTP, RTCP statistics from WebRTC to provide feedback around streaming quality to customers | Calls SDK, Live SDK |
| cloud recording | Any other user-generated content | Cloud recording records streamed voice and video on the server | Calls SDK, Live SDK |
| local recording | Any other user-generated content | Local recording records streamed voice and video on device | Live SDK |
| remote notification ACK, VoIP push notification ACK | Other diagnostic data | If you're using remote push notification or VoIP push notification for direct call, we would collect ACK from push notification to analyze delivery | Calls SDK |
| ticket | Customer support | Ticket and ticket information from Desk SDK is used for customer support | Desk SDK |
## 14. Contact us
Company is the controller of the personal information we process under this Privacy Notice. Sendbird has designated the Chief Privacy Officer and the department to oversee the overall management of personal information and to handle any inquiries, complaints, or requests for damage relief related to personal information processing.
If you have any questions about our privacy practices or this Privacy Notice, or to exercise your rights as detailed in this Privacy Notice, please contact us at:
Sendbird, Inc.
100 S Ellsworth Ave, Suite 900
San Mateo, CA 94401
USA
Chief Privacy Officer (CPO)
Name: Brandon Jeon
Email: [\[email protected\]](/cdn-cgi/l/email-protection#d3a3a1baa5b2b0aa93a0b6bdb7b1baa1b7fdb0bcbe)
Personal Information Protection Department: Information Security Team
Email: [\[email protected\]](/cdn-cgi/l/email-protection#4636342f3027253f0635232822242f34226825292b)
### Sendbird, Inc.
**100 S Ellsworth Ave, Suite 900**
**San Mateo, CA 94401 USA**
Brandon Jeon
Chief Privacy Officer (CPO)
[\[email protected\]](/cdn-cgi/l/email-protection#84f4f6edf2e5e7fdc4f7e1eae0e6edf6e0aae7ebe9)
Information Security Team
Personal Information Protection Department
[\[email protected\]](/cdn-cgi/l/email-protection#87f7f5eef1e6e4fec7f4e2e9e3e5eef5e3a9e4e8ea)
View previous versions of our Privacy Notice
[ See archive](https://sendbird.com/privacy-notice/archive)
## Delight.ai vs Zendesk
Source: https://delight.ai/compare/zendesk-vs-delight
## Evaluating Zendesk?
Zendesk logs the ticket. Delight.ai remembers the customer.
## Customers
    
    
    
   
## Key metrics
- 7B+ Conversations/month
- 300M+ Monthly active users
- 99.9%+ Uptime SLA
- 10+ yr Enterprise infra
## Already decided to leave Zendesk?
Two resources built specifically for teams migrating off Zendesk.
- [ Migrating from Zendesk
See the $0 seat fee pricing comparison and how pre-trained AI actionbooks replace your existing workflows.](https://delight.ai/zendesk-migration-desk)
- [ Migration demo
Migrate from Zendesk to Delight Desk in one click and bring over your ticket history, macros, and more.](https://delight.ai/zendesk-demo)
## Delight.ai vs Zendesk: AI-native, not an add-on.
| Capability | Delight AI agent | Zendesk |
|---|---|---|
|AI architecture|Agentic AI built into the core of the platform, not a separate module.|AI agents ship as a paid Advanced AI add-on, layered onto existing ticketing.|
|AI readiness|Pre-trained on your resolved tickets from day one, not a blank Copilot.|Requires a populated knowledge base before resolving tickets.|
|Pricing model|Predictable enterprise pricing; no per-resolution fees; Desk is free.|Per-seat pricing plus AI add-on and usage-based resolution fees.|
|Multi-agent collaboration|Specialized agents hand off inside one case; the customer never repeats themselves.|Different AI agents can be published per channel, but they don't collaborate or hand off within a conversation.|
|AI governance & safety|Trust OS governs responses with policy enforcement, observability, and full audit trails.|Quality Score and admin-configured guardrails evaluate interactions after they're resolved.|
## Beyond what Zendesk offers — All native to delight.ai.
### Agent Memory Platform
Persistent profiles across every interaction, with shared context.
### Trust OS
Full visibility with built-in policy and controls.
### Omnipresence
All channels in one place with full context across interactions.
### Sendbird Desk
Built-in helpdesk with AI-to-agent handoffs.
## Where Zendesk is strong — Where we differ
When to choose each platform
| Feature | Delight AI agent | Zendesk |
|---|---|---|
|Customer memory|Persistent across every session and channel.|Builds a continuous profile visible to agents; history carries over.|
|AI governance|Built in from day one with observability and audit trails.|Governance after the fact with Quality Score and guardrails.|
|Pricing predictability|One enterprise contract — no stacked seat fees or per-resolution charges.|Seat pricing plus a $50/agent/month AI add-on and usage-based resolution fees.|
|Platform completeness|AI-native with agentic automation built into the core.|Advanced AI sits on top of a ticketing system built for a pre-AI era.|
If your team still runs on tickets
Zendesk's macros and SLA rules run deep for teams still tracking support as tickets, not conversations.
Off-the-shelf apps, not custom integration
1,800+ pre-built apps move fast, but you're limited to whatever functionality each one shipped with.
Established, and resistant to change
Two decades of deployments make Zendesk the safe pick, and the slow-to-change one.
## Questions worth asking any AI vendor, including us.
- Is your AI built to close tickets, or to build customer relationships?
Ticketing AI optimizes for closing tickets quickly. Delight.ai focuses on reducing ticket volume, autonomous resolution, and relationship outcomes.
- Does your AI personalize or just respond?
Personalization requires persistent memory across sessions; Delight.ai builds continuous profiles from every interaction.
- Does your AI wait for customers to reach out, or does it engage them first?
For You Conversations enables proactive outreach at key moments (delays, renewals, inactivity).
- What does your AI actually own, and what does it depend on?
Delight.ai ships Desk natively, so helpdesk, agent workspace, ticketing, and AI are one system.
- When the AI makes a decision, can your team see why?
Trust OS provides visibility into decisions, what was said, why, and whether policies were followed — audit trails for regulated industries.
- Are you measuring the interaction or the outcome?
Ticketing platforms focus on response times and handle time; Delight.ai is optimized for downstream outcomes like retention and churn.
- Does your AI work across every channel your customers use?
Omnipresence carries a single thread across chat, voice, email, and SMS with full context.
- Does the customer experience belong to you or the vendor?
Delight.ai is fully white-label so your brand owns the interface, voice, and escalation logic end to end.
## Stop logging tickets. Start remembering customers.
[Contact sales](https://delight.ai/contact-sales)
## Zendesk pricing: what you'd actually pay
Source: https://delight.ai/tools/zendesk-pricing-calculator
## Zendesk pricing: what you'd actually pay
Delight charges for the work AI handles, not the size of your team. AI is built into the platform, and customer context carries across every channel and session. Enter your team size and monthly ticket volume to compare costs and see potential savings when switching from Zendesk.
### Inputs
- Paid agent seats — Used to price the Zendesk side.
- Tickets per month — Total monthly conversations. Used to price the Delight side.
---
## Zendesk base plan
Auto-matched to team size
- Support Team — $19/agent/mo
- Suite Team — $55/agent/mo
- Suite Growth — $89/agent/mo
- Suite Professional — $115/agent/mo
- Suite Enterprise — $169/agent/mo (custom)
- Suite Enterprise Plus — $249/agent/mo (custom)
Auto-matched: teams around 90 agents usually run Suite Professional. Edit anytime.
## Zendesk add-ons (per seat)
- AI / Copilot — +$50
- WEM — +$50
- Contact center — +$83
- Advanced data privacy — +$50
## Estimated annual cost with Zendesk
Estimated at $0.37 per AI-resolved conversation, assuming ~65% of tickets resolve with AI.
**$124,200/year**
---
## See your savings with Delight
Get your personalized savings estimate.
[See estimated savings](#)
### Comparison
- Zendesk — Annual cost: $124,200 per year (estimate; depends on industry and channel mix)
- Delight — Priced per resolved conversation; estimate shown based on entered ticket volume and assumed AI resolution rate
 Ready to see your exact number?
[Talk to sales](https://delight.ai/contact-sales)
## Top reasons teams switch from Zendesk to Delight
### Pay for AI work, not human headcount
Your costs are based on AI usage, so adding human agents won’t increase your bill when ticket volume stays the same.
### AI that's native to your helpdesk
AI is built into Delight’s platform, so you don’t have to pay extra each month for add-ons that aren’t fully integrated with your helpdesk.
### Never make customers start over
Delight remembers customer context across channels and sessions, making every interaction faster, more personal, and more helpful.
## Frequently asked questions
### How much does Zendesk cost per agent?
Zendesk's published list prices range from $19/agent/mo (Support Team) up to $249/agent/mo (Suite Enterprise Plus), billed annually. Mid-sized teams often use Suite Growth or Suite Professional ($89–$115/agent/mo). Add-ons like Copilot, WEM, and Contact Center are priced separately.
### What determines my total Zendesk bill?
Four levers: seat count, base plan tier, which add-ons are active, and contract term. Seat count is typically the largest and most variable factor.
### How is the Delight estimate calculated?
Delight prices per resolved conversation rather than per seat. We assume a 65% AI resolution rate on your entered ticket volume, map resolved tickets to a volume tier, and show a range across that tier's band to account for monthly fluctuation.
### Does this calculator use current Zendesk pricing?
Yes — figures reflect Zendesk's public US list pricing as of the most recent update. Enterprise and Enterprise Plus use reference prices and may require custom quotes.
### What if my ticket volume is outside the listed tiers?
Below 10,000 or above 100,000 resolved conversations per month, the nearest tier's per-resolution rate applies. Contact us for a custom quote tailored to your actual volume.
### Can I get an exact number instead of a range?
Yes — the displayed range reflects a tier band. Talk to sales to get a precise quote sized to your exact ticket volume and team.
## Want to go deeper on Zendesk?
### Why are companies leaving Zendesk?
[Explore](https://delight.ai/blog/industry/why-are-companies-leaving-zendesk)
### Zendesk migration: Keep your data, skip the rebuild
[Explore](https://delight.ai/blog/industry/zendesk-migration)
### Can I use Zendesk for free?
[Explore](https://delight.ai/blog/industry/can-i-use-zendesk-for-free)
## Compare
Source: https://delight.ai/compare
## delight.ai — Agent-as-a-Service
The smart AI concierge that turns every customer moment into recognition, loyalty, and growth.
[View demo](https://delight.ai/demo-request)
## Key features
### Memory, For You Conversations, Never Disconnected, Agent Steward
- Memory
- For You Conversations (FYC)
- Always-connected across channels
- Agent Steward (autonomous ownership across systems)
## The platform to turn deflection into resolution
Personalization, connection, and autonomous resolution — built into a single platform.
##### Omnipresence
Always on chat, voice, SMS, email, and WhatsApp. Now with Voice 2.0.
[Explore](https://delight.ai/omnipresent)
##### FYC + AMP
A living, always-updated understanding of every customer.
- [Explore FYC](https://delight.ai/for-you-conversations-fyc)
- [Explore AMP](https://delight.ai/agent-memory-platform-amp)
##### Agent Steward
AI that takes full ownership from first message to final confirmation — across every party, every step.
[Explore](https://delight.ai/autonomous-agent)
##### Trust OS
AI that polices, improves, and learns from every conversation — zero touch.
[Explore](https://delight.ai/trust-os)
## Autonomous resolution — One owner, start to finish
Agent Steward sits between your customer and every system, team, and partner, coordinating everything in parallel so nothing falls through the cracks.
Delight every customer and turn them into lifelong fans.
## Core capabilities
### Agent memory: A living understanding of every customer
Delight.ai builds a living understanding of every customer — who they are, what they love, and what they need next.
### For-you conversations (FYC): Hyper-personalized service
Powered by memory, delight.ai makes every conversation personal and every customer valued.
### Memory + FYC: Customer loyalty and brand obsession
Every conversation builds memory. Every memory makes the next conversation more personalized, sparking unexpected delight.
## Omnipresence: Always present, always connected
### Omnichannel continuity: Turn every pause into progress
Delight.ai maintains memory across chat, SMS, email, and voice. Reengage after a drop-off without missing a beat.
### Voice AI: The most human way to stay connected
Delight.ai's voice support feels natural, personal, and instant — in any language.
### Proactive presence: Always a step ahead
Like a true concierge, delight.ai anticipates customer needs before they ask. Turn every moment of service into a moment of surprise.
## AI purpose-built for your industry’s toughest challenges
Your customers' problems don't look like everyone else's. Your AI shouldn't either. Delight.ai is tuned to the workflows, urgency, and stakes that define your industry.
- Retail  [Explore](https://delight.ai/industries/retail)
- B2B  [Explore](https://delight.ai/industries/b2b)
- Healthcare  [Explore](https://delight.ai/industries/healthcare)
- On-demand  [Explore](https://delight.ai/industries/on-demand)
- Financial services  [Explore](https://delight.ai/industries/financial-services)
- Travel & hospitality  [Explore](https://delight.ai/industries/travel-and-hospitality)
## Build on Trust OS — Deliver with confidence
### Trust OS: Responsible AI powered by observability, oversight, and control
- Build — Build with confidence and ease. Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Validate early, before it matters. Detect, analyze, and fix potential issues long before they impact your customers.
- Evaluate — Stay in control, always. Monitor results in real time, measure impact, and continuously optimize your business performance.
Build-Test-Evaluate loop
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
## A Sendbird product proven at scale
Built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
## The world’s leading brands deliver with delight.ai
    
### Customer highlights
> "Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations."
> Martina Panza — Head of CX, Norse Atlantic Airways
[Explore](https://delight.ai/customers/norse-atlantic-airways)
> "From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows that turn onboarding into ongoing adoption."
[Explore](https://delight.ai/customers/mixpanel)
> "The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is more than a vendor. They're a true collaborative partner who works alongside us to solve challenges from the same perspective."
Eugene Kim — CEO, Hanssem
[Explore](https://delight.ai/customers/hanssem)
> "Where relationships build repeat business. Hipages helps 33,000 tradies build trust through real-time, personalized communication, turning one-off jobs into lasting customer relationships across 12 million projects."
[Explore](https://delight.ai/customers/hipages)
## Scale, reliability, and integrations
- 7 billion+ conversations every month
- 300M+ monthly active users
- 99.9%+ uptime
Integrate with your customer service tech stack and gain enterprise white-glove support.
## Delight AI concierge FAQs
Find answers to common questions about AI concierges.
**What is an AI concierge?**
An AI concierge is an AI agent trained on your knowledge base and customer conversations to provide exemplary service autonomously. It remembers preferences from real-time conversations and applies that knowledge to future interactions.
**What use cases can an AI concierge handle?**
You can train your AI concierge across the customer lifecycle — sales, support, proactive reengagement, renewals, and routine tasks like rescheduling appointments or providing order updates.
**Can delight.ai AI concierges improve operational efficiency?**
Yes. They handle routine tasks to free human teams for higher-value work, improving speed and consistency while reducing manual effort.
**How can I monitor and improve my delight.ai AI concierge?**
The platform monitors agent adherence to [Actionbooks](https://delight.ai/builder) (your standard operating procedures), provides performance metrics, and offers AI-generated insights for optimization and oversight.
#### Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
## Reports
Source: https://delight.ai/reports
## Meet delight.ai — Agent-as-a-Service
The smart AI concierge that turns every customer moment into recognition, loyalty, and growth.
[View demo](https://delight.ai/demo-request)
## The platform to turn deflection into resolution
Personalization, connection, and autonomous resolution — built into a single platform.
### Omnipresence
Always on chat, voice, SMS, email, and WhatsApp. Now with Voice 2.0.
[Explore](https://delight.ai/omnipresent)
### FYC + AMP
A living, always-updated understanding of every customer.
[Explore FYC](https://delight.ai/for-you-conversations-fyc) · [Explore AMP](https://delight.ai/agent-memory-platform-amp)
### Agent Steward
AI that takes full ownership from first message to final confirmation — across every party, every step.
[Explore](https://delight.ai/autonomous-agent)
### Trust OS
AI that polices, improves, and learns from every conversation — zero touch.
[Explore](https://delight.ai/trust-os)
## Autonomous resolution — One owner, start to finish
Agent Steward sits between your customer and every system, team, and partner, coordinating everything in parallel so nothing falls through the cracks.
## Delight every customer and turn them into lifelong fans
### Agent memory: A living understanding of every customer
Delight.ai builds a living understanding of every customer — who they are, what they love, and what they need next.
### For-you conversations (FYC): Hyper-personalized service
Powered by memory, delight.ai makes every conversation personal and every customer valued.
### Memory + FYC: Customer loyalty and brand obsession
Every conversation builds memory. Every memory makes the next conversation more personalized, sparking unexpected delight.
## Omnipresence: Always present, always connected
### Omnichannel continuity: Turn every pause into progress
Delight.ai maintains memory across chat, SMS, email, and voice. Reengage after a drop-off without missing a beat.
### Voice AI: The most human way to stay connected
Delight.ai's voice support feels natural, personal, and instant — in any language.
### Proactive presence: Always a step ahead
Like a true concierge, delight.ai anticipates customer needs before they ask.
## AI purpose-built for your industry’s toughest challenges
Your customers' problems don't look like everyone else's. Delight.ai is tuned to the workflows, urgency, and stakes that define your industry.
 Retail [Explore](https://delight.ai/industries/retail)
 B2B [Explore](https://delight.ai/industries/b2b)
 Healthcare [Explore](https://delight.ai/industries/healthcare)
 On-demand [Explore](https://delight.ai/industries/on-demand)
 Financial services [Explore](https://delight.ai/industries/financial-services)
 Travel & hospitality [Explore](https://delight.ai/industries/travel-and-hospitality)
## Build on Trust OS. Deliver with confidence
### Trust OS: Responsible AI powered by observability, oversight, and control
- Build — Design, test, and launch your AI agent in days, not months, using tools that simplify every step.
- Test — Detect, analyze, and fix potential issues long before they impact your customers.
- Evaluate — Monitor results in real time, measure impact, and continuously optimize your business performance.
Build–Test–Evaluate loop
AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
## A Sendbird product proven at scale
Built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
## The world’s leading brands deliver with delight.ai
> "Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations."
> Martina Panza — Head of CX, Norse Atlantic Airways
[Explore Norse case study](https://delight.ai/customers/norse-atlantic-airways)
Other customer highlights:
- Mixpanel: AI concierge guides users through tailored onboarding and adoption. [Explore](https://delight.ai/customers/mixpanel)
- Hanssem: 90%+ agent accuracy in POC; strong partnership. [Explore](https://delight.ai/customers/hanssem)
- Hipages: Helps tradies build trust across 12 million projects. [Explore](https://delight.ai/customers/hipages)
## Connecting the world with AI concierges
Powering 300 million users every month.
- 7 billion+ conversations every month
- 300M+ monthly active users
- 99.9%+ uptime
## Integrate with your customer service tech stack
Delight.ai connects with existing systems to deliver autonomous, personalized service across channels.
## Delight AI concierge FAQs
Q: What is an AI concierge?
A: An AI concierge is an AI agent trained on your knowledge base and conversations to provide exemplary, autonomous service and remember preferences across interactions.
Q: What use cases can an AI concierge handle?
A: From sales to support, proactive reengagement, and renewal — trained for any point in the customer lifecycle.
Q: Can delight.ai AI concierges improve operational efficiency?
A: Yes. They automate routine tasks (rescheduling, order updates, recommendations, rebooking), freeing staff for higher‑value work.
Q: How can I monitor and improve my delight.ai AI concierge?
A: Trust OS provides monitoring against Actionbooks (standard operating procedures), performance metrics, and AI-generated insights with full oversight and control. [Builder / Actionbooks](https://delight.ai/builder)
## Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
## Tools
Source: https://delight.ai/tools
## Meet delight.ai — Agent-as-a-Service
The smart AI concierge that turns every customer moment into recognition, loyalty, and growth.
[View demo](https://delight.ai/demo-request)
---
## Key capabilities
- **Memory** — A living, always-updated understanding of every customer.
- **For-you conversations (FYC)** — Hyper-personalized service powered by memory.
- **Never Disconnected** — Omnichannel continuity across chat, voice, SMS, email, and WhatsApp.
- **Agent Steward** — AI that owns the whole lifecycle: first message to final confirmation.
- **Trust OS** — Observability, oversight, and control for responsible AI operations.
### Quick links
- [Omnipresence](https://delight.ai/omnipresent)
- [Explore FYC](https://delight.ai/for-you-conversations-fyc)
- [Explore AMP](https://delight.ai/agent-memory-platform-amp)
- [Agent Steward](https://delight.ai/autonomous-agent)
- [Trust OS](https://delight.ai/trust-os)
---
## The platform to turn deflection into resolution
Personalization, connection, and autonomous resolution — built into a single platform.
### Omnipresence
Always-on chat, voice, SMS, email, and WhatsApp. Now with Voice 2.0. [Explore](https://delight.ai/omnipresent)
### FYC + AMP
A living, always-updated understanding of every customer. Explore: [FYC](https://delight.ai/for-you-conversations-fyc) · [AMP](https://delight.ai/agent-memory-platform-amp)
### Agent Steward
AI that takes full ownership from first message to final confirmation — across every party, every step. [Explore](https://delight.ai/autonomous-agent)
### Trust OS
AI that polices, improves, and learns from every conversation — zero touch. [Explore](https://delight.ai/trust-os)
---
## Autonomous resolution — one owner, start to finish
Agent Steward sits between your customer and every system, team, and partner, coordinating everything in parallel so nothing falls through the cracks.
Delight every customer and turn them into lifelong fans.
## Agent memory: a living understanding of every customer
Delight.ai builds a living understanding of who customers are, what they love, and what they need next.
## For-you conversations (FYC): hyper-personalized service
Powered by memory, delight.ai makes every conversation personal and every customer valued.
## Memory + FYC: customer loyalty and brand obsession
Every conversation builds memory. Every memory makes the next conversation more personalized, sparking unexpected delight.
---
## Omnipresence: always present, always connected
### Omnichannel continuity
Delight.ai maintains memory across chat, SMS, email, and voice. Reengage after a drop-off without missing a beat.
### Voice AI
Natural, personal, instant voice support in any language.
### Proactive presence
Anticipates customer needs and proactively engages to turn service into surprise.
---
## AI purpose-built for your industry
Your customers' problems don't look like everyone else's. Delight.ai is tuned to the workflows, urgency, and stakes that define your industry.
- Retail — [Explore](https://delight.ai/industries/retail)
- B2B — [Explore](https://delight.ai/industries/b2b)
- Healthcare — [Explore](https://delight.ai/industries/healthcare)
- On-demand — [Explore](https://delight.ai/industries/on-demand)
- Financial services — [Explore](https://delight.ai/industries/financial-services)
- Travel & hospitality — [Explore](https://delight.ai/industries/travel-and-hospitality)
---
## Build on Trust OS — deliver with confidence
### Trust OS: responsible AI powered by observability, oversight, and control
- Build — Design, test, and launch your AI agent in days, not months.
- Test — Validate early: detect, analyze, and fix potential issues before they impact customers.
- Evaluate — Monitor results in real time and continuously optimize performance.
Build-Test-Evaluate loop: AI you can trust, continuously tested and improved with every conversation to enhance accuracy and reliability.
Trust OS provides brands full visibility, oversight, and control for transparent and compliant AI operations.
---
## A Sendbird product proven at scale
Built on Sendbird’s enterprise-grade infrastructure, trusted by global brands and powering billions of conversations.
### Trusted by (selected)
   
And many more global brands across industries.
---
## Customer stories (selected)
> "Delight.ai has been a game changer. Our CX team is highly satisfied and more proactive than ever. It’s transforming how we manage customer conversations."
Martina Panza — Head of CX, Norse Atlantic Airways
[Explore Norse case study](https://delight.ai/customers/norse-atlantic-airways)
> "From first click to full value. Mixpanel gets customers there faster. An AI concierge that guides each user through tailored workflows."
— Mixpanel
[Explore Mixpanel case study](https://delight.ai/customers/mixpanel)
> "The delight.ai agent's 90%+ accuracy rate during our POC gave us the confidence we needed. Sendbird is more than a vendor — a true collaborative partner."
Eugene Kim — CEO, Hanssem
[Explore Hanssem case study](https://delight.ai/customers/hanssem)
---
## Scale, reliability, and integrations
- 7 billion+ conversations every month
- 300M+ monthly active users
- 99.9%+ uptime
Integrate with your customer service tech stack. 
---
## Delight AI concierge FAQs
### What is an AI concierge?
An AI concierge is an AI agent trained on your knowledge base and customer conversations to provide exemplary service autonomously. It remembers preferences and applies learning across future interactions.
### What use cases can an AI concierge handle?
Train the concierge across the customer lifecycle: sales, support, proactive reengagement, renewals, rescheduling, order updates, local recommendations, and more.
### Can delight.ai AI concierges improve operational efficiency?
Yes. They handle routine tasks (rescheduling, order updates, proactive outreach), freeing staff to focus on high-value guest experiences.
### How can I monitor and improve my delight.ai AI concierge?
Delight.ai includes monitoring for adherence to [Actionbooks](https://delight.ai/builder) (your SOPs), agent performance metrics, and AI-generated insights for continuous optimization and full oversight.
---
## Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
## Zendesk alternative LP - New page builder test
Source: https://delight.ai/zendesk-alternative-lp-new-page-builder-test
## Zendesk wasn't built for the AI era. Delight was.
Your AI agents resolve up to 83% of support tickets and turn conversations into upsell opportunities, increasing revenue by 20%. Unlimited seats are included, so you can replace your legacy helpdesk without paying per user.
[Delight your customers](https://delight.ai/zendesk-migration-wizard)
    
    
    
   
Conversations/month 7B+
Monthly active users 300M+
Uptime SLA 99.9%+
Enterprise infra 10+ yr
## Migrate from Zendesk in one click
VIDEO CHAPTER
0:00 Introduction to Zendesk migration
0:26 One-click migration
1:50 Turn tickets into Actionbooks
[Contact sales](https://delight.ai/contact-sales) | [Learn more](https://delight.ai/zendesk-migration-wizard)
### Watch the Zendesk migration demo
## Already decided to leave Zendesk?
Two resources built specifically for teams migrating off Zendesk.
- [ Migrating from Zendesk](https://delight.ai/zendesk-migration-desk)
See the $0 seat fee pricing comparison and how pre-trained AI actionbooks replace your existing workflows.
- [ Migration demo](https://delight.ai/zendesk-demo)
Migrate from Zendesk to Delight Desk in one click and bring over your ticket history, macros, and more.
## Delight.ai vs Zendesk :
AI-native, not an add-on.
### Key differences
- **AI architecture**
- Delight: Agentic AI built into the core of the platform, not a separate module.
- Zendesk: AI agents ship as a paid Advanced AI add-on, plus usage-based resolution fees layered onto existing ticketing.
- **AI readiness**
- Delight: Pre-trained on your resolved tickets from day one, not a blank Copilot.
- Zendesk: Requires a populated knowledge base connected before agents can resolve tickets.
- **Pricing model**
- Delight: Predictable enterprise pricing; no per-resolution fees; Desk (native helpdesk) is free.
- Zendesk: Per-seat pricing plus a separate AI add-on and usage-based resolution fees.
- **Multi-agent collaboration**
- Delight: Specialized agents hand off inside one case so the customer never repeats themselves.
- Zendesk: Different AI agents can be published per channel but don't collaborate or hand off within a conversation.
- **AI governance & safety**
- Delight: Trust OS governs every response with built-in policy enforcement, observability, and full audit trails.
- Zendesk: Quality Score and admin-configured guardrails evaluate interactions after they're resolved.
## Beyond what Zendesk offers. All native to delight.ai.
### Agent Memory Platform
Persistent profiles across every interaction, with shared context.
### Trust OS
Full visibility with built-in policy and controls.
### Omnipresence
All channels in one place with full context across interactions.
### Sendbird Desk
Built-in helpdesk with AI-to-agent handoffs.
## Where Zendesk is strong — and where we differ
When to choose each platform:
- **Customer memory**
- Delight: Persistent across every session and channel; shared context is visible to agents.
- Zendesk: Builds a continuous profile; history carries over for agents.
- **AI governance**
- Delight: Built in from day one with observability and audit trails.
- Zendesk: Governance tools evaluate interactions after the fact.
- **Pricing predictability**
- Delight: One enterprise contract; no stacked seat fees or per-resolution charges.
- Zendesk: Layered, consumption-based pricing with seat fees plus AI add-ons.
- **Platform completeness**
- Delight: AI-native platform with agentic automation built into the core.
- Zendesk: Advanced AI is layered on top of a ticketing system designed before the AI era.
### If your team still runs on tickets
Zendesk's macros and SLA rules are deep for teams tracking support as tickets rather than conversations.
### Off-the-shelf apps, not custom integration
Zendesk offers 1,800+ pre-built apps that speed deployment but limit custom functionality to what each app provides.
### Established, and resistant to change
Zendesk's long history makes it a safe, stable choice — and slower to change.
## Questions worth asking any AI vendor, including us
- **Is your AI built to close tickets, or to build customer relationships?**
- Ticketing platforms optimize ticket metrics (close and deflect). Delight optimizes relationships via persistent memory, autonomous resolution, and proactive outreach.
- **Does your AI personalize or just respond?**
- Personalization requires persistent memory across sessions and channels. Agent Memory Platform builds continuous profiles from conversations, purchases, and signals.
- **Does your AI wait for customers to reach out, or engage them first?**
- Most ticketing AI is reactive. Delight can proactively initiate outreach (e.g., delays, renewals) with "For You Conversations."
- **What does your AI actually own, and what does it depend on?**
- Some vendors sell AI as an add-on layered onto a separate ticketing system. Desk ships natively with Delight, so helpdesk and AI are one system.
- **When the AI makes a decision, can your team see why?**
- Trust OS provides full visibility into every AI decision, including audit trails and policy enforcement — important for regulated industries.
- **Are you measuring the interaction or the outcome?**
- Ticketing metrics focus on first response and handle time. Delight measures downstream outcomes like retention, upgrades, and churn.
- **Does your AI work across every channel your customers use?**
- Many platforms keep channel threads separate. Omnipresence carries one thread across chat, voice, email, and SMS with full context.
- **Does the customer experience belong to you or the vendor?**
- Delight is fully white-label so your brand controls the interface, voice, personality, and escalation logic.
## Stop logging tickets. Start remembering customers.
[Contact sales](https://delight.ai/contact-sales)
## Zendesk alternative LP - New page builder test - video block fix
Source: https://delight.ai/zendesk-alternative-lp-new-page-builder-test-video-block-fix
## Evaluating Zendesk?
## Zendesk wasn't built for the AI era. Delight was.
Your AI agents resolve up to 83% of support tickets and turn conversations into upsell opportunities, increasing revenue by 20%. Unlimited seats are included, so you can replace your legacy helpdesk without paying per user.
[Delight your customers](https://delight.ai/zendesk-migration-wizard)
### Customers and scale
    
    
    
Conversations/month 7B+
Monthly active users 300M+
Uptime SLA 99.9%+
Enterprise infra 10+ yr
## Migrate from Zendesk in one click
VIDEO CHAPTER
0:00 Introduction to Zendesk migration
0:26 One-click migration
1:50 Turn tickets into Actionbooks
[Contact sales](https://delight.ai/contact-sales) [Learn more](https://delight.ai/zendesk-migration-wizard)
#### Watch the Zendesk migration demo
## Already decided to leave Zendesk?
Two resources built specifically for teams migrating off Zendesk.
- [ Migrating from Zendesk
See the $0 seat fee pricing comparison and how pre-trained AI actionbooks replace your existing workflows.](https://delight.ai/zendesk-migration-desk)
- [ Migration demo
Migrate from Zendesk to Delight Desk in one click and bring over your ticket history, macros, and more.](https://delight.ai/zendesk-demo)
## Delight.ai vs Zendesk: AI-native, not an add-on
| Capability | Delight AI agent | Zendesk |
|---|---:|:---|
| AI architecture | Agentic AI built into the core of the platform, not a separate module. | AI Agents ship as a paid Advanced AI add-on with usage-based fees layered onto existing ticketing. |
| AI readiness | Pre-trained on your resolved tickets from day one. | Requires a populated knowledge base before resolving tickets. |
| Pricing model | Predictable enterprise pricing; Desk (native helpdesk) included free; no per-resolution fees. | Per-seat pricing plus separate AI add-on and usage-based resolution fees. |
| Multi-agent collaboration | Specialized agents hand off inside one case; customer never repeats themselves. | Different agents can be published per channel but don't collaborate within a conversation. |
| AI governance & safety | Trust OS governs every response with policy enforcement, observability, and full audit trails. | Quality Score and admin guardrails evaluate interactions after they're resolved. |
## Beyond what Zendesk offers — All native to delight.ai
### Agent Memory Platform
Persistent profiles across every interaction, with shared context.
### Trust OS
Full visibility with built-in policy and controls.
### Omnipresence
All channels in one place with full context across interactions.
### Sendbird Desk
Built-in helpdesk with AI-to-agent handoffs.
## Where Zendesk is strong — Where we differ
### When to choose each platform
| Area | Delight AI agent | Zendesk |
|---|---:|:---|
| Customer memory | Persistent across every session; continuous profile across channels. | Builds a continuous profile visible to agents; history carries over for agents. |
| AI governance | Built in from day one with full observability and audit trails. | Governance after the fact via Quality Score and guardrails post-resolution. |
| Pricing predictability | One enterprise contract; no stacked seat fees or per-resolution charges. | Seat pricing plus AI add-on (~$50/agent/month) and usage-based resolution fees. |
| Platform completeness | AI-native — agentic automation built into the core. | Advanced AI add-on sits on top of a pre-AI-era ticketing system. |
### If your team still runs on tickets
If your team still tracks support as tickets, Zendesk's macros and SLA rules run deep.
### Off-the-shelf apps, not custom integration
1,800+ pre-built apps move fast but limit you to each app's shipped functionality.
### Established, and resistant to change
Two decades of deployments make Zendesk a safe, but slow-to-change, pick.
## Questions worth asking any AI vendor, including us
- Is your AI built to close tickets, or to build customer relationships?
- Ticketing platforms optimize closing tickets quickly. Delight.ai optimizes relationships via persistent memory, autonomous resolution, and proactive outreach.
- Does your AI personalize or just respond?
- Personalization requires memory across sessions and channels. Agent Memory Platform maintains a continuous profile so the AI knows the customer before the conversation begins.
- Does your AI wait for customers to reach out, or does it engage them first?
- Most ticketing AI is reactive. "For You Conversations" lets the AI initiate outreach at key moments (delays, renewals, inactivity).
- What does your AI actually own, and what does it depend on?
- Some platforms sell AI as an add-on on top of your ticketing system. Desk ships natively with delight.ai, so the helpdesk, agent workspace, ticketing, and AI are one system.
- When the AI makes a decision, can your team see why?
- Trust OS provides full visibility into every AI decision, what it said, why, and whether it followed your process—critical for regulated industries.
- Are you measuring the interaction or the outcome?
- Ticketing platforms focus on response time and handle time. Delight.ai is optimized for outcomes: retention, upgrades, and churn.
- Does your AI work across every channel your customers use?
- Many platforms thread conversations separately by channel. Omnipresence carries one thread across chat, voice, email, and SMS with full context.
- Does the customer experience belong to you or the vendor?
- Delight.ai is fully white-label so your brand owns the interface, voice, personality, and escalation logic end to end.
## Stop logging tickets.
Start remembering customers.
[Contact sales](https://delight.ai/contact-sales)
## ROI Calculator v2
Source: https://delight.ai/tools/roi-calculator-v2
## Delight.ai AI agent ROI calculator
What industry are you in?
Step 1 of 3
---
1
---
2
---
3
## Step 01 — What industry are you in?
- Retail
- Shopping assistance, order support, and returns at scale
- Travel & Hospitality
- Trip planning, disruption management, and in-stay concierge
- Consumer Health
- Appointment scheduling, billing support, and subscription follow-up
- On-Demand
- Delay management, payment recovery, and dispute resolution
- Software & Technology
- Onboarding, technical support, and account growth
- Home Services
- Call booking, dispatch delays, and post-job follow-up
- Automotive
- Service booking, repair status, and dealer follow-up
- Other
- Don't see your industry? Get a cross-industry estimate.
Next
1
Step 01
---
2
Step 02 — ROI Calculator
## Home redesign
Source: https://delight.ai/home-redesign
## Your enterprise revenue engine
An AI agent that grows and protects revenue across the customer lifecycle by driving purchases and upsells and recovering stalled transactions.
[View demo](https://delight.ai/demo-request)
Slide 1 of 4
- 
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- 10x
as likely to buy, book, or sign up for customers who engage with Delight vs. who don’t
- 20%+
higher average order value across live retail deployments
## Converting customer experience into revenue
[](https://delight.ai/customers/bjs)
Bigger baskets
Jennie
Which coffee is smooth, not bitter?
[](https://delight.ai/customers/norse-atlantic-airways)
Higher booking value
Aaliyah
Can I get more legroom?
[](https://delight.ai/customers/mixpanel)
Account expansion
Haruto
How do I unlock this feature?
## Built to grow revenue from every conversation
### 1. Understand the customer
[Explore Agent Memory Platform](https://delight.ai/agent-memory-platform-amp)
Identity · Preferences · Intent
### 2. Reach out at the right moment
[Explore Omnipresence](https://delight.ai/omnipresent)
Channels: chat, SMS, WhatsApp, email, voice
### 3. Take the right action to grow revenue
[Explore AI agent](https://delight.ai/ai-agent)
Actionbook:
- Book
- Upgrade
- Renew
### 4. See revenue opportunities through to completion
[Explore Agent Steward](https://delight.ai/autonomous-agent)
Day 1: Coordinate
Day 3: Follow up
Day 7: Complete
### 5. Follow your business rules
[Explore Trust OS](https://delight.ai/trust-os)
Boundaries · Approvals · Oversight · Improvement
## Every industry has a moment that moves revenue
Retail
Discover • Convert • Reorder
[Learn more](https://delight.ai/industries/retail)
Travel
Book • Upgrade • Rebook
[Learn more](https://delight.ai/industries/travel-hospitality)
Software
Activate • Adopt • Expand
Healthcare
Qualify • Enroll • Refill
[Learn more](https://delight.ai/industries/healthcare)
Home & field service
Book • Dispatch • Complete
[Learn more](https://delight.ai/industries/field-services)
## Built to protect revenue when service goes wrong
Your biggest orders and bookings can take days and several parties to put right. Delight coordinates everyone involved to resolve the issue and protect the customer relationship.
delight.ai
I’ll take care of everything.
- Pull order details
- Reserve a replacement
- Get refund approved
- Book the delivery
- Send confirmation
### Agent · Steward
One conversation, full resolution
- Order Agent · SystemOrder System
Order #48213 pulled, damage logged
- Stock Agent · EmailWarehouse
Replacement reserved, ships today
- Approval Agent · DeskCS Lead Approval
$38 shipping refund approved
- Delivery Agent · CallCarrier
Redelivery · Thursday before 12 PM
## Your customers’ trust is a responsibility we take seriously
Proven at scale
Built on Sendbird’s decade of experience powering customer conversations. Infrastructure that handles over 7 billion conversations a month, ready for your next stage of growth.
Built for enterprise standards
Meet the demands of doing business at scale, with security, privacy, and compliance at the foundation.
AI you can stand behind
Every conversation is a promise to your customer. Give AI more responsibility while keeping your business standards and your customers’ trust intact.
## Find your revenue opportunities
[Talk to an expert](https://delight.ai/contact-sales)
## Common questions about the revenue AI agent
**What is a revenue AI agent?**
Delight is the customer-facing revenue AI agent. It remembers who each customer is, understands what they need now, and uses commercial judgment to choose the next best action, so more customers convert and stay. It works across the whole journey, from qualification and purchase through post-purchase service, retention, and expansion.
**How is this different from an AI SDR or other revenue AI?**
Most revenue AI is built for your team. AI SDRs prospect, copilots draft the follow-up, CRM agents update the record, coaching tools score the rep. Their user is a seller. Delight’s user is your customer — the shopper on your site, the guest part-way through a booking, the member in your app. It holds the buying conversation itself and carries it through to a confirmed order.
**Does Delight still handle customer service?**
Yes. It resolves support requests across chat, email, and voice, and coordinates the ones that cross systems, teams, and outside partners until they are resolved. The same agent that answers a service question is the one that recognizes the revenue opportunity inside it.
**Which channels does it work across, including voice?**
Chat, SMS, WhatsApp, email, and voice. Voice matters most where the revenue moment is a phone call — a missed call to a home services business, a booking change mid-trip, a quote request that would otherwise reach voicemail. The agent picks up, handles it, and brings in a person when it should. It holds one thread across every channel, so a conversation that starts in chat can finish on the phone without the customer repeating themselves.
**Do we need to rebuild our product data or systems first?**
No. The agent works with the catalogue, records, and customer history you already have. It connects to Salesforce, Zendesk, ServiceNow, Intercom, Freshworks, Sprinklr, and NICE CXone, and draws knowledge from Confluence, Notion, and Google Drive. Commerce, booking, and payment systems connect through their own APIs.
**Which industries is it built for?**
Retail, travel and hospitality, software, and home and field services — anywhere a customer conversation affects whether someone buys, books, or comes back.
## AI Sales Agent
Source: https://delight.ai/ai-sales-agent
## The AI sales agent that turns conversations into revenue
Qualify the visitors who are ready to buy. Personalize the pitch to what they actually need. Recover the sale that almost got away.
[Book a personalized demo](https://delight.ai/contact-sales) [See how it works](#how-an-ai-sales-agent-works)
### What is an AI sales agent?
An AI sales agent is an AI-native platform you connect to your business that helps grow revenue. The agent talks with customers, understands what they need, and moves them toward the right decision.
### Beyond the AI SDR: an AI sales agent that remembers every buyer and closes the sale.
#### AI SDR
Automates outreach, qualifies prospects, and books meetings to build pipeline for your sales team.
#### Customer-facing agent
Delight.ai's AI sales agent works with buyers already on your site. It answers their questions, remembers what they've told you, and guides them through to purchase.
### Delight AI Index
[Read the Delight AI Index →](https://delight.ai/reports/ai-index)
Delight.ai's own research, the Delight AI Index, found that 71% of consumers already use AI customer service, but only 57% like the experience they're getting. An AI sales agent exists to personalize a customer’s entire experience, improving their experience and closing this gap. It can be the difference between a business that answers customers and one that grows revenue from every conversation.
#### 71%
of consumers already use AI customer service
#### Only 57%
like the experience they're getting
### How an AI sales agent compares to a chatbot
A chatbot follows a script. An AI sales agent sees what a customer needs before they ask, and acts on it.
| Capability | AI sales agent | Chatbot | AI SDR |
|---|---:|---:|---:|
| Works inbound | Yes | Yes | No |
| Works outbound | Warm audiences only | No | Yes, cold outreach |
| Personalizes | Yes, every message | No, it follows a script | First message only |
| Recovers stalled sales | Yes | No | No |
| Remembers across sessions | Yes | No | Limited to CRM notes |
### How an AI sales agent grows your revenue
#### Qualify & route new visitors
A new visitor might be a genuine buyer, or just browsing. Delight.ai reads the signals and routes each visitor to the appropriate next step.
**How it works:** memory plus an Actionbook match intent to the right person.
#### Reduce buying friction
A technical question shouldn't mean repeating yourself three times. Delight.ai pulls account data, documentation, and past conversations into one answer.
[**Mixpanel**](https://delight.ai/customers/mixpanel): roughly 10 minutes of manual account verification, gone.
#### Personalize at the point of sale
Shoppers describe what they want in their own words, and Delight.ai's Agent Memory Platform (AMP) translates that into the solutions that actually fit.
[**Hanssem**](https://delight.ai/customers/hanssem): purchase conversion rose 30% in four weeks.
#### Recover a stalled sale
A stalled cart, quote, or application can still close. Delight.ai remembers exactly where the customer stopped and re-engages on the right channel.
**How it works:** Delight.ai picks up mid-checkout, mid-quote, or mid-signup, exactly where the customer left off.
### Selling B2B?
The B2B AI agents guide goes deeper.
[ B2B AI agents guide](https://delight.ai/ebooks/b2b-ai-agents)
### How Delight.ai's AI sales agent works
- Know the customer. Agent Memory Platform remembers history, preferences, intent. [Explore](https://delight.ai/agent-memory-platform-amp)
- Reach out at the right moment. Omnipresence spans chat, SMS, WhatsApp, email, voice. [Explore](https://delight.ai/omnipresent)
- Take the next best action. A person sets the Actionbooks, which let the agent decide what happens next. [Explore](https://delight.ai/product-release/actionbook-editor)
- Stay with it until it's done. Agent Steward carries context through every handoff. [Explore](https://delight.ai/autonomous-agent)
- Stay within your rules. Trust OS keeps a full audit trail, so you can prove what the agent said, review every conversation, and train it to improve. [Explore](https://delight.ai/trust-os)
### Results from real Delight.ai deployments
- [30% higher purchase conversion in four weeks — Hanssem (Leading furniture retailer)](https://delight.ai/customers/hanssem)
- [10 min → 0: Account questions answered instantly, with no manual lookup — Mixpanel (Product analytics)](https://delight.ai/customers/mixpanel)
- [More per visit: Members who chat with the AI convert at a higher rate and buy more each visit — BJ's Wholesale Club (Membership warehouse retail)](https://delight.ai/customers/bjs)
### See Delight.ai's AI sales agent in action
One conversation. Every stage of the sale.
[Book a personalized demo](https://delight.ai/contact-sales)
### Frequently asked questions
**What is an AI sales agent?**
An AI sales agent is software that holds a real conversation with a customer or prospect and [takes the next useful step on its own](https://delight.ai/ai-agent): qualifying intent, recommending a product, or helping someone finish a purchase. Unlike a scripted chatbot, it reasons about context and adapts when the conversation goes somewhere the script didn't expect.
**What's the difference between an AI sales agent and a chatbot?**
A chatbot answers questions on a fixed decision tree and breaks on the first unexpected one. An AI sales agent reasons about what a buyer actually needs, adapts as the conversation shifts, and can [act on the outcome](https://delight.ai/product-release/actionbook-editor), qualifying a lead, recommending a product, or booking a next step, instead of just responding.
**Can Delight.ai handle both customer support and sales?**
Yes. Delight.ai [resolves customer issues](https://delight.ai/ai-customer-service) and helps customers buy, book, or upgrade. It [remembers their needs](https://delight.ai/agent-memory-platform-amp) across conversations and [channels](https://delight.ai/omnipresent), so they don’t have to start over. When a support conversation reveals a relevant buying opportunity, the same agent can help—while putting the customer’s original need first.
**Can an AI sales agent replace our human sales reps?**
No. Delight.ai's agent handles the repetitive volume: qualifying, first-line questions, routing, and follow-up on stalled conversations. It hands off anything ambiguous, high-stakes, or relationship-critical to a person, [with the full conversation history attached](https://delight.ai/autonomous-agent). The goal is freeing your team for the conversations that actually need them, not reducing headcount.
**How does pricing work for an AI sales agent?**
Delight.ai prices on conversation-based usage, with unit costs that decrease at scale, not a flat per-seat fee. [Delight Desk](https://delight.ai/desk) itself has a $0 seat fee; you pay per successful AI resolution, not per person on your team. Exact pricing depends on your channels, volume, and which capabilities you need, which is easiest to [walk through on a call](https://delight.ai/contact-sales).
## Agentic Commerce
Source: https://delight.ai/agentic-commerce
## Agentic commerce that actually sells
Agentic commerce is an AI agent guiding a shopper from browsing to checkout, in one conversation, on your business's behalf.
[Book a personalized demo](https://delight.ai/contact-sales) [See how it works](#how)
## What is agentic commerce?
Sell-side agentic commerce is the process of an AI agent guiding a shopper through first question to completed purchase. Think of it as a personal shopping assistant for your customers. Delight.ai builds that agent, so shoppers can complete a purchase without leaving the chat.
## Two sides of agentic commerce
Shoppers are starting to send their own AI agents to buy. Businesses need an agent of their own to sell.
### Buy-side: An agent for the shopper
Finds products, compares options, and makes purchases on the shopper’s behalf.
### Sell-side: An agent for your business
Answers questions, recommends products, and helps customers complete a purchase. That’s what Delight.ai builds—for conversations with customers or their AI agents.
## One agent from first question to repeat purchase
Shopper intent → Business outcome
- Discover
- Choose
- Buy
- Get help
- Buy again
One shared memory across every channel
## The product features behind agentic commerce
### Understands the request
Against the business's own catalog and the customer's history.
### Remembers what a shopper already told it
Powered by Agent Memory Platform ([AMP](https://delight.ai/agent-memory-platform-amp)), so someone who dropped off yesterday can pick up right where they left off today.
### Acts within rules the business writes and reviews
Called an [Actionbook](https://delight.ai/product-release/openbook), so nothing the agent does on its own ships without a person signing off first.
## How agentic commerce grows your revenue
Fewer places to lose the sale.
One agent carries the conversation from question to close, instead of losing the customer at a handoff to a form, a queue, or a different channel.
Attribution that holds up.
One thread from first question to sale means the business can actually trace which conversation drove which purchase.
## Built for any business where customers ask before they buy
### [Retail and ecommerce](https://delight.ai/industries/retail)
Shopper
Will this sectional fit through a 30-inch doorway?
Delight.ai
This one won't. The 3-piece version will, and it comes in the same fabric. Want me to add it to your cart?
Business outcome **Sale closed in chat**
[Explore retail and ecommerce →](https://delight.ai/industries/retail)
### [Software and B2B](https://delight.ai/industries/technology)
Admin
I'm on day 3 of the trial and SSO setup keeps failing.
Delight.ai
I can see your workspace settings. Here's the fix for your setup, step by step.
Business outcome **Trial kept on track to paid**
[Explore software and B2B →](https://delight.ai/industries/technology)
### [Home services](https://delight.ai/industries/home-services-and-warranty)
Homeowner
It's 9 p.m. and my AC just quit. Can someone come?
Delight.ai
A technician can be there tomorrow morning. Here's your quote. Want me to book it?
Business outcome **Job booked after hours**
[Explore home services →](https://delight.ai/industries/home-services-and-warranty)
## Retailers are already selling in conversation
30% higher purchase conversion in four weeks
> “Within just four weeks of implementing Delight Agentic Commerce, our purchase conversion rate increased by approximately 30%. In particular, customers who engaged with AI showed a conversion rate nearly 10 times higher than those following the traditional purchase journey.”
>
> Chief Business Officer, Hanssem
Hanssem — Leading furniture retailer
[Read the story →](https://delight.ai/customers/hanssem)
More per visit
Members who used Bev, BJ's Delight.ai shopping assistant, converted at a higher rate and bought more per visit than those who didn't.
One assistant handles product discovery, savings, and membership care, so a product question can become a basket.
BJ's Wholesale Club — Membership warehouse retail
[Read the story →](https://delight.ai/customers/bjs)
## Frequently Asked Questions
**What is agentic commerce?**
Sell-side agentic commerce is the process of an [AI agent](https://delight.ai/glossary/ai-agent) guiding a shopper through first question to completed purchase. Think of it as a personal shopping assistant for your customers. Delight.ai builds that agent, so shoppers can complete a purchase without leaving the chat.
**What's the difference between buy-side and sell-side agentic commerce?**
Buy-side agentic commerce is a consumer's own AI agent, finding and paying for something on that person's behalf. Sell-side agentic commerce is the business's own AI agent, working on its behalf to close the sale with the customer. Sell-side agentic commerce is what Delight.ai's product facilitates.
**Is the Agentic Commerce Protocol (ACP) the same thing as what Delight.ai does?**
No, [Agentic Commerce Protocol](https://www.agenticcommerce.dev/) refers to Stripe and OpenAI's payment layer, built so a shopper's own AI agent can check out on a merchant's site, while Delight.ai's agent works on the business's side of that process. Delight.ai understands the customer's request and completes the sale, whether the customer is a person or an agent acting on behalf of a person.
**How does an AI agent actually complete a purchase without a human involved?**
It follows a business's own predefined, reviewed rules for what it can do on its own, called [Actionbooks](https://delight.ai/product-release/action-books-management), combined with a memory of the customer's history and context. Inside those rules, it can recommend a product, apply an offer, and complete a transaction. If a customer request is out of the scope set by Actionbooks, Delight hands off to a person.
**Can AI agents make purchases on a shopper's behalf?**
Yes, in specific, guarded flows AI agents can make purchases on a shopper’s behalf. A sell-side agent like Delight.ai's [can guide a shopper to the right product](https://delight.ai/for-you-conversations-fyc) and complete the purchase inside a conversation. A buy-side consumer agent can check out through a protocol like ACP where a merchant supports it.
**Does this replace our sales or support team?**
No, Delight.ai does not replace your sales or support teams. Delight.ai's agent takes on the routine version of a conversation, so the team can spend time on the cases that actually need a person, like a complex return or an unusual request.
## See what a sell-side agent could do for your business.
[Book a demo](https://delight.ai/contact-sales)
## Delight Security Test
Source: https://delight.ai/security-test
## Delight Security
At Delight, security isn't just a feature, it's our DNA. Trust is earned, and we earn yours by keeping your data safe.
[Talk to an expert](https://delight.ai/contact-sales)
### Customers
## Best in class internal security
Delight uses an automation-first approach to implement advanced internal security measures.
- FIDO2 MFA & device trust to prevent phishing
- Automated Detection & Response
- Automated Compliance
- DevSecOps approach
## Enterprise-grade reliability & performance
Proven to perform at global scale, Delight powers 7 billion+ conversations every month—delivering high availability, low latency, and real-time responsiveness, even in mission-critical environments.
- Global infrastructure built for scale
- Real-time monitoring and 24/7 incident response
- Trusted by enterprises across regulated industries
## Data privacy & residency options
Maintain full control over where your data resides, how it’s stored, and who can access it—helping you meet regulatory and internal data governance requirements.
- Regional hosting to support data residency needs
- Clear data ownership and processing transparency
- Flexible data retention settings aligned to your policy
## Advanced security features & controls
Built-in security features give you full control over how you protect customer data—so you can align Delight to your internal policies and compliance needs.
- End-to-end encryption and TLS in transit
- Granular role-based access controls
- Full audit logs for visibility and control
## Security resources & compliance support
Partner with us on security
Security is a shared responsibility we take seriously. To report vulnerabilities, contact [\[email protected\]](/cdn-cgi/l/email-protection). Acknowledgment or reward is at our sole discretion.
Security docs for your team
We understand security is top priority for our customers, and we have detailed guides on the security features available for our customers to secure their Delight implementation.
Security compliance & accreditation
Delight is committed to data security and privacy, adhering to industry-leading frameworks including GDPR, CCPA, HIPAA, and SOC 2 Type 2.
## Security resources and insights
- [](https://delight.ai/blog/product/introducing-trust-os-v2) Introducing Trust OS 2.0 — Eight new capabilities for running AI agents in production with full visibility, precise control, and continuous improvement.
- [](https://delight.ai/blog/ai-for-cx/omnipresent-ai-for-customer-service) Omnipresent AI for customer service: what it is, why it matters, and how to get started.
- [](https://delight.ai/blog/ai-for-cx/proactive-ai-customer-service) From messaging to intelligence: proactive AI customer service and omnichannel AI agents.
- [](https://delight.ai/blog/ai-for-cx/agentic-ai-omnichannel-customer-service) How agentic AI improves omnichannel customer service.
## Enterprise-grade security,
fully transparent.
[View trust center](https://trust.sendbird.com/)
## AI Booking Agent
Source: https://delight.ai/ai-booking-agent
## The AI booking agent that turns calls into bookings
Stop losing jobs to voicemail. With an AI booking agent, callers hang up with a confirmed appointment, even after hours.
[Book a personalized demo](https://delight.ai/contact-sales) [See how it works](#how-it-works)
## What a missed call costs
When no one answers, 82% of callers say they’ll try a competitor instead ([CallRail, 2025](https://www.callrail.com/blog/missed-calls-cost-businesses-more-than-ever)). For a business that books by phone, each missed call is a job that goes somewhere else.
- 60 — Missed calls a week
- 24 — Lost bookings a week
- $7,200 — Lost revenue a week
- $374,400 — Lost revenue a year
## What happens to a call after hours
A homeowner’s AC stops cooling at 9:40 p.m., and they call an HVAC company. Here’s how that call plays out with voicemail, and with Delight.ai’s [Voice AI Agent](https://delight.ai/voice).
### Without an AI booking agent
1. 9:40 p.m.
The call goes to voicemail
2. 9:41 p.m.
The homeowner hangs up and calls another company
3. 9:43 p.m.
The other company books the job
4. 8:00 a.m.
Your team calls back. The job is already booked elsewhere
### With an AI booking agent
1. 9:40 p.m.
The agent picks up on the first ring
2. 9:41 p.m.
It asks about the problem and checks open slots
3. 9:43 p.m.
It books the visit and confirms the time
4. 8:00 a.m.
The job is already on the morning schedule
That’s what an AI booking agent does: it answers every call and books the job while the caller is still on the line.
## What is an AI booking agent?
An AI booking agent is software that answers calls and messages, then books the appointment before the conversation ends.
## How an AI booking agent compares to answering services, IVR, and chatbots
An answering service takes a message. An IVR sends callers through a menu. A chatbot answers questions. An AI booking agent books the appointment before the call ends.
| Feature | AI booking agent | Answering service | IVR (phone menu) |
|---|---|---|---|
| Books the appointment | On the call | Takes a message | Routes to a queue |
| After hours | 24/7 | Depends on your plan | Goes to voicemail |
| How callers start | Say what they need | Talk to an operator | Press through a menu |
| Unusual requests | Follows your rules | Depends on the script | Press 0 or hold |
| Handoff to your team | Full conversation attached | Short message | Caller starts over |
## How an AI booking agent recovers revenue
- Capture missed and after-hours calls
Calls that would hit voicemail at night, on weekends, or in peak season get answered and booked.
**How it works:** the agent picks up 24/7 and books into your existing schedule.
- Reschedule without the front desk
Callers move or cancel appointments themselves, and the schedule is updated before the office opens.
**How it works:** changes go straight to your scheduling system, with a text confirmation.
- Sell while you book
A repair call can end with the repair booked and a replacement estimate on the calendar.
**How it works:** Actionbooks tell the agent when an add-on fits and what to offer.
## How Delight.ai's AI booking agent works
- Answer every call.
The Voice AI Agent picks up 24/7 and handles background noise, accents, and interruptions. [Explore](https://delight.ai/voice)
- Understand the request.
Callers say what they need in their own words instead of pressing through a menu. [Explore](https://delight.ai/agent-memory-platform-amp)
- Book the slot.
Appointments go straight into your existing scheduling system. [Explore](https://delight.ai/autonomous-agent)
- Confirm by text.
The caller gets a confirmation text before hanging up. [Explore](https://delight.ai/omnipresent)
- Hand off with context.
Complex calls go to your team in Delight Desk with the full conversation. Trust OS sets the handoff rules and records every decision. [Explore](https://delight.ai/desk)
## What customers say
Our customers don’t want to navigate phone trees or wait on hold. They want to talk to someone who can help.
Mark Farley, President
Dr. Cool Services
[Read the case study →](https://delight.ai/customers/dr-cool-services)
## AI booking agent FAQs
### How much does an AI phone answering service cost?
The cost of an [AI phone answering service](https://delight.ai/blog/industry/answering-service-for-home-services) varies by vendor, from flat monthly plans to per-minute or per-conversation fees. Delight.ai charges conversation-based usage fees that drop per unit as volume grows. Its helpdesk, [Delight Desk](https://delight.ai/desk), has a $0 seat fee, so you pay per AI resolution and not per agent.
### What is the best AI answering service?
The best AI answering service is the one that finishes the job your callers want done. A basic answering service covers callers who only need to leave a message. Callers who need an appointment are better served by one that books in your scheduling system and [hands off with full context](https://delight.ai/blog/product/introducing-intelligent-voice-handoff), as Delight.ai does.
### Can AI answer my business phone after hours?
Yes, [AI can answer your business phone after hours](https://delight.ai/voice), including weekends and holidays. After-hours callers get a booked appointment and a text confirmation, just like daytime callers. Anything outside your rules goes to your team with the full conversation attached.
### Is an AI booking agent the same as an AI virtual receptionist?
An AI booking agent overlaps with an [AI virtual receptionist](https://delight.ai/blog/industry/ai-receptionist-home-services) but goes a step further. A virtual receptionist answers and routes calls and often takes messages. An AI booking agent completes the booking during the call, sends the confirmation, and carries the customer’s history into the next conversation.
### What is an AI IVR system?
An [AI IVR system](https://delight.ai/blog/product/introducing-ivr-navigation) is a phone system that understands what callers say instead of asking them to [press keys through a menu](https://delight.ai/blog/ai-for-cx/ai-ivr). Callers state the reason for the call once. The AI agent then answers the question, books the appointment, or routes the call to the right person.
### Can an AI chatbot book appointments?
A basic chatbot can’t book appointments, but an [AI agent working in chat](https://delight.ai/blog/ai-agent/ai-agent-vs-chatbot) can. Customers can book in chat, SMS, WhatsApp, or voice under one set of booking rules. A customer who starts in chat and calls an hour later picks up where they left off.
## Every missed call is revenue you can recover
A personalized demo shows how Delight.ai turns after-hours calls into revenue.
[Book a personalized demo](https://delight.ai/demo-request)
## AI CX agent
Source: https://delight.ai/ai-cx-agent
## The AI CX agent for support and sales
Most AI customer service is built to close tickets fast. Delight.ai’s AI CX agent resolves each request, then helps the customer complete the purchase, signup, or renewal that comes next.
[Contact sales](https://delight.ai/contact-sales) [See how it works](https://delight.ai/demo-request)
### AI CX agent proof
[60% → 80%
Containment in two weeks
Norse Atlantic Airways
2026
Read the story →](https://delight.ai/customers/norse-atlantic-airways)
[48% → ~86%
Resolution rate in 5 months
Hanssem
2025
Read the story →](https://delight.ai/customers/hanssem)
73%
Average deflection within 90 days
Delight.ai — Vendor-reported
### What is an AI CX agent?
An AI CX agent is an AI customer service agent that resolves requests end-to-end and completes the next action on behalf of the customer. It remembers context across channels and hands off to a human with full history when a case requires judgment.
### AI CX agent vs. chatbot vs. agent copilot
| Feature | AI CX agent | Chatbot | Agent copilot |
|---|---:|---:|---:|
| Who it serves | Your customer | Your customer | Your human agent |
| What it does | Resolves the request and completes the action | Answers scripted questions | Suggests replies for the human agent to send |
| Customer context | Carries history across channels and sessions | Starts over each session | Limited to what the human agent can see |
| Revenue role | Acts on buying intent inside the service conversation | Hands off or ends the chat | Depends on the human agent |
| Escalation | Hands off with full context attached | Often loses context at handoff | Already with a human |
### How does an AI CX agent turn service into revenue?
Your service team supports customers who are close to a purchase. An AI CX agent answers their question, then helps them buy, upgrade, or sign up.
#### Turn service into sales
The last question before a purchase is often a service question. On an event page, the AI agent answers a visitor’s registration questions and helps them register in the same chat.
#### Reach customers before they ask
An AI agent can start the conversation inside a moment the customer already cares about. When a plan is up for renewal, the agent can reach out with an upgrade that fits how they use the product.
Delight.ai’s [proactive AI](https://delight.ai/product-release/proactive-chat-ai-that-reaches-out-first) picks the channel that suits the moment, so outreach reads as service rather than a campaign.
#### Finish the signup that stalled
Sometimes a customer starts a signup, gets stuck on a question, and leaves. The agent can follow up, answer the question, and collect what’s missing right in the chat.
[Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) keeps everything the customer already entered, so they never type it twice.
### What will AI support automation save your team?
Revenue is the bigger opportunity, but the support savings come first, and they’re easy to measure. Add your monthly ticket volume, team size, and cost per agent to see your three-year savings and how many agents you could free up for higher-value work.
[Calculate your ROI](https://delight.ai/tools/roi-calculator)
### How does an AI CX agent know when to sell?
A good salesperson knows when to make an offer and when to provide assistance. Delight.ai’s AI CX agent works the same way, using what it knows about each customer to suggest their next step.
- Know the customer. AMP brings together each customer’s history, preferences, and current intent, so the agent never starts a conversation cold. [Explore](https://delight.ai/agent-memory-platform-amp)
- Reach out at the right moment. Omnipresence carries that context across chat, SMS, email, voice, WhatsApp, and in-app, so a customer who switches channels never starts over. [Explore](https://delight.ai/omnipresent)
- Take the next best commercial action. Step-by-step flows your operations team writes, called Actionbooks, let the agent book, order, update, and resolve instead of only answering. [Explore](https://delight.ai/product-release/openbook)
- Stay with it until it’s done. Agent Steward keeps a multi-step case moving across sessions and systems, through waits, follow-ups, and channel changes. [Explore](https://delight.ai/autonomous-agent)
- Act within your boundaries. Permissions and approval steps decide what the agent handles on its own and when a person steps in. [Explore](https://delight.ai/trust-os)
### What customers say about Delight.ai
> "We are moving past the idea of ‘support bots.’ The future is one unified agent for sales and support."
> Alf Lim — Chief Product Officer, Norse Atlantic Airways
> [Read the story →](https://delight.ai/customers/norse-atlantic-airways)
> "Whether a member is browsing for a grill, looking up club hours, or asking about a renewal, they’re talking to the same Bev. That’s how a real member-team member relationship is supposed to feel."
> Scott Vandegrift — VP of Product, BJ’s Wholesale Club
> [Read the story →](https://delight.ai/customers/bjs)
> "Our support agents were spending the first ten minutes of every interaction just figuring out who they were talking to. The actual problem-solving came second."
> Kathleen Matthews — Global Support, Mixpanel
> [Read the story →](https://delight.ai/customers/mixpanel)
### Which channels can an AI CX agent cover?
One AI CX agent covers every channel in 100+ languages, so your team sees one conversation history no matter where the customer started.
- Voice — Answers in under a second, handles noise and interruptions, and works with Genesys, Zendesk, NICE, and Amazon Connect.
[See Voice AI](https://delight.ai/voice)
- Chat and in-app — Lives on your website and inside your app, so customers get help without leaving the page they're on.
- SMS and WhatsApp — Runs natively in the apps customers already use, with no download or redirect.
- Email — Keeps full-thread context and cuts response times from 24 to 48 hours down to minutes.
Your human agents work alongside the AI agent in [Delight Desk](https://delight.ai/desk), the AI-native helpdesk. Desk has a $0 seat fee, because you should pay for successful AI resolutions, not seats.
### How do you keep an AI CX agent in control?
An AI agent that can sell needs tighter guardrails than one that only answers questions. [Trust OS](https://delight.ai/trust-os), Delight.ai’s governance and observability layer, lets you decide what the agent does on its own and shows you every action it takes.
- **Actionbooks stay drafts** until a person on your team approves them.
- **Citations** show the source behind every response, so customers and your team can verify it.
- **Low-confidence flagging** sends answers that may be wrong or off-brand to review automatically.
- **Deactivate AI** pauses all automated responses instantly without deleting your configuration.
- **Audit logs** record who changed what and when, with role-based access control on every setting.
Delight.ai meets GDPR and CCPA requirements and holds a SOC 2 Type II report and a HIPAA Type I report. See the full [security and compliance details](https://delight.ai/security).
### AI CX agent FAQs
**What is the difference between an AI CX agent and a chatbot?**
A chatbot answers scripted questions, while an AI CX agent resolves requests end-to-end and completes actions. An AI CX agent also acts on buying intent during a service conversation, which turns support from a cost center into a source of revenue.
**How is Delight.ai’s AI CX agent priced?**
Delight.ai’s AI CX agent is priced on conversation-based usage fees, and unit costs decrease as volume grows. Delight Desk, the [helpdesk](https://delight.ai/desk) where your human agents work, has a $0 seat fee. You pay per successful AI resolution, never per agent, so your costs track the outcomes the agent delivers rather than the size of your team.
**Does an AI CX agent replace the support team?**
No, an AI CX agent does not replace your support team. The agent handles routine requests end to end and hands off anything ambiguous, high-stakes, or emotionally sensitive to a person, with [full context attached](https://delight.ai/agent-memory-platform-amp). That frees your team to focus on the conversations where human judgment protects the relationship and the revenue behind it.
**How do you stop an AI CX agent from giving wrong answers?**
To stop an AI CX agent from giving wrong answers, Delight.ai’s Trust OS cites the source behind every response, flags low-confidence answers for review, and logs the reasoning behind each decision. [Actionbooks](https://delight.ai/product-release/openbook) stay drafts until your team approves them, and Test Center checks agent behavior before changes reach customers. If something goes wrong, Deactivate AI pauses automated responses instantly.
**Does an AI CX agent work with Zendesk and Salesforce?**
Yes, an AI CX agent, like Delight.ai, works with Zendesk and Salesforce, along with other [existing tools](https://delight.ai/integrations) such as Confluence, Google Drive, and internal APIs, without a migration project. [Teams leaving Zendesk](https://delight.ai/zendesk-migration-desk) can move to Delight Desk, which imports ticket history and turns past resolutions into draft Actionbooks, so the agent starts from your own data.
**How long does it take to launch an AI CX agent?**
How long it takes to launch an AI CX agent depends on your channels and integrations. Norse Atlantic Airways replaced a legacy chatbot with its AI agent in under two months, and AllAthlete went live in about 3 weeks. Actionbooks built from your own resolved tickets give the agent a trained starting point instead of a blank one.
### Build an AI CX agent around your revenue goals
Book a personalized demo to see how Delight.ai’s AI CX agent acts on the buying signals in your service conversations.
[Book a demo](https://delight.ai/contact-sales)
## TEMP delete — FYC JA block source
Source: https://delight.ai/temp-delete-fyc-ja-block-source
## For You Conversations: Personalized recommendations that drive sales
[View demo](https://delight.ai/demo-request)
## Help customers find what fits, and buy it
-  Recommend from purchase history
Your agent uses past purchases, preferences, and budget to suggest products that fit each customer.
-  Work toward your revenue goals
Choose what your agent aims for, such as cross-sell or larger orders, and when it should focus on support instead.
-  Open with what matters to the customer
Your agent starts from details the customer has shared, like a birthday coming up, and offers something that fits.
## Retail & ecommerce: guide each shopper to the right product
[Explore retail](https://delight.ai/industries/retail)
-  Compare products for each shopper
Narrow the options to products that fit each shopper's needs, budget, and past purchases.
-  Answer sizing and stock questions
Check sizes, availability, and delivery options so shoppers can decide in the conversation.
-  Suggest what goes with the cart
Offer items that match what's in the cart or what the shopper bought before, then add them to the cart.
## Travel & hospitality: offer upgrades that fit each traveler
[Explore travel & hospitality](https://delight.ai/industries/travel-and-hospitality)
-  Offer the upgrades travelers prefer
Suggest seats, rooms, and add-ons based on each traveler's past trips and stated preferences.
-  Use loyalty tier to shape each offer
Tailor each offer to the traveler's loyalty status and booking history.
-  Rebook disrupted trips early
Reach travelers when a flight changes and offer a new option that fits their preferences, like an aisle seat.
## Software & technology: help each account get more from its plan
[Explore software & technology](https://delight.ai/industries/technology)
-  Check the account before answering
Your agent looks up each account's plan, access, and setup before it replies.
-  Suggest a plan when usage grows
Explain limits with the account's own usage data and suggest the plan that fits.
-  Guide customers to features they own
Point customers to features in their plan that they haven't set up yet.
## Memberships & subscriptions: help members use what they pay for
[Explore B2B](https://delight.ai/industries/technology)
-  Remind customers before credits expire
Let customers know when credits or benefits are about to expire, on the channel they use.
-  Suggest where to use what's left
Recommend ways to use remaining credits, based on what each customer has done recently.
-  Follow up across channels
Continue the same conversation over SMS, email, or WhatsApp without losing context.
## See memory and personalization at work
#### Each conversation starts from what your agent already knows
Your agent remembers past conversations, preferences, and orders.
It keeps the same context across chat, SMS, email, and voice.
Customers pick up where they left off, which makes it easier to buy again.
### How brands turn conversations into revenue
Quote 1 of 3
  
- ### BJ's Wholesale Club
> “Bev gives us a new way to help members across the journey, from finding the right product to getting more value out of their membership. It makes the experience feel more personal, more useful, and more connected.”
Scott Vandegrift — VP of Product, BJ’s Wholesale Club
[Read the BJ’s story](https://delight.ai/customers/bjs)
- ### Norse Atlantic Airways
> “We are moving past the idea of ‘support bots.’ The future is one unified agent for sales and support—a single digital concierge that knows you, helps you, and travels with you.”
Alf Lim — Chief Product Officer, Norse Atlantic Airways
[Read the Norse story](https://delight.ai/customers/norse-atlantic-airways)
- ### Mixpanel
> “The AI knows if you’re asking because you don’t have access yet, or because you’re trying to configure something advanced. In the first case, we’re converting curiosity into revenue. In the second, we’re driving deeper product adoption.”
Kathleen Matthews — Global Support, Mixpanel
[Read the Mixpanel story](https://delight.ai/customers/mixpanel)
## How For You Conversations connects to the rest of Delight
**Agent Memory Platform (AMP)**
The customer history behind each recommendation
[Explore](https://delight.ai/agent-memory-platform-amp)
**Omnipresence**
One continuous agent wherever customers engage
[Explore](https://delight.ai/omnipresent)
**Trust OS**
Control what your agent recommends and offers
[Explore](https://delight.ai/trust-os)
Delight agent runs on the infrastructure behind 7 billion+ conversations a month.
**Enterprise-grade scalability and reliability**
Built on the world’s #1 communications API platform, trusted by industry leaders from on-demand, healthcare, retail, and more.
**Ironclad security and compliance**
Our infrastructure meets the highest degree of compliance and security requirements, including GDPR, SOC 2 (Type II), HIPAA/HITECH, ISO 42001.
**Privacy-first data governance**
Your data remains yours—never accessed, used, or shared. With PII protection, AES256 encryption, data masking, and region-specific storage, all secured by TLS 1.3.
## Revenue AI agent resources
[ How For You Conversations Make Every Interaction Feel Personal
Discover how brands can deliver experiences that are consistent, intelligent, and deeply human with For You Conversations from delight.ai.](https://delight.ai/blog/product/for-you-conversations)
[ Introducing AMP: The Agent Memory Platform
Agent Memory Platform (AMP) is delight.ai’s intelligence layer that gives your AI agents a living memory and a business brain. It connects what your agent remembers about every customer with what your business wants to achieve.](https://delight.ai/blog/product/introducing-agent-memory-platform)
[ What is an AI shopping assistant? How top options compare
Learn what an AI shopping assistant is and how leading platforms differ in channel coverage, workflow depth, and more to help you choose the right fit.](https://delight.ai/blog/ai-for-cx/ai-shopping-assistant-comparison)
[ How does real-time customer engagement impact your business?
Real time customer engagement is usually about response speed. Here's why proactive AI customer service, reaching out first, closes the gap reactive tools miss.](https://delight.ai/blog/ai-for-cx/real-time-customer-engagement)
## For You Conversations FAQs
How personal conversations work, what they draw on, and how your team controls them.
**What is For You Conversations?**
For You Conversations (FYC) is how Delight's revenue AI agent personalizes each conversation. It draws on what [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) knows about the customer to open with what's relevant, recommend what fits, and help the customer buy, book, or upgrade.
**How does For You Conversations help grow revenue?**
Your agent suggests products that match each customer's past purchases, preferences, and budget, answers the questions that hold up a purchase, and can add items to the cart. You choose the goal, such as cross-sell or larger orders, and your agent works toward it in each conversation.
**How is For You Conversations different from basic personalization?**
Basic personalization fills in a name or shows one offer to a whole segment. For You Conversations adapts each recommendation and reply to one customer's history, preferences, and current intent, and keeps that context when the customer switches channels.
**Can the agent reach out before a customer asks?**
Yes. With [Nudge Message](https://delight.ai/blog/ai-for-cx/real-time-customer-engagement), your agent can start the conversation when a shopper stalls on a page or leaves a form unfinished. With [Omnipresence](https://delight.ai/omnipresent), it can follow up on the channel each customer prefers when something relevant happens, such as a restock or an expiring offer.
**How does my team control what the agent recommends?**
Your team sets goals and rules in plain language, such as which products to feature or when to focus on support. With [Trust OS](https://delight.ai/trust-os), you can test changes before rollout, review conversations, and flag topics such as discount requests for review.
**Which channels does For You Conversations work across?**
Chat, SMS, WhatsApp, email, and [voice](https://delight.ai/voice). The same customer memory carries across each channel, so a conversation that starts in chat can continue by email or phone without the customer repeating themselves.
**Does For You Conversations improve over time?**
Yes. Each conversation adds to what your agent remembers, so the next recommendation starts with more context. Your team can trace each memory to its source and correct or delete it.
**How do I measure revenue from For You Conversations?**
Export conversation-level data with the [Data Export API](https://delight.ai/product-release/data-export-api) and join it to your order or booking data in your BI tool. You can then report conversion and order value for the conversations your agent handled. Learn more about [Analytics](https://delight.ai/analytics).
## Find your revenue opportunities
See where personal recommendations can help your customers buy.
[Talk to an expert](https://delight.ai/contact-sales)
## Home Service Demo Request
Source: https://delight.ai/homeservice-demo-request
## Watch Delight handle a home service repair
From intake to technician match, Delight verifies the plan, texts appointment slots to the member's phone while the voice call is still running, and confirms the technician live — all before they hang up.
Home service booking · Service Repair Booking
Timeline:
- 0:21 Emergency call initiated
- 0:33 Identity verification
- 0:55 Omnipresent channel coordination
- 1:22 Booking confirmed / Live Technician Coordination
- 2:33 Case status inquiry
- 3:29 Steward dispatches sub-agent
- 4:26 New ETA confirmed
- 5:00 Autonomous resolution
[Contact sales](https://delight.ai/contact-sales) · [Home services and warranty](https://delight.ai/industries/home-services-and-warranty)
## See delight.ai in action for home services
Learn how delight.ai can support your home services team.
### Explore our features
- [](https://delight.ai/omnipresent) **Omnipresence** — Support that stays ahead, on every channel
- [](https://delight.ai/for-you-conversations-fyc) **For You Conversations** — Hyper-personalized service
- [](https://delight.ai/agent-memory-platform-amp) **Agent Memory Platform** — Know who customers are, and what they need next
- [](https://delight.ai/steward-demo-request) **Agent Steward** — AI that owns every step from first message to final confirmation
- [](https://delight.ai/trust-os) **Trust OS** — Zero-touch AI that learns and improves every conversation
## AI built for multi-party coordination
Agent Steward coordinates and holds every party on one thread until it's done: homeowner, contractor, parts supplier, adjuster.
[Explore Agent Steward](https://delight.ai/autonomous-agent)
### Key capabilities
-  **Automated rescheduling** — Finds and confirms new appointment windows automatically. No callbacks required.
-  **Proactive parts updates** — Tracks delays and updates homeowners with revised ETAs before they need to ask.
-  **Claims status sync** — Adjusters and homeowners receive updates automatically. No manual follow-up.
-  **Smart case escalation** — Flags stalled cases and alerts your team automatically. Nothing falls through the cracks.
## Delight runs calls and texts together. No bookings lost.
Delight combines voice and SMS so bookings get confirmed in real time. Open slots, addresses, and confirmations are handled while customers stay on the call.
- Voice + SMS: confirmation arrives during the call
- Verification, matching, and address checks in one step
- 24/7 coverage without adding headcount
- EDI & TMS: status posted automatically
[ Explore Voice AI](https://delight.ai/voice)
### How Delight helps handle more jobs without adding headcount
- 
- 
#### Dr. Cool Services
> "Delight handles the calls we used to miss and follows up on the jobs we used to forget. It's like having a CSR who never clocks out."
Dr. Cool Services
[Read the story](https://delight.ai/customers/dr-cool-services)
#### hipages
Australia's #1 home services marketplace on inbound volume, booking automation, and homeowner satisfaction.
> "Homeowners expect an answer the moment they reach out. Delight handles every inbound surge without adding a single seat — and our booking rate went up."
hipages
[Read the story](https://delight.ai/customers/hipages)
## Other AI agents stop at the booking. Delight doesn't stop until the job is done.
[Talk to an expert](https://delight.ai/contact-sales)
## Iris voice demo — test
Source: https://delight.ai/iris-voice-demo-test
## Missing Component: **irisDemo**
This document notes that the component "irisDemo" is missing and provides troubleshooting steps, examples, and diagnostic commands to add or register it.
### Symptoms
- Build or runtime error: component not found (for example, "Module not found" or "Unknown component: irisDemo").
### Common causes
- File or export missing (component not exported as default or named export).
- Incorrect import path or filename case-sensitivity.
- Component not registered globally or not added to router/component registry.
- Build tool excluding the file (exclude rules) or unresolved path aliases.
### Quick fixes
1. Verify the component file exists and exports correctly:
```js
// src/components/irisDemo.jsx (React example)
export default function IrisDemo(props) {
return IrisDemo
;
}
```
```js
// src/components/irisDemo.vue (Vue single-file component example)
IrisDemo
```
2. Check import and registration examples:
```js
// React
import IrisDemo from "./components/irisDemo";
// use
// Vue (local registration in a component)
import IrisDemo from "@/components/irisDemo.vue";
export default {
components: { IrisDemo },
};
```
3. If using a global registry or app-level registration, register the component:
```js
// Vue global registration
import { createApp } from "vue";
import IrisDemo from "./components/irisDemo.vue";
const app = createApp(App);
app.component("IrisDemo", IrisDemo);
app.mount("#app");
```
4. Rebuild and verify case-sensitive filesystems:
- Run the dev server or build and inspect logs for the precise error.
- On Linux/macOS, ensure filename case matches the import path.
### Diagnostic commands
- Search the repository for references:
```bash
# from project root
rg "irisDemo" || grep -R "irisDemo" .
```
- Quick check of module exports (Node):
```bash
node -e "console.log(require('./src/components/irisDemo').default)"
```
### When to escalate
- If the file exists and imports/exports appear correct but the error persists, inspect bundler aliases, tsconfig/jsconfig path mappings, and module resolution rules.
- Collect and provide: the exact error message, relevant build config (webpack/rollup/vite), tsconfig/jsconfig, and the component file path for further debugging.
## Frames to try · Delight Eyes
Source: https://delight.ai/frames
## Sample products from a Delight.ai demo
 From your call with Iris
## Three frames to try
Have a look, then tell Iris the name of the one you like.
### Juniper
$140
Black · Thin rectangle
Delight Eyes Basics
Say "Juniper"
### Harlow
$180
Tortoiseshell · Soft round
Marlowe & Co.
Say "Harlow"
### Reed
$210
Silver · Lightweight metal
Kestrel
Say "Reed"
# Seasons & Events
## Delight Spark 2026
Source: https://delight.ai/events/spark
## DelightSpark 2026
May 7th | 12:30-6:30pm | L4 San Francisco
Where visionary CX leaders come to build tomorrow’s AI-first concierge experiences
Join us May 7, 2026 in San Francisco for Delight Spark, an invitation-only gathering where we'll unveil what's next in AI-powered customer experience.
Last time, we introduced delight.ai, your AI concierge. This time, we're going further. If you're building the future of how brands and customers connect, you need to be in the room when it happens.
## Meet our speakers
John Kim
Co-Founder & CEO, delight.ai
Shailesh Nalawadi
Head of Product Management, delight.ai
## Agenda
### KEYNOTE
1:00-1:45pm
The Next Era of AI Concierge
Meet the first AI agent that orchestrates people, systems, and other AI to own the outcome.
John Kim, CEO, delight.ai
### Fireside chat
2:15-2:35pm
When AI Becomes the Orchestrator
What happens when AI orchestrates every team, system, and stakeholder and nobody drops the thread.
Alf Lim, CPO, Norse Atlantic Airways
### Fireside chat
2:35-3:00pm
AI Concierge in Retail
How a national retailer built a concierge that remembers every member, personalizes every interaction, and sells like your best associate.
Anirudh Bhargava, SVP, BJ’s Wholesale Club & Scott Vandegrift, VP of Product, BJ’s Wholesale Club
### Group Panel
3:15-3:45pm
Building AI, Running on AI: How the Companies Making AI Actually Run on It
The leaders behind today’s most advanced AI companies on what they’ve learned deploying AI inside their own.
Eric Jing, Co-Founder and CEO, Genspark & Cal Rueb, Head of Applied AI, Anthropic
## Location
### L4 San Francisco
Where: 945 Market St, San Francisco, CA 94103
When: May 7, 2026 at 12:00-6:30pm
[View larger map](https://maps.app.goo.gl/Y6iMCNKhcKBipcsJA)
## Revisit Spark 2025
[View replays](https://delight.ai/events__old/spark-2025__old)
## CX on the Thames
Source: https://delight.ai/events/cx-on-the-thames
October 7, 2026 | 6:00–8:30 PM | Westminster Pier, London
## CX on the Thames
Join delight.ai for an unforgettable evening on the Thames, bringing together CX leaders during Engage Customer Summit 2026. Step aboard for a private evening cruise past London’s most iconic landmarks—an intimate setting to connect with peers, exchange ideas, and build meaningful relationships after the first day of the Summit.
[Request an invitation](#)
### Event schedule
#### Arrival
**6:00–6:15 PM**
Boarding at Westminster Pier
Guests arrive and board the boat at Westminster Pier. Please ensure you arrive on time—the boat will depart promptly, and late arrivals will unfortunately be unable to join once we leave the pier.
#### Welcome
**6:15–6:30 PM**
Welcome & Introduction
Mike Bohnett, Chief Revenue Officer at delight.ai, will share a short introduction to delight.ai and how AI-powered conversations are shaping the future of customer experience.
#### Networking
**6:30–8:30 PM**
Thames Sunset Cruise & Networking Reception
Enjoy a relaxed evening of networking with CX leaders while cruising past some of London’s most iconic landmarks. Canapés and drinks will be served throughout the journey.
#### Departure
**8:30 PM**
Disembarking at Westminster Pier
The evening concludes back at Westminster Pier. We look forward to continuing the conversation throughout day two of Engage Customer Summit and beyond.
### What to expect
London’s skyline at sunset, an evening on the Thames and plenty of space for meaningful peer conversations. We’ll keep things relaxed and intentional, with a few discussion prompts woven in around how AI is reshaping customer experience and engagement. It’s an opportunity to step away from the Summit, connect with leaders navigating similar challenges, and leave with new relationships and ideas to carry into day two and beyond.
## CX at sunset Miami Beach
Source: https://delight.ai/events/cx-sunset-miami
June 3, 2026 | 6:30 – 8:30 PM | Hyatt Regency Miami
## CX at Sunset
Join delight.ai for an unforgettable evening on the water, bringing together aviation and travel leaders after the first day of Aviation Festival Americas. Step away from the conference floor and onto a private yacht for an exclusive sunset cruise across Biscayne Bay. An intimate setting where airline and travel executives driving CX and digital transformation can connect, exchange ideas, and share what is actually moving the needle.
[Request an invitation](#)
### Event schedule
#### Boarding
5:45 – 6:00 PM
Guests arrive and board the yacht at Hyatt Regency Miami. Please ensure you arrive on time — the boat will depart promptly and late arrivals will unfortunately not be able to join once we leave the marina.
#### Welcome & Introduction
6:30 – 6:45 PM
Charles Studt, Chief Marketing Officer at Sendbird, will share a short introduction to delight.ai and how the new era of AI-concierge is shaping the future of customer experience.
#### Cruise
6:45 – 8:15 PM
Biscayne Bay Sunset Cruise and Networking Reception
Enjoy a relaxed evening of networking with aviation and travel leaders aboard a private yacht as we cruise past Star Island and the Miami skyline at golden hour.
#### Departure
8:15 – 8:30 PM
Disembarking at the Hyatt Regency Miami
The evening concludes back at the Hyatt Regency Miami. We look forward to continuing the conversation through day two of Aviation Festival Americas and beyond.
### What to Expect
Golden hour over Biscayne Bay, an evening on the water, and plenty of room for the kind of peer conversation that actually sticks. We will keep things relaxed and intentional, with a few discussion prompts woven in around how AI is reshaping the airline passenger experience to help the group go deeper when the moment is right. This is a space for leaders to step off the conference floor, connect with others working through the same questions, and leave with a few new relationships and ideas worth carrying into the rest of the week.
## A Taste of Delight | Nicosi | San Antonio
Source: https://delight.ai/hitec-san-antonio
Jun 17, 2026 | 5:30 - 7:30 PM | Nicosi, 221 Newell Avenue, San Antonio
## A Taste of Delight
Join delight.ai for a curated evening in San Antonio, bringing together hospitality and travel technology leaders for an unforgettable night. Step away from the conference and into something entirely different. A private, immersive dining experience at San Antonio's only Michelin-starred dessert bar, where great food sets the stage for even better conversation. An intimate gathering for the leaders shaping the future of guest experience and digital transformation in hospitality. A space to connect with the right people, share what is actually working, and go deeper on the ideas that matter most.
[Request an invitation](#)
### Event schedule
WELCOME
5:15 - 5:30 PM
Arrival & Welcome
Guests are encouraged to arrive ahead of 5:30 PM. The experience begins promptly and late arrivals may not be accommodated once the evening is underway.
Dinner
5:30 - 7:00 PM
The Tasting Experience
An intimate 8-course experience seated around an open kitchen where chefs guide you through each course as it arrives. Each plate explores one of four flavor profiles: acidic, umami, bitter, and sweet, elevated to their most refined and surprising expressions, with beverage pairings and the story behind every plate revealed throughout the evening. This is a full dining experience from start to finish.
Closing
7:00 PM
The Final Course
### What to expect
The setting is intentional. A small group of hospitality technology leaders, a world-class culinary team, and an evening where the conversation can breathe. Nicosi observes a no-photography policy to keep guests fully present throughout the experience. We will keep things focused, with a few discussion prompts woven in around how AI is reshaping the guest experience throughout the evening. This is a space for leaders to step away from the conference floor, connect with others working through the same questions, and leave with connections and perspectives worth carrying forward.
## Mediterranean CX Catamaran at Barcelona
Source: https://delight.ai/events/mediterranean-cx-catamaran-barcelona
Jun 9, 2026 | 7:30 – 9:45 PM | Port Olímpic, Barcelona
## The Mediterranean CX Catamaran
Join delight.ai for an unforgettable evening on the water, bringing together senior retail leaders in Barcelona. Step away from the day’s agenda and onto a private catamaran for an exclusive sunset cruise along the Mediterranean coast. An intimate setting where retail executives driving CX and digital transformation can connect, exchange ideas, and share what is actually moving the needle.
[Request an invitation](#)
### Event schedule
- Boarding — 19:30
Guests arrive and board the catamaran at Port Olímpic. Please arrive on time — the boat will depart promptly, and late arrivals will unfortunately not be able to join once we leave the marina.
- Welcome — 19:45 – 20:00
Welcome & Introduction
Mike Bohnett, CRO at Sendbird, will share a short introduction to delight.ai and how the new era of AI concierge is shaping the future of customer experience.
- Cruise — 20:00 – 21:45
Mediterranean CX Catamaran & Networking Reception
Enjoy a relaxed evening of networking with senior retail leaders aboard a private catamaran as we cruise along the Barcelona coastline at golden hour.
- Departure — 21:45
Disembarking at Port Olímpic, Barcelona
The evening concludes back at Port Olímpic. We look forward to continuing the conversation through day two of Shoptalk Barcelona and beyond.
### What to expect
Golden hour along the Mediterranean coast, an evening on the water, and plenty of space for the kind of peer conversation that actually sticks. We'll keep things relaxed and intentional, with a few discussion prompts woven in around how AI is reshaping the retail and eCommerce customer experience to help the group go deeper when the moment is right. This is a space for leaders to step away from Shoptalk Europe, connect with others working through the same challenges, and leave with a few new relationships and ideas worth carrying into the rest of the week.
## Spark 2026 | Recordings
Source: https://delight.ai/spark-2026
## Sessions from Delight Spark 2026
Thanks for your interest in Delight Spark 2026. Fill out the form to access the full recording with product updates and insights from industry leaders.
### Access recordings from Spark 2026
## Meet our speakers
Eric Jing
Co-Founder & CEO, Genspark
Alf Lim
CPO, Norse Atlantic Airways
Scott Vandegrift
VP of Product, BJ’s Wholesale Club
Anirudh Bhargava
SVP, BJ’s Wholesale Club
Cal Reub
Head of Applied AI, Anthropic
John Kim
Co-Founder & CEO, delight.ai
Shailesh Nalawadi
Head of Product Management, delight.ai
Mike Bohnett
CRO, delight.ai
Charles Studt
CMO, delight.ai
LeighAnne Manwiller
Author, AI Hype to AI Readiness
Yeji Yoon
Chief of Staff, delight.ai
## Agenda
### The Next Era of AI Concierge
John Kim, Delight.ai CEO
Meet the first AI agent that orchestrates people, systems, and other AI to own the outcome.
### When AI Becomes the Orchestrator
Alf Lim, CPO, Norse Atlantic Airways
What happens when AI orchestrates every team, system, and stakeholder and nobody drops the thread.
### How to Deploy AI Orchestration
Delight.ai
A walkthrough of setting up an AI agent that coordinates across teams and systems to drive complex issues to close, without handing off.
### AI Concierge in Retail
Scott Vandegrift, VP of Product BJ’s Wholesale Club
How a national retailer built a concierge that remembers every member, personalizes every interaction, and sells like your best associate.
### How to Deploy Memory & Personalization
Delight.ai
See every decision your AI makes, score its confidence in real time, and catch risks specific to your vertical before they reach customers.
### Building AI, Running on AI
Eric Jing, Co-founder & CEO, Genspark & Cal Reub
Cal Reub, Head of Applied AI, Anthropic
The leaders behind today’s most advanced AI companies on what they’ve learned deploying AI inside their own.
### State of AI in CX
Delight.ai
Do your customers trust AI more than you think, or less? Does your industry handle it better than others, or worse? New survey data across retail, travel, healthcare, financial services, and tech. Find out where you stand.
### The Taylor Swift Playbook for Building Fans
Ken Hughes, Headline Speaker
Why the brands that win aren't just solving problems — they're building fandoms. How AI concierge experiences create the emotional loyalty that turns one-time buyers into lifelong advocates.
## Revisit Spark 2025
[ View replays](https://delight.ai/events/spark-2025)
## CCW Las Vegas ancillary (summer 2026)
Source: https://delight.ai/events/ccw-las-vegas
June 23 | 7:00 PM | Michelob ULTRA Arena, Las Vegas, NV
## CX on the Court
Join delight.ai for an exclusive evening at Michelob ULTRA Arena as the Las Vegas Aces take on the New York Liberty. A private suite experience bringing together customer experience and contact center leaders during CCW Summer 2026. An intimate gathering for the leaders shaping the future of customer experience and AI — with prime half-court views, premium hospitality, and space to connect with the right people away from the conference floor.
[Request an invitation](#)
### Schedule
#### Departure — 5:15 PM
Charter departure
Step aboard the private charter and enjoy the ride to Michelob ULTRA Arena as the evening gets underway.
#### Arrival — 5:30 PM
Arrive at Michelob ULTRA Arena
Head up to the private half-court suite and take in the views. Enjoy drinks, a spread of food, and good conversation as the pre-game energy builds.
#### Tipoff — 7:00 PM
Tipoff: Las Vegas Aces vs. New York Liberty
Watch one of the WNBA's marquee matchups from your private suite. Two of the league's top teams go head-to-head in what promises to be an electric evening. Enjoy a hosted bar and curated catering all evening, with room to connect, unwind, and take in the game alongside fellow CX leaders.
#### Finale — End of game
Photo on the court
Close out the night with an exclusive group photo on the court — a one-of-a-kind moment to remember.
### What to expect
An evening built for genuine connection. A carefully selected group of customer experience leaders, a world-class matchup, and a private suite with room to breathe. The energy of a live WNBA game creates a natural, relaxed setting where conversation flows without the formality of a conference room. Whether you're catching up with familiar faces or meeting someone new, this is the kind of evening that turns a conference trip into something memorable.
## Boston Converse event
Source: https://delight.ai/events/designed-to-delight-boston
Aug. 11, 2026 | 6:00 – 8:00 PM | Converse, 1 Lovejoy Wharf, Boston, MA
## Designed to Delight
Join delight.ai for a private evening at the iconic Converse flagship on Boston's Lovejoy Wharf. An intimate gathering for the leaders shaping the future of AI-powered customer experience, brought together for great conversation, great drinks, and a one-of-a-kind creative experience. Design your own pair of custom Converse sneakers, enjoy passed bites and a full bar, and connect with the people building what's next. No agenda. No panels. Just the right people in the right space.
[Request an invitation](#)
### Event schedule
WELCOME
6:00 PM
Arrival & welcome
Guests are encouraged to arrive at 6:00 PM. The experience begins promptly.
EVENT
6:00 – 8:00 PM
Design your own Converse
Design a fully custom pair of sneakers with the Converse team guiding you through every option including various silhouettes, colors, and customizations. Each guest leaves with a pair that's entirely their own.
END TIME
8:00 PM
Closing remarks
We'll wrap up the night with a few words of thanks. May you leave with good conversations, new connections, and a pair of shoes you'll actually wear.
## What to expect
A private buy-out of one of Boston's most recognizable brand spaces, a creative hands-on activity, and a curated group of AI and technology leaders who are all navigating the same shifts. This isn't a networking reception, it's a chance to actually connect, make something with your hands, and leave with a pair of shoes and a few conversations worth continuing. We'll share the full attendee list ahead of the event so you know exactly who you'll be in the room with. Because at the end of the day, it's all about the connections you walk away with. Spots are limited to 20 guests.
## Seattle Executive Dinner Gifting LP
Source: https://delight.ai/events/seattle-executive-dinner
## Seattle AI & CX Executive Dinner
## Pick something you'll actually love
As a thank-you for joining us tonight, we'd love to send something your way. Pick one gift below and we'll send it straight to your door — our way of saying the delight was all ours.
[See gift options](#gifts)
### Claim your gift
---
## Gift options
- **A dozen assorted artisan cookies**
A dozen carefully crafted cookies. Because a little sweetness goes a long way, and nothing spreads happiness quite like a great cookie.
- **Single origin coffee set**
Three delightfully distinct roasts from women’s co-ops in Honduras and Rwanda. Chocolate wafer, maple, orange blossom honey. One happy cup at a time.
- **Devi Brut Rose**
A pale pink French sparkling rosé with fine bubbles and ripe red berries. Delightfully light, endlessly festive. Pop it whenever you feel like celebrating something, or nothing at all.
- **Snake plant**
A hard-to-kill desk companion that brings a little happy greenery to any space. Thrives in any light; water every 2–3 weeks. Low effort, high delight.
- **Bonanza Cabernet Sauvignon**
A California Cab built for the simple, happy pleasures of everyday life. Luscious fruit, dark chocolate, silky tannins. Delightfully drinkable over a great meal or a quiet evening.
- **Vice Tour golf balls**
A dozen Vice Tour balls stamped with the delight.ai logo. For the golfer who finds their happy place on the fairway, now with a little brand delight on every drive.
## Dreamforce HH - Delight After Hours - Sept. 2026
Source: https://delight.ai/delight-after-hours
Sept. 15, 2026 | 6:00 6 9:00 PM | SoMa, San Francisco | Dress code: Smart casual
## Delight After Hours
Join delight.ai for an evening in San Francisco you would not find on your own. We're bringing together a small, curated group of CX and AI leaders at a speakeasy in SoMa. From the street, it looks exactly like the working pawn shop it used to be. Getting through the door requires a trade. Inside, it is worth what you gave at the door.
[Request an invitation](#)
### Schedule
#### ARRIVAL
6:00 6 6:30 PM
**The Entry**
Find the pawn shop. Bring something worth trading.
#### INTROS
6:30 6 7:45 PM
**Welcome**
A brief welcome from the delight.ai team before the night opens up.
#### MAIN EVENT
7:45 6 8:45 PM
**The Exchange**
Enjoy an evening of curated cocktails, tapas, and good company.
#### CLOSING
8:45 6 9:00 PM
**Last Call**
Close out the night with one final round inside one of San Francisco's most unexpected hideaways.
### What to expect
A speakeasy built inside a former pawn shop, mood-lit interiors, and a guest list small enough that the conversations actually go somewhere. This is a space for CX and AI leaders to step away from the week, connect with others working through the same questions, and leave with a few things worth keeping.
## Events
Source: https://delight.ai/events
## DelightSpark 2026
May 7th | 12:30-6:30pm | L4 San Francisco
Where visionary CX leaders come to build tomorrow’s AI-first concierge experiences
Join us May 7, 2026 in San Francisco for Delight Spark, an invitation-only gathering where we'll unveil what's next in AI-powered customer experience.
Last time, we introduced delight.ai, your AI concierge. This time, we're going further. If you're building the future of how brands and customers connect, you need to be in the room when it happens.
### Meet our speakers
- 
John Kim
Co-Founder & CEO, delight.ai
- 
Shailesh Nalawadi
Head of Product Management, delight.ai
### Agenda
- KEYNOTE
**1:00-1:45pm — The Next Era of AI Concierge**
Meet the first AI agent that orchestrates people, systems, and other AI to own the outcome.
John Kim, CEO, delight.ai
- Fireside chat
**2:15-2:35pm — When AI Becomes the Orchestrator**
What happens when AI orchestrates every team, system, and stakeholder and nobody drops the thread.
Alf Lim, CPO, Norse Atlantic Airways
- Fireside chat
**2:35-3:00pm — AI Concierge in Retail**
How a national retailer built a concierge that remembers every member, personalizes every interaction, and sells like your best associate.
Anirudh Bhargava, SVP, BJ’s Wholesale Club & Scott Vandegrift, VP of Product, BJ’s Wholesale Club
- Group Panel
**3:15-3:45pm — Building AI, Running on AI: How the Companies Making AI Actually Run on It**
The leaders behind today’s most advanced AI companies on what they’ve learned deploying AI inside their own.
Eric Jing, Co-Founder and CEO, Genspark & Cal Rueb, Head of Applied AI, Anthropic
### Location
#### L4 San Francisco
Where: 945 Market St, San Francisco, CA 94103
When: May 7, 2026 at 12:00-6:30pm
[View larger map](https://maps.app.goo.gl/Y6iMCNKhcKBipcsJA)
### Revisit Spark 2025
[View replays](https://delight.ai/events__old/spark-2025__old)
## Spark 2025
Source: https://delight.ai/events/spark-2025
## Spark 2025
Revisit this year’s conversations on-demand - just fill out a short form to watch.
Nov 19 2025, 2:30pm
### Opening Keynote
Join Sendbird CEO and Co-founder John Kim as he unveils a bold new vision for the future of AI in customer experience, where every brand learns who you are, remembers what matters, and delights you before you even ask.
CEO KEYNOTE
Nov 19 2025, 3:00pm
### Product Premiere
What if your brand could give every customer their own concierge? Join Shailesh Nalawadi, Head of Product, for a live debut of an AI concierge that remembers, personalizes, and acts.
LIVE DEBUT AI
Nov 19 2025, 3:50pm
### Panel: From support tool to intelligent partner
Hear how AI leaders at Doordash and Mixpanel are transforming customer experiences - from automation to truly intelligent partnerships.
DOORDASH MIXPANEL
Nov 19 2025, 4:30pm
### AI Leaders Forum
AI marks a fundamental shift, it's rewriting how businesses think, build, and grow. Listen in as industry pioneers share what this new era means for leadership, growth, and the human experience.
AI LEADERS HUMAN EXPERIENCE
Nov 19 2025, 5:00pm
### Panel: Building AI-ready product organization
As AI reshapes how products are built and delivered, readiness becomes a competitive edge. Hear from Carlos, CEO of Product School, and Anique Drumright, VP of Product at Rippling, on what it takes to build an AI-ready organization.
AI LEADERS HUMAN EXPERIENCE
## Sessions - Spark 2025
Source: https://delight.ai/events/spark-2025/on-demand
## Sessions - Spark 2025
Nov 19 2025, 2:30pm
### Opening Keynote
Join Sendbird CEO and Co-founder John Kim as he unveils a bold new vision for the future of AI in customer experience, where every brand learns who you are, remembers what matters, and delights you before you even ask.
CEO KEYNOTE
Nov 19 2025, 3:00pm
### Product Premiere
What if your brand could give every customer their own concierge? Join Shailesh Nalawadi, Head of Product, for a live debut of an AI concierge that remembers, personalizes, and acts.
LIVE DEBUT AI
Nov 19 2025, 3:50pm
### Panel: From support tool to intelligent partner
Hear how AI leaders at Doordash and Mixpanel are transforming customer experiences - from automation to truly intelligent partnerships.
DOORDASH MIXPANEL
Nov 19 2025, 4:30pm
### AI Leaders Forum
AI marks a fundamental shift, it's rewriting how businesses think, build, and grow. Listen in as industry pioneers share what this new era means for leadership, growth, and the human experience.
AI LEADERS HUMAN EXPERIENCE
Nov 19 2025, 5:00pm
### Panel: Building AI-ready product organization
As AI reshapes how products are built and delivered, readiness becomes a competitive edge. Hear from Carlos, CEO of Product School, and Anique Drumright, VP of Product at Rippling, on what it takes to build an AI-ready organization.
AI LEADERS HUMAN EXPERIENCE
## ShopTalk Fall - CX on Broadway
Source: https://delight.ai/cx-on-broadway
## CX on Broadway
Join delight.ai for an exclusive evening in Nashville, bringing together customer experience leaders during Shoptalk Fall. A private, stylist-guided shopping experience at Boot Barn, right on Nashville's Broadway strip. An intimate gathering for the leaders shaping the future of retail and customer experience. A space to connect with the right people, exchange ideas, and walk away with perspectives that matter.
[Request an invitation](#)
### Schedule
#### ARRIVAL — 6:00 PM
Check in at Boot Barn
Start your experience by checking in at Boot Barn, then get ready for an evening of shopping and connection.
#### WELCOME — 6:15 PM
Welcome from delight.ai team
Join the delight.ai team for a warm welcome and event kickoff.
#### MAIN EVENT — 6:20 – 8:00 PM
Shop, sip, and socialize
Enjoy a guided shopping experience with Boot Barn stylists, paired with light bites, beer and wine, and good conversation.
## What to expect
An evening curated for genuine connection. A small group of retail and customer experience leaders, guided one-on-one by Boot Barn's stylists, with room to browse, try things on, and talk without the noise of the conference floor. Broadway is known for neon and crowds, but for a few hours the store is just ours. Conversation moves easily throughout the evening, with plenty of space to connect with others navigating the same shifts in retail and AI.
# Resources
## Your playbook for on-demand AI agents that keep transactions moving
Source: https://delight.ai/ebooks/on-demand-ai-agents
## Your playbook for on-demand AI agents that keep transactions moving
[Download ebook](#download-ebook "Download ebook")
On-demand platforms run on trust, which is fragile and easy to lose. When a delivery is delayed, a payment fails, or a dispute goes unresolved, your customers churn, providers leave, and the ops team inherits a mess of friction—all of which could have been prevented with the right AI agent.
In this ebook, you'll learn how on-demand AI agents:
- Turn delivery delays into trust-building moments
- Recover failed payments before customers abandon
- Support provider onboarding without human intervention
- Maintain job context as providers switch channels
- Resolve disputes with evidence that protects all parties
Plus, see how leading on-demand platforms are deploying AI agents successfully—keeping every party in your marketplace well-coordinated and happy.
## Your playbook for on-demand AI agents
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources "View more")
### Related resources
- [](https://delight.ai/webinars/trust-stack)
[The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
- [](https://delight.ai/ebooks/ai-agents-for-home-services)
[Home Services AI Agent Use Cases Playbook](https://delight.ai/ebooks/ai-agents-for-home-services)
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar)
[The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## B2B AI Agent Use Cases Playbook
Source: https://delight.ai/ebooks/b2b-ai-agents
## B2B AI Agent Use Cases Playbook
[Download ebook](#download-ebook "Download ebook")
> "Without Delight.ai agents, scaling our growth plans wouldn't be possible. It's the engine powering faster, smarter customer support."
>
> — Kathleen Matthews, Senior Support Engineer, [Mixpanel](https://delight.ai/customers/mixpanel)
### About this ebook
A practical look at how B2B SaaS teams put Delight.ai's AI agents to work across the customer journey, from onboarding through renewal, with five real actionbook examples for support, adoption, growth, and retention.
### What you'll learn
Download this ebook to discover how B2B AI agents can:
1. **Guide onboarding** — walk new customers from signup to first value, resolving workspace and integration blockers before they cause churn
2. **Resolve technical support** — diagnose API errors, integration failures, and product bugs in real time, without a human handoff for most cases
3. **Drive feature adoption** — spot underused features and manual workarounds, then proactively recommend the automation that saves the most time
4. **Surface customer growth** — flag expansion-ready accounts by usage and plan capacity, and route qualified upgrades to sales with full context
5. **Prevent churn** — catch usage drops and login inactivity early, and re-engage at-risk accounts before they start thinking about canceling
### Your playbook for B2B AI agents
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources "View more")
[](https://delight.ai/webinars/trust-stack)
[](https://delight.ai/ebooks/ai-agents-for-home-services)
[](https://delight.ai/webinars/delight-ai-index-2026-webinar)
[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## Travel & Hospitality AI Agent Use Cases Playbook
Source: https://delight.ai/ebooks/ai-agents-for-travel-hospitality
## Travel & Hospitality AI Agent Use Cases Playbook
[Download ebook](#download-ebook "Download ebook")
Norse Atlantic Airways deployed Delight.ai's AI agent, named Freya, to replace a legacy chatbot in under two months. Freya's containment rate climbed from 60% to 80% within two weeks. Download this ebook to learn how you can apply the same playbook Freya runs for Norse Atlantic Airways to your own guest journey.
> "We are moving past the idea of 'support bots.' The future is one unified agent for sales and support—a single digital concierge that knows you, helps you, and travels with you."
>
> — Alf Lim, Chief Product Officer, [Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways)
### About this ebook
A practical look at how travel and hospitality teams use Delight.ai's AI agents across the guest journey: pre-trip planning, active travel, and post-trip follow-up. Every moment becomes a chance to build loyalty, instead of losing the traveler to a bad experience.
### What you'll learn
Download this ebook to discover how travel and hospitality AI agents can:
1. **Plan trips and recommend destinations** — interpret vague travel intent (budget, occasion, travel party) and match it to specific destinations, hotels, and packages using past bookings and loyalty status
2. **Recover abandoned bookings** — diagnose why a traveler hesitated (price, dates, uncertainty) and re-engage them across web, app, email, SMS, chat, and voice with a fix, not just a reminder
3. **Manage disruptions** — monitor flight status, weather, and booking systems in real time, then proactively rebook and communicate compensation before the traveler has to ask
4. **Act as an in-stay concierge** — recommend dining, activities, and upgrades based on guest preferences and real-time context, and book them directly in the conversation
5. **Resolve guest support issues** — handle baggage claims, cancellations, and insurance questions instantly across channels, using full booking history instead of routing to a human
### Your playbook for AI agents for travel & hospitality
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources "View more")
[](https://delight.ai/webinars/trust-stack)
The Trust Stack: How to design an AI agent that earns its autonomy
[](https://delight.ai/ebooks/ai-agents-for-home-services)
Home Services AI Agent Use Cases Playbook
[](https://delight.ai/webinars/delight-ai-index-2026-webinar)
The Delight AI Index 2026: The trust paradox reshaping CX
[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## Your playbook for AI agents for financial services that build trust, stay compliant, and grow customer value
Source: https://delight.ai/ebooks/ai-agents-for-financial-services
- [eBooks](https://delight.ai/ebooks)
## Your playbook for AI agents for financial services that build trust, stay compliant, and grow customer value
[Download ebook](#download-ebook "Download ebook")
Every customer moves through your financial services experience in five critical phases: discovery, onboarding, everyday banking, financial growth, and retention. This playbook shows you how AI agents for financial services deliver personalized experiences across the entire customer lifecycle—from product eligibility to activation to winback—all without compromising security or compliance.
In this ebook, you’ll learn how AI agents for financial services:
- Automate product discovery with personalized recommendations
- Guide customers from approval to transaction
- Resolve repetitive inquiries at scale instantly
- Drive financial growth and revenue with proactive engagement
- Retain at-risk customers and win back lost customers
Plus, see how leading financial services companies are deploying AI agents successfully—turning prospects into active customers and transactional users into lasting relationships.
## Your playbook for AI agents for financial services
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources "View more")
### Related resources
- [](https://delight.ai/webinars/trust-stack)
The Trust Stack: How to design an AI agent that earns its autonomy
- [](https://delight.ai/ebooks/ai-agents-for-home-services)
Home Services AI Agent Use Cases Playbook
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar)
The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## Retail AI Agent Use Cases Playbook
Source: https://delight.ai/ebooks/retail-ai-agents
## Retail AI Agent Use Cases Playbook
[Download ebook](#download-ebook "Download ebook")
## About this ebook
A practical look at how retail teams use Delight.ai's AI agents across the three decisive moments in the shopper journey: pre-sale discovery, in-store decision, and post-sale care. Every moment becomes a chance to convert and retain, instead of losing the sale to friction.
## What you'll learn
Download this ebook to discover how retail AI agents can:
1. **Guide product discovery** — interpret shopper intent from natural language, images, or incomplete queries, and recommend products using their full purchase history
2. **Recover abandoned carts** — diagnose why a shopper left (fit, price, shipping) and re-engage them across web, app, email, SMS, chat, and voice with a fix, not just a reminder
3. **Guide shoppers to products in-store** — give real-time, aisle-level directions using store layout and inventory data, on any channel or kiosk
4. **Resolve customer service issues** — handle WISMO, returns, and membership questions instantly across channels, using full order history instead of routing to a human
## Your playbook for retail AI agents
Discover how to move from adoption to transformation. Free to download. No spam.
## Keep reading
[View more](/resources "View more")
## Related resources
- [The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
[](https://delight.ai/webinars/trust-stack)
- [Home Services AI Agent Use Cases Playbook](https://delight.ai/ebooks/ai-agents-for-home-services)
[](https://delight.ai/ebooks/ai-agents-for-home-services)
- [The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
[](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [Why memory is AI's most critical bottleneck (MindMakers podcast)](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## Healthcare AI Agent Use Cases Playbook
Source: https://delight.ai/ebooks/healthcare-ai-agents
- [eBooks](https://delight.ai/ebooks)
- Healthcare AI Agent Use Cases Playbook
## Healthcare AI Agent Use Cases Playbook
[Download ebook](#download-ebook "Download ebook")
## About this ebook
A practical look at how healthcare teams use Delight.ai's AI agents across the patient journey: pre-visit, during care, and post-visit follow-up. Every step becomes a chance to reduce friction and improve outcomes, without adding headcount.
## What you'll learn
Download this ebook to discover how healthcare AI agents can:
1. **Schedule appointments** — check real-time provider availability, verify insurance coverage, and guide patients through visit prep in one conversation
2. **Verify insurance** — answer coverage, copay, and prior-authorization questions instantly, in plain language instead of jargon
3. **Support care teams** — aggregate patient data across EMRs, CRMs, and analytics platforms so care teams get answers without logging into multiple systems
4. **Coordinate post-visit care** — proactively schedule follow-ups, coordinate prescriptions, and share lab results before patients have to ask
## Your playbook for healthcare AI agents
Discover how to move from adoption to transformation. Free to download. No spam.
## Keep reading
[View more](/resources "View more")
- [](https://delight.ai/webinars/trust-stack) The Trust Stack: How to design an AI agent that earns its autonomy
- [](https://delight.ai/ebooks/ai-agents-for-home-services) Home Services AI Agent Use Cases Playbook
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## eBook: From hype to AI readiness
Source: https://delight.ai/ebooks/from-hype-to-ai-readiness
## eBook: From hype to AI readiness
[Download ebook](#download-ebook "Download ebook")
Build AI agents with confidence by preparing your people, data, and systems for what comes next.
AI can transform customer experience, but only if your organization is ready for it. This guide helps you bridge the gap between hype and real results. Learn how to evaluate your current state, align on outcomes, and adopt AI agents like delight.ai with clarity and trust.
### In this ebook you will learn:
- How to assess organizational readiness across people, data, and systems
- How to define CX-aligned goals and success metrics
- How to map customer inquiries and workflows to identify automation opportunities
- How to evaluate AI platforms beyond demos and features
- How to measure, iterate, and scale AI responsibly
Start your AI readiness journey with guidance built for real-world CX.
## From hype to AI readiness
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources "View more")
- [](https://delight.ai/ebooks/ai-agents-for-home-services)
Your playbook for home services AI agents that book, coordinate, and close the loop
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar)
The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
Why memory is AI's most critical bottleneck
- [](https://delight.ai/webinars/spring-product-update)
From AI that answers to AI that owns: The delight.ai spring product update
[View more](/resources "View more")
## AI readiness 101: Close the gap between AI hype and real results
Source: https://delight.ai/webinars/ai-readiness-101-webinar
## AI readiness 101: Close the gap between AI hype and real results
On-demand webinar | Watch anytime
[Get instant access](#watch "View Video")
## Speakers
**Shailesh Nalawadi**
Head of Product Management
**LeighAnne Manwiller**
Product Marketing Manager
## Close the gap between AI hype and real results
Most teams overestimate their AI readiness—and it costs them months of implementation delays, missed ROI targets, and customer trust.
The gap between deploying an AI agent and being truly ready for one is wider than most organizations realize. Turning an impressive AI demo into a production-ready agent requires organizational readiness. That means aligned stakeholders, AI-ready knowledge and data systems, documented workflows, and clear success metrics. Without this foundation, pilots stall and production deployments break down.
In this on-demand session, LeighAnne Manwiller, author of *From AI Hype to AI Readiness*, and Shailesh Nalawadi, AI Transformation Architect at Sendbird, offer a practical introduction to AI readiness that cuts through the hype. Learn what separates organizations that deploy successfully in 90 days from those still struggling months later, and identify the gaps holding your team back.
Most teams don’t realize they’re not ready for AI until implementation starts to break. This session shows you how to identify those gaps early and avoid months of delays.
In this session, you’ll learn:
- **The AI readiness gap** most CX leaders overlook
- **The three pillars of AI readiness:** organizational, infrastructure, and strategic
- **Where to start** based on your maturity, resources, and priorities
- **How to evaluate AI platforms** beyond demos and features
- **What “AI-ready” actually looks like** across people, systems, and operations, with real examples
Walk away with a clear view of your readiness and actionable next steps to prepare your organization for AI agents that deliver real impact. Whether you’re exploring AI or actively evaluating platforms, this session gives you a practical framework to move forward with confidence.
## Watch AI Readiness 101 on demand
Practical guidance. Real-world examples. Clear next steps to move from AI hype to real results.
## Keep reading
[View more](/resources "View more")
- [](https://delight.ai/webinars/trust-stack) The Trust Stack: How to design an AI agent that earns its autonomy
- [](https://delight.ai/ebooks/ai-agents-for-home-services) Your playbook for home services AI agents that book, coordinate, and close the loop
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## Why multimodal data is the next enterprise AI advantage
Source: https://delight.ai/podcasts/mindmakers-podcast-cody-coleman
- [Podcasts](https://delight.ai/podcasts)
- Why multimodal data is the next enterprise AI advantage
## Why multimodal data is the next enterprise AI advantage
March 9 2026
**Listen to the MindMakers Podcast:** [](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
In a world that increasingly runs on product photos, YouTube clips, and Zoom recordings, business systems built for structured data are already falling short. As a result, enterprises are drowning in multimodal content that their tools can neither understand nor leverage.
For Cody Coleman, CEO and Co-Founder of [Coactive AI](https://www.coactive.ai/), this disconnect is one of the biggest blockers to AI transformation. “There is a massive ocean of unstructured content that current business tools weren’t built to handle,” he explains.
Without multimodal AI—models or systems that can understand visual, audio, and video content—organizations are overlooking an entire class of business intelligence and leaving money on the table.
In this episode of *MindMakers*, [delight.AI](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder) CEO [John Kim](https://www.linkedin.com/in/doshkim) sits down with [Cody Coleman](https://www.linkedin.com/in/codyaustun/overlay/about-this-profile/) to discuss how organizations can move beyond structured data, unlock value from multimodal content, and set themselves up for success in the AI-driven future.
### The multimodal shift vs the content understanding gap
For decades, business systems were designed for structured inputs, such as tables, rows, and logs. But today, the majority of information flowing through many organizations is multimodal and unstructured: images, videos, audio, PDFs, livestreams, and IoT sensor feeds.
Coleman breaks the shift to rich content into three core drivers:
1. **Work is increasingly visual-first.** From Zoom meetings to product photography to UGC-filled social feeds, visual communication now dominates knowledge work.
2. **Consumer behavior is multimodal.** Product discovery now happens through visual search, TikTok video snippets, AR try-ons, and image-driven commerce.
3. **Enterprises capture more visual content than ever.** Cameras, sensors, robots, and customer uploads generate millions of rich media assets per day—far more than humans can manually review or tag.
However, none of this content fits neatly into structured systems.
> “Classic database systems treat images and video as blobs. They’re not good at understanding the actual content,” he explains.
This has left vast amounts of high-value content to sit unanalyzed and unused, simply because legacy tools are unable to interpret it. In an era where structured data is just a slice of the enterprise pie, and data-hungry AI systems are becoming essential—organizations must shift to embracing multimodal AI that can understand images, video, audio, and text together.
### Multimodal AI models are just the start
Multimodal AI technology is advancing rapidly, so AI leaders might assume that simply adopting a foundation model will unlock value from their underutilized content. However, Coleman warns that models alone won’t suffice, even as the technology becomes more advanced.
> “No single model will ‘just work’ for the wide variety of content, constraints, and edge cases enterprises deal with,” he says.
In his experience, model-only approaches fall short because:
- Different content types require different models
- Domain-specific signals still need customization
- Business teams need usable interfaces—not raw model outputs
To move from “we have the data” to “we can use the data,” enterprises need more than multimodal models—they need the right AI infrastructure that can:
- Orchestrate multiple specialized models
- Extract context directly from visual content
- Integrate outputs into real workflows
In other words, without the right underlying system to support their multimodal capabilities, even the best models won’t deliver value.
### The solution: Turn unstructured content into a searchable signal
Cody believes the missing ingredient in most enterprise AI strategies is a content intelligence layer that transforms raw multimodal content into machine-readable data.
This is what Coactive AI provides: a multimodal AI platform that turns images, video, and audio into structured, searchable, and analyzable signals. This way, enterprises can finally process and leverage massive rich datasets securely and at scale.
> “We want to make it easy to search and analyze visual content by effectively turning images and video into machine-readable signals,” he says.
Here’s how the multimodal AI platform works:
- **Model-agnostic architecture** uses the best-in-class model(s) for each use case and content type.
- **Automated context extraction** pulls meaningful signals directly from images, video, and audio.
- **AI APIs for developers** enable integration, customization, and large-scale orchestration.
- **Interfaces for business teams** support search, tagging, moderation, and analytics—no machine learning expertise required.
- **Enterprise-scale + trust features** built for environments that require security, governance, and massive throughput.
*Coactive’s fast dynamic tagging enables computers to quickly get a rich, contextual understanding of visual content.*
By abstracting the multimodal AI stack, Coactive enables enterprises to index, search, and analyze their rich content for widespread use. Companies like Pinterest and Meta have built this kind of infrastructure internally, Coleman notes, but at the cost of tens or hundreds of millions of dollars. Coactive makes multimodal AI accessible as a platform, so enterprises don’t have to rebuild their infrastructure from scratch to get value from rich content.
### Real-world examples of a multimodal AI layer
Coactive’s approach to multimodal AI is already unlocking new value across industries. For example:
#### 1. Better search and discovery for Thomson Reuters
The news organization relied on manual metadata entry for content, which was slow and inconsistent, as reporters didn’t always tag everything relevant. Before, searching the Reuters database for “Kamala on SNL” could return just a single result.
With Coactive, semantic context is extracted directly from video and images, making content discovery far more complete and relevant. This helps reporters and newsrooms find more of what they need from visual content faster.
#### 2. Faster, safer content moderation for Fandom
Previously, Fandom manually reviewed ~60,000 user-uploaded images per day. With Coactive, review time fell from two days to 250 milliseconds. This achieved a 50% cost savings while increasing the accuracy of the company’s AI content moderation, resulting in safer communities at scale.
#### 3. Visual analytics & signal extraction
Structured data alone can’t deliver certain capabilities—such as turning images and video into machine-readable signals. By turning the pixels in visual content into machine-readable features, companies can power a variety of AI workflows and systems, including:
- Recommendation engines
- Content understanding
- Compliance workflows
- Creative analytics
- Risk detection
These are insights that structured data alone could never provide.
### The multimodal opportunity: Where AI meets content understanding
While much of the AI world remains focused on text, Coleman and the Coactive team believe the next frontier lies in visual and sensor data. He says that many industries are already undergoing this transformation, including:
- **Healthcare:** telemedicine, diagnostic imaging
- **Life sciences:** microscopy, pathology, high-throughput imaging
- **Retail:** visual search, UGC understanding
- **Industrial & robotics:** camera + depth + spatial sensor fusion
- **Media & entertainment:** content production, rights management
Across all these domains, the bottleneck isn’t the availability of data, but the lack of infrastructure to understand it. He gives an example from the life sciences:
> “Microscopes are now high-throughput, generating thousands of images no human can review,” he notes. “As robots capture sensor data—vision, depth, spatial—multimodal AI becomes essential for making sense of it.”
### Next steps for embracing multimodal AI
As multimodal content becomes the dominant form of enterprise data, organizations that invest in content intelligence infrastructure—not just models—will gain the greatest advantages. After all, structured data alone can’t power the next generation of AI applications.
Coleman stresses that enterprises will only benefit if their infrastructure keeps pace with the latest developments. This means implementing an abstraction layer, or a system that unifies content, orchestration, and AI reasoning. This is his larger vision for Coactive:
> “We need a new operating system to help humans and machines work together with a shared understanding of visual data,” he explains. “Just like CPUs needed Windows or macOS, foundation models need platforms that connect content and AI.”
What should leaders look for in a content intelligence layer? In Coleman’s view, a winning layer should abstract model complexity, allowing enterprises to use the best model for each task. It should also unify structured and unstructured data into a single semantic layer, orchestrate and integrate workflows across technical and non-technical teams, and evolve with foundation models.
Ultimately, this will enable leaders to transform their unstructured content into a signal for both humans and intelligent systems, paving the way for reliable automation, innovation, and returns from AI.
Want to learn more about multimodal AI with Cody Coleman of Coactive AI? Listen to the full episode of *MindMakers* on [Spotify](https://open.spotify.com/episode/7nO6aUCSiDwKAfAi3HOVmI?si=150126801a6f4cd1) or [Apple Podcasts](https://podcasts.apple.com/us/podcast/how-the-founder-of-coactive-ai-is-bridging-the/id1840021178?i=1000739289183).
Curious how [delight.ai](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder) enables multimodal AI for customer experience? Just [contact sales](https://delight.ai/contact-sales).
- [The multimodal shift vs the content understanding gap](#section-anchor-id-0 "The multimodal shift vs the content understanding gap")
- [Multimodal AI models are just the start](#section-anchor-id-1 "Multimodal AI models are just the start")
- [The solution: Turn unstructured content into a searchable signal](#section-anchor-id-2 "The solution: Turn unstructured content into a searchable signal")
- [Real-world examples of a multimodal AI layer](#section-anchor-id-3 "Real-world examples of a multimodal AI layer")
- [The multimodal opportunity: Where AI meets content understanding](#section-anchor-id-4 "The multimodal opportunity: Where AI meets content understanding")
- [Next steps for embracing multimodal AI](#section-anchor-id-5 "Next steps for embracing multimodal AI")
Keep reading
[View more](/resources "View more")
[](https://delight.ai/webinars/trust-stack)
The Trust Stack: How to design an AI agent that earns its autonomy
[](https://delight.ai/ebooks/ai-agents-for-home-services)
Your playbook for home services AI agents that book, coordinate, and close the loop
[](https://delight.ai/webinars/delight-ai-index-2026-webinar)
The Delight AI Index 2026: The trust paradox reshaping CX
[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## Beyond text: Why video AI is the future of enterprise intelligence
Source: https://delight.ai/podcasts/mindmakers-podcast-jae-lee
## Beyond text: Why video AI is the future of enterprise intelligence
[Watch now](https://www.youtube.com/watch?v=EBQXp8ibsGA)
March 9 2026
**Listen to the MindMakers Podcast:** [  ](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [  ](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [  ](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
Large language models (LLMs) have captivated the world with their text-based capabilities. But while businesses rush to adopt these tools, they are overlooking the largest and fastest-growing part of their data footprint: video. From sports broadcasts to manufacturing lines, video content is exploding, and almost none of it is usable or searchable to AI in its raw form.
For Jae Lee, co-founder and CEO of [TwelveLabs](https://www.twelvelabs.io/), video is the next frontier of enterprise AI. In this episode of MindMakers, [delight.ai](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder) CEO John Kim sits down with Jae Lee to discuss why video AI is essential in a future powered by AI agents, and how enterprises can turn their unstructured footage into actionable intelligence.
### Why text-only AI isn't enough
Language models are powerful, but limited. They do not experience the world, so they learn and understand it through text, reading descriptions as a proxy for reality. By contrast, humans learn through sight, sound, and time, perceiving the world years before we can speak.
Lee points out that, if we want AI to truly understand and operate in human-like ways, these systems must be grounded in this same kind of perceptual understanding.
No one doubts we live in a visual-first world. An intelligent system should be built to understand that world, not just read about it.
In Lee's view, video is the ideal substrate for machine intelligence. It is truly multimodal, combining visuals, audio, space, and time. It also reflects not just what is in a scene, but how it changes, and how entities within interact and evolve.
By processing video effectively, AI can gain a context-rich understanding that more closely resembles human intelligence. Meanwhile, the world is generating more video than ever, more than any existing data center could reasonably process.
> We realized it wasn't just us trying to make sense of insane amounts of video. This was a global problem waiting to be solved.
>
> - Jae Lee, Co-Founder and CEO of TwelveLabs
### Building the full-stack video intelligence layer
Most of today's AI infrastructure is built around text generation, not video understanding. The hardware and the optimizations are tuned to feed tokens into an auto-regressive model and get tokens back out. Though perfect for chatbots and [AI concierges](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder), it is not ideal for processing petabytes of video.
The challenge is that video is dense but also redundant. Not much changes frame to frame. Treating each frame as a token so a model can predict the next one works for short clips, but breaks down when it needs a deep understanding of lengthier content and massive archives. Instead of asking how do we generate the next frame, Lee asked how do we turn this video into a compact representation of meaning?
The solution is multimodal embeddings, numerical representations that encode what is happening visually and auditorily over time. TwelveLabs built their own video-centric infrastructure, custom preprocessing and indexing for different video types (a fast-clip sports broadcast is more demanding than a podcast), and tightly integrated the infrastructure with the models. This allows video to be ingested, indexed, searched, and described with speed, accuracy, and cost-effectively.
We wanted end-to-end control so large-scale semantic video search would be both fast and affordable, says Lee.
The result is a full-stack video intelligence layer designed for real-world enterprise workloads. It transforms static video archives into something that both humans and AI agents can navigate semantically.
### Use cases for AI video intelligence
Before TwelveLabs, most video search relied on manual notes, tags, and hours of scrubbing through footage. Even sophisticated organizations were operating with workflows closer to analog than AI.
TwelveLabs changes this by enabling semantic search and summarization across entire archives and metadata. Teams can look for ideas, actions, emotions, or specific moments using text, images, or reference clips.
Across industries, AI-driven semantic video search can save serious time and money:
- **Sports broadcasting:** Sports teams can now automatically generate highlights from their own footage.
- **Media and entertainment:** Editors can assemble rough cuts in minutes, rather than hours.
- **Government and intelligence:** Analysts can piece together multi-camera events with greater clarity than was previously possible.
Across these and other sectors, video AI can transform footage from a passive asset into a searchable, usable knowledge surface with real business value.
### Misconceptions about video AI
When customers first approach Lee, they often assume a single model will handle everything. But he insists that different modalities and domains require different semantic layers. For example, the embeddings used for PDFs differ from those optimized for video.
In his experience, the best video systems are built around a stack of models and tools stitched together into cohesive workflows, rather than a single monolithic model.
> It's almost never this model or that model. It's usually this one plus that one, working together in a stack that fits your data and your use cases.
>
> - Jae Lee, Co-Founder and CEO of TwelveLabs
### Ethical considerations
Beyond delivering results for customers, Lee must contend with various ethical concerns around video intelligence, especially surveillance. How do you prevent misuse? What do you do when ethically gray customers come knocking? How do you balance national security interests with civil liberties?
Lee says TwelveLabs has already declined to work with certain customers and content categories they weren't comfortable enabling. Sometimes, the nature of the content itself is something they do not want their team exposed to. Other times, the intended use case is misaligned with the kind of world they want to help build. Passing in these situations isn't easy, but he considers it part of the responsibility of building the foundational infrastructure of tomorrow's world.
### Takeaways on video AI
Many leaders still see AI as an efficiency tool, something that speeds up existing workflows. But as Lee points out, the current moment is about far more than marginal improvements. It's about a structural shift in how organizations store knowledge, make decisions, and create value.
This tech doesn't just make you more efficient. It changes what's possible, says Lee.
In his view, leaders who benefit most from this shift will focus on three things:
- **Have a strong POV on AI direction:** You do not need to be a model architect to lead today, but you can't outsource your understanding. The difference between seeing AI as a chatbot and seeing it as a new computing substrate determines whether you play defense or redefine your category.
- **Prepare data (especially video) for agentic AI systems:** Video is the largest, richest, least-used part of enterprise data footprints, but remains invisible to AI agents if not indexed and semantically searchable. Structuring unstructured data will unlock entirely new workflows that text-only systems cannot handle.
- **Accept incumbency as fragile during architectural shifts:** Just as Intel lost ground when computing moved beyond its core paradigm, today's market leaders can fall behind if they treat AI as a bolt-on tool instead of a transformative one. New competitors with AI-native infrastructure can quickly leapfrog the laggards.
In the end, Lee and TwelveLabs consider video to be the best substrate for machine intelligence going forward, as it enables intelligent systems to see the world, better understand it, and perform accordingly.
Want to learn more about video AI with Jae Lee of TwelveLabs? Listen to the full episode of MindMakers on [Spotify](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) or [Apple Podcasts](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178).
Curious how [delight.ai](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder) supports video AI for customer experience? Just [contact sales](https://delight.ai/contact-sales?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder).
- [Why text-only AI isn't enough](#section-anchor-id-0)
- [Building the full-stack video intelligence layer](#section-anchor-id-1)
- [Use cases for AI video intelligence](#section-anchor-id-2)
- [Misconceptions about video AI](#section-anchor-id-3)
- [Ethical considerations](#section-anchor-id-4)
- [Takeaways on video AI](#section-anchor-id-5)
Keep reading
[View more](/resources)
[ The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
[ Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
[ The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
[ Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources)
## Why 95% of enterprise AI fails—and why success is closer than you think
Source: https://delight.ai/podcasts/mindmakers-podcast-kevin-chung
## Why 95% of enterprise AI fails—and why success is closer than you think
[Watch now](https://www.youtube.com/watch?v=R2GYL_fzp5I)
March 9 2026
**Listen to the MindMakers Podcast:** [](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
Every week, the headlines trumpet a new rollout of [enterprise AI agents](https://delight.ai/enterprise-grade/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder). Yet behind the scenes, most of these projects stall out before they ever deliver results, often failing in unexpected and costly ways. In fact, according to a recent report from MIT, as many as [95% of enterprise AI projects fail](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/).
For Kevin Chung, Chief Strategy Officer at [WRITER](https://writer.com/), the problem is rarely the technology itself—but the people, processes, and assumptions behind it. "Ninety-five percent of enterprise AI projects don’t fail because of the tech," Kevin says. "They fail in adoption—the last mile is where transformation either happens or stalls."
On the debut episode of MindMakers, Sendbird CEO [John Kim](https://www.linkedin.com/in/doshkim) sat down with [Kevin Chung](https://sg.linkedin.com/in/thekevinchung) to unpack why AI success is so elusive—and why most enterprises are closer to ROI than they realize.
### Fixing the last-mile problem in enterprise AI
Kevin has yet to see an AI project fail because the model wasn’t powerful enough. The breakdown happens when it’s time to embed AI into workflows, empower employees, and scale. But he points out that this "last-mile" failure often starts much earlier with misconceptions about what it takes to succeed.
"A lot of people mistakenly think you just adopt AI, buy the software, and it will work," he says. "But AI doesn’t work off the shelf for enterprises. It’s pretty tricky."
The challenge isn’t just technical, it’s organizational. Teams are expected to learn a new industry, build solutions, and keep pace with a rapidly evolving technology, all at once. Meanwhile, it’s frustrating to spend hours configuring components and crafting the right prompts, only to see new use cases pop up every month.
Kevin likens the experience to drinking from a firehose: overwhelming and unmanageable. Faced with a flood of tasks, decisions, and the attendant uncertainty—"Is it time to pivot? Was any of this worth it?"—even motivated enterprises risk losing momentum and watching projects go under before adoption. He encourages organizations to focus on change management rather than just in-house solutions, as success ultimately hinges on the people who believe in the mission and advance it.
> "Number one, you gotta get your people empowered. Number two, you actually have to start promoting the people. And promoting in a sense of [not just] giving them the tools, but actually finding those champions who can be your AI advocates, right? The AI leaders in the organization who are gonna effectively drive forward those initiatives."
>
> — Kevin Chung
### Treat AI like a new hire, not software
One of Kevin’s biggest lessons: companies that thrive with AI treat it like a new teammate, not just another tool. "You onboard it, you train it, you assign it a champion, just like you would with a new hire," he says.
This shift in mindset is critical. Tools can be ignored, but teammates get integrated, developed, and supported. By treating AI like a member of the workforce and allowing trust to grow over time, enterprises are far more likely to see adoption stick.
The friction between AI and humans is real. A recent study finds that [41% of Gen-Z and Millennial employees](https://www.inc.com/ben-sherry/employees-are-sabotaging-their-companys-ai-strategy-according-to-a-new-survey/91162740) report sabotaging their company's AI strategy. Kevin understands the pushback. If AI doesn’t directly work for what employees want, they’re naturally going to resist, stick to old tools, and divert from adoption in favor of their own strategy.
> 41% of Gen-Z and Millennial employees report sabotaging their company's AI strategy.
>
> — Inc.com study
"You can’t just throw AI over the wall and expect people to use it," Kevin says. "Trust is built when you’re hands-on with your teams, showing them how AI supports their goals and then scaling from there."
### From pilots to productivity
Another reason AI initiatives fail is that they never get beyond the pilot phase. In fact, a recent RAND report shows that [80% of AI pilots never make it to production](https://www.rand.org/pubs/research_reports/RRA2680-1.html)—a failure rate nearly double that of traditional IT projects.
Kevin acknowledges that enterprises juggle a myriad of moving parts and considerations as they try to prove concepts. But at a certain point, he says, it’s time to stop tinkering and start scaling.
"We’re seeing the [AI] leaders move beyond experiments into hundreds of deployed AI agents," Kevin says. "That’s where the ROI shows up—tens of thousands of hours reclaimed and teams freed up for higher-value work."
One of those leaders is Ryder, a global logistics and transportation company. Despite early hesitancy, Ryder pushed forward into scaled deployments. Today, they’ve launched hundreds of custom vertical AI agents, proving what’s possible when adoption sticks.
For Kevin, Ryder is proof that enterprise AI can work when it’s done right. Pilots can validate potential, but until organizations appoint AI champions and scale deployment, they’ll never realize the full benefits.
### Building trusted AI through partnerships
For most enterprises, the barrier to AI success isn’t whether the technology works—it’s whether they can trust it while navigating toward transformation. Kevin stresses the importance of partnerships with technical AI experts, which are becoming more abundant and effective every day. "You really do need a good guide to get you there," he says.
This is because AI isn’t plug-and-play for enterprises. Every deployment has to be customized to the organization’s workflows, and even within the same industry, no two companies are alike because they have different processes.
That’s why WRITER takes a consultative approach, working closely with enterprises. "We understand their business at the deepest level. We do the use case mapping. We’re like a part of their team in many cases. That’s the way we’ve been able to be successful," Kevin says.
> "We understand their business at the deepest level. We do the use case mapping. We’re like a part of their team in many cases. That’s the way we’ve been able to be successful."
>
> — Kevin Chung
By handling the heavy lifting, WRITER frees internal teams to focus on what they know best: change management, adoption, and empowerment. This way, enterprises can chart a course for change without losing momentum.
### A proven playbook for trusted enterprise AI
At a time when most AI projects are failing, Kevin Chung and WRITER have established a playbook that delivers reliable results. The difference isn’t cutting-edge models or massive datasets—it’s well-supported people and processes. When motivated employees have both the mission and the backing to push forward, AI projects gain the traction they need to succeed.
Kevin’s playbook comes from recognizing a familiar pattern in the AI space, one he first observed during his career in enterprise technology, including mobile payments at Google and cloud storage at Dropbox. The pattern? Savvy enterprises didn’t try to build the groundbreaking technology themselves. Instead, they waited until it was enterprise-ready and then focused on integrating it effectively. He realized the same principle applied to AI.
WRITER’s own evolution mirrors this lesson. "We started as a niche translation tool," Kevin recalls. "But enterprises needed more—they needed AI that could work across functions. That’s how WRITER grew into a full-stack AI agent platform." By stepping in to handle the technical complexities of full-stack AI implementation, WRITER enables organizations to play to their strengths and position themselves for lasting success.
In the end, rather than trying to reinvent the wheel, Kevin encourages organizations to focus foremost on change management and leave the rest to expert partners. Upholding this conventional enterprise dynamic in the evolving AI space has enabled WRITER to turn numerous AI projects into operational solutions that deliver real results. Kevin knows others can do the same.
Catch the full conversation with Kevin Chung on MindMakers to hear more lessons from WRITER’s AI playbook—and discover how your company can be part of the 5% that succeed.
Want to learn about delight.ai's enterprise-ready AI agents? You can [contact sales](https://delight.ai/contact-sales).
- [Fixing the last-mile problem in enterprise AI](#section-anchor-id-0)
- [Treat AI like a new hire, not software](#section-anchor-id-1)
- [From pilots to productivity](#section-anchor-id-2)
- [A proven playbook for trusted enterprise AI](#section-anchor-id-3)
### Keep reading
[View more](/resources "View more")
- [The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
- [Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
- [The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## How to scale healthcare AI without risking patient data
Source: https://delight.ai/podcasts/mindmakers-podcast-pelu-tran
- [Podcasts](https://delight.ai/podcasts)
- How to scale healthcare AI without risking patient data
## How to scale healthcare AI without risking patient data
[Watch now](https://www.youtube.com/watch?v=k-8SPABZtCY "View Video")
March 9 2026
**Listen to the MindMakers Podcast:** [](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
AI in healthcare is uniquely promising, offering gains not just in operational efficiency, but also in human health and longevity. Despite this, AI adoption lags far behind industries like retail, finance, and customer service. According to a Federal Reserve Bank survey, [44% of metro-area hospitals use AI](https://www.stlouisfed.org/on-the-economy/2025/jul/use-ai-health-care-workplace-us-experience#:~:text=On%20a%20national%20level%2C%2043.9,not-metro-adjacent%20hospitals.), and only 18% of non-metro hospitals do.
For Pelu Tran, CEO and co-founder of [Ferrum Health](https://ferrumhealth.com/), the obstacle isn’t the AI models themselves—but the lack of infrastructure and safety layers required to make them reliable. “AI tends to move fast and break things,” he says. “But in healthcare, the AI can’t break. Not if you’re trying to find cancer or follow up on urgent findings when lives are at stake.”
After more than a decade deploying AI in highly-regulated clinical environments, Tran has seen what works—and what doesn’t. On this episode of MindMakers, Sendbird CEO [John Kim](https://www.linkedin.com/in/doshkim) sits down with [Pelu Tran](https://www.linkedin.com/in/pelutran/) to unpack what it takes to drive adoption and move healthcare AI to production safely.
### Safety and security slow AI adoption in healthcare
As a Stanford-trained physician turned founder, Pelu Tran has spent the last decade building AI platforms for clinical use. He’s clear on why adoption lags. “Healthcare is one of the most conservative, complex, and slow-moving industries,” he says. “We’re still on pagers and fax machines in a lot of this industry.”
In healthcare, AI failure isn’t just costly or non-compliant—it’s dangerous. Wary of both regulatory exposure and patient risk, hospitals are hesitant to send population-scale patient data to early-stage startups. For this reason, the biggest blockers to adoption are data security, HIPAA compliance, and patient safety.
“The mantra [in healthcare] is 'do no harm'—so flashy demos don’t work.” Since trust, compliance, and reliability features aren’t optional, every AI tool must clear a high bar before it can be deployed. Implementing these safety layers slows adoption, but also serves as essential safeguards for the patients that AI is ultimately meant to help.
### Clinicians push ahead, leaders pull back
The uncomfortable truth is that many doctors are already using AI tools like ChatGPT—often without approval—because it saves time and helps patients. For Tran, blocking AI use is unrealistic.
“I’ve spoken to executives whose main goal is to stop doctors from sending patient data to ChatGPT,” he says. “But the truth is, you’ll never stop people from using tech they want to use. The job of hospital AI administrators, IT teams, and clinical leadership is to figure out which tools doctors need to do a good job and enable safe, compliant use.”
Many hospitals are creating roles like Chief AI Officer and forming AI councils to steward adoption. But too often, Tran says, these groups act like blockers—throwing up hurdles, checkpoints, and vetting every tool through endless committees. Instead, their role should be guiding standards and enabling business units to directly adopt tools that solve their most pressing problems. “That’s how hospitals will adopt the right tools faster and more predictably,” Tran says.
> “You’ll never stop people from using tech they want to use. The job of hospital AI administrators, IT teams, and clinical leadership is to figure out which tools doctors need to do a good job and enable safe, compliant use.”
>
> — Pelu Tran, CEO and co-founder of Ferrum Health
### The regulatory paradox: Both accelerator and brake
For a sector bound to a fast-evolving AI regulatory landscape, Tran sees encouraging signs of progress. “In 2018, there were about 70 FDA-cleared AI tools,” he says. “Today, there are over 1,000.”
While acknowledging this rapid acceleration at the federal level, Tran says the momentum comes with a caveat: nearly one-third of U.S. states are introducing AI laws around bias, transparency, and safety. This leaves organizations caught in the middle—with federal regulators pushing for faster adoption, while state governments raise the bar for AI compliance.
The result is a paradox: regulation is both driving AI adoption and slowing it, depending on where organizations operate. To move forward, healthcare leaders need more than compliance, but standardized AI governance and infrastructure that make it safe to deploy AI at scale.
### For startups: Find where AI delivers value first
In Tran’s experience, the healthcare sector is “expert mode” for startups. Hospitals see hundreds of AI tools pitched every year, but can only realistically adopt two or three. “Unless you’re solving a top-three problem for a health system, you won’t scale,” he cautions. “Founders need brutal honesty. Your problem might be important to you, but unless it’s a priority for them, adoption won’t happen.”
He sees immediate opportunity in creating provider efficiency: addressing physician shortages, reducing administrative burden, automating low-value tasks, and coordinating care across complex systems. Hospitals can’t afford to add more administrators, so “AI has to step in to automate low-value tasks, coordinate care, and free providers to focus on patients.” Rather than focus on flashy diagnostic tools, startups should target enduring pain points. By aligning with healthcare’s incremental pace and delivering practical wins, startups will find the sweet spot for adoption today while building lasting trust with responsible, [enterprise-grade AI](https://delight.ai/enterprise-grade).
> “Unless you’re solving a top-three problem for a health system, you won’t scale.”
>
> — Pelu Tran, CEO and co-founder of Ferrum Health
### The Ferrum Health approach: Separate models from patient data
Progress in a highly regulated sector like healthcare isn’t about disruption, but rather incremental transformation. That’s why Tran compares the industry's adoption to a "Maslow’s hierarchy of AI needs."
Today, most hospitals are at the bottom: fragmented pilots scattered across departments, deployed without oversight. But for AI to be widely adopted as a strategic capability for a hospital or healthcare system, multiple layers of scalable enterprise infrastructure and an AI stack must be built in. To climb higher, organizations need three core layers:
1. Standardized onboarding for AI models
2. A common architecture for secure deployment
3. A unified intelligence layer for observability and oversight
Ferrum’s approach also involves decoupling AI models from patient data, helping hospitals move up the hierarchy at their own pace without risk. This involves:
- **A secure model hub** firewalled from patient data where vendors can publish and refine AI models.
- **A local deployment fabric** (cloud, hybrid, or on-premises) that runs models inside hospital systems—without sending patient data out.
“We separate model innovation from security,” Tran says. “Think of it like the App Store for healthcare AI.” This approach allows hospitals to retain control of sensitive data while enabling vendors to innovate and scale their models safely within the system. Combined with standardized onboarding and unified data, Ferrum Health gives hospitals the ability to deploy AI without compromising compliance, privacy, or patient trust.
### Slow, separate, secure: The path to trusted AI in healthcare
Currently, healthcare AI is held back not by models themselves but by a lack of proper infrastructure, governance, and leadership. Flashy demos may win headlines, but hospitals need safe, scalable systems that prioritize patient privacy and trust. AI startups must recognize these realities if they're to win adoption.
Ferrum’s strategy—separating AI models from patient data, standardizing onboarding, and building a unified layer of oversight and governance—shows a clear path to how AI in healthcare can move beyond pilots, without compromising compliance or safety.
To hear more lessons from Ferrum’s AI playbook, catch the full conversation with [Pelu Tran on MindMakers](https://www.youtube.com/watch?v=k-8SPABZtCY)—and see how your organization can move AI pilots to production while ensuring secure, responsible innovation that puts patients first.
Want to learn about delight.ai’s enterprise-ready AI agents? You can [contact sales](https://delight.ai/contact-sales).
- [Safety and security slow AI adoption in healthcare](#section-anchor-id-0 "Safety and security slow AI adoption in healthcare")
- [Clinicians push ahead, leaders pull back](#section-anchor-id-1 "Clinicians push ahead, leaders pull back")
- [The regulatory paradox: Both accelerator and brake](#section-anchor-id-2 "The regulatory paradox: Both accelerator and brake")
- [For startups: Find where AI delivers value first](#section-anchor-id-3 "For startups: Find where AI delivers value first")
- [The Ferrum Health approach: Separate models from patient data](#section-anchor-id-4 "The Ferrum Health approach: Separate models from patient data")
- [Slow, separate, secure: The path to trusted AI in healthcare](#section-anchor-id-5 "Slow, separate, secure: The path to trusted AI in healthcare")
### Keep reading
[View more](/resources "View more")
- [](https://delight.ai/webinars/trust-stack) [The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
- [](https://delight.ai/ebooks/ai-agents-for-home-services) [Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) [The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) [Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## From models to orchestration: How Moveworks built an enterprise AI platform that works
Source: https://delight.ai/podcasts/mindmakers-podcast-bhavin-shah
- [Podcasts](https://delight.ai/podcasts)
- From models to orchestration: How Moveworks built an enterprise AI platform that works
## From models to orchestration: How Moveworks built an enterprise AI platform that works
[Watch now](https://www.youtube.com/watch?v=prI3VrsFbqE)
March 9 2026
**Listen to the MindMakers Podcast:** [  ](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&nd=1&dlsi=12d80f92d67a4c70) [  ](https://embed.podcasts.apple.com/us/podcast/mindmakers-podcast/id1840021178) [  ](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
At a time when [95%](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/) of GenAI projects fail, Bhavin Shah, co-founder and CEO of Moveworks, defied the odds—building an [enterprise AI agent platform](https://delight.ai/enterprise-grade) now set to be acquired by ServiceNow for $2.85 billion.
It all began with a narrow goal: to resolve IT tickets for enterprises faster. But after listening to customers, Shah saw a bigger pain point—employees needed a way to quickly find information, complete tasks, and act across systems without friction.
This insight reshaped [Moveworks](https://www.moveworks.com/) from a single-use tool into a company-wide AI orchestration layer that brings consumer-grade speed and simplicity to enterprise workflows. While conversational AI has been widely adopted, few have made it enterprise-ready. “It’s not just about answering questions,” he says. “It’s about taking actions that actually finish the job.”
On the latest episode of MindMakers, Sendbird CEO [John Kim](https://www.linkedin.com/in/doshkim) sits down with [Bhavin Shah](http://linkedin.com/in/bhavinnicholasshah) to unpack how he navigated Moveworks’ evolution from startup to scale to acquisition. You’ll learn:
- The difference between “weak ROI” and “strong ROI” in AI
- How to build AI that wins trust through reliable execution
- Why a good night’s sleep is the #1 productivity hack
## The opportunity: Bring consumer speed to enterprise AI
Shah was inspired to pivot after realizing that consumer technology was racing ahead while enterprise systems lagged far behind. “If I can hail a car in two minutes, why can’t I reset my multi-factor authentication just as fast at work?” he laughs.
In the era of Uber, DoorDash, and WhatsApp, Shah realized that enterprise employees expected the same fast, seamless experiences at work, but enterprise systems weren’t up to it. Reflecting on this broader shift toward short-form, real-time, convenient communications, Shah saw an opportunity to bring consumer-grade speed and simplicity to enterprise AI workflows.
“We became the front door for employees—one place to search, act, and get work done,” he says. The company’s AI agent assistant now integrates across Slack, Teams, and the web, using natural language understanding (NLU) to retrieve data, complete forms, reset passwords, or initiate workflows across enterprise systems such as SAP, ServiceNow, and Workday.
“We saw that gap and decided to close it,” he says.
## The LLM inflection point
Before Moveworks pivoted, Shah and his team had already built domain-specific models for IT and HR that could resolve tickets and handle employee requests with precision. Then came ChatGPT in 2022, and large language models (LLMs) changed everything.
“Overnight, we could handle every question across every department,” he recalls. The LLMs gave Moveworks a single, generalized AI brain capable of understanding any function, any role, and any request. What started as an IT assistant was now becoming a unified intelligence layer that could access data and act across once-siloed systems using natural language. The Moveworks mission was no longer just about support, but transforming how work gets done.
It was the start of a new path for Shah, one with fresh challenges. “The LLMs became the brain—but we still needed to build the arms and legs that actually do the work across enterprise systems.”
## The messy middle: AI infrastructure is the real “magic”
While many AI startups chase elegant use cases, Shah describes enterprise AI as “squarely in the messy category.” To him, success isn’t about building a single effective model, but making hundreds of systems talk to each other reliably.
In his view, this need for deep integration and orchestration is what separates enterprise AI from consumer AI. AI’s magic is rooted in the infrastructure that ensures consistent execution at scale, making systems run 24/7 across departments for thousands of users.
To achieve this, Moveworks built its platform from the inside out: custom AI agent-ready APIs, reliable data pipelines, and end-to-end [AI integrations](https://delight.ai/integrations) that reliably turn AI answers into trusted actions. Unlike plug-and-play AI tools suitable for consumer brands, enterprises demand consistency, reliability, and measurable outcomes, or procurement teams will call you out.
“If you can’t deliver value over and over again,” he says, “after a year or two, the procurement team comes to you and says, ‘Hey, you didn’t deliver what you promised.’” Initial delight only matters if the AI is reliable in production.
## Product-market fit: Earning the right to scale
In the fast-moving world of AI, product-market fit is less of a milestone and more of a moving target for Shah. “You reach it for a moment, then everything from competitors, markets, expectations all changes,” he says.
He credits Moveworks’ traction to its consistent execution and clear metrics: win rates above 40%, sales reps closing at 110% of quota, and one in four new deals coming from referrals.
“Vision doesn’t grant scale, delivery does,” Shah says. “When your customers start referring you, that’s when you know you’re hitting real pain and solving for it.” It’s a reminder that in enterprise AI, success isn’t about hype or model size. It’s about having internal trust in results and employee uptake.
> “Vision doesn’t grant scale—delivery does. When your customers start referring you, that’s when you know you’re hitting real pain and solving for it.”
>
> — Bhavin Shah, Co-founder and CEO, Moveworks
## Weak vs. strong ROI
Shah draws a sharp line between incremental productivity gains and true AI transformation, or what he refers to as the difference between weak ROI and strong ROI.
“Weak ROI leaves when the employee leaves, taking their domain knowledge with them,” he explains. “Strong ROI changes how the business runs; it’s transformative at a deep level.” [Research from McKinsey](https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/successful-transformations) confirms that end-to-end workflow transformation is one of the strongest predictors of lasting ROI with enterprise technology, AI included.
In Shah’s view, most failed AI projects suffer from “DIY syndrome”—enterprises trying to piece together vector databases, custom APIs, and models without the necessary integrations or expertise. The result is brittle systems that don’t scale.
His advice is simple but pointed: build a strong foundation with trusted vendors and proven infrastructure, as is common in transformational periods. “You can’t Home Depot your way to enterprise AI,” he says. “You need the right platforms, the right partners, and the right level of execution.”
> “You can’t Home Depot your way to enterprise AI. You need the right platforms, the right partners, and the right level of execution.”
>
> — Bhavin Shah, Co-founder and CEO, Moveworks
## Frothy markets vs flexible foundations
In today’s AI economy, where startups can go from zero to $100M ARR in just 18 months, Shah acknowledges that speed matters, but so does staying power. “We’re in the era of popcorn growth: up fast, down faster, with few true winners.”
Some call it an AI bubble while others say it’s the new normal. For Shah, the strategy is the same: learn fast, move fast, and build for change ahead. “You learn in two years what used to take five,” he says. “The upside is, you find out a lot faster whether your idea is real.”
This mindset guided the Moveworks evolution. Rather than chasing every new trend, the company invested in flexible foundations for AI—prioritizing interchangeable foundation models, multimodal inputs, stable system interconnects, and consistent quality of service across integrations. In a volatile market, Shah argues, long-term winners are the ones who focus on durability, consistency, and compounding outcomes over short-term hype.
## The importance of leadership, execution, and good sleep
Shah closes by saying that what truly scales AI isn’t just speed, but execution, reliability, and responsibility. The future of AI, he says, won’t be won by those who simply move and break things, but by companies that deliver reliably. His secret to being an effective leader and navigating Moveworks to a multibillion-dollar acquisition? Good sleep.
“If I sleep well, I’m a better father, husband, manager, and listener,” he says. It’s a metaphor for the philosophy that brought Moveworks to acquisition: in a world of complex and evolving technologies, the fundamentals of human systems—rest, clarity, execution—are still the backbone of progress and performance. The same focus on fundamentals is essential to building AI systems that are enterprise-ready.
Listen to the full episode of *MindMakers* to hear Shah’s and Moveworks’ playbook for building enterprise AI that performs—and what separates the short-lived AI tools from what truly sticks and scales.
- [The opportunity: Bring consumer speed to enterprise AI](#section-anchor-id-0)
- [The LLM inflection point](#section-anchor-id-1)
- [The messy middle: AI infrastructure is the real “magic”](#section-anchor-id-2)
- [Product-market fit: Earning the right to scale](#section-anchor-id-3)
- [Weak vs. strong ROI](#section-anchor-id-4)
- [Frothy markets vs flexible foundations](#section-anchor-id-5)
- [The importance of leadership, execution, and good sleep](#section-anchor-id-6)
## Keep reading
[View more](/resources)
[ The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
[ Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
[ The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
[ Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources)
## Why data unification is crucial to trusting enterprise AI agents
Source: https://delight.ai/podcasts/mindmakers-podcast-manish-sood
## Why data unification is crucial to trusting enterprise AI agents
[Watch now](https://www.youtube.com/watch?v=8eR9M-9bzas "View Video")
**March 9 2026**
**Listen to the MindMakers Podcast:** [](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
When Manish Sood, founder and CEO of [Reltio](https://www.reltio.com/), asked his enterprise customers what they wanted from AI transformation, the majority said they wanted true agentic AI systems that could reason, act, and adapt in real time. “Seventy-five percent of what customers now want wasn’t solvable with prior-gen SaaS software,” says Sood. “It requires an agent’s ability to reason and make decisions.”
This shift from traditional automation to decision-based agentic workflows marks the dawn of a new era in what enterprise software can solve for. But in Sood’s eyes, unlocking this business advantage requires more than deploying an AI workforce. Success begins with the data that AI agents rely on to operate effectively. Without a foundation of quality, context-rich data, their autonomy invites chaos.
In this episode of MindMakers, Sendbird CEO [John Kim](https://www.linkedin.com/in/doshkim) sits down with [Manish Sood](http://linkedin.com/in/manishsood) to unpack why data-first architecture is vital to scaling and managing agentic AI you can trust—and what enterprise leaders get wrong about AI transformation. You’ll learn:
- Why success with agentic AI begins with data readiness and unification.
- How Reltio built a bridge from data integrity to scalable enterprise AI agents.
- How Liberty Mutual cut its call response times from seconds to milliseconds with unified agent-ready data
## The challenge: Fragmented data vs AI agent decisioning
Unlike traditional IT, AI agents can’t operate in a silo. To be effective, they need to access, interpret, and act on live data from multiple systems—CRM, support desk, logistics—all at once.
> “Real-time, unified context is the difference between a generic action or a precise action that’s effective in the real world,” says Sood.
However, when he founded Reltio over a decade ago, Sood encountered a problem that no analytics dashboard could fix: data chaos. Since the start of the SaaS era, he says, enterprises have gone from three or four core systems to an average of 300–600 disconnected software tools.
In expanding their tech stacks, organizations inadvertently fragmented their view of customers, products, and operations. Marketing, Sales, and Compliance each had their own slice of truth, but no one had the whole picture. What used to simply result in operational drag has evolved into a critical barrier to deploying real-time, decision-making [enterprise AI agent](https://delight.ai/enterprise-grade) systems.
## The solution: Data-first agentic AI architecture
Reltio was founded to solve this enterprise problem of data and software fragmentation. Its core mission: to unify every data source across the enterprise into a single, trusted foundation that all systems can use.
Thanks to the modular design of agentic AI, it’s now possible to create a unified, data-first ecosystem where agents operate securely and effectively at scale. Rather than feeding models inconsistent or incomplete information, data-first systems ground the reasoning of agents in a single source of truth. When every decision and action starts with the same verified context, organizations can scale AI that’s accurate, safe, and trustworthy.
> “We’re moving from application-first to data-first architecture,” Sood says. “Instead of many silos serving the data, there’s a shift to one unified data layer serving many processes.”
At Liberty Mutual, for example, Reltio connected hundreds of disjointed systems into a unified customer view. What once took 5–8 seconds to resolve at the insurer’s call center now happens in around 300 milliseconds. This same infrastructure now powers AI agent–driven call scripts, next-best actions, and personalized cross-sells—all generated in real time and aligned with broader business goals and customer experience (CX) standards.
By unifying data for AI agents, Sood and Reltio not only created the foundation that all high-performance agentic systems rely on—they delivered the 360° customer view that CX teams have chased after for so long.
## The AI adoption playbook: Top-down meets bottom-up
AI adoption in the enterprise isn’t just a matter of a technology overhaul, in Sood’s view, but of bold leadership. He believes success emerges when top-down urgency is paired with bottom-up enablement that meet in the middle to create quality products and ROI from AI development.
This means embedding AI engineers across departments to drive practical experimentation, helping teams discover what AI can automate, enhance, or reimagine in their daily workflows.
> “Give every team an AI engineer and an AI-first mandate,” he says. “Rethinking these core processes from an AI-first perspective is key to reinventing how each function works.”
This isn’t about replacing people, but about scaling agents’ impact exponentially through agentic systems that never stop learning or executing.
The C-suite’s role, Sood says, is to set the vision and inspire confidence. Yet many leaders remain hesitant, worried about job displacement, hallucinations, or loss of control over their digital workforce. In doing so, they risk missing the far larger opportunity ahead. Grounding AI in a trusted system of context and unified data is that key step that enables organizations to produce precise, accurate, and scalable AI outcomes—turning uncertainty into measurable ROI.
## Velocity is the real moat
Once organizations feel they can trust AI, the key differentiator becomes velocity. In this new landscape, Sood says, what separates winners from losers isn’t their coding, compute power, or model choice. It’s their time to value.
> “Models will commoditize over time,” says Sood. “Time-to-outcome is what will become the moat.”
To deliver faster, Reltio has adopted a forward-deployed engineering model: embedding small, hands-on teams with customers to co-create solutions before productizing them. As with Liberty Mutual, these forward-deployed teams focus on the proof of concept (POC) first, prove value, then move to productize.
Delight.ai takes a similar approach with its AI concierge for CX and support, collaborating directly with customers to deploy AI agents that are safe, observable, and enterprise-grade from day one.
## The future: Category consolidation and the UI of AI
Sood foresees a coming wave of AI platform consolidation and pricing model transformation. The per-seat SaaS model that was built for human users will give way to per-agent, compute, or transaction-based pricing.
> “Per-seat pricing will fade. Expect compute, per-agent, or transaction-based pricing models in the future,” says Sood.
He also warns against long-range planning in an era where traditional innovation cycles are proving cumbersome. Rather than planning in five to ten-year horizons, stay more agile by looking two to three years ahead to better align with the state of the AI market.
As data unification and agentic architectures mature, Sood sees the next shift being how humans interact with AI-powered software. In this future, employees won’t navigate complex dashboards; they’ll simply ask for information, and the AI will generate the most relevant visualization or response in the moment. The user interface (UI) of AI itself will become dynamic and personalized, offering a visual reflection of agentic reasoning in action behind the screen.
## Data-first AI = Customer-first AI
In Sood’s view, AI isn’t just another tool—it’s a complete shift in architecture and emphasis for enterprises. The success of Reltio’s playbook in unifying enterprise data for agentic AI operations suggests that tomorrow’s AI winners will be those who turn data readiness and unification into performance today, forging a stable foundation from which to innovate and expand into the agent-driven future.
For delight.ai, this conversation reinforces a core truth: trusted AI for CX depends on a dual foundation of unified data and trust. Without a core of unified context and transparent AI governance, enterprises will struggle to demonstrate value, validate performance, and productize AI profitably.
> “Agentic CX is the combination of unified data plus effective governance. This is the only way teams can know—not just hope—that their agents are safe, secure, and trustworthy.”
>
> —John Kim
Listen to the full episode of *MindMakers* with Manish Sood of Reltio to learn how data-first architectures are shaping the next wave of enterprise AI.
Ready to discover how delight.ai enables data-first architectures for agents with our [Trust OS governance framework](https://delight.ai/blog/product/introducing-trust-os-for-ai-agents) and enterprise-grade AI infrastructure? Just [contact sales](https://delight.ai/contact-sales).
- [The challenge: Fragmented data vs AI agent decisioning](#section-anchor-id-0 "The challenge: Fragmented data vs AI agent decisioning")
- [The solution: Data-first agentic AI architecture](#section-anchor-id-1 "The solution: Data-first agentic AI architecture")
- [The AI adoption playbook: Top-down meets bottom-up](#section-anchor-id-2 "The AI adoption playbook: Top-down meets bottom-up")
- [Velocity is the real moat](#section-anchor-id-3 "Velocity is the real moat")
- [The future: Category consolidation and the UI of AI](#section-anchor-id-4 "The future: Category consolidation and the UI of AI")
- [Data-first AI = Customer-first AI](#section-anchor-id-5 "Data-first AI = Customer-first AI")
## Keep reading
[View more](/resources "View more")
[](https://delight.ai/webinars/trust-stack)
The Trust Stack: How to design an AI agent that earns its autonomy
[](https://delight.ai/ebooks/ai-agents-for-home-services)
Your playbook for home services AI agents that book, coordinate, and close the loop
[](https://delight.ai/webinars/delight-ai-index-2026-webinar)
The Delight AI Index 2026: The trust paradox reshaping CX
[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## How Augment Code is redefining developer velocity with AI
Source: https://delight.ai/podcasts/mindmakers-podcast-guy-gur-ari
## How Augment Code is redefining developer velocity with AI
[Watch video](https://www.youtube.com/watch?v=-9DkRTETRqQ "View Video")
March 9 2026
**Listen to the MindMakers Podcast:** [](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
AI has already changed how developers write code—but the next leap forward isn’t about generating more lines. It’s about understanding context at scale.
In this episode of *MindMakers,* Augment Code Co-Founder [Guy Gur-Ari](https://www.linkedin.com/in/guy-gur-ari/) chats with Sendbird CEO [John Kim](https://www.linkedin.com/in/doshkim/) about why developer velocity isn’t about typing speed but comprehension—and how agentic systems that reason across entire codebases are redefining enterprise engineering.
From feature builds to incident response, Guy shares how Augment’s context engine helps teams move beyond code completion into autonomous workflows—and why success with AI depends as much on behavioral change as on technology.
### What developers actually want from AI
When Guy and his team began exploring how AI could boost productivity, they noticed something surprising: engineers weren’t asking for more code—they were asking for more understanding.
> "When we started Augment, we realized developers don’t need another autocomplete plugin. They need something that understands their codebase and can reason about it intelligently."
>
> — Guy Gur-Ari, Co-Founder, Augment Code
Traditional coding tools predict the next line, but rarely grasp how everything connects. True velocity, Guy says, comes from reducing context switching.
> "Our model understands structure—it knows how files connect, what calls what. When it changes something, it’s aware of the consequences across the system."
Augment’s context engine helps AI agents navigate multi-million-line codebases across teams of hundreds or thousands—managing dependencies and architecture in real time.
That level of comprehension turns AI from a helper into a collaborator—one that understands not just what’s written, but how everything fits together.
Every company, Guy notes, has unwritten rules—naming patterns, file structures, problem-solving habits. Capturing that "vibe" is what separates a true AI teammate from a generic tool. Context isn’t just technical; it’s cultural.
### Beyond assistance: How agentic systems redefine developer velocity
This evolution from code prediction to intelligent collaboration marks the rise of agentic systems—AI that doesn’t just complete tasks, but understands purpose and scope.
> "This isn’t theoretical anymore. We already have customers where agents open and close tickets, debug, analyze logs, even respond to incidents," Guy says.
These agents now manage entire workflows—accelerating development, testing, and incident response while reducing cognitive load for human engineers.
As AI generates and tests code faster than humans can review it, enterprises face a new limit: the code review bottleneck. Augment’s agents help close that gap by validating, debugging, and refining work in context.
Velocity is no longer bound by human attention—it’s multiplied by machine understanding.
### The human shift: From coders to conductors
Even with these advances, Guy notes that adoption isn’t just a tooling challenge—it’s a behavioral one.
> "Developers love typing—it’s tactile. It’s muscle memory. Asking them to stop and instead guide an agent—it’s unnatural, and it takes a while to adjust."
Moving from writing code to orchestrating intelligent systems asks developers to think differently—less about syntax, more about intent and communication.
> "Prompting is communication. The clearer you are, the better the model performs. It’s like leading a team—if you’re vague, you’ll get vague results."
Tomorrow’s best engineers won’t just write code; they’ll lead collaborative systems. Developer velocity becomes less about output and more about clarity, orchestration, and shared context.
### Building trust in autonomy
As AI takes on more responsibility, trust becomes the true measure of readiness. Transparency, oversight, and clear permissions ensure that autonomy builds confidence—not risk.
> "When the agent gets access to more things—codebases, production, data—the risks rise. You need permissions, controls, guardrails. Without that, you’re taking big risks."
For Guy, governance isn’t bureaucracy—it’s infrastructure. The more autonomy an agent has, the stronger the foundation of visibility and accountability must be.
Trust doesn’t slow innovation; it sustains it.
### The next horizon: Customizable software and the agent experience
Looking ahead, Guy believes the falling cost of software creation will reshape how we build entirely.
> "As the cost of code generation goes to zero, do we still live in a world where you need one company to build an app for millions of users to use, or can it become a lot more customizable? Maybe I can now create my own apps, or maybe take some sort of template of an app, but do a lot of customization on it to suit exactly my needs."
If generating software becomes effortless, the challenge shifts from building for the masses to enabling individual customization.
> "Right now there are all these walls between apps—I can’t easily connect my emails to my calendar or my logs in production to the code. They’re all living in silos. A term I really like that’s starting to come up is ‘AX,’ or agent experience—building your product not just for humans, but to be really easy for agents to use."
This idea of agent experience reimagines software as a connected ecosystem—where humans and agents collaborate seamlessly. It’s a natural extension of Augment’s mission: making software not just faster, but smarter and more adaptive.
### The leadership lesson: Adoption takes work
Even with all this progress, Guy reminds us that transformation isn’t just technical—it’s human.
> "It’s rarely the tech that fails. It’s the change management. Tools work—the hard part is helping people change their habits."
Organizations that succeed will treat adoption as a cultural evolution, not a rollout. Helping teams work confidently with AI means leading with empathy and patience.
Because real innovation happens when technology and people grow together.
Listen to the full conversation with Guy Gur-Ari, Co-Founder of Augment Code, on *MindMakers* to hear how contextual agents are transforming how enterprises build, test, and ship software.
Ready to explore delight.ai's agent-ready infrastructure? We can break down the silos between all of your systems, so that our AI agents have all the data and context they need to support your customers. [Contact sales](https://delight.ai/contact-sales) to learn more.
- [What developers actually want from AI](#section-anchor-id-0 "What developers actually want from AI")
- [Beyond assistance: How agentic systems redefine developer velocity](#section-anchor-id-1 "Beyond assistance: How agentic systems redefine developer velocity")
- [The human shift: From coders to conductors](#section-anchor-id-2 "The human shift: From coders to conductors")
- [Building trust in autonomy](#section-anchor-id-3 "Building trust in autonomy")
- [The next horizon: Customizable software and the agent experience](#section-anchor-id-4 "The next horizon: Customizable software and the agent experience")
- [The leadership lesson: Adoption takes work](#section-anchor-id-5 "The leadership lesson: Adoption takes work")
### Keep reading
- [](https://delight.ai/webinars/trust-stack) [The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
- [](https://delight.ai/ebooks/ai-agents-for-home-services) [Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) [The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) [Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## eBook: Leading the AI agent leap in customer service
Source: https://delight.ai/ebooks/ai-customer-service-transformation-guide
## eBook: Leading the AI agent leap in customer service
[Download ebook](#download-ebook "Download ebook")
### What every customer support leader should know before diving into AI
AI is becoming essential to modern customer service—but successful implementation is harder than it looks. While the promise is clear—faster support, greater scale, better experiences—most initiatives stall in pilot mode or erode customer trust.
This ebook offers a practical roadmap for support leaders ready to move from experimentation to impact. Learn how to build AI agents you can trust, avoid common pitfalls, and scale with confidence.
Download this ebook to learn:
- How to move beyond pilot purgatory
- What makes an AI agent enterprise-ready
- How to turn unpredictable AI behavior into consistent, measurable performance
- Strategies to ensure safety, compliance, and reliability at scale
- How leading brands like Redfin and Lotte Homeshopping made AI work in production
### Leading the AI agent leap in customer service
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources "View more")
- [](https://delight.ai/webinars/trust-stack) The Trust Stack: How to design an AI agent that earns its autonomy
- [](https://delight.ai/ebooks/ai-agents-for-home-services) Your playbook for home services AI agents that book, coordinate, and close the loop
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) Why memory is AI's most critical bottleneck
[View more](/resources "View more")
## eBook: Say goodbye to these 8 support problems—thanks to agentic AI
Source: https://delight.ai/ebooks/how-agentic-ai-solves-eight-support-problems
## eBook: Say goodbye to these 8 support problems—thanks to agentic AI
[Download ebook](#download-ebook "Download ebook")
March 9 2026
Customer support doesn’t have to be a game of whack-a-mole for your team and a never-ending frustration for your customers.
Repetitive inquiries, long wait times, and rising ticket volumes aren’t just inefficient—they’re expensive. There’s a better way.
Agentic AI is transforming customer support from a reactive, resource-heavy function into a proactive, strategic engine for customer satisfaction and operational efficiency. By combining real-time knowledge retrieval, intelligent action execution, and natural conversational engagement, enterprise-grade **AI agents can resolve up to 70% of tickets at first contact (FCR).**
The result? Faster, more satisfying support, at a lower cost.
In this ebook, you’ll learn how agentic AI helps enterprises:
- Reduce support workload and burnout
- Automate repetitive and complex workflows
- Deliver 24/7, specialized, and multilingual support across multiple communication channels
- Turn support insights into product and CX improvements
## Say goodbye to these 8 support problems—thanks to agentic AI
Discover how to move from adoption to transformation. Free to download. No spam.
## Keep reading
[View more](/resources "View more")
- [](https://delight.ai/resources/ai-readiness-101-webinar) [AI readiness 101: Close the gap between AI hype and real results](https://delight.ai/resources/ai-readiness-101-webinar)
- [](https://delight.ai/resources/spark-resource) [Join us May 7, where we'll unveil what's next in AI-powered customer experience.](https://delight.ai/resources/spark-resource)
- [](https://delight.ai/resources/mindmakers-podcast-cody-coleman) [Why multimodal data is the next enterprise AI advantage](https://delight.ai/resources/mindmakers-podcast-cody-coleman)
- [](https://delight.ai/resources/mindmakers-podcast-jae-lee) [Beyond text: Why video AI is the future of enterprise intelligence](https://delight.ai/resources/mindmakers-podcast-jae-lee)
[View more](/resources "View more")
## eBook: 5 key questions to vet an AI agent platform
Source: https://delight.ai/ebooks/key-questions-to-vet-an-ai-agent-platform
## eBook: 5 key questions to vet an AI agent platform
[Download ebook](#download-ebook)
How to separate real AI platforms from vendor promises.
Every vendor says their AI agent is fast, smart, and scalable. But how do you know which platform can actually deliver in the real world at enterprise scale?
This short guide gives you the **five most important questions** every CX leader should ask before selecting an AI agent platform. Whether you’re just exploring or building a shortlist, this framework will help you evaluate trust, transparency, and performance beyond the buzzwords.
**You’ll learn how to assess:**
- Real-world testing and validation capabilities
- Platform transparency and traceability
- Update control and governance
- Recovery protocols and fallbacks
- Global scalability and compliance
Download the guide and choose your next AI platform with clarity and confidence.
### 5 key questions to vet an AI agent platform
Discover how to move from adoption to transformation. Free to download. No spam.
### Keep reading
[View more](/resources)
### Related resources
- [](https://delight.ai/ebooks/ai-agents-for-home-services) Your playbook for home services AI agents that book, coordinate, and close the loop
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) Why memory is AI's most critical bottleneck
- [](https://delight.ai/webinars/spring-product-update) From AI that answers to AI that owns: The delight.ai spring product update
[View more](/resources)
## Building for scale: Organizational best practices for AI-first customer experience
Source: https://delight.ai/ebooks/building-for-scale
## Building for scale: Organizational best practices for AI-first customer experience
[Download white paper](#download "Download ebook")
### Most AI agent programs plateau before they scale. The difference is how you build the team around them.
Scaling AI agents is less a technology problem and more an organizational one. The companies that move successfully from AI pilot to full production share a common set of practices: a dedicated AI Manager, a tight core team across Product, Engineering, and Operations, and a formal governance structure built before it's urgently needed.
This white paper synthesizes Delight.ai's AI management experience across hundreds of enterprise deployments — including a real-world [case study from Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways), one of the first airlines to formally define the AI Manager role — into a practical framework for building the team, structure, and culture your AI agent program needs to scale.
**In this white paper you will learn:**
- Why the **AI Manager** is the most critical and most commonly missing role in enterprise AI implementation — and what plateau looks like without it
- How to structure your **core team** across Product, Engineering, and Operations for continuous AI agent improvement
- The **three-layer organizational model** every scaled AI program needs: Core Team, Extended Team, and AI Council
- The **six most common gaps** in enterprise AI strategy — from knowledge management failures to measurement mistakes — and how to close them
- How to apply the right structure at every stage of your **AI readiness** journey: pilot, production, and scale
Build the organizational foundation that turns a promising AI pilot into a lasting competitive advantage.
## Building for scale
Free to download. No spam.
### Keep reading
[View more](/resources "View more")
- [](https://delight.ai/resources/spark-resource) Join us May 7, where we'll unveil what's next in AI-powered customer experience.
- [](https://delight.ai/resources/mindmakers-podcast-benny-yufei-chen) The four overlooked requirements to operationalize enterprise AI
- [](https://delight.ai/resources/ai-readiness-101-webinar) AI readiness 101: Close the gap between AI hype and real results
- [](https://delight.ai/resources/mindmakers-podcast-cody-coleman) Why multimodal data is the next enterprise AI advantage
[View more](/resources "View more")
## The four overlooked requirements to operationalize enterprise AI
Source: https://delight.ai/podcasts/mindmakers-podcast-benny-yufei-chen
- [Podcasts](https://delight.ai/podcasts)\n- The four overlooked requirements to operationalize enterprise AI\n\n## The four overlooked requirements to operationalize enterprise AI\n\n\n\nApril 14 2026\n\n**Listen to the MindMakers Podcast:** [](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)\n\nThe AI model market is expanding rapidly: the number of major [AI models](https://delight.ai/glossary/ai-model) released annually has [grown more than 10x since 2019](https://hai.stanford.edu/ai-index/2024-ai-index-report). In the experience of Benny Yufei Chen, co-founder of Fireworks AI, chasing the latest models has become a costly distraction for organizations. As enterprise leaders work to operationalize AI, the bottleneck Chen sees is rarely in model intelligence, but in operational alignment.\n\nAfter years of working on PyTorch infrastructure at Meta, Chen launched [Fireworks AI](https://fireworks.ai/) to help companies run and optimize their large language models (LLMs) without sacrificing adaptability. Having watched hundreds of AI systems move from research into production, he's noticed a pattern: organizations tend to underestimate what it takes to operationalize AI, and often to their detriment.\n\n"We're here to help businesses scale AI sustainably—so they don't scale into bankruptcy," Chen says.\n\nIn this episode of MindMakers, delight.ai CEO John Kim sits down with Chen to unpack what it really takes to scale AI in production, and the four requirements most organizations overlook.\n\n## Requirement 1: Inference efficiency\n\nThe economics of enterprise generative AI are becoming a serious concern. According to Andreessen Horowitz, [model inference costs can account for up to 80–90% of the total cost](https://a16z.com/navigating-the-high-cost-of-ai-compute/) of running large AI systems in production. As usage grows, these costs can outpace the value the system generates. Most organizations can build a good AI pilot, says Chen, but fewer ask whether they can operate it sustainably at scale.\n\nMeanwhile, token consumption is rising fast. Currently, Fireworks AI processes trillions of tokens per day across its thousands of customers, and Chen believes demand could grow dramatically as companies move toward AI-first workflows. What appears like a simple interaction with an [AI agent](https://delight.ai/glossary/ai-agent) can involve dozens of internal reasoning steps and tens of thousands of tokens behind the scenes.\n\n"Inference was always taking the lion's share," he says. As organizations move from experimentation to production, Chen insists they must consider computational efficiency.\n\n## Requirement 2: Model optionality\n\nChen always reminds organizations: today's best model will soon be surpassed. This creates a genuine strategic challenge for leaders: how to pick the right model for now while staying adaptable in a rapidly evolving market?\n\n"It's very hard for buyers to pick which model to go for," Chen says. Committing too deeply to a single provider poses real risks: model switching can mean rewriting [AI prompts](https://delight.ai/glossary/ai-prompts), re-running evaluations, and validating that AI performance hasn't regressed since deployment. This is why Fireworks AI supports a broad portfolio of frontier, proprietary, and [open-source AI](https://delight.ai/glossary/open-source-ai) models, allowing customers to experiment and adapt without unnecessary lock-in.\n\n## Requirement 3: Disciplined AI evaluation\n\nAI systems don’t just need to be scalable and flexible; they also need to be trustworthy. This requires rigorous AI evaluation, yet Chen finds that most organizations skip this step. "When talking with our customers, we find a large majority of them don't have evals," he says. Gartner predicted that [over 60% of generative AI deployments](https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025) would fail to meet expectations due to poor governance and inadequate evaluation, and this prediction is bearing out in 2026.\n\nEvaluation frameworks, such as delight.ai’s readiness framework, establish the feedback loops teams need to test, iterate, and deploy AI safely. Without them, organizations can't answer basic questions: Is the new model actually better? Did a recent update introduce regressions? Is the system improving over time? As AI systems grow more complex, that lack of measurement becomes a significant blind spot, hiding serious risks and costs.\n\n## Requirement 4: Organizational alignment\n\nEven the best-designed AI system will underperform without the right people and processes to support it. The biggest gains from AI come from [redesigning existing workflows around AI automation](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#:~:text=High%20performers%20use%20AI%20to,change%2C%20and%2013%20percent%20increases.), not "bolting it on". However, Gartner research shows [60% of organizational change efforts fail to meet their goals](https://www.gartner.com/en/newsroom/press-releases/2024-10-28-gartner-hr-research-finds-organizations-are-in-the-midst-of-a-reset-most-are-not-prepared), largely because teams underestimate the human and operational work involved.\n\nChen encourages organizations to let AI do its part. While AI can only automate what's been clearly defined by humans in quality documentation, he often reminds companies: "Don't try to put yourself on the hook for repetitive work. Make a bot be on the hook for it." AI lets teams accomplish more in less time, especially when encoding skills and designing AI agents. "It is now much easier to be a 10× engineer than before," Chen says.\n\nThe companies that benefit most from AI don’t simply give employees new tools—they redesign work itself as they redesign their AI workflows. Doing so helps ensure operational and cultural alignment, plus ensure better AI performance over time as human teams grow their skills in parallel to these new AI systems.\n\n## Enterprise AI success begins with genuine readiness\n\nEach of these overlooked requirements to operationalize AI—efficient inference, model flexibility, robust evaluation, and fully redesigned workflows—points to a larger theme: [AI readiness](https://delight.ai/glossary/ai-readiness). Readiness is a measure of an organization's preparedness to adopt AI safely and sustainably across its technology, operations, and culture.\n\nAccording to Cisco’s 2025 AI Readiness Index, organizations that are highly AI-ready are [four times more likely](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf) to move pilots into production and 50% more likely to see value from their investments. Readiness makes AI innovation a repeatable, cross-functional process, helping teams stay agile in a fast-moving market by sidestepping the pitfalls associated with failed AI projects. And ultimately, it's what builds trust.\n\n"People watch you for your consistency," Chen says. "If you're not consistent, it is very hard to win trust." That consistency comes down to four things: infrastructure that doesn't fail, evaluation systems that catch problems early, workflows designed for repeatability, and teams that know how to supervise automated systems. When these elements are in place, supporting model intelligence, organizations gain the confidence to automate more complex workflows from a proven foundation of responsible AI that operates safely and reliably at scale.\n\n*To learn more about how* [*Fireworks AI*](https://fireworks.ai/) *scales AI models with maximum efficiency and flexibility, listen to the full MindMakers podcast on* [Apple Podcasts](https://podcasts.apple.com/us/podcast/how-fireworks-ai-is-powering-the-inference-layer-of/id1840021178?i=1000761297990) *or* [Spotify](https://open.spotify.com/episode/5rnin3cdE4HcZW0laEZiBi?si=2XDfqRT8TTOX-2VNqDp8VA).\n\n*Wondering if your organization is truly AI-ready?*\n\n*Take the* [AI readiness assessment](https://delight.ai/tools/ai-readiness-intro) *for CX from delight.ai. Or* [contact sales](https://delight.ai/contact-sales) *to learn more.*\n\n- [Requirement 1: Inference efficiency](#)\n- [Requirement 2: Model optionality](#)\n- [Requirement 3: Disciplined AI evaluation](#)\n- [Requirement 4: Organizational alignment](#)\n- [Enterprise AI success begins with genuine readiness](#)\n\n## Keep reading\n\n[View more](/resources)\n\n[](https://delight.ai/webinars/trust-stack-design-ai-agent) [](https://delight.ai/ebooks/ai-agents-for-home-services) [](https://delight.ai/webinars/delight-ai-index-2026-webinar) [](https://delight.ai/podcasts/mindmakers-podcast-bercovici)\n\n[View more](/resources)\n
## From AI that answers to AI that owns: The delight.ai spring product update
Source: https://delight.ai/webinars/spring-product-update
- [Webinars](https://delight.ai/webinars)
- From AI that answers to AI that owns: The delight.ai spring product update
## From AI that answers to AI that owns: The delight.ai spring product update
On-demand webinar | Watch anytime
[Get instant access](#watch "View Video")
### Speakers
Shailesh Nalawadi
Head of Product Management
Kibeom Lee
Head of AI/ML at delight.ai
### This is our biggest product update yet.
AI is handling more customer conversations than ever. But the hardest 30% — the issues that involve refunds, exceptions, credits, and multi-party coordination — still end up with a human. Not because AI isn't capable. Because no one has built trusted AI that can actually own the outcome.
Until now.
We're introducing our biggest product update yet: the first fully autonomous AI agent built for long-horizon, async customer issues. The kind that spans multiple parties, multiple systems, and hours or days to resolve. No handoffs. No dropped context. Full ownership, start to finish.
More autonomy requires more trust. That's why we're also releasing Trust OS 2.0, a complete governance layer that lets AI improve itself continuously while keeping your team in control of every guardrail. And with Voice 2.0, your AI doesn't wait for the phone to ring. It calls first.
Join Shailesh Nalawadi, Head of Product at delight.ai, for a live walkthrough of all three releases.
### You'll see
- The first fully autonomous agent handle a complex, multi-party customer issues end-to-end—without a single human handoff
- Trust OS 2.0's Zero-Touch Improvement loop in action: AI researching, fixing, and deploying its own improvements
- The graduated autonomy ladder—and what it looks like to move from human-approved AI to fully autonomous resolution, step by step
Walk away knowing exactly what these products do, how they fit into your stack, and whether they're right for your team. Stick around for live Q&A—we'll answer everything.
### Watch the delight.ai spring product update on demand
From AI that answers to AI that owns.
### Keep reading
[View more](/resources "View more")
- [](https://delight.ai/webinars/trust-stack) [The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
- [](https://delight.ai/ebooks/ai-agents-for-home-services) [Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) [The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) [Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
[View more](/resources "View more")
## Why memory is AI's most critical bottleneck
Source: https://delight.ai/podcasts/mindmakers-podcast-bercovici
## Why memory is AI's most critical bottleneck
May 5 2026
**Listen to the *MindMakers* podcast:** [  ](https://open.spotify.com/show/2y2Lu9GPTxuXhPclS5sT4d?go=1&sp_cid=e6545b7ae20c820d04fa3fff83f21549&utm_source=embed_player_p&utm_medium=desktop&nd=1&dlsi=12d80f92d67a4c70) [  ](https://embed.podcasts.apple.com/us/podcast/mindmakers/id1840021178) [  ](https://www.youtube.com/playlist?list=PLcEvyGCx7NKgIjjowuMsFe7msbKbi6ngx)
Storage used to be something you bought once and forgot about. That era ended the moment trillion‑parameter models and AI agent swarms arrived. Memory — how models access, cache, and work with context — has become the defining challenge in AI infrastructure, the layer that determines whether your GPU investments actually deliver or stall out.
In this episode of *MindMakers*, [WEKA](https://www.weka.io/) Chief AI Officer [Val Bercovici](https://www.linkedin.com/in/valbercovici/) and Sendbird CEO [John Kim](https://www.linkedin.com/in/doshkim) discuss:
- why memory architecture matters more than most people realize
- how the industry is shifting from prompt engineering to context engineering
- what happens when you remove the memory bottleneck entirely
Val brings two decades of infrastructure experience across NetApp, SolidFire, and early Kubernetes development. But it's his current role that frames the conversation. As WEKA's Chief AI Officer, he's building a market category around the very problem this episode unpacks. The conversation starts there.
### The Chief AI Officer role is temporary (and that's the point)
The title "Chief AI Officer" is everywhere right now. Companies are hiring them. VCs are asking for them. But Val sees the role as fundamentally transitional — more like "internet person" from the dot-com era than a permanent C-suite fixture.
> *"I do believe it's transitional. Like most cliches, you're really successful in your role if you work yourself out of a job."*
>
> — Val Bercovici, Chief AI Officer, WEKA
The real work, Val explains, is making sure organizations take full advantage of what AI has to offer. Right now, that means myth-busting, internal education, and helping teams sort through what to act on, what to ignore, and where the real risks are. Strategy is fundamental to the role, along with technical leadership both inside and outside the company.
Because the role follows the rapid evolution of the science itself, the day-to-day activities of the role are changing fast, Val notes. In two years, this role will barely resemble what it is today. But the category Val is building at WEKA — context memory storage — points to the problem he believes will outlast the title.
### The memory wall is the real constraint on AI scaling
Memory is the technical bottleneck for the industry right now, and most infrastructure conversations aren't treating it that way.
Val breaks it down: the working memory that transformers use — technically called the key-value cache — is the real memory hog, not the model weights themselves.
A 100,000-token context translates to about a megabyte of raw data, but once you embed and vectorize it across 10,000 to 20,000 dimensions, that balloons to roughly 50 gigabytes. And that memory has to be expensive, scarce, high-bandwidth memory co-packaged with the GPUs themselves.
> *"This solves a first-principles GPU scientific computing problem, because you're always trading GPU floating-point operations for memory. AI is the most successful scientific application on GPUs ever."*
>
> — Val Bercovici, Chief AI Officer, WEKA
You're out of that high-bandwidth memory before you even begin, Val says. Then the spillover starts: from HBM to CPU DRAM shared across the motherboard, and from there to storage tiers — with what Val calls a "Grand Canyon" of performance between each level.
WEKA's approach, as Val describes it, repackages those storage tiers as memory — maintaining throughput and latency across a thousand times more capacity.
> *"The net effect is you go from a scarcity mindset of infrastructure resources — memory in particular — to an abundance mindset. Because now you have, at least economically, a thousand times the capacity of memory without any sacrifice in latency or throughput."*
>
> — Val Bercovici, Chief AI Officer, WEKA
Once you solve the memory constraint, everything downstream opens up. Context gets deeper. Concurrency scales. The applications you can build shift entirely.
### Context engineering is the new frontier
Val points to a major shift that happened over the past year: the move from prompt engineering to context engineering. It's directly tied to memory scarcity — the fact there will always be some level of constraint in what the GPU can process in real time, despite other tiers being available.
The science and engineering are both evolving rapidly, Val explains. Models still don't understand memory tiers or memory hierarchies — they understand very scarce, limited memory. So engineers optimize around that constraint: KV cache compression, context compaction, summarization after a certain number of turns. These techniques help, but they're workarounds. They manage scarcity instead of solving it.
Val's alternative is what WEKA calls the "token warehouse." The idea: instead of compacting and discarding context to stay within limits, you prepare it once — embedded, vectorized, ready — and keep it permanently available for low-cost cache reads. No redundant reprocessing every session. No eviction. Nvidia's engineers describe a similar approach as local pre-fill global decode.
> *"We've given this a lot of thought, and we actually coined a term around it called a token warehouse. One side of the memory discussion with AI engineers focuses on compaction — preserving short-term memory in markdown files, SQLite databases, Postgres, context graphs. But they're basically pre-embedded, pre-vectorized structures."*
>
> — Val Bercovici, Chief AI Officer, WEKA
The token warehouse concept is still early, but the research momentum is real. Val points to TensorMesh's Cache Blend paper — work on partial prefix matching that moves beyond brute-force full-prefix approaches — as a sign the field is shifting from proof-of-concept to engineering discipline.
### Agent swarms need concurrent memory, not just capacity
The conversation around agents today focuses on what a single agent can do. Val's conviction is sharper: no one will ever run just one agent. It'll always be swarms working in parallel.
Instead of summarizing and compacting context to fit within limits, Val describes a future where you give full context to a new sub-agent for each sub-task, letting it fill up its own context window. That parallelization — concurrent agents, agent swarms — changes the memory math entirely.
> *"The biggest value of memory today is without any additional expense, without more CAPEX or energy OPEX, you can have a high level of concurrency. In a best-case scenario, we estimate about 10x. Even at modest scale, we're seeing 6.5x — another way of saying it is 550% more concurrent tokens without latency sacrifice, without any more GPU spend or any more energy spend."*
>
> — Val Bercovici, Chief AI Officer, WEKA
Those numbers reframe the economics of agent deployment. If you can run 6x more concurrent agents on the same hardware, you're saving money and making architectures viable that weren't before. A customer service system that dispatches 10 specialized sub-agents per ticket, a code review pipeline that checks security, performance, and style in parallel — these patterns only work if the memory layer can keep up.
Val calls the discipline behind this "context platform engineering" — architecting memory so swarms of agents can operate at scale without hitting the constraints that shut down single-agent systems.
> *"I've always believed that as agents really take off and become valuable, no one will be running just an agent. It'll always be a swarm of agents working in parallel concurrently. So this concurrent agent swarm engineering is something we've termed context platform engineering."*
>
> — Val Bercovici, Chief AI Officer, WEKA
Organizations already thinking in terms of agent swarms will find that solving memory concurrency first gives them a structural advantage — and we're only at the beginning of understanding how large that gap can be.
### Memory awareness is moving to the model level
So far, every solution Val has described — bridging the memory wall, engineering context persistence, scaling agent concurrency — treats memory as an infrastructure problem solved below the model. The model consumes context; engineers figure out how to feed it efficiently. But Val points to a shift that changes that dynamic entirely: models themselves are starting to understand memory.
He cites DeepSeek 4's preview paper introducing a concept called Engram — the first public indication that foundation models are becoming architecturally aware of memory hierarchies, not just consuming whatever context gets handed to them.
> *"It is the first public indication — and we're sure the closed commercial frontier labs are doing this — of awareness of memory tiers at the model level, not just at the inference server level."*
>
> — Val Bercovici, Chief AI Officer, WEKA
This matters because it closes the loop. Models need a clear sense of what memory they have access to. When models know how to use both short‑term and long‑term memory, they can decide what to keep close and what to fetch later — much like a developer choosing between cache and disk. New attention mechanisms such as radix and ring attention are pushing this even further, expanding how well models can reason over long contexts.
For Val, this reinforces the central point: the teams that truly solve memory will be the ones capable of building AI systems that operate on deep context, high concurrency, and real‑time memory management across the entire stack.
And because each layer strengthens the next, the advantage compounds. Better memory infrastructure unlocks more concurrency; models that understand memory tiers make smarter use of it. And as a result, companies that move early achieve faster infrastructure and access to application architectures their competitors can’t run at all.
Listen to the full conversation with [Val Bercovici, Chief AI Officer of WEKA, on *MindMakers*](https://www.youtube.com/watch?v=d6z3ZkLQSI0) to hear more about how memory changes the AI competitive landscape, why the SaaS business model faces new pressure from AI agents, and what infrastructure leaders need to think about right now.
Ready to explore delight.ai's agent-ready infrastructure? We break down the silos between your systems, giving our AI agents the context they need to reliably support your customers. [Contact sales](https://delight.ai/contact-sales) to learn more.
- [The Chief AI Officer role is temporary (and that's the point)](#section-anchor-id-0 "The Chief AI Officer role is temporary (and that's the point)")
- [The memory wall is the real constraint on AI scaling](#section-anchor-id-1 "The memory wall is the real constraint on AI scaling")
- [Context engineering is the new frontier](#section-anchor-id-2 "Context engineering is the new frontier")
- [Agent swarms need concurrent memory, not just capacity](#section-anchor-id-3 "Agent swarms need concurrent memory, not just capacity")
- [Memory awareness is moving to the model level](#section-anchor-id-4 "Memory awareness is moving to the model level")
### Keep reading
[View more](/resources "View more")
- [ The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)
- [ Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)
- [ The Delight AI Index 2026: The trust paradox reshaping CX](https://delight.ai/webinars/delight-ai-index-2026-webinar)
- [ From AI that answers to AI that owns: The delight.ai spring product update](https://delight.ai/webinars/spring-product-update)
[View more](/resources "View more")
## The Delight AI Index 2026: The trust paradox reshaping CX
Source: https://delight.ai/webinars/delight-ai-index-2026-webinar
## The Delight AI Index 2026: The trust paradox reshaping CX\n\nOn-demand webinar | Watch anytime\n\n[Get instant access](#watch-now "View Video")\n\n\n\n### Speakers\n\n\n\nCharles Studt\n\nCMO at delight.ai\n\n\n\nLeighAnne Manwiller\n\nProduct Marketing Manager\n\n**Consumers want everything AI promises, from 24/7 access to instant resolution to experiences that feel effortlessly personal. But the moment something goes wrong, the blame doesn’t land on the AI that failed them. It lands on the brand that deployed it.**\n\n71% of consumers have already interacted with AI in customer service. 83% hold the brand accountable when it fails, not the AI vendor. 64% expect AI to outperform a human rep. The era of AI accountability is here, and most brands don’t yet know where they stand.\n\nIn March 2026, delight.ai surveyed U.S. consumers across five industries to find out. The result is the [Delight AI Index](https://delight.ai/reports/ai-index), a benchmark that measures consumer sentiment about AI in customer service across five pillars. The overall U.S. score is 51. That’s a tipping point, not a win.\n\n**Join Charles Studt, CMO of delight.ai, and LeighAnne Manwiller, Product Marketing Manager**, for a deeper dive into the data first revealed at [Delight Spark 2026](https://delight.ai/spark-2026). You’ll see exactly where consumer trust in AI customer service stands today, where the competitive gaps are, and which segments of your audience will determine how far your AI strategy can actually go.\n\n### What you’ll learn\n\n- **The five pillars of the Delight AI Index** — Trust & Confidence, Resolution Effectiveness, Brand Alignment, Comfort with Autonomy, and Emotional Resonance, plus where U.S. consumer sentiment scored on each\n- **The trust paradox redefining brand accountability in the AI era** — why 83% of consumers blame your brand (not your AI vendor) when something goes wrong\n- **Industry benchmarks for Retail, Travel, Tech & Media, Healthcare, and Financial Services** — and the human-preference patterns that reveal where AI is working and where it isn’t\n- **The biggest competitive differentiator in AI service today** — what 70% of consumers say would genuinely delight them\n- **How generational and gender differences should shape your AI strategy** — why women, who use customer service most, will determine the ceiling on AI autonomy\n\n**Walk away with a clear view of where your industry stands**, plus the playbook to elevate your Delight AI Score from baseline to benchmark long before your competitors do.\n\n---\n\n### Keep reading\n\n- [](https://delight.ai/webinars/trust-stack) [The Trust Stack: How to design an AI agent that earns its autonomy](https://delight.ai/webinars/trust-stack)\n- [](https://delight.ai/ebooks/ai-agents-for-home-services) [Your playbook for home services AI agents that book, coordinate, and close the loop](https://delight.ai/ebooks/ai-agents-for-home-services)\n- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) [Why memory is AI's most critical bottleneck](https://delight.ai/podcasts/mindmakers-podcast-bercovici)\n- [](https://delight.ai/webinars/spring-product-update) [From AI that answers to AI that owns: The delight.ai spring product update](https://delight.ai/webinars/spring-product-update)\n\n[View more](/resources "View more")\n
## Home services AI agent use cases playbook
Source: https://delight.ai/ebooks/ai-agents-for-home-services
- [eBooks](https://delight.ai/ebooks)\n- Home services AI agent use cases playbook\n\n## Home services AI agent use cases playbook\n\n[Download ebook](#download "Download ebook")\n\n\n\n> \"Our customers don't want to navigate phone trees or wait on hold. They want to talk to someone who can help. We needed technology that feels like talking to a real person—because that's what our brand represents.\"\n>\n> — Mark Farley, President, [Dr. Cool Services](https://delight.ai/customers/dr-cool-services)\n\n### About this ebook\n\nA practical look at how home services teams use Delight.ai's AI agents across the five decisive moments in every job: the first call, mid-job updates, multi-visit coordination, warranty claims, and post-job follow-up. No job gets lost to a slow response or a missed follow-up.\n\n### What you'll learn\n\nDownload this ebook to discover how home services AI agents can:\n\n1. **Handle inbound calls** — verify coverage, check technician availability, and confirm a booked appointment before the customer hangs up\n2. **Manage delays** — proactively text a new arrival window before the customer thinks to call, or resolve it live if they call in first\n3. **Coordinate parts and multi-visit jobs** — track a backordered part from order to arrival and automatically book the return visit once it ships\n4. **Coordinate warranty claims** — keep the homeowner, contractor, parts supplier, and adjuster updated in one thread instead of four disconnected ones\n5. **Follow up after every job** — trigger the satisfaction check, review request, invoice follow-up, and seasonal reminder automatically once a job closes\n\n- [About this ebook](#section-anchor-id-0 "About this ebook")\n- [What you'll learn](#section-anchor-id-1 "What you'll learn")\n\n### Your playbook for home services AI agents\n\nDiscover how to move from adoption to transformation. Free to download. No spam.\n\n### Keep reading\n\n[View more](/resources "View more")\n\n[](https://delight.ai/ebooks/voice-ai-home-services)\n\nVoice AI for home services: Buyer's guide to AI agents\n\n[](https://delight.ai/webinars/trust-stack)\n\nThe Trust Stack: How to design an AI agent that earns its autonomy\n\n[](https://delight.ai/webinars/delight-ai-index-2026-webinar)\n\nThe Delight AI Index 2026: The trust paradox reshaping CX\n\n[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)\n\nWhy memory is AI's most critical bottleneck\n\n[View more](/resources "View more")
## The Trust Stack: How to design an AI agent that earns its autonomy
Source: https://delight.ai/webinars/trust-stack
## The Trust Stack: How to design an AI agent that earns its autonomy
[Save your seat](#register "View Video")
Kibeom Lee
Head of AI/ML at delight.ai
**The failure that scares us most looks exactly like success.**
Most AI agent failures don't look dramatic in the moment. An agent gets talked into a conversation it should have shut down, or quietly invents a policy that was never real and a customer acts on it before anyone notices. The customer usually finds the problem before the company does.
Join Kibeom Lee, Head of AI/ML at delight.ai, as he breaks down the Trust Stack, the four-layer framework delight.ai uses in production to catch failures before your customers do. The goal isn't a smarter model. It's a system that catches itself, and that earns AI agents more autonomy without losing control of what they do.
**What you'll walk away with**
- How to tell if an agent's "success" is actually hiding a failure
- The four-layer framework for earning AI agents more autonomy, one layer at a time
- A simple way to check whether any workflow you're automating is ready for more trust
Save your seat and see exactly where your own AI agents stand on the trust ladder.
## Register for our upcoming live webinar: The Trust Stack
Join us live on September 29 at 10 AM PT / 1 PM ET.
## Keep reading
[View more](/resources "View more")
- [](https://delight.ai/ebooks/ai-agents-for-home-services) Your playbook for home services AI agents that book, coordinate, and close the loop
- [](https://delight.ai/webinars/delight-ai-index-2026-webinar) The Delight AI Index 2026: The trust paradox reshaping CX
- [](https://delight.ai/podcasts/mindmakers-podcast-bercovici) Why memory is AI's most critical bottleneck
- [](https://delight.ai/webinars/spring-product-update) From AI that answers to AI that owns: The delight.ai spring product update
[View more](/resources "View more")
## Voice AI for home services: Buyer's guide to AI agents
Source: https://delight.ai/ebooks/voice-ai-home-services
- [eBooks](https://delight.ai/ebooks)
## Voice AI for home services: Buyer's guide to AI agents
[Download ebook](#download-ebook "Download ebook")
> "Delight handles the calls we used to miss and follows up on the jobs we used to forget. It's like having a CSR who never clocks out."
>
> — Mark Farley, President, [Dr. Cool Services](https://delight.ai/customers/dr-cool-services)
## About this ebook
For home services owners and operations leads, missed calls, callbacks, and forgotten follow-ups add up to lost jobs. This buyer's guide to voice AI for home services businesses shows where your jobs slip away. Discover how a voice AI agent books within your rules, and how to test different providers with your own calls.
## What you'll learn
Download this ebook to learn how to:
1. Find where jobs slip away across unanswered calls, bookings that end in a callback, and estimate follow-ups that never happen.
2. Get your operation ready by checking scheduling access, writing down your booking rules, and deciding when a call goes to your team.
3. Pick a first use case out of 7 inbound and outbound options, like service booking or estimate follow-up, and know what to measure for each.
4. Compare 4 types of provider with the questions that expose red flags and a scorecard for testing finalists on your own calls.
5. Launch a pilot you can measure with a step-by-step checklist, agreed go-live criteria, and a comparison against today's results.
## Discover how to move from adoption to transformation
Free to download. No spam.
## Keep reading
[View more](/resources "View more")
[](https://delight.ai/webinars/trust-stack)
The Trust Stack: How to design an AI agent that earns its autonomy
[](https://delight.ai/ebooks/ai-agents-for-home-services)
Home services AI agent use cases playbook
[](https://delight.ai/webinars/delight-ai-index-2026-webinar)
The Delight AI Index 2026: The trust paradox reshaping CX
[](https://delight.ai/podcasts/mindmakers-podcast-bercovici)
Why memory is AI's most critical bottleneck
[View more](/resources "View more")
# Product Updates
## Desk Glossary: Translation without rules is just expensive guessing
Source: https://delight.ai/product-release/desk-glossary
## Desk Glossary: Translation without rules is just expensive guessing
Jan 26, 2026
Every industry has terms that must stay precise. "Dasher" is a job title, not a generic courier. "Sprint" is a two-week cycle, not a run. "Prior authorization" is an insurance workflow, not a polite request. "Wire transfer" means something different in the US than "Bank transfer" in the UK. Addresses shouldn't become foreign streets. UI labels need to match what users actually see on screen.
The list goes on. "Surge pricing" in ride-sharing. "Backlog" in SaaS. "Confirmation number" in travel. "Loot box" in gaming. "Deductible" in insurance. "Add to cart" in e-commerce. Brand names, feature names, role titles, status messages, error codes, compliance terms—every industry has language that can't be left to guesswork.
Today, we're releasing Glossary in Desk—giving you full control over how your terms translate across languages and regions.
### How it works
- **CSV-based management**: Upload your terms, descriptions, and translations in one file. No complex setup, no dev work required.
- **Do Not Translate rules**: Lock terms that should never change—brand names, addresses, compliance terms like "HIPAA" or "SDK"—so they stay exactly as-is.
- **Contextual matching**: Add descriptions so the engine knows "Order" means a food delivery request, not a command. Right translation, every time.
- **Country-level localization**: US customers see "Wire transfer" while UK sees "Bank transfer." One glossary handles regional differences across 15+ languages.
- **Instant updates**: Change a term and it applies to all tickets immediately—even ongoing ones. No syncs, no stale translations.
Glossary ensures your support team speaks your product's language—in every language.
## Staging Environment: A new dedicated testing layer between dev and production
Source: https://delight.ai/product-release/staging-environment
- [Product release](/product-release)\n- Staging Environment: A new dedicated testing layer between dev and production\n\n## Staging Environment: A new dedicated testing layer between dev and production\n\nJan 7, 2026\n\n**Delight.ai** already provided [Development and Production environments](https://delight.ai/product-release/deployment-and-production-mode-stay-in-control-at-scale) for managing AI agent configurations. However, enterprises building production-grade AI agents face the same challenges as any mission-critical software: complex integrations with payment systems, SSO providers, and internal APIs; the high cost of production failures; and the need to prove security and compliance before go-live. Development environments use simplified setups that miss real-world conditions, meaning the first time teams discover integration issues or configuration errors is often in front of customers.\n\nStaging Environment brings enterprise-grade deployment workflows to **delight.ai**. As part of [Trust OS](https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents) and our scale-proven infrastructure pillar, it de-risks AI deployments by giving teams the critical testing layer between development and production where they can validate changes with real configurations, catch integration issues before they cause outages, and deploy with confidence.\n\n### How it works\n\n- **Production-mirror testing**: Test with actual production configurations, integrations, and security settings in a safe environment that won't impact live customers or leak sensitive data.\n- **Last checkpoint before production**: Validate that all changes work together as a complete system, catching subtle integration issues that only appear under real-world conditions.\n- **Sequential deployment workflow**: Enforced Development → Staging → Production path ensures every change passes through proper validation gates, reducing deployment failures and costly rollbacks.\n- **Risk reduction at scale**: Prevent downtime, SLA penalties, and reputational damage by catching bad releases before they reach customers.\n- **Flexible when needed**: Hide or skip Staging for simpler workflows, automatically adjusting the promotion path to Development → Production for faster iteration.\n- **Version control and rollback**: Track every deployment version with full history and roll back configurations when issues arise, just like software teams manage code releases.\n\nStaging Environment brings software development best practices to AI agent deployment. As part of delight.ai's Trust OS, it ensures workspace-level assets are tested with the same rigor as mission-critical applications, giving enterprises the confidence to deploy AI agents that integrate seamlessly with their systems and meet security and compliance requirements.
## Selective Deployment 2.0: You had control, now you have visibility
Source: https://delight.ai/product-release/selective-deployment-v2
## Selective Deployment 2.0: You had control, now you have visibility
Dec 17, 2025
**Delight.ai** already offered [selective deployment](https://delight.ai/product-release/selective-deployment-and-rollback), allowing teams to choose which shared assets (Knowledge, Actionbooks, Tools, and Message Templates) to promote from Development to Production.
Selective Deployment 2.0 shows exactly what's changing before you deploy and extends coverage to workspace-level settings like Profile, CSAT, and Safeguards that directly impact agent behavior. Teams now have full visibility and control over every change before it reaches production.
### How it works
- **Visual difference comparison**: See exactly what changed in each asset with side-by-side or inline views before you deploy.
- **Expanded scope**: Other Settings (Profile, CSAT, Safeguards, Workspace General) are now included in selective deployment with full diff visibility and version control.
- **Automatic change detection**: The system identifies modified settings and assets, surfacing any unintended updates before they go live.
- **Read-only preview**: Review changes safely without risk of accidental edits during the approval process.
- **Rollback for settings**: Roll back workspace configuration changes, not just assets, to previous deployment versions.
This enhancement to selective deployment gives teams complete visibility into what's changing across assets and settings. It reduces deployment risks, improves audit trails for compliance, and makes it easier to troubleshoot issues when they arise.
## Activity Log 2.0: From black box to open book
Source: https://delight.ai/product-release/activity-log-v2
## Activity Log 2.0: From black box to open book
Dec 2, 2025
**Delight.ai** has been an early advocate for transparency in AI agent decision-making. Our [Activity Log](https://delight.ai/product-release/activity-trails-full-visibility-into-how-your-ai-agent-acts) already provided visibility into agent responses, giving teams a foundation for understanding how agents behave. Now we're taking that transparency further with detailed reasoning paths that show exactly which knowledge sources, actionbooks, and tools shaped each response.
Explain AI responses an enhancement to Activity Log and part of Trust OS — [delight.ai](https://delight.ai/)'s suite of features designed to help customers confidently test, deploy, and continuously improve AI agents in production.
### How it works
- **Enhanced reasoning visibility**: Go beyond basic activity logs to see the complete chain of knowledge sources, actionbooks, and tools behind each agent response.
- **Confidence indicators**: Low-confidence responses are automatically flagged when the agent is less certain, surfacing answers that may need review.
- **Evidence-based verification**: Trace the exact logic path to confirm agents are pulling from correct sources and following intended workflows.
- **From observation to optimization**: Move beyond monitoring to actively improving agent performance with detailed insights into decision patterns.
- **Built for continuous improvement**: Identify knowledge gaps, refine prompts, and optimize agent quality based on concrete evidence rather than assumptions.
Explain AI Responses deepens our commitment to transparency, giving our customers the detailed visibility they need to trust and improve their AI agents. As part of [delight.ai's Trust OS](https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents), it helps teams verify accuracy, catch issues early, and continuously enhance agent quality with confidence.
## Conversation Search: Find any conversation, instantly
Source: https://delight.ai/product-release/message-search
- [Product release](/product-release)\n\n## Conversation Search: Find any conversation, instantly\n\nNov 26, 2025\n\n\n\nAI–customer conversations contain valuable signals from feature requests, pain points, automation gaps, to sentiment patterns. But without a way to search across thousands of interactions, these insights stayed buried. Teams had to manually review conversations hoping to spot trends, or worse, make decisions based on guesswork instead of data.\n\nMessage Search brings full-text search to delight.ai's conversation view. Find any word or phrase across all AI–customer interactions instantly, turning buried insights into immediate action.\n\n*Search results, highlighted phrase*\n\n### What you can find\n\n#### Customer pain points:\n\n- Search "refund" to find all refund requests and identify common cancellation reasons\n- Search "frustrated" or "disappointed" to surface conversations with negative sentiment\n- Search "not working" or "error" to catch technical issues customers are experiencing\n\n#### Product opportunities:\n\n- Search "wish" or "would be great if" to discover feature requests\n- Search competitor names to see how customers compare your offering\n- Search "upgrade" or "premium" to find upsell opportunities\n\n#### Automation gaps:\n\n- Search "speak to human" or "real person" to identify where AI agents struggle\n- Search "I don't understand" to find confusing responses that need improvement\n- Search specific product names to see if AI provides accurate information\n\n#### Compliance and quality:\n\n- Search policy terms to verify AI agents handle them correctly\n- Search brand-specific phrases to ensure consistent messaging\n- Search error messages to catch system issues early\n\n### How it works\n\n- **Search keywords or phrases**: Type what you're looking for and see matching conversations in seconds\n- **Review with context**: See highlighted search terms within full conversation threads\n\nMessage Search transforms customer conversations from scattered data into actionable intelligence, helping teams make smarter decisions about customer experience, product development, and automation strategy.
## Omnichannel continuity: Turn every pause into progress
Source: https://delight.ai/product-release/omnichannel-continuity
- [Product release](/product-release)
## Omnichannel continuity: Turn every pause into progress
Nov 18, 2025
Omnipresence gives [delight.ai](https://delight.ai/) a single, unified AI agent that follows the customer across every channel — maintaining context, continuity, and personalization from one touchpoint to the next.
### How it works
- Cross-channel continuity: One agent persists across web, app, SMS, email, social, and voice.
- Context that travels: The agent remembers past interactions and picks up instantly where the customer left off.
- Proactive engagement: It can step in before issues escalate, guiding users in real time.
Omnipresence delivers seamless, personalized support anywhere customers go to reduce friction, build trust, and drive stronger engagement and conversions.
## For You Conversations (FYC)
Source: https://delight.ai/product-release/for-you-conversations
- [Product release](/product-release)\n- For You Conversations (FYC)\n\n## For You Conversations (FYC)\n\nNov 18, 2025\n\n\n\nAI agents traditionally handled each conversation the same way for every user, providing generic responses without adapting to individual interests, history, or behavior. This created flat, one-size-fits-all interactions that lacked relevance and missed opportunities to keep customers engaged or guide them toward meaningful next steps.\n\n### What's new\n\nFor You Conversations (FYC) brings personalized, dynamic conversation flows to [delight.ai](https://delight.ai/). FYC tailors each interaction to the individual customer by leveraging their preferences, past actions, and real-time context to create experiences that feel curated, relevant, and uniquely “for them.”\n\n\n\n### How it works\n\n- Personalized conversation paths: The AI shapes dialogue based on each customer’s interests, behavior, and engagement patterns.\n- Context-aware suggestions: FYC recommends next steps, products, content, or support actions that match each user’s needs.\n- Adaptive engagement: As the customer interacts, the conversation adjusts in real time to stay helpful, compelling, and aligned with their goals.\n\nFYC boosts relevance and connection, delivering personalized experiences that keep customers engaged while naturally guiding them toward business objectives, increasing satisfaction, trust, and revenue.
## Agent Memory Platform (AMP)
Source: https://delight.ai/product-release/agent-memory-platform
- [Product release](/product-release)
- Agent Memory Platform (AMP)
## Agent Memory Platform (AMP)
Nov 18, 2025
Traditional AI agents only resolved customer issues in the moment. They lacked memory, offered no proactive guidance, and didn’t help move customers toward broader business goals, resulting in one-sided interactions.
Agent Memory Platform (AMP) brings long-term memory and business intent together to power [delight.ai](https://delight.ai/). It transforms AI agents from reactive problem-solvers into proactive, context-aware assistants that support both customer needs and business outcomes.
How it works
------------
- Long-term memory: Captures and maintains evolving customer context across all interactions.
- Business intent: Encodes your goals—conversion, retention, efficiency—into the agent’s decision-making.
- Combined intelligence: The agent personalizes conversations and guides customers toward desired actions.
AMP delivers personalized, trust-building experiences while helping businesses reach their objectives, creating customer relationships that strengthen loyalty and drive revenue.
## Collect instant user feedback on every AI agent message
Source: https://delight.ai/product-release/collect-instant-user-feedback-on-every-ai-agent-message
- [Product release](/product-release)\n\n## Collect instant user feedback on every AI agent message\n\nOct 31, 2025\n\n\n\nEnd users can share quick feedback on individual AI agent messages, helping your team continuously refine model quality and conversational accuracy. With a simple up/down control and optional short comment, users can instantly flag helpful or inaccurate responses. Feedback is visible to human agents and admins directly within the Dashboard, empowering data-driven improvements to both AI and human performance.\n\n### What's new\n\n- **Per-message feedback**: Users can rate each AI agent generated message (up/down) and add short notes (up to 100 characters) to explain the rating. Feedback can be modified or deleted before the chat is resolved.\n- **Flagged messages view**: Access a consolidated list of all rated messages (filter by positive or negative) with timestamps and conversation links. Monitor patterns over time through a trend chart that visualizes positive vs. negative feedback, helping identify opportunities for AI model improvement.\n\n\n\n### Why it matters\n\nThis creates a lightweight, actionable feedback loop between your users and your AI agent so you can better understand where the AI performs well, where it falls short, and how human or model interventions impact satisfaction over time.
## Embed interactive forms in agent responses
Source: https://delight.ai/product-release/embed-interactive-forms-in-agent-responses
- [Product release](/product-release)
## Embed interactive forms in agent responses
Oct 16, 2025
Go beyond static replies by embedding interactive forms directly into the conversation flow. This removes the need to redirect users to external pages or manually follow up for missing details, helping agents guide customers through faster, more efficient interactions.
With this update, AI agents can now include multiple form types within a chat:
- **Free-form text**: collect open-ended responses
- **Email or phone number collection**: capture and validate customer email addresses and/or phone numbers
- **Single or multi select**: prompt users to select one predefined option or select multiple options
- **One-Time Password (OTP)**: verify user identity securely within the conversation
### Why it matters
Interactive forms help improve first-contact resolution by capturing complete, structured information upfront. They create a seamless, guided experience where customers can provide the exact information agents need (such as contact details, issue specifics, or identity verification) without ever leaving the chat. This reduces handle time by minimizing repetitive back-and-forth exchanges and enables more conversion-driven workflows, such as lead qualification or account setup. In addition, OTP verification adds a secure layer of trust and compliance.
## Customizable post-chat CSAT survey
Source: https://delight.ai/product-release/customizable-post-chat-csat-survey
## Customizable post-chat CSAT survey
Sep 30, 2025
Every business measures satisfaction differently. An e-commerce brand might prefer a quick thumbs-up/down or emoji reaction after a purchase chat to maximize response rates. A healthcare provider may require an explicit yes/no resolution check with follow-up questions to meet compliance standards. A SaaS company might choose a detailed 1–5 scale with comments to analyze account-level trends and understand the why behind each rating.
Now, with **Customizable CSAT Surveys**, you can design and manage feedback your way:
- **Configure survey format**: Choose rating types (stars, emoji, numeric) and add optional follow-up questions.
- **Customize appearance**: Apply branding, titles, and themes to match your customer experience.
- **Set triggering logic**: Decide when and how surveys are delivered (e.g., post-chat, after resolution).
- **Track results**: View responses and trends in reporting for actionable insights.
Collect feedback that fits your business, while keeping it consistent and on-brand everywhere.
## Sendbird AI agent is ISO 42001 certified
Source: https://delight.ai/product-release/sendbird-ai-agent-is-iso-42001-certified
- [Product release](/product-release)
- Sendbird AI agent is ISO 42001 certified
Sep 29, 2025
## Sendbird AI agent is ISO 42001 certified
We’re proud to announce that Sendbird is now ISO/IEC 42001 certified, a globally recognized standard for Artificial Intelligence Management Systems (AIMS). This certification demonstrates our deep commitment to building AI responsibly and ensures our AI Agents are designed, deployed, and managed with safety, transparency, and risk controls at their core.
## Why this matters
ISO 42001 is the first international standard focused on AI governance. Achieving it means our customers can trust that Sendbird:
- **Manages AI risks responsibly** from bias and fairness to privacy and security.
- **Implements structured AI governance** so models are tested, monitored, and improved safely.
- **Meets the highest compliance expectations** helping customers stay ahead of evolving global regulations around AI.
While ISO/IEC 42001 is a milestone that few companies globally have reached, it came as a natural progression of [Trust OS](https://sendbird.com/blog/introducing-trust-os-for-ai-agents) for us. With responsible AI already deeply embedded in our development practices, this milestone wasn’t a challenge — it was a confirmation of our commitment to a new standard for observability, oversight, and control of AI agents. We’re proud to be recognized as an early leader in operationalizing responsible AI at scale.
## What’s next
This achievement reflects years of investment in building trustworthy, enterprise-grade products for our customers. ISO 42001 isn’t the finish line, it’s a foundation. As one of the few companies holding this certification, we’ll continue to advance responsible AI and help our customers navigate new regulations with confidence.
## Humans as AI evaluators
Source: https://delight.ai/product-release/humans-as-ai-evaluators
- [Product release](/product-release)
- Humans as AI evaluators
## Humans as AI evaluators
Sep 23, 2025
AI agents are capable of managing more conversations than a human agent could. But with that scale comes increased risks like hallucinations, tone drift, or compliance issues. Automated tests help catch some mistakes, but your team still wants to see, judge, and confirm for themselves. With **humans as AI evaluators**, a part of [Trust OS](https://sendbird.com/blog/introducing-trust-os-for-ai-agents), you have increased human oversight into AI evaluations. Review, grade, and refine AI-handled conversations with the same rigor as QA—ensuring accountability, compliance, and trust at scale.
### What’s new
- **Assign evaluators**: Designate reviewers by role, business function, or language.
- **Configurable criteria**: Evaluate what matters most—tone, clarity, solution effectiveness, and more.
- **Evaluator console**: View, filter, and manage all conversations under review in one place with interactive views.
- **Keyboard shortcuts**: Easily move between conversations and submit evaluations all with keyboard shortcuts to streamline high-volume evaluations.
In the agentic-first era, AI can produce many invalid responses—judged for tone, reasoning, and outcome. With humans as AI evaluators, your agent understands where mistakes were made and learns from them. QA should be a system design principle with actionable next steps, not a spot-check process.
## Multiple active conversations per customer
Source: https://delight.ai/product-release/multiple-active-conversations
- [Product release](/product-release)
## Multiple active conversations per customer
Sep 12, 2025
Your customers often have more than one question at a time. Now, they can manage them all without waiting for one issue to resolve first. With **multi-conversation support in the AI agent messenger**, your customers can start and maintain multiple ongoing conversations simultaneously.
### Why this matters
Previously, customers had to either wait until a conversation was closed before starting a new one, or pile multiple unrelated questions into a single thread making it harder to track, report, and resolve issues efficiently. With multiple active conversations, your support experience becomes:
- **More flexible**: End users can open new conversations for new topics without interrupting existing threads.
- **More productive**: Support teams can handle questions in parallel, so longer-running issues don’t block faster resolutions.
- **More accurate**: Cleaner separation of topics improves conversation history and reporting accuracy.
### The business impact
The multiple active conversations per end user feature delivers measurable improvements across customer experience, team efficiency, and reporting accuracy:
- **Happier customers**: Customers get answers to multiple questions without waiting, driving higher CSAT and NPS.
- **Faster support**: Agents can resolve issues in parallel, reducing response and resolution times.
- **Cleaner reporting**: Each issue is tracked separately, improving data accuracy and insights.
## Desk AI Copilot
Source: https://delight.ai/product-release/desk-ai-copilot
- [Product release](/product-release)
- Desk AI Copilot
## Desk AI Copilot
Sep 9, 2025
In customer support, seconds matter. Every extra click, every manual search, every keystroke delays the moment a customer feels helped. Those seconds add up — to longer wait times, frustrated customers, and agents stretched thin.
[**Desk AI Copilot**](https://sendbird.com/ai-agent/desk/ai-copilot) **flips that script.** It's not just faster, it's smarter. Built to work hand-in-hand with your team, Copilot drafts polished, on-brand replies in an instant. It pulls answers from the right sources without breaking an agent's flow. And it transforms each conversation from a drawn-out task into a fast, confident resolution.
## What's new
- **Generate replies in seconds**. Copilot instantly drafts contextual, on-brand answers so agents spend less time typing and more time solving.
- **Knowledge at your fingertips.** Agents can ask Copilot to search knowledge and context on the fly — from policies to past tickets — and add answers directly into replies.
- **Boost customer satisfaction.** With faster, more accurate responses, customers feel heard and helped. Copilot makes every interaction count.
## Time is your team's most valuable resource
Support is a race against the clock. Every wasted minute is a lost opportunity to build trust, retain a customer, and scale your business. Desk AI Copilot is built to protect that time.
- Agents spend less time searching, drafting, and switching between tools.
- Customers spend less time waiting and more time being helped.
- Your team spends more time where it counts: building lasting relationships.
Desk AI Copilot turns time saved into customers retained.
## Voice AI agents: Faster, smarter, and built with empathy
Source: https://delight.ai/product-release/voice-ai-agents-faster-smarter-and-built-with-empathy
- [Product release](/product-release)
## Voice AI agents: Faster, smarter, and built with empathy
Aug 26, 2025
Traditional voice support relied on human agents or robotic phone bots that struggled with natural speech, accents, and real-world noise. Conversations were disconnected from other channels, forcing customers to repeat themselves. Voice AI changes this by bringing natural, real-time voice conversations to [delight.ai](https://delight.ai/) — enabling your AI agent to handle calls instantly, understand human speech, and maintain full context across channels.
### How it works
- **Natural, real-time voice conversations**: Understands accents, interruptions, and noisy environments while responding instantly and conversationally.
- **Omnichannel continuity:** The same AI agent handles both voice and chat, so conversations can move between phone, SMS, email, or in-app channels without losing context.
- **Customizable voice and tone:** Choose languages, accents, personality, and speaking style to match your brand’s voice.
Voice AI delivers fast, natural support on the channel customers trust most — while reducing workload, improving consistency, and helping businesses scale personalized service that builds loyalty.
## Citations: AI answers you can trust and explore
Source: https://delight.ai/product-release/citations-ai-answers-you-can-trust-and-explore
- [Product release](/product-release)
## Citations: AI answers you can trust and explore
Aug 13, 2025
Your AI agent just got even better at helping customers help themselves. With **AI agent Citations,** every AI answer can now show exactly where the information came from, linking directly to your help center articles, documentation, or trusted external knowledge.
Instead of stopping at a short AI agent reply, customers can click through to explore the full details, learn more at their own pace, and get confident answers. That means more engagement with the content you’ve already invested in, and fewer tickets for your team to handle.
### The impact on your support operations
- **Drive self-service engagement:** Every AI interaction becomes an entry point to your existing content library, turning quick exchanges into meaningful engagement with your resources.
- **Reduce repeat questions:** Customers can click through to explore details without opening a support ticket.
- **Maximize your content investment**: Get more value from the Help Center and resources you’ve already built.
Citations work automatically with your existing setup. Simply turn it on and start giving customers answers they can explore, trust, and act on.
## GPT-5 now live in Sendbird AI agent
Source: https://delight.ai/product-release/gpt-5-now-live-in-sendbird-ai-agent
- [Product release](/product-release)
## GPT-5 now live in Sendbird AI agent
Aug 8, 2025
At Sendbird, we build AI agents that are fast, capable, and built for real-world complexity. We’re excited to bring GPT‑5 to production inside the Sendbird AI agent platform, powering deeper reasoning, better context retention, and more reliable automation where it matters most.
### Where GPT‑5 makes a difference
GPT‑5 isn’t a plug-and-play upgrade. At Sendbird, large language models sit within a carefully structured system: from Actionbooks and orchestration logic to observability and evaluation layers. We’ve integrated GPT‑5 into key areas of the Sendbird AI agent platform, not as a blanket replacement, but as a targeted upgrade where the model clearly improves real-world performance.
Based on extensive internal testing and benchmarks, GPT‑5 has been deployed in areas where it delivers measurable gains:
- **Long-context reasoning for better omnichannel continuity:** GPT‑5 maintains longer context across long, multi-turn conversations. Whether a customer jumps from web chat to WhatsApp or voice, GPT‑5 helps our agent follow the thread.
- **Sharper understanding of ambiguity:** GPT‑5 interprets vague or incomplete inputs with higher accuracy, prompting for clarification and guiding users toward resolution faster. Vague requests like “I need help with my plan” are clarified faster with smarter follow-ups and clearer next steps.
- **Reliable execution of multi-step execution:** Our AI agent uses Actionbooks to automate structured workflows like returns, reorders, or cancellations. GPT‑5 increases accuracy and consistency across these flows to reduce failure and improve containment.
### Our model selection approach
We don’t believe in model monocultures. Instead of relying on a single foundation model, we match the best model to each task, which is based on real-world performance, cost-efficiency, and user impact.
Some examples of how we apply in practice:
- GPT‑5: Powers longer context reasoning and automation, structured workflows, and omnichannel conversations - where deep reasoning and memory make a clear difference.
- Gemini Flash: Used for real-time safeguards and hallucination detection, where fast and consistent behavior is essential.
- Claude Sonnet 4: Used in Test Center (part of Trust OS) to evaluate conversation quality and multi-turn reasoning in more complex, in-depth decision environments.
This modular approach lets us continuously optimize for speed, safety, and task-fit without compromising trust or agility.
### Business impact
We didn’t upgrade to GPT‑5 to chase benchmarks, we adopted it to solve real customer service problems. With GPT‑5 powering key parts of the Sendbird AI agent, businesses can now automate more complex customer interactions without sacrificing accuracy, context, or control.
- **Handle vague requests** with smarter clarification and faster routing.
- **Resolve multi-step workflows** end-to-end, without manual intervention.
- **Maintain context across channels** with no repeated steps.
Our commitment is simple: AI agents that are fast, accurate, and capable. If you’re ready to see it in action, we’re here.
## Trust OS: The foundation for responsible AI agents
Source: https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents
- [Product release](/product-release)
## Trust OS: The foundation for responsible AI agents
Jul 30, 2025
Many AI agent platforms give agents real responsibility — access to data, ability to take action, and to interact with customers — but provide little transparency, oversight, or control. That makes AI risky to scale: mistakes are hard to detect, misconfigurations can slip through, and enterprises can’t reliably govern what their AI does. Trust OS solves this by delivering an enterprise-grade framework for accountability, visibility, and safe scaling. It turns AI agents from black boxes into governable, traceable, and reliable digital teammates.
### How it works
#### Observability
- Full visibility into every AI decision and interaction, including detailed logs show which actions, API calls or (“Actionbooks”) were triggered, what knowledge sources were used, and why the AI responded the way it did.
- Ability to run simulated conversations before deployment (Conversation Benchmark Testing) to catch regressions or unwanted behavior early.
*Run test conversations to monitor your AI agent*
#### Control
- Version control and environment separation: track prompts/config changes, compare them side-by-side, and roll back if needed.
- Role-based access control (RBAC) and deployment gating so teams decide who can make changes, when and how — preventing unintended changes or misuse.
*Side-by-side comparison of changes to your AI agent’s operating procedures*
#### Oversight & human-in-the-loop safety
- Human review via agent scorecards: evaluate AI performance on tone, accuracy, relevance, and compliance.
- Auto-flagging of risky content: hallucinations, policy violations, harmful content or unexpected behavior are surfaced automatically and can be stopped or reverted.
*Control who on your team can do what with role-based access control (RBAC)*
*Use a human-in-the-loop scorecard for AI evaluation*
#### Scale-proven infrastructure
- Built on a platform already handling billions of messages per month globally; now extended to AI agent workloads with multilingual, high-volume, enterprise-ready stability.
- Supports safe scaling of many AI agents across regions, teams, and languages without sacrificing control or compliance.
*AI agents are multilingual and conversations can be instantly translated*
Trust OS gives enterprises the transparency, safeguards, and controls needed to deploy AI agents with real power, ensuring decisions are traceable, behavior is safe, and outcomes are reliable. It transforms AI from a risky experiment into a scalable, trustworthy business asset.
## Automatic message translation in Sendbird Desk
Source: https://delight.ai/product-release/automatic-message-translation
- [Product release](/product-release)
## Automatic message translation in Sendbird Desk
Jul 28, 2025
Language barriers slow down support and frustrate both agents and customers. Today, we’re removing that friction with Automatic **Message Translation** in [**Sendbird Desk**](https://sendbird.com/products/chat-messaging/customer-support) — our native ticketing system.
## What it does
**Human agents can now:**
- Read customer messages in their preferred language instantly
- Automatically translate replies into the customer's language
- Communicate clearly without switching tools or losing context
**Customer experience:**
- Always receives responses in their preferred language
- Clear, localized communication without added wait time or confusion
## True multilingual continuity from AI to human
Whether a customer starts with an AI agent or is routed to a human, Automatic Message Translation keeps the entire conversation consistent. It maintains the same language and context, creating a seamless multilingual support experience from start to finish.
## The AI-native standard for modern support
As support shifts from tickets to real-time conversations, global scale shouldn’t compromise the experience. Sendbird unifies AI and human support through our AI Agent and [Sendbird Desk](https://sendbird.com/products/chat-messaging/customer-support), which is purpose-built for the AI era and trusted by enterprises worldwide.
This launch is another step toward making Sendbird Desk the AI-native support standard for the post-ChatGPT era where automation and humans work together at global scale.
## Outcome-based testing: The end of shallow AI validation
Source: https://delight.ai/product-release/outcome-based-testing
- [Product release](/product-release)
## Outcome-based testing: The end of shallow AI validation
Jul 23, 2025
Here’s the brutal truth about AI deployment: Sounding helpful isn’t the same as being helpful. Your AI can respond with empathy, structure, and polish — and still fail entirely.
**Because what matters isn’t how well the AI agent speaks, but whether it** ***gets the job done***.
Yet, most AI testing stops at semantic similarity. Checking if the AI’s response sounds close enough to a reference answer. It’s better than nothing, but it completely misses the point. An AI can sound correct, pass the test, and still fail to solve the customer’s problem.
**Sendbird’s Outcome-Based Testing changes that.** It goes deeper, measuring whether your AI understood the user’s intent, executed the right logic, and delivered the actual result. It’s how you move from “the bot sounded fine” to “the issue is resolved.”
### The problem it solves
Traditional AI testing asks:
- *“Did the AI say something reasonable?”*
But what you really need to know is:
- *“Did the AI actually help the user accomplish their goal?”*
Let’s take a simple example:
User intent: *“Schedule a meeting for tomorrow at 2pm”*
- **Traditional test:** AI responds helpfully and discusses scheduling
- **Outcome-based test:** AI actually creates the calendar event
In traditional testing, the AI passes if it *talks* about scheduling. But if it doesn’t actually *create* the calendar event, the task is still undone — and no one catches it.
**Outcome-based testing** doesn’t just evaluate how the AI sounds, it verifies whether the right action was taken, the right workflows triggered, and the user’s goal achieved.
### How outcome-based testing works
- **Intent recognition**
- Understands the real goal behind the user messages
- Can be defined manually or extracted via LLM
- Goes beyond keywords to capture actual intent
- **Outcome validation**
- Compares the AI’s result to what should’ve happened
- Tracks if the correct outcome was delivered
- Focuses on what the AI did, not what it said
- **Workflow verification**
- Actionbook check: Was the right business logic triggered?
- Tool check: Were the correct integrations used?
### The business impact
**Outcome-based testing turns AI quality from a guessing game into a measurable system.** You’re no longer relying on surface-level responses, you’re validating whether the AI understood the user, triggered the right workflows, and delivered the actual result.
That means fewer silent failures, fewer escalations, and fewer customers left hanging.
For enterprise teams, it’s more than improved QA. It’s how you de-risk AI in production. You can now test your AI like any critical system: with clear goals, real metrics, and accountability. Advanced outcome-based testing now available. [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Data export API: Turn AI agent conversations into actionable business intelligence
Source: https://delight.ai/product-release/data-export-api
## Data export API: Turn AI agent conversations into actionable business intelligence
Jul 16, 2025
Enterprise teams need more than chat logs, they need performance data that drives decisions. With Sendbird’s new **Data Export API**, you can access detailed conversation and performance data from your AI Agent and feed it directly into your BI or reporting systems.
Designed for enterprise teams who demand full observability, this API gives you structured access to conversation-level and daily performance data. Whether you’re auditing resolution outcomes, tuning your AI workflows, or reporting on CSAT across business units, **you now own the source of truth.**
## Why it matters
- **Complete data ownership:** Export conversation metrics, message logs, CSAT scores, resolution outcomes, and handoff rates on your schedule.
- **System integration:** Pull data in JSON or CSV format to feed your existing reporting stack and business intelligence tools.
- **Performance clarity:** Track macro trends like resolution rates and sentiment alongside granular conversation details, all in one structured dataset.
## What’s included
- **Conversation-level exports:** Get full metadata, user/agent message counts, sentiment, resolution type/time, hallucination flags, handoff and abandonment markers.
- **Message logs:** Trace individual messages with hallucination indicators, flagged content, and AI activity logos.
- **Daily rollups:** Aggregate key metrics like resolution rates, CSAT scores, handoff percentages, and user behavior patterns for trend analysis.
## Security essentials are built in
PII masking protects sensitive content, exports are retained securely for 7 days, and the API follows the same structure as other Sendbird exports to make it easy to integrate and scale.
Now available for all AI agent customers. [Contact us](https://sendbird.com/ai/contact-sales) to request access.
## Selective deployment & rollback: Built for control in complex teams
Source: https://delight.ai/product-release/selective-deployment-and-rollback
- [Product release](/product-release)
## Selective deployment & rollback: Built for control in complex teams
Jul 1, 2025
**Enterprise AI agents aren't managed by one person. They're shaped by many.** Knowledge teams, tool builders, compliance leads, product owners. When everything auto-deploys together, one bad change brings down the whole system.
Now, you’re in control. With **Selective Deployment**, you decide exactly what ships to production — and what stays in dev.
### Deploy only what’s ready
Take command of your deployment pipeline with precision controls:
- **Pick and choose** which Actionbooks, Knowledge files, and settings go live.
- **Change list preview** shows detailed edits, additions, and disabled items.
- **Smart filters** and search capabilities for fast selection.
- **Dependency warnings** alert you if something critical is left behind.
- **Auto-included tools and templates** ensure nothing breaks.
### Roll back, without rework
Need to undo a bad deploy or revert to a known-good version? We've got you covered:
- **Restore any deployment** from the past 30 days
- **Get** **alerts for deleted or outdated content**
- **Partial rollbacks** with built-in guardrails to avoid inconsistencies
### Designed for multi-team operations
Built for the way enterprise teams actually work:
- **Teams can move independently**, without stepping on each other
- **Ship confidently**, knowing only verified updates are included
- **Every deploy is tracked** — who deployed what, and when
**You build carefully. Now you can deploy the same way.**
## Flagged message monitoring
Source: https://delight.ai/product-release/flagged-message-monitoring
- [Product release](/product-release)\n- Flagged message monitoring\n\n## Flagged message monitoring\n\nJun 30, 2025\n\n![Flagged message monitoring](https://sendbird.imgix.net/cms/Flagged-message-monitoring.webp?fm=webp&fit=max&w=904&q=100&dpr=1.4&sharp=5 \"Flagged message monitoring\")\n\n**Your AI is out there handling real conversations, but not everyone plays nice.**\n\nYou’ve set the guardrails: no harmful content, blocked phrases, protections against prompt injection. But when users test those boundaries, do you know what’s actually happening? Are your safeguards working as intended?\n\nWith **Flagged Message Monitoring**, you get a dedicated dashboard that tracks every time a safeguard is triggered. Whether it’s a banned keyword, a hostile prompt, or an attempted exploit — you’ll see exactly what users tried, what flagged it, and how your AI responded.\n\n### What this means for your team\n\n- **Close the loop between build and monitor.** The rules you set during setup — from banned words to safety policies — are now fully traceable in real conversations.\n- **Understand every violation.** Each flag includes a transparent explanation of what triggered it, so reviewers can respond quickly and accurately.\n- **Spot new threats. Strengthen safeguards.**Identify patterns in how users attempt to circumvent your guardrails and strengthen your defenses accordingly.\n- **Refine your policies.** Use flagged conversations as direct feedback to adjust thresholds, add new terms, or update detection logic.\n- **Document compliance.** Generate clean audit trails to show how your AI handles violations and upholds safety standards.\n\n\n\n### What’s included\n\n- **Comprehensive tracking** monitors violations across all your safeguard categories—guardrails, adversarial attacks, banned phrases, and harmful content detection.\n- **Clear explanations** show exactly what triggered each flag, so your team understands the violation without guesswork.\n- **Trend analysis** reveals patterns in violation attempts over daily, weekly, and monthly views—helping you spot emerging attack vectors.\n- **Filterable conversation list** lets you drill down by violation type, user ID, or time period to investigate specific incidents.\n- **Direct conversation links** provide full context around each flagged interaction for thorough review.\n\nThis feature is part of Sendbird’s broader commitment to building **accountable, responsible AI agents for customer service.** From what you define to what your AI says — we’re closing the loop with visibility, control, and safeguards that scale.
## One-click convert to knowledge: Teach your AI agent from real conversations
Source: https://delight.ai/product-release/one-click-convert-to-knowledge
- [Product release](/product-release)
## One-click convert to knowledge: Teach your AI agent from real conversations
Jun 17, 2025
Your AI agent is only as good as the knowledge it has, but no knowledge base is ever truly complete. Customers surface new edge cases. Teams make one-off policy calls. And **sometimes your best training data isn't in a doc—it's in the conversations you're already having.**
With **One-Click Convert to Knowledge**, you can instantly convert any past conversation into structured, reusable knowledge for your AI Agent. Whether it’s a golden resolution to replicate, a failure to avoid, or a policy gap you haven’t documented yet — you can now teach your AI how to handle it with zero friction.
### Why it matters
- **Capture real-world insights.** The best knowledge comes from actual conversations, not just documentation. Now you can harvest those insights as they happen.
- **Close gaps immediately.** Save critical moments before they disappear into chat history. No waiting for documentation sprints or backlog cleanups.
- **Train from reality.** Every save makes your AI Agent smarter based on how your team actually works, not just how you think it should work.
### Put it to work
- Save high-performing conversations as AI reference points
- Flag poor outcomes to prevent repeat mistakes
- Document edge cases and new policies on the fly
- Build AI coverage between major knowledge base updates
Now available for all AI agent customers. Find a conversation → Click **\"Create knowledge snippet\"** → Your AI learns instantly.
## AI hallucination detection & alerts
Source: https://delight.ai/product-release/ai-hallucination-detection-alerts
- [Product release](/product-release)
## AI hallucination detection & alerts
May 22, 2025
**AI mistakes are inevitable. What matters is how quickly you detect them, and how effectively you respond.**
With **Hallucination Detection** and **Visible Flag Reasons**, you’ll get real-time alerts when your AI Agent goes off-script — plus a clear explanation of *why* each response was flagged. Whether it’s a low-confidence answer, an ungrounded claim, or a policy violation, you’ll see exactly what triggered the flag and what action to take.
These tools give your team the visibility, context, and control needed to monitor quality, reduce risk, and continuously improve AI performance.
### How it works
- **Real-time detection** flags potentially problematic responses based on confidence thresholds and content analysis.
- **Clear reasoning** explains why each message was flagged—whether it's low confidence, policy violations, or ungrounded content.
- **Actionable review queue** lets you approve, correct, escalate, or dismiss flagged responses with full context.
- **Trend monitoring** shows hallucination patterns over time so you can spot systemic issues before they spread.
- **Webhook integration** connects alerts to your existing monitoring and escalation workflows.
### What this means for you
- **Speed beats damage control.** Catching problematic responses in real-time prevents customer frustration and protects your brand reputation.
- **Context drives better decisions.** Understanding why something was flagged helps you review faster and train your AI more effectively.
- **Scale without fear.** Reliable monitoring means you can expand your AI agent’s responsibilities without worrying about unexpected failures slipping through.
**These features are part of Sendbird’s broader commitment to building responsible, accountable AI Agents for customer service.** From observability to control, we’re investing in the infrastructure that makes AI reliable at scale — so you can deploy with confidence, improve continuously, and stay in control.
## Sendbird localized AI agent built for real-world scale
Source: https://delight.ai/product-release/sendbird-localized-ai-agent-built-for-real-world-scale-and-accuracy
- [Product release](/product-release)\n\n## Sendbird localized AI agent built for real-world scale\n\nMay 13, 2025\n\n![Sendbird localized AI agent built for real-world scale](https://sendbird.imgix.net/cms/geo-aware_availability.webp?fm=webp&fit=max&w=904&q=100&dpr=1.4&sharp=5 \"Sendbird localized AI agent built for real-world scale\")\n\nMore AI agents treat localization as a translation layer only, as if changing the language is enough.\n\nBut global companies know the truth: operating across countries means navigating different regulations, support policies, workflows, and expectations. These differences don’t just affect users — they shape how your teams create, manage, and scale content across regions.\n\nAt Sendbird, we build for the complex reality of how global businesses actually operate – across countries, languages, and locations, with tens of thousands of knowledge articles, country-specific policies, and real differences across regions.\n\nThat’s why we built the **Sendbird localized AI agent** — not just to translate, but to **retrieve and act on the right knowledge for every country, language, and user context**.\n\n### What’s new\n\nNow, a single Sendbird AI agent can dynamically adapt to each user’s country, language, and operational context – delivering the right knowledge, the right workflows, and the right experience, every time.\n\n- **Geo-tagged & context-aware knowledge & actionbooks:** Knowledge sources and actionbooks can be tagged global or country-specific. The AI dynamically selects the right set of actions based on the user’s country and region.\n- **Country & language-aware retrieval (RAG):** We filter and rank knowledge based on country, language, and fallback logic — ensuring the AI only sees what’s relevant to each user’s locale.\n- **First-class language configs with precision fallback logic:** You define which languages are fully supported in each country — and in what order of preference. The AI uses this config to make smart decisions: when to translate a user’s query, when to run dual-language retrieval, and when to avoid showing content from irrelevant markets.\n\n\n\n### Built for real global operations\n\nGlobal enterprises don’t just need translation. Whether you’re managing 10 markets or 50, the challenge is scaling answers that stay precise across languages, regulations, and teams.\n\nHere’s how it works in practice:\n\n**User in Japan, speaks English:** A user asks in English. Instead of defaulting to global answers, the AI retrieves Japan-specific policies written in Japanese, translates them back to English, and responds. The result? A fully localized answer — in the user’s language — that reflects Japan’s rules, not just global defaults.\n\n**User in Germany, speaks Turkish:** Turkish isn’t a supported language in Germany’s knowledge base. The AI translates the query into German, retrieves only German and global content, and replies in Turkish. The user gets local German policies in their language — not mismatched content from Turkey.\n\n### Localization isn’t just a language problem, it’s a retrieval problem\n\nYour business doesn't operate in one language, one policy, or one workflow. Your AI shouldn’t either.\n\nSendbird’s localized AI Agent brings structure to that complexity. It dynamically adapts to user location, language, and your content coverage — ensuring each response is both locally accurate and globally consistent.\n\nThe result? One AI agent that scales across markets without sacrificing operational relevance. [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Deployment and production mode: Stay in control at scale
Source: https://delight.ai/product-release/deployment-and-production-mode-stay-in-control-at-scale
- [Product release](/product-release)
## Deployment and production mode: Stay in control at scale
May 8, 2025
If your business manages multiple teams, product lines, services, or regions, keeping your AI agents aligned can quickly become complicated.
Imagine you run a marketplace with separate AI agents for buyers and sellers. Your buyer agent helps users discover products and manage returns, while your seller agent handles onboarding, payouts, and support.
Each agent has distinct content, scripts, and workflows. Until now, updating content for sellers risked confusing your buyers, and vice versa—leading to unintended customer friction.
**Mistakes like these happen because most AI platforms aren’t built for flexibility. You either deploy everything at once—or not at all.**
We are fixing that.
### What’s new
Sendbird now offers clear separation between two environments for every AI agent:
- **Development:** A safe workspace to build, test, and adjust your entire AI experience, including knowledge bases, Actionbooks, canned responses, integrations, and more, without touching live interactions.
- **Production:** A stable, read-only environment for your customers. Fully protected from unexpected or unapproved changes..
This unlocks precise control over how you operate at scale:
- **Shared workspace assets:** Easily manage common resources such as communication style, brand safeguards, knowledge content, Actionbooks, and integrations across multiple agents.
- **Tailored deployments**: Independently update agents to precisely target your audience segments or business units (e.g., buyer vs. seller), ensuring only the intended agents are affected.
- **Clear visibility:** Changelogs and deployment histories clearly show what changed, who published, and when—perfect for compliance and operational transparency.
### Move faster while staying in control
AI is never static. Teams are constantly iterating—new knowledge, new flows, new content. But in fast-moving environments, that speed often comes at a cost: rushed rollouts, unclear ownership, and unexpected breakage in live systems.
**With this update, you don’t have to choose between velocity and control.**
Now, every agent has a clear boundary between staging and production. Your teams can build and test freely, while your live experiences stay consistent and protected.
Here’s what this unlocks:
- **Faster iteration, safer launches.** Teams can test changes freely without fear of breaking production—no bottlenecks, no fire drills.
- **Targeted rollouts at scale.** Whether you’re managing 2 agents or 20, across regions, products, or user types, you can deploy exactly where needed—by product line, region, user segment, or platform surface—without unintended side effects.
- **Build trust, internally and externally**. Changelogs and deployment history bring full accountability to AI operations—so you always know what’s live, what’s changed, and who made it happen. Perfect for compliance, troubleshooting, or exec visibility.
Deployment and production mode is live now for all Sendbird AI Agent customers. [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## AI agent scorecards: Bring oversight and accountability to AI
Source: https://delight.ai/product-release/ai-agent-scorecards-bring-oversight-and-accountability-to-ai
- [Product release](/product-release)
## AI agent scorecards: Bring oversight and accountability to AI
May 1, 2025
In every support operation, human agents are managed — agents aren’t just hired and forgotten.
You coach them, measure their quality, give feedback, and help them improve.
This is how great support organizations stay great.
But when it comes to AI agents, most teams treat them differently. They’re launched once — and left to operate on autopilot. No regular oversight. No conversation reviews. No feedback loops. No growth.
That gap creates silent risks — bad conversations, lost customers, missed opportunities to improve.
**Good AI isn’t just self-correcting. Good AI is managed.** Just like your best teams are.
### Scorecards for AI agent
At Sendbird, **we believe building a great AI agent doesn’t end at launch.**
It’s about ongoing oversight, continuous learning, and operational excellence – just like managing a human support team.
AI agent scorecards give you the power to:
- Review conversations one by one, right inside the dashboard.
- Score across four key quality categories: Tone, clarity and readability, relevance, and solution effectiveness.
- Tag common issues with preset reasons for faster, structured reviews.
- Leave coaching notes to capture context, patterns, and feedback.
And it doesn’t stop at review:
With enough scorecards, your feedback becomes fuel — helping AI agents improve over time. The more you evaluate, the better your AI can become.
Now, your AI agents can be evaluated and coached with the same rigor as your human agents — and grow from the feedback.
AI agent scorecards are now available on Sendbird AI agent for customer service.
Turn your AI agents into managed, accountable members of your team.
## Activity trails: Full visibility into how your AI agent acts
Source: https://delight.ai/product-release/activity-trails-full-visibility-into-how-your-ai-agent-acts
- [Product release](/product-release)
## Activity trails: Full visibility into how your AI agent acts
Apr 30, 2025
When a customer talks to your AI agent, trust is built (or broken) with every response. Yet until now, you could only see the final answer, not the path that created it.
But behind every message, there’s a hidden world:
- What context did the AI use?
- What knowledge guided its answer?
- What actions were taken to get there?
Until now, it’s been hard to see. You could review the final output, but the decision path remained a black box, leaving teams guessing, not knowing.
**Sendbird AI agent activity trails** change that. Now you can see the full decision path, with full visibility into the context, data, and actions that shaped every AI response. Giving you the clarity to trust not just what your AI says, but exactly how it got there.
### What’s new
With Activity Trails, Sendbird AI now lets you:
- **See the full context behind every response**, including goals, communication style, guidelines, and user attributes.
- **Understand which knowledge and tools were used**, trace every answer back to the exact articles, Actionbooks, or APIs involved.
- **Follow the decision process, step-by-step**, from the first welcome message to tool activations to human handoffs, with full visibility into each step.
### Beyond visibility: Know when to intervene
It’s more than just seeing the decision path, you see when the path went off course. Every AI message is automatically flagged if it:
- Contains a hallucination
- Triggered a safeguard (e.g. harmful content, adversarial prompt)
- Violates your communication policies
Combined with full trail visibility, this gives you a multi-dimensional view into every AI response – **what was said, why it was said, and whether it crossed the line.**
This isn’t just observability — it’s protection.
**Confidence comes from clarity.**
When you can see exactly what powered every AI response — the context, the knowledge, the actions — you unlock real visibility into how your AI operates.
That visibility builds trust, giving you the transparency to stand behind every conversation.
And with trust comes true confidence: the confidence to launch without fear, to scale without surprises, and to know your AI is working exactly as intended — no black boxes, no guesswork.
Activity Trails is live now for all Sendbird AI agent customers. [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## End-to-End AI agent testing — Build trust before you go live
Source: https://delight.ai/product-release/end-to-end-ai-agent-testing-build-trust-before-you-go-live
- [Product release](/product-release)
## End-to-End AI agent testing — Build trust before you go live
Apr 28, 2025
It’s 2 a.m. and a subscription cancellation request pings your bot.
One wrong API call, and the customer wakes to a double-charge.
By 7 a.m. your inbox is a crime scene—chargeback alerts, angry tweets, finance in full-stop mode.
Most testing stalls at a single FAQ—one question, one answer, and done. But real conversations have follow-ups, API tool calls, context switches, and edge cases.
**Real risk hides deeper in the dialogue, not in the first reply.**
**Sendbird’s E2E Test changes that.**
Now you can simulate the entire customer journey—every message, every action, every decision—*before* you ship. Most importantly, no two customers are ever the same.
Real conversations vary by user, they are on different plans, statuses, and scenarios. **Sendbird’s E2E Test ensures your AI agent adapts — to every customer.**
### What’s new
With **E2E AI Agent test**, Sendbird AI now lets you:
- **Test full conversations**: Upload real customer < > agent dialogues to replay and test the entire back-and-forth.
- **Simulate real-world flows:** Trigger tool calls, hit live or mocked APIs, and mirror the complexity of real production environments.
- **Catch breakdowns before customers do:** Automatically detect where the AI drifts from the expected human behavior, and spotlight exactly where it goes off track.
- **Trace issues back to source and improve:** Every failure links you back to the exact Actionbook step or Knowledge Base article, so you know *exactly* what to fix.
- **Growing your customer testing library:** Our E2E Test isn’t a one-time check. Customers can continuously accumulate real-world scenarios, building a dynamic, ever-expanding library of critical tests over time.
- **Schedule regular health checks:** Run your full test suite daily, weekly, or monthly to catch issues early and maintain AI performance over time.
### Why it matters
Confidence comes from testing every message, not hoping for the best. With Sendbird E2E test, you can now:
- **Launch with confidence**. Know your AI agent can handle full conversations, not just scripted FAQs.
- **Sleep better at night**. No 2 a.m. surprises, no customer-facing disasters.
- **Protect customer trust**. Every message your AI sends either builds loyalty—or risks losing it.
- **Move faster without compromise**. Cover real-world complexity without slowing down launches.
**E2E Test** is live now for all Sendbird AI Agent customers.
Launch with confidence. Sleep easy. Trust your AI — because you’ve tested the whole journey.
## Sendbird’s unified support: AI agent & Desk
Source: https://delight.ai/product-release/unified-ai-agent-support-suite
- [Product release](/product-release)\n- Sendbird’s unified support: AI agent & Desk\n\n## Sendbird’s unified support: AI agent & Desk\n\nApr 2, 2025\n\n\n\nSupport today is stitched together, not streamlined. AI tools, ticketing systems, and messaging platforms may be technically integrated, but they are functionally isolated.\n\nData moves between systems, but not in real time. Not with full context. Customer history is stored, but buried across tabs and tools.\n\nAgents see the tickets, but not the full conversation that led up to it. Support workflows are automated, but only *within* each tool, not *across* them.\n\n**Connected tools aren’t the same as a connected experience.** We’re changing that.\n\n## Meet Sendbird Desk + AI agent\n\n[**Sendbird Desk**](https://sendbird.com/products/chat-messaging/customer-support) is our first-party support software, built from the ground up with in-app messaging, ticketing, and agent tools in a single platform.\n\nNow, with **AI agent tightly integrated**, Desk becomes the first truly unified support suite – where automation and human agents work together in one thread, with full context and nothing lost in translation. No middleware, no hacks, no context lost in translation. From first touch to final resolution, it’s one seamless experience—for agents, ops, and customers alike.\n\n\n\n## What makes it different?\n\nMost AI agent support tools integrate *around* your help desk, not into it.\n\nWhen an AI agent connects to third-party ticketing platforms via limited APIs. That restricts what AI can access – agent states, ticket status, full history – and limits how smoothly it can escalate or resolve issues. That means shallow context, clunky escalations, and brittle handoffs that leave your customers frustrated.\n\n**With Sendbird Desk + AI Agent**, everything lives in one ecosystem. That means:\n\n- **Shared channel, no context lost:** AI and human agents operate within the same Sendbird channel - no need to pass transcripts during escalation.\n- **Deeper visibility for human agents:** agents can see the entire AI - user conversation instantly, with zero lag or repeated work.\n- **One product, one partner**: No third-party dependencies. One vendor, one contract, one integration to maintain.\n- **Consistent analytics, shared CSAT**: Measure and optimize performance across AI and human support with shared analytics and CSAT tracking.\n\n## Lean by design, and built for clarity\n\nLegacy platforms are bloated, they are stacked with unused features, complex interfaces, and hidden costs. They slow teams down and drive up the cost.\n\nSendbird Desk + AI Agent is lean by design.\n\nWith messaging, ticketing, and AI in a single, unified platform, every feature is intentional, with no redundancy or excess.\n\nThe result is faster onboarding, minimal maintenance, and a better agent experience. So your team spends less time managing complexity and more time delivering results.\n\n\n\n**The future of support isn’t stitched together. It’s unified.** With Desk + AI Agent, you can streamline operations, elevate customer experience, and scale support with confidence – all from a single platform. [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Proactive Chat: AI that reaches out first
Source: https://delight.ai/product-release/proactive-chat-ai-that-reaches-out-first
## Proactive Chat: AI that reaches out first
Mar 28, 2025
Picture this: **An order is clearly delayed – your system knows it.** The customer knows it too. But they still have to dig through your help center, hit chatbot dead ends, and repeat their issue three times before someone helps.
**A storm is about to ground flights – your system sees the alerts.** But even your premium travelers have to contact you first to explore their options.
**A long-time subscriber hasn’t logged in for 4 months.** You do nothing, until they hit “Cancel.”
**A driver’s license is about to expire.** But your AI stays quiet – until the platform blocks the user.
The frustration? Totally avoidable. The data? Already in your hands.
And yet, nothing happens.
Most AI agents just wait – sitting quietly until a customer raises their hand. By then, it’s too late. They’re already frustrated. Already thinking about leaving.
**What if support moved first? What if your AI reached out before your customer did?**
## What’s new
**Proactive Chat** gives the AI agent the power to move first, reaching out before frustration builds, before churn begins, before a ticket even exists.
With [Proactive AI agents](http://sendbird.com/ai-agent/omnipresent), you can now:
1. **Detect key signals using real-time triggers** based on user behavior, system events, or external APIs/webhooks.
2. **Engage across channels:** Activate proactive messages simultaneously across in-app chat, SMS, WhatsApp, and more – wherever your users are.
3. **Deliver personalized, contextual messages.** Use customer-specific data and historical context to tailor every message. Making it feel timely, relevant, and human.
Each [proactive AI interaction](http://sendbird.com/ai-agent/omnipresent) is built on three core components:
1. **Dynamic conditions.** Define real-time triggers based on user attributes, activity, delivery status, or system events. Decide *when*, *why*, and *how* the AI should engage first.
2. **Prior knowledge.** Leverage existing context like VIP status, past purchases, or usage patterns to personalize messaging and build trust instantly.
3. **Channel orchestration.** Choose *where* and *how many* channels to activate. From a single message to a coordinated multi-channel experience delivered simultaneously.
## Why it matters
Reactive support puts your business on defense. [Proactive AI chat](http://sendbird.com/ai-agent/omnipresent) flips the script, helping you intercept issues early, reduce ticket volume, and build customer trust before frustration sets in. With Proactive Chat, you can:
- Reduce inbound volume and cut operational costs
- Prevent churn by engaging high-risk users early
- Drive conversion by stepping in when intent is high
- Turn support into a brand moment, not a damage control effort
And your team stays in full control – defining triggers, copy, timing, and performance tracking in one place.
Proactive Chat is how modern support gets ahead of problems. Now available on [Sendbird AI agent for customer service](https://sendbird.com/). [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Sendbird AI agent on WhatsApp
Source: https://delight.ai/product-release/sendbird-ai-agent-on-whatsapp
## Sendbird AI agent on WhatsApp
Mar 25, 2025
We’re bringing the Sendbird AI agent to the world’s most popular messaging app.
Your customers already trust WhatsApp. With over 2 billion users globally, it’s not just the most popular messaging app - it’s a daily habit, it’s where conversations *start and stick*.
Now, your support can live there too, with **Sendbird AI agent for WhatsApp**.
### What’s new
You can now deploy your AI agent on WhatsApp to deliver fully automated, human-like customer support, without customers ever leaving the app.
### Why it matters
**Customer expectations have changed. They want fast, effortless support – on channels they already use.** WhatsApp delivers exactly that. By bringing the Sendbird AI Agent to WhatsApp, you can meet customers where they are with real-time, human-like, and contextual conversations.
**There’s no app to download, no login to remember, and no learning curve.** Customers send a message like they would to a friend, and the AI agent handles the rest – from resolving issues to capturing feedback and closing the loop. It’s automation that feels intuitive, familiar, and personal - not transactional.
**Behind the scenes, teams stay in full control.** Configure consent messages, set conversation-close timing, track CSAT, and monitor performance – all from one place. This is scalable support without the trade-offs: personal, fast, and easy to manage.
**Modern support that’s fast, familiar, and always-on, now available on Sendbird AI agent for customer service.** [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Salesforce supported as a knowledge source
Source: https://delight.ai/product-release/salesforce-supported-as-a-knowledge-source
- [Product release](/product-release)
## Salesforce supported as a knowledge source
Mar 20, 2025
Customer service teams depend on fast, accurate information. But knowledge is often scattered – spread across multiple tools, buried in outdated uploads, or requiring constant manual updates.
With Salesforce knowledge source integration, AI agents can now pull answers directly from live Salesforce Knowledge articles. No more outdated documents. No more manual syncing. Just **real-time, authoritative responses from your company’s single source of truth.**
## What this means for customer service teams:
- **Accurate, up-to-date responses**: AI agents retrieve the latest policies, product details, and operating instructions directly from Salesforce knowledge.
- **Zero manual effort**: No need for teams to manually upload or update documents. Everything stays in sync automatically.
- **Faster resolutions**: AI agents provide consistent, context-rich answers, reducing escalations and improving customer satisfaction.
Salesforce now joins the expanding list of supported knowledge sources, including [Confluence](https://sendbird.com/ai-agent/product-release/confluence-knowledge-source), website URLs, direct file uploads, and Zendesk. Available now on [Sendbird AI agent for customer service](https://sendbird.com/).
## Sprinklr integration for AI to human handoff and knowledge
Source: https://delight.ai/product-release/sprinklr-integration-for-ai-to-human-handoff-and-knowledge
- [Product release](/product-release)
- Sprinklr integration for AI to human handoff and knowledge
## Sprinklr integration for AI to human handoff and knowledge
Mar 20, 2025
Support gets smarter and more seamless with the **Sendbird AI agent + Sprinklr integration**. Now you can escalate conversations to human agents directly in Sprinklr, while also training your AI using your existing Sprinklr knowledge base.
With Sendbird AI agent's integration with Sprinklr, you can:
- **Auto-route conversations:** Seamlessly transfer chats from AI to human agents in Sprinklr for smooth, real-time handoffs when a human touch is needed.
- **Train AI with Sprinklr knowledge**: Instantly enhance AI accuracy by syncing your Sprinklr knowledge base to deliver faster, context-aware answers to customers.
Drive faster resolutions, lighten the load on your agents, and create better customer experiences using the tools you already trust. [Contact us](https://sendbird.com/ai/contact-sales) to get started.
## Multi-language AI agent that not just translates
Source: https://delight.ai/product-release/multi-language-localization
## Multi-language AI agent that not just translates
Mar 18, 2025
Most AI agents claim to "support" 100+ languages. But in reality, they’re just translating the same answers across regions - like running a global customer support through Google Translate.
Real support isn’t just about language. It’s about context, culture, and operational know-how. Knowing not just what to say, but how and why things work differently in each market.
That’s why global enterprises don’t rely on a single, centralized team to handle customer questions in different languages. Instead, they build localized teams, each with deep expertise in their regions.
That’s exactly how Sendbird’s Multi-language AI agents work.
### How are we different?
- Independent AI agent, not just translation. Instead of a single AI bot translating responses, each AI agent operates independently—just like a real-world regional team.
- Own knowledge base & internal data. Every AI agent pulls from region-specific sources, ensuring answers aren’t just translated, but contextually correct.
- No cross-region interference. AI agents operate independently, eliminating mismatched responses across markets.
Built for enterprise operations. Each AI agent can connect to different CRMs, helpdesks, and internal databases, mirroring how global businesses actually operate.
For global brands, this means an AI agent team that feels as real as a local team - scalable, compliant, and truly built for the way enterprises operate.
### How it works
Create up to 5 independent AI agents per language, region, or teams.
Set AI agent’s language. This determines the language used initially by the AI Agent to welcome the user.
Configure the welcome messages, suggested replies depending on the language set.
Manage AI agent’s knowledge, manuals, settings, and data individually, making sure AI agents follow language or region specific policies.
## Version Control for Actionbooks & Profile
Source: https://delight.ai/product-release/action-books-management
- [Product release](/product-release)\n- Version Control for Actionbooks & Profile\n\n## Version Control for Actionbooks & Profile\n\nMar 18, 2025\n\n\n\nKeeping AI agents aligned with your business isn’t a one-and-done task, it is an ongoing process. As your operations evolve, so do the instructions guiding your AI. But updating them without visibility or testing? That’s a recipe for inconsistency.\n\nTracking changes manually? Inefficient. Deploying untested updates? Unpredictable.\n\nThat’s why we’re introducing **Version control for Actionbooks & Profile,** so you can track, test, and refine AI behavior with confidence.\n\n### What’s different?\n\n- **Track every change.** Easily see what’s been updated in your AI agent’s instructions over time.\n- **Test before going live.** Try out new instructions in a safe environment before applying them to real customer interactions.\n- **Revert anytime.** If an update doesn’t work as expected, roll back to a previous version with one click.\n- **Better control.** Keep a clear record of all changes to ensure AI agents follow the right guidelines.\n\n\n\nFor teams managing AI agents, this means more control, fewer surprises, and AI that consistently delivers the right experience. Now available on [Sendbird AI agent for customer service](https://sendbird.com/).\n\n[Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Sendbird AI agent team — specialized AI agents for every role
Source: https://delight.ai/product-release/team
- [Product release](/product-release)
- Sendbird AI agent team — specialized AI agents for every role
## Sendbird AI agent team — specialized AI agents for every role
Mar 14, 2025
A single AI agent handling every conversation — sales, support, onboarding — for every customer and every situation?
That’s not how real teams work. And it’s definitely not how your business runs. With Sendbird AI agent team, you can build a team of specialized AI agents, where each agent has its own role, purpose, inputs, and outputs. Each agent comes with its own knowledge, manual, and guardrails, purpose-built for the job it’s meant to do.
### Why this matters
Even if a single AI agent follows different manuals and playbooks, it’s still pulling from the same shared knowledge base and working under the same global rules.
Even worse, the insights from every conversation get lumped together — mixing sales signals, support trends, and churn risks into one messy pool of data.
That creates problems across the board:
- **Data governance risks.** Sensitive customer data from support cases can leak into sales recommendations, creating compliance issues and damaging customer trust.
- **Regulatory and compliance challenges.** Different markets, products, and conversation types need their own data handling rules — rules a single agent can’t fully enforce.
- **Blurry performance tracking.** You can’t optimize AI for sales, support, and retention when all the data is blended — leaving you guessing what’s working, what’s failing, and how to improve.
With AI agent team, each AI agent stays in its lane:
- **Data stays clean.** Each agent has its own knowledge base and conversation history, so sales, support, and retention data never overlap.
- **Access stays controlled.** Each agent only accesses the data, systems, and tools it needs, reducing governance risks.
- **Compliance stays intact.** Agents follow region or product-specific policies, supporting local regulations and industry standards.
- **Performance stays clear.** Track and optimize each agent’s conversations, outcomes, and impact, with clean data and role-specific insights you can optimize.
Your human teams are built with specialized roles for a reason — your AI should work the same way.
With the AI agent team, every agent is purpose-built, governed, and optimized for its specific job, working seamlessly together to power your entire customer journey. Now available on [Sendbird AI agent for customer service](https://sendbird.com/).
## Sendbird AI agent now available on email
Source: https://delight.ai/product-release/sendbird-ai-agent-now-available-on-email
- [Product release](/product-release)
## Sendbird AI agent now available on email
Mar 13, 2025
We’re bringing the [Sendbird AI agent](https://sendbird.com/ai-agent) to your customers’ inbox.
Email is the backbone of customer support for many businesses – and for good reason. But traditional email support is slow, manual, and hard to scale. Customers wait. Tickets pile up. Resolution times stretch.
With **Sendbird AI agent for email**, your team can deliver instant, intelligent, and fully automated support—directly through your existing email channels.
### What’s new
You can now deploy the Sendbird AI agent to your support email address. It reads and understands incoming messages, replies with human-like responses, and resolves issues end-to-end, without human intervention.
### Why it matters
- **Email remains essential.** It’s one of the most widely used support channels – and often the first point of contact for customers.
- **Expand your reach, without expanding your team.** Automate high volumes of email inquiries without sacrificing quality.
- **Human-like support, at scale.** The AI understands context and tone, delivering fast, natural responses.
- **Frictionless for customers.** No apps, no portals – just email. Customers write in, the AI handles the rest.
For customers, it’s just email.
For your team, it’s automation that scales.
With **Sendbird AI agent for Email**, your team can deliver always-on, enterprise-grade support through the world’s most familiar channel – without adding complexity or cost.
## Freshworks integration for AI to human handoff
Source: https://delight.ai/product-release/freshworks-integration
## Freshworks integration for AI to human handoff
*Mar 11, 2025*
Your AI agent can resolve many conversations, but some situations require a human touch. A clunky handoff frustrates customers, disrupts workflows, and leaves agents scrambling for context.
With **Sendbird AI agent integration with Freshworks**, AI agents can escalate conversations directly to Freshchat, keeping all the conversations in one place.
### Why it matters
- **Faster resolutions**: AI hands off conversations instantly, so human agents can step in without delay.
- **No disruptions**: Customers don't need to switch platforms or repeat themselves. Conversations stay within the same chat.
- **Complete context:** AI-generated transcripts and summaries ensure agents have all the information they need.
- **Unified insights**: Track AI resolution rates, handoff rate, and customer satisfaction - all in one place.
For support teams, this means smoother workflows, faster responses, and better customer experiences.
Now available on [Sendbird AI Agent for customer service](https://sendbird.com/). [Contact us](https://sendbird.com/ai/contact-sales) to learn more.
## Sendbird AI agent on SMS
Source: https://delight.ai/product-release/sms
## Sendbird AI agent on SMS
Mar 3, 2025
"Hey, I need help"
That’s how most customer support conversations start. Simple, direct. But here is the problem - most support systems aren’t built for real conversations.
Instead, you get a ticket. A number in a queue. Maybe an email response - only hours later. Maybe you get stuck in a chatbot loop, pressing buttons, just hoping to talk to someone who can actually fix the problem.
We think there’s a better way. So we built an AI agent for SMS.
[**SMS AI agent demo**](http://sendbird.com/ai-agent/sms)
### AI Agent on SMS
A fully automated customer service [AI agent that texts ](http://sendbird.com/ai-agent/sms)like a human and solves your issue, end-to-end, over SMS. You send a message. That’s it. No apps. No waiting. No dead ends. Just a real and fluid conversation that gets things done.
### Why SMS?
- **Always accessible** – The most universal support channel. No downloads. No internet required.
- **Preferred by modern customers** – 82% of Millennials and 74% of Gen Z favor real-time, digital-first service (from [source](https://wpengine.com/resources/resilience-connecting-a-locked-down-generation/)).
- **Feels natural** – Customers reach out just like they would to a human—by sending a simple text.
### How it works
1. **Add a phone number.** To add a phone number, talk to a Sendbird representative. We will assist you with adding a new phone number that the AI Agent will be using to communicate via SMS.
2. **Activate SMS as a channel.** Enable [AI customer support on SMS](https://sendbird.com/ai-agent/sms) and integrate it with your existing workflows.
3. **Set up smart automation.** View channel-specific messages sent from the AI Agent and configure automated flows, such as conversation auto-close time and messages sent once an [AI SMS conversation](https://sendbird.com/ai-agent/sms) closes.
4. **Monitor and optimize performance.** Filter and analyze [AI SMS engagement](https://sendbird.com/ai-agent/sms), resolution rates, and CSAT scores.
With an [AI agent on SMS](https://sendbird.com/ai-agent/sms), businesses gain real-time support and actionable insights, and a frictionless way to engage with customers. Now available on [Sendbird AI agent for customer service](https://sendbird.com/). [Contact us](https://sendbird.com/ai/contact-sales) to learn more or try our [SMS AI agent demo](http://sendbird.com/ai-agent/sms).
## Confluence now supported as a knowledge source
Source: https://delight.ai/product-release/confluence-knowledge-source
- [Product release](/product-release)
- Confluence now supported as a knowledge source
## Confluence now supported as a knowledge source
Feb 4, 2025
Your teams already capture critical processes, product details, and internal knowledge in Confluence. Now, your AI agents can tap directly into that content — staying up-to-date without manual uploads or syncing.
With Confluence connected, your AI agents:
- Pull answers directly from live Confluence pages, no manual uploads.
- Stay up-to-date with your latest policies, product info, and internal processes.
- Handle complex questions by tapping into detailed internal documentation.
Confluence joins our growing list of supported knowledge sources, including website URLs, direct file uploads, text snippets, Salesforce, and Zendesk knowledge base. Now available on [Sendbird AI agent for customer service](https://sendbird.com/).
## Sound Notification: When a new message arrives, users hear it
Source: https://delight.ai/product-release/sound-notification
- [Product release](/product-release)
## Sound Notification: When a new message arrives, users hear it
Mar 3, 2026
Your AI agent is already doing its job — handling the question, maintaining context, sending the right response at the right moment. The conversation is working. The problem is attention. A user switches tabs, gets pulled into a meeting, and the agent's response sits there unread until the session expires and the work starts over.
Sound Notification plays an audio alert the moment a new agent message arrives while the user is on a different browser tab. On mobile, a push notification is sent instead.
### How it works
- **Tab-away alert:** When a new message arrives and the user is not on the chat tab, an audio cue fires immediately. No manual checking required.
- **Mobile push:** On mobile devices, push notifications replace the audio alert so users are reached wherever they are on their device.
- **Zero friction for the user:** There is nothing to configure or opt into. The alert fires as part of the existing chat experience.
- **SDK-level activation:** Sound Notification is enabled through the React SDK web integration, giving teams control over rollout without additional infrastructure.
The conversation was already worth having. This makes sure it gets finished.
## Low CSAT Analysis: Filter bad conversations by failure type
Source: https://delight.ai/product-release/low-csat-analysis
## Low CSAT Analysis: Filter bad conversations by failure type
Mar 1, 2026
A CSAT of 1 or 2 is a signal, not a diagnosis. Was the AI's answer off-topic? Did the customer reject the handoff? Was the refund too small? Or did the situation break down before the conversation even started, a delayed delivery or wrong order that had nothing to do with how the AI responded? Without that distinction, every failed conversation looks the same, and you end up iterating on the wrong thing.
Low CSAT Analysis brings AI-powered failure categorization into the Conversations tab, giving support managers and AI Agent admins a structured way to find, filter, and understand dissatisfied conversations.
### Find the right conversations
Filter by Low CSAT to surface only CSAT 1–2 conversations. Stack it with the AI Confidence filter, which now operates at the conversation level rather than the message level, to surface conversations where the AI both underperformed and expressed uncertainty. That combination gives you the shortest path to the conversations that most need attention.
### Understand why they failed
Each conversation is automatically tagged with one of five failure categories:
- **Inadequate Response:** The AI answered the wrong question, or gave a vague non-answer
- **Inadequate Compensation:** The customer pushed back on a refund or coupon offer
- **Escalation Refusal:** The customer declined a handoff to a human agent
- **Situation Frustration:** The customer was upset about something external. The AI did its job. This is not an agent problem.
- **Others:** Does not fit the above
Click into any flagged conversation and the Insights panel shows the category and a plain-text reason behind it, ordered by impact.
### Track it over time
The trend chart updates to reflect whichever filters are active. Watch whether a spike in Inadequate Response failures is a one-week anomaly or a pattern that points to a gap in your agent configuration.
## Gradual Rollout: Test with real traffic, commit when you're confident
Source: https://delight.ai/product-release/gradual-rollout
- [Product release](/product-release)
## Gradual Rollout: Test with real traffic, commit when you're confident
Mar 10, 2026
When a CX or AI team deploys a change, every user finds out at the same time whether it works. Even a fast rollback means thousands of real conversations already routed to a broken version. For high-volume deployments, that is not a testing strategy.
Gradual Rollout lets teams validate actionbook and model changes against live traffic before committing to a full deployment.
### How it works
- Automatic candidate storage: Every version promoted from Dev to Staging is saved as a candidate automatically. No tagging, no manual steps.
- Two-slot architecture: Production runs a Live slot (current version) and an Idle slot (candidate under test). Conversations split between them by ratio.
- Configurable traffic split: Set the initial ratio from the dashboard using integers between 1 and 99. Start at 1%, increase as confidence grows.
- Conversation-level metrics: Every conversation is tagged Live or Idle. Resolution Rate, CSAT, and other core metrics are broken down by slot and by ratio segment.
- Promote or rollback in one action: Candidate wins - swap slots, Idle becomes Live at 100%. Something looks wrong - cut Idle to 0% immediately. The Live slot never changes.
- Model comparison support: Not limited to actionbook changes. Teams evaluating model updates can route live traffic to a candidate and compare performance before committing.
## Human Initiated Proactive Email in Desk: Reach customers first, without leaving your support queue
Source: https://delight.ai/product-release/human-initiated-proactive-email-in-desk
- [Product release](/product-release)
## Human Initiated Proactive Email in Desk: Reach customers first, without leaving your support queue
Mar 25, 2026
Support has always been reactive by design. A customer runs into a problem, opens a ticket, waits. That model works — until you know something your customer doesn’t yet: a delayed shipment, a scheduled downtime, a billing change. At that point, waiting for them to come to you isn’t neutral. It’s a choice to let the frustration build.
Starting today, agents can [initiate proactive emails](https://sendbird.com/docs/desk/guide/v1/proactive-chats/proactive-chats) — composing rich, formatted outbound messages directly from the Desk dashboard, without switching tools or exporting contact lists.
### How it works
- **Start from where you already work:** Agents initiate proactive emails from Desk dashboard — no separate campaign tool, no exports.
- **Send to new customers on the spot:** If the recipient isn’t in the system yet, agents can create a new customer record at send time. The platform automatically creates both the Sendbird user and Desk customer record.
- **No ticket until they reply:** A ticket is created only when the customer responds — consistent with how Proactive Chat already works. Until then, outbound emails sit in the Proactive Chat queue, where a new Channel column distinguishes email from Messenger threads.
- **Follow up any time:** Agents can send follow-up messages to the proactive email thread before the customer has responded, and any agent can do it — not just the original sender.
## AI Agent Urgent Notice: Push real-time notices without a deployment
Source: https://delight.ai/product-release/ai-agent-urgent-notice
- [Product release](/product-release)
## AI Agent Urgent Notice: Push real-time notices without a deployment
Apr 22, 2026
When a payment gateway goes down or a service disruption hits, customers open a chat session looking for answers. Until now, the only way to get a notice in front of them was to wait on a deployment. That process is too slow for real-time issues. The result: avoidable inbound from customers who just didn't know what was going on.
Starting today, support teams can publish an Urgent Notice directly from the AI Agent dashboard with no deployment required. The notice appears as the first message customers see when they open a new chat session. The AI agent continues handling conversations as normal after the notice is shown.
### How it works
- **No deployment required:** Publish directly from AI Agent Settings. It takes effect immediately, no engineering ticket needed.
- **Notice appears first, agent keeps going:** The notice is the first message customers see when they open a new session. The AI agent continues responding normally after that.
- **Context for the agent, not just customers:** The Agent Context field is appended to the system prompt — the AI agent can answer related follow-up questions on the fly, not just display a static message.
- **Dashboard reflects what's live:** When an Urgent Notice is active, the agent status updates to Active (Urgent Notice) — your team always knows what customers are seeing.
## Dynamic Safeguards: Context-aware responses when safeguards trigger
Source: https://delight.ai/product-release/dynamic-safeguards
## Dynamic Safeguards: Context-aware responses when safeguards trigger
May 13, 2026
Safeguards are the line between an AI agent you can trust in front of customers and one you can't. They catch harmful content, prompt injection attempts, banned terms, and false context before any of it shapes the rest of the conversation. But once a safeguard fires, what users see has historically been the same fixed message. A confused shopper, a jailbreak attempt, and a competitor name on the banned list all get the same line.
That works as a safety net but not as a brand experience. A user whose innocent question happened to brush a banned word reads the same canned apology as someone trying to attack the agent. The detection is doing the right thing. The response is doing the bare minimum.
Dynamic Safeguards replaces the fixed fallback with a safeguard actionbook that generates the response in real time, branching by trigger type and using the full conversation context.
### How it works
- **Actionbook mode for safeguard responses:** Connect any actionbook to your safeguard follow-up action. When a safeguard triggers, the actionbook runs instead of returning the default message.
- **Per-trigger branching:** The actionbook receives the flagged type (`harmful_content`, `adversarial_attack`, `context_injection`, `banned_words`) and the flagged reason as input. Write distinct instructions for each, so an adversarial probe and a banned word brush get different responses.
- **Full conversation and user context:** The actionbook has access to the entire conversation history and any user context objects you've configured, including membership tier, region, and language. VIPs can get a more empathetic response. Users in regulated regions can see the right disclaimer.
- **Backup message as safety net:** A backup safeguard message is required even in Actionbook mode. If the actionbook fails or times out, the backup takes over, so users always get a response.
Dynamic Safeguards is part of [Trust OS](https://delight.ai/trust-os), turning the moment a safeguard fires from a dead end into a response that still sounds like your brand.
## Zero-Touch Improvement: Autonomous AI agent optimization with human oversight built in
Source: https://delight.ai/product-release/zero-touch-improvement
- [Product release](/product-release)
- Zero-Touch Improvement: Autonomous AI agent optimization with human oversight built in
## Zero-Touch Improvement: Autonomous AI agent optimization with human oversight built in
May 7, 2026
Every AI agent eventually plateaus. You’ve refined prompts, iterated through actionbooks, and tuned test scenarios. Your agent handles 95% of cases well. The remaining 5% (novel scenarios, edge cases that require judgment, policy drift from last quarter’s reality) aren’t solved by adding more test cases.
An AI that scores 100% on 50,000 test conversations has mastered your test suite, not customer service. Optimization within existing boundaries is a ceiling. What breaks through it is a mechanism to learn from what’s happening now, not what you prepared for.
[Zero-Touch Improvement (ZTI)](https://delight.ai/blog/product/introducing-zero-touch-improvement) is an autonomous improvement system that researches failures from real conversations, proposes precise actionbook changes, tests them against the cases that failed, and deploys, progressing through four graduated autonomy levels at the pace your team is ready for.
### How it works
- **Research from real conversations**: ZTI scans actual customer conversations, not test cases, to identify failure patterns. It clusters failures by root cause and surfaces the specific actionbook branch, the scenario type, and how many conversations were affected.
- **Precise actionbook proposals**: ZTI doesn’t suggest updating a prompt in the abstract. It generates an actionbook diff showing exactly what’s being added, removed, or modified, reviewed and approved by your team before anything changes.
- **Iterative test-and-deploy loop**: Every proposed fix is tested against the exact conversations that failed. ZTI iterates until the failure set is resolved, then deploys to production. Pass rates at each iteration are visible throughout.
- **Four autonomy levels**: ZTI operates across four graduated autonomy levels. At L1, the AI proposes every change and humans approve. At L2, the AI runs the improvement loop within approved boundaries while humans steer direction. At L3, it operates fully autonomously with no human gate required. Progression between levels is based on AI-human decision match rate, not a schedule.
- **Approval gates at every level**: Even at L3, you define the boundaries, including financial thresholds, compliance requirements, and sensitive topics. The AI cannot autonomously change anything you’ve scoped out.
Zero-Touch Improvement is part of [Trust OS](https://delight.ai/trust-os), turning every human decision at Level 2 into a lesson that makes full autonomy at Level 3 possible.
## Agent Steward: AI that owns complex customer issues end to end
Source: https://delight.ai/product-release/agent-steward
## Agent Steward: AI that owns complex customer issues end to end
May 7, 2026
A cancelled flight that needs a refund, a rebooking, and a hotel extension involves three requests, four systems, and two third parties. Today’s AI handles the first message, maybe the second, then transfers. A human then spends hours re-investigating, emailing partners, waiting for approvals, and circling back to the customer. Five minutes of AI. Five hours of human coordination.
That handoff is by design because of the ceiling of what AI can do. Three gaps explain why. First, businesses aren’t ready to hand high-stakes decisions to AI. Second, some cases take longer than one conversation to resolve. And third, no current AI can coordinate across multiple systems and third parties on its own.
[Agent Steward](https://delight.ai/blog/product/introducing-agent-steward) closes all three gaps. It’s the first AI agent that takes full ownership of complex customer issues — investigating, coordinating across systems, deciding within your guardrails, and following up across channels — until the case is closed, however long it takes.
### How it works
- **End-to-end case ownership**: Steward doesn’t hand off. It dispatches specialized sub-agents for each system or party involved, coordinates them in parallel, and holds the full case until resolution.
- **Async resolution across sessions**: When resolution takes hours, Steward keeps working in the background and reaches out to the customer when there’s an update.
- **Graduated autonomy**: Start at Level 1 (Steward investigates and proposes, human approves every decision). Advance to Level 2 (autonomous resolution for specific approved use cases) and Level 3 (Steward handles novel cases and proposes new resolution playbooks) as AI-human match rates build trust.
- **Full activity trail**: Every case has a complete, timestamped log — what was triggered, what calls were made, what was approved, how long each step took. Any team member can open any resolved case and trace the full resolution.
- **Built for complex resolution industries**: Designed for use cases where resolution requires coordinating across multiple systems or third parties — travel rebooking and refunds, food delivery coordination, healthcare follow-up, fraud investigation, and cross-channel retail returns.
[Agent Steward](https://delight.ai/autonomous-agent) closes the trust, time, and coordination gaps that have kept AI resolution rates plateaued — giving enterprises a path to true end-to-end automation for their most complex customer issues.
## A/B Testing: Know whether your AI agent changes actually worked
Source: https://delight.ai/product-release/a-b-testing
## A/B Testing: Know whether your AI agent changes actually worked
May 7, 2026
When you update an actionbook, swap a model, or refine your knowledge base, you expect improvement. But "no obvious regressions" and "statistically meaningful improvement" are two different things. Without rigorous measurement, you can't tell whether a change moved your metrics — or whether the movement you're seeing is just noise.
[Gradual Rollout](https://delight.ai/product-release/gradual-rollout) gives you a safe path to ship. A/B Testing answers the harder question: did the change actually work?
A/B Testing lets you state a hypothesis upfront, run two versions of your agent against real traffic, and receive a statistical reliability score — so you know whether a result is real before you fully commit to a change.
## How it works
- **Hypothesis-driven setup**: Before the test starts, define your hypothesis and select a target metric. The system estimates the number of conversations needed to reach statistical confidence and how long that will take at your current traffic volume.
- **Parallel traffic split**: Two versions run simultaneously against real conversations. Traffic is divided between them for the duration of the test, and each version's metrics are tracked independently.
- **Statistical reliability score**: When you end the test, results are evaluated against your hypothesis at three tiers — High (reliable enough to act on), Medium (trending but not confirmed), or Low (insufficient data to draw a conclusion).
- **AI analysis report**: Every completed test generates a report covering what changed, what improved, which secondary metrics moved, and suggested next steps — turning test results into a roadmap for the next iteration.
- **Secondary metric monitoring**: Metrics that shifted in the wrong direction are flagged with their own reliability score — so you can distinguish a real tradeoff from noise before making a decision.
A/B Testing is part of [Trust OS 2.0](https://delight.ai/trust-os) alongside Gradual Rollout. Use Gradual Rollout to limit blast radius when shipping a change. Use A/B Testing to confirm it moved the metric you care about.
## Openbook: Visual flow navigation for your Actionbooks
Source: https://delight.ai/product-release/openbook
## Openbook: Visual flow navigation for your Actionbooks
Apr 28, 2026
Actionbooks grow. What starts as a clean set of rules grows into nested conditionals, branching intents, and stacked policy changes over time. The editing part is manageable, but finding the right thing to edit isn't. When legal updates the return window, which condition needs to change? When something behaves unexpectedly, which branch is responsible? You open the source view and start scrolling through thousands of characters of nested markup.
When updates feel like too much work, teams stop making them, and actionbooks drift until the AI is running on logic nobody's reviewed in months.
[Openbook](https://delight.ai/blog/product/introducing-openbook) renders your actionbook as a visual flow, giving you the 10,000-foot view of your entire document structure, so you can orient first and edit precisely.
### How it works
- **Flow view**: Toggle from Source to Flow view and the entire actionbook opens as a structured visual map — section headers at the top, intent blocks branching below, conditional paths fanning out, every node labeled. See how everything connects before you touch any of it.
- **Click-to-navigate**: Click any node in the flow and the editor jumps directly to that section. No scrolling, no line counting, no searching.
- **Tree panel**: A hierarchical outline mirrors the full actionbook structure at a glance — every section, every intent block, every conditional branch.
- **Works alongside Actionbook Editor**: Openbook and [Actionbook Editor](https://delight.ai/blog/product/introducing-actionbook-editor) share the same workspace. Find the node in the flow, click, land in the editor at that exact section, make the change, save.
Openbook is the "See it" pillar of [Trust OS](https://delight.ai/trust-os) - making AI logic visible and navigable so teams can maintain it with confidence at any scale of complexity.
## Actionbook Editor: A reliable WYSIWYG environment for authoring your AI’s rules
Source: https://delight.ai/product-release/actionbook-editor
- [Product release](/product-release)
## Actionbook Editor: A reliable WYSIWYG environment for authoring your AI’s rules
Apr 13, 2026
Operations teams need to update AI logic quickly, without depending on engineering for every change or worrying whether an edit will behave as expected.
The updated [Actionbook Editor](https://delight.ai/blog/product/introducing-actionbook-editor) consolidates editing to a single WYSIWYG surface and pairs it with a real-time read-only Viewer panel so authors always see what the AI will see before saving.
### How it works
- **Single editing surface**: One panel, one mode. Author and update actionbook logic directly with consistent, reliable rendering at any level of complexity.
- **Toolbar-based block insertion**: Add conditional logic, key points, note annotations, and formatted text directly from the toolbar.
- **Real-time Viewer panel**: The right panel updates live as you edit. Switch between the Source tab and the Steps tab to verify what the AI sees without saving.
- **Click-to-navigate**: Click any component in either Viewer tab and the editor scrolls directly to the corresponding section on the left. Instant orientation without leaving the editing surface.
- **Built-in Tester:** Test changes against your actual AI agent without leaving the screen.
Actionbook Editor is a part of [Trust OS 2.0](https://delight.ai/trust-os) — making it practical for operations teams to own and maintain AI behavior directly, without requiring engineering involvement for every change.
## Thinking Messages: Real-time status updates while your AI agent works
Source: https://delight.ai/product-release/thinking-messages
- [Product release](/product-release)
## Thinking Messages: Real-time status updates while your AI agent works
Mar 26, 2026
The moment between a user’s question and an agent’s answer is usually a second or two. Three animated dots fill the space, but they don't say anything. Whether the agent is writing a plain response or running three API calls in the background, what users see looks identical. As reasoning models take longer to work through complex problems, that silence grows, and the dots become an even weaker signal.
Users don’t know if the agent understood the question, is retrieving their account, or got stuck in a loop. That uncertainty creates friction that has nothing to do with how good your agent actually is.
[Thinking Messages](https://delight.ai/blog/product/thinking-messages) replaces the generic typing indicator with custom, per-tool status messages, so users always know what's happening while they wait.
### How it works
- **Per-tool message configuration**: Write a specific status for each tool call in your agent’s actionbook. Each tool gets its own message, written in your voice.
- **Real-time display**: The message appears the moment a tool is invoked and replaces the typing indicator for the duration of that call. When the tool completes, the normal response flow resumes.
Thinking Messages is part of [Trust OS](https://delight.ai/trust-os), turning the space between question and answer from silence into a signal.
## WhatsApp Handoff Support: When a conversation needs a human, you're already in the right channel
Source: https://delight.ai/product-release/whatsapp-handoff-support
- [Product release](/product-release)
## WhatsApp Handoff Support: When a conversation needs a human, you're already in the right channel
Jun 25, 2026
WhatsApp is where a lot of your customers already are. For businesses that rely on it as a primary support channel, any gap in coverage is a customer experience problem. Until now, AI agents could handle conversations on WhatsApp, but there was no path to escalate to a human when needed.
WhatsApp Handoff Support closes that gap. Operators can now configure their AI Agent to transfer WhatsApp conversations to a human agent in Desk, with full conversation history carried over so the handoff is seamless for both the agent and the customer.
### What's new
- **Automatic channel type passthrough:** When a WhatsApp conversation is handed off, the ticket is automatically tagged with the channel source (AI agent - WhatsApp). Human agents always know where a conversation came from without any manual tagging.
- **Full conversation history in Desk:** After handoff, the human agent sees the complete prior AI conversation in Desk and picks up new messages from the user in real time, with full context intact.
- **WhatsApp-native replies:** After handoff, human agents reply directly in WhatsApp. The customer stays in the channel they started in. The full 24-hour messaging window applies, consistent with Meta’s business messaging policy.
WhatsApp Handoff Support extends AI Agent's omnichannel coverage to WhatsApp, so your team is never cut off from customers who need them most, regardless of which channel they’re on.
## Custom Safeguard Categories: Define what your AI flags, not just how it responds
Source: https://delight.ai/product-release/custom-safeguard-categories
## Custom Safeguard Categories: Define what your AI flags, not just how it responds
Jun 18, 2026
Every AI agent ships with the same four safeguard categories out of the box: harmful content, adversarial attacks, context injection, and banned words. For most deployments, that coverage is a solid foundation. But a financial services agent has different exposure than a retail one. A healthcare assistant has compliance-sensitive topics that generic safeguard lists don't anticipate. A B2B platform may need to catch competitor mentions before they reach a customer.
Until now, operators could only extend detection through a limited set of banned words or phrases. Topics that fell outside the four built-in categories and couldn't be captured through keyword matching simply went unflagged.
Custom Safeguard Categories changes that. Dashboard operators can now define up to 10 detection categories of their own, tailored to their product, their industry, and their risk profile — without touching the built-in logic.
### What's new
- **Custom detection categories:** Add up to 10 custom safeguard categories per agent, tailored to your product, industry, and risk profile.
- **Granular control over default categories:** Enable or disable individual built-in categories based on your use case. Adversarial attack detection always stays on. Everything else is configurable.
- **Analytics for custom categories:** Custom categories appear in Flagged Messages and safeguard rate statistics alongside the four built-in ones, giving your team a complete picture of what's being caught across the board.
- **Version history for safeguard config:** Changes to your safeguard configuration are automatically tracked, so you have a clear audit trail of what was turned on, turned off, or redefined and when.
Custom Safeguard Categories is part of [Trust OS](https://delight.ai/trust-os), giving operators the detection layer that matches their actual risk surface — not just a generic one.
## Delight Desk updates: Macros, voice handoff, and one queue for every channel
Source: https://delight.ai/product-release/delight-desk-updates
## Delight Desk updates: Macros, voice handoff, and one queue for every channel
Jul 9, 2026
[Delight Desk](https://delight.ai/desk) could save a canned reply, but not the actions around it — no status change, no reassignment, no tags. A voice call handed off by the AI agent had nowhere to go but an external contact center, so it left Desk (and its history) entirely. And chat, email, and voice each behaved like separate tools, even though agents needed to work all three from one place. Individually, each of these was a reason a team stayed on their old helpdesk instead of moving to Desk.
This release closes all three, so there's nothing left holding your team back from making the switch.
### What's new
- **Macros:** Delight Desk now supports macros. A single command posts a reply and applies ticket actions together, such as status, priority, assignee, team, and tags.
- **Voice handoff to a human agent:** When the AI agent can't resolve a call, it hands off to a Delight Desk agent on the same live call. The full transcript and context arrive with it. No re-dialing, no asking the customer to repeat themselves.
- **Every call becomes a ticket:** Calls handled or handed off by the AI agent now generate a Desk ticket automatically, with the full transcript attached, so voice gets the same history and reporting as every other channel.
- **One queue for every channel:** Chat, email, and voice tickets now live in a single Desk queue, so agents work every channel from the same place instead of switching tools mid-conversation.
Taken together, these updates let a support team run macros, calls, chat, and email entirely inside [Delight Desk](https://delight.ai/desk), without giving up the workflows they already depend on.
## Delight Knowledge: One knowledge base for your AI agent, help center, and agents
Source: https://delight.ai/product-release/delight-knowledge
- [Product release](/product-release)\n- Delight Knowledge: One knowledge base for your AI agent, help center, and agents\n\n## Delight Knowledge: One knowledge base for your AI agent, help center, and agents\n\nJul 9, 2026\n\n\n\nUntil now, Knowledge in Delight.ai was built to feed the AI agent using other sources, snippets, and embeddings, not to author long-form articles or host a public-facing help center. That's fine if all you need is AI retrieval. But it left a real gap for any team whose knowledge base doubles as their customer-facing help center. There was nowhere to author it, version it, and publish it as its own site.\n\nDelight Knowledge is that missing layer. Your team authors and organizes articles once. From there, the same content reaches your AI agent's answers, your public help center, and the in-ticket panel your agents already work from.\n\n\n\n### What's new\n\n- **Knowledge base authoring:** Create, organize, and version categories, sections, and articles directly in Delight Knowledge, with multi-language support and draft, published, and internal states.\n- **Hosted public help center:** Publish a branded, customer-facing help center with your logo, colors, and your own custom domain. It's searchable and live in minutes, with no separate web hosting required.\n- **Zendesk Guide migration with SEO preserved:** Your entire [Zendesk Guide](https://delight.ai/zendesk-migration-desk) migrates over, including articles, categories, sections, media, and translations. Automatic redirects keep your existing help center links and search rankings intact.\n- **In-ticket knowledge for agents:** Human agents see suggested articles and can search Delight Knowledge directly inside a ticket, with [AI Copilot](https://delight.ai/desk) answers grounded in the same content.\n- **One source of truth:** The same knowledge base feeds your AI agent, your public help center, and your agents' in-ticket answers. Update an article once, and it's current everywhere.\n\nThe result is consistency. Customers get the same accurate answer whether they search the help center, ask the AI agent, or reach a human on a ticket.
## Zendesk Migration: Keep your tickets, workflows, and muscle memory with one click
Source: https://delight.ai/product-release/zendesk-migration
## Zendesk Migration: Keep your tickets, workflows, and muscle memory with one click
Jul 9, 2026
For a lot of teams, the reason they're still on Zendesk isn't the product anymore, it's the exit. Migrating means rebuilding a knowledge base from scratch, retraining agents on a new tool, and starting an AI agent from zero. That project alone is enough to keep teams paying Zendesk's seat fees long after they've decided they want out.
[Migrating from Zendesk](https://delight.ai/zendesk-migration-desk) no longer takes a project team. Connect your Zendesk account, and [Delight Desk](https://delight.ai/desk) imports your tickets, knowledge base, macros, and users automatically, with redirects in place so your existing links and search rankings carry over. Your AI agent isn't starting blank either. It arrives already trained on your own resolved tickets, ready before your first conversation.
### What's new
- **Full ticket & history migration:** Tickets, conversation history, status, priority, requester, assignee, and custom fields all migrate from Zendesk into Desk, so agents keep working without a gap.
- **Knowledge base migration with SEO preserved:** Zendesk categories, sections, authors, and articles migrate into [Delight Knowledge](https://delight.ai/product-release/delight-knowledge), with automatic redirects so your existing help center links and search rankings keep working.
- **Macros come with you:** Zendesk macros and chat shortcuts migrate into Desk's macro system, so agents keep the shortcuts they already rely on instead of rebuilding them by hand.
[Zendesk migration](https://delight.ai/zendesk-migration-desk) is built for teams who've outgrown their old helpdesk but never had a good reason to actually leave. The switch now takes minutes instead of a migration project. [Delight Desk](https://delight.ai/desk) itself stays free, forever.
## Delight Agent MCP: Your agent's data, in every AI you use
Source: https://delight.ai/product-release/delight-agent-mcp
- [Product release](/product-release)
## Delight Agent MCP: Your agent's data, in every AI you use
Aug 5, 2026
CX and operations teams managing AI agents spend significant time moving between the tools where their AI works and the platforms where they analyze it. You check your resolution rate in one tab, search for a transcript in another, pull quality signals from a third, then try to hold it all in context while forming a view. That's not analysis. That's logistics.
The Delight Agent MCP opens a new interface for that work. Connect any MCP-compatible AI client directly to your live agent data and ask the questions you already have, in the environment you're already in.
### How it works
- **Ask questions instead of switching tabs**: Instead of opening the dashboard, running a filter, copying a conversation ID, and pulling quality signals in a separate tab, you ask one question and get a complete answer. You stay in one place. Your thinking stays intact.
- **Investigate faster, in context**: When something looks off, you can go from "resolution rate dropped Tuesday" to the specific conversations, the transcripts, and the related quality signals without leaving your AI environment. No reformatting. No exports. No losing your train of thought.
- **Surface signals your dashboard was never built to show**: For the first time, you can answer questions that the dashboard has never been able to answer: which knowledge items your agent is ignoring, why configured tools never actually fire, and exactly where in a specific conversation the agent misunderstood the customer.
- **Make changes, not just observations**: Create and update actionbooks, tools, knowledge sources, and agent configuration, from identity and instructions to safeguards, CSAT, and handoff, directly from your AI client. Diagnose an issue and fix it in the same conversation. No dashboard, no context switch.
The Delight Agent MCP is available now for Claude Code, Claude.ai, ChatGPT, Cursor, and Codex.
## Nudge Message: Your AI agent can speak first, right when it matters
Source: https://delight.ai/product-release/nudge-message
## Nudge Message: Your AI agent can speak first, right when it matters
Jul 31, 2026
Until now, support only started when a customer chose to open the messenger and explain their situation from scratch. There was no way for the agent to step in at the moment something actually went wrong, a form abandoned, a page lingered on too long, a step dropped mid-flow, without the customer making the first move. And when they finally did reach out, they were often re-explaining a situation the business had already seen play out.
Nudge Message flips that. The moment something happens, a form abandoned, a page lingered on, a step skipped, the agent can reach out first with a short message above the chat, so customers already know help is there before they ever tap in.
### What's new
- **Speaks first, right when it matters:** A short message appears above the chat the moment something goes wrong, instead of waiting for the customer to reach out.
- **Two ways to greet customers:** A nudge can simply open the chat with the usual welcome message, or open the chat already mid-conversation, so nothing needs to be repeated.
- **Two message types:** Send a fixed message for the fastest response, or let the AI write one tailored to the page and moment.
Harper, delight.ai's own AI agent, [is already running Nudge Message live](https://delight.ai/blog/ai-for-cx/real-time-customer-engagement), catching customers who start a form and don't finish.
## AI Suggestions: Your AI agent can tell you what your knowledge base is missing
Source: https://delight.ai/product-release/ai-suggestions
- [Product release](/product-release)
## AI Suggestions: Your AI agent can tell you what your knowledge base is missing
Aug 27, 2026
A knowledge gap is the hardest problem to fix because you never hear about it. A customer asks your AI agent something the [knowledge base](https://delight.ai/product-release/delight-knowledge) doesn't cover, gets a vague answer or none at all, and moves on without filing a ticket. Nothing breaks, nothing gets logged, and the same gap costs you the same conversation again tomorrow. The only way to find these was to read transcripts and hope you noticed a pattern.
AI Suggestions turns that silence into a list. Your agent tracks the questions it fails to answer, writes up what the missing knowledge should say, and hands it to you as something to review rather than something to research.
### What's new
- **A running list of what's missing:** Every gap your agent found on its own, in one place, with a one-line summary of each and filters to separate what you've handled from what you haven't.
- **A draft answer, already written:** Each suggestion carries a proposed title and content, so publishing means reviewing and adjusting wording, not starting from a blank form.
- **The conversations behind every suggestion:** Open any suggestion and read the actual customer questions that triggered it. You never have to take a suggestion on faith.
- **Nothing goes live unreviewed:** AI writes the draft. A person decides whether it becomes knowledge.
Your agent already knows where it's weakest. AI Suggestions is how it tells you, and how you close the gap in a few minutes instead of a few weeks.
## Delight Voice: From the first ring to the case record, without starting over
Source: https://delight.ai/product-release/delight-voice
- [Product release](/product-release)
## Delight Voice: From the first ring to the case record, without starting over
Oct 1, 2026
A voice agent rarely loses the sale while it’s talking. It loses it on the way to the next step. The caller gets transferred and repeats everything, the line opens with a phone menu nobody gets past, the record lands nowhere, or the caller is asked to read a code out loud. Nothing looks broken, since the call connected and the ticket closed. The booking, order, or renewal just goes quiet, and the only way to find out why was to replay recordings and hope you spotted a pattern.
Delight Voice closes those gaps. Your voice agent now carries the caller, the context, and the next step through every handoff, phone menu, and system it touches, and hands your team a finished outcome instead of a mess to reconstruct.
### What's new
- **A handoff that carries the whole story.** The caller stays on the line while Delight confirms a person picked up and briefs them first. If nobody answers, Delight leaves a message and files a case with a callback number. [See how Intelligent Voice Handoff works](https://delight.ai/blog/product/introducing-intelligent-voice-handoff)
- **A transfer that gets past the phone menu.** On menus your business has mapped, Delight presses the right keys and briefs whoever answers, so transfers don’t stall at “press 1.” [See how IVR Navigation works](https://delight.ai/blog/product/introducing-ivr-navigation)
- **Your numbers, your queues, your case record.** Delight answers on your own numbers, hands callers off as a standard phone transfer, and sends each resolved case to systems like Salesforce, Zendesk, and ServiceNow. [See how Universal Telephony works](https://delight.ai/blog/product/introducing-universal-telephony)
- **The keypad for the digits nobody should say out loud.** One-time codes, consent, and post-call ratings go through the keypad instead of being spoken, masked in the transcript and beep-masked in stored audio. [See how Secure Caller Keypad Input works](https://delight.ai/blog/product/introducing-secure-caller-keypad-input)
On a call, voice leads, and the context follows the customer everywhere else. Delight Voice keeps each call moving until the booking, order, or renewal is actually done.
[**Talk to an expert →**](https://delight.ai/voice)
## Multi-Ticket View: Monitor multiple conversations from a single screen
Source: https://delight.ai/product-release/multi-ticket-view
- [Product release](/product-release)\n\n## Multi-Ticket View: Monitor multiple conversations from a single screen\n\nSep 22, 2026\n\n\n\nSupervising a busy queue meant opening one ticket, reading it, closing it, and opening the next. Admins keeping an eye on several live conversations had to cycle through them one by one, and by the time they circled back to a conversation that was going sideways, the moment to step in had often passed.\n\nMulti-Ticket View puts those conversations on one screen, so admins can follow every conversation at once and act the moment one needs attention.\n\n\n\n### What's new\n\n- **Summarize or translate on the spot:** Get an AI summary of a long conversation, or translate one in another language, right from its panel without opening the full ticket.\n- **Act without leaving the grid:** Reassign a ticket, update its tags, change its status, or keep it from auto-closing, all from the panel menu.\n- **A layout built for how you supervise:** Arrange up to 25 tickets in the grid size you choose, drag them into place, resize panels, and pin the conversations you can't lose track of.\n- **Live conversations, full context:** Every panel updates as new messages arrive, and a details panel shows the full ticket information for whichever conversation you're focused on.\n\nSupervising a queue no longer happens one ticket at a time. Admins see every conversation they care about at a glance and know exactly where to step in.
## SLA Management: Stay on top of every resolution deadline
Source: https://delight.ai/product-release/sla-management
- [Product release](/product-release)
- SLA Management: Stay on top of every resolution deadline
## SLA Management: Stay on top of every resolution deadline
Sep 22, 2026
Promising customers a resolution time is easy. Keeping that promise across hundreds of open tickets is hard without a system that tracks every deadline, and most teams only find out about a missed target after the fact.
SLA Management builds that system into [Delight Desk](https://delight.ai/desk). Set a resolution target for each ticket priority, and Desk tracks every ticket against it, surfaces the ones running out of time, and escalates any ticket that misses its deadline.
### What's new
- **Targets that match the urgency:** Set resolution time targets for each channel and ticket priority, so an urgent outage and a routine question are each held to the right standard.
- **See what's at risk before it's late:** A dedicated SLA view puts the closest deadline first, flags tickets in amber as time runs short, and shows exactly how overdue a breached ticket is.
- **SLA status on every ticket:** Open any ticket to see its target, whether it was met, and how long resolution actually took.
- **Automatic escalation:** When a ticket misses its target, Desk can raise its priority automatically so it gets attention right away instead of sitting in the queue.
- **Proof you're keeping your promises:** SLA performance reporting shows how consistently your team meets its targets over time.
Your SLAs are commitments to your customers. With SLA Management, Delight Desk keeps track of every one and gives your team time to act before a deadline slips.
## Custom Report Dashboard: Build the report your team actually needs in Delight Desk
Source: https://delight.ai/product-release/custom-report-dashboard
- [Product release](/product-release)
## Custom Report Dashboard: Build the report your team actually needs in Delight Desk
Sep 22, 2026
Until now, [Delight Desk](https://delight.ai/desk) showed every team the same fixed reports, with no single view of how AI agents and human agents performed side by side.
Custom Report Dashboard gives every team a dashboard it shapes itself. Pick the metrics, add the filters, arrange the layout, and share it with the people who need it. With AI agent and human agent data in one place, you can see where your AI agent is carrying the load and where your human agents make the difference.
### What's new
- **AI agent and human agent performance in one view:** Compare how AI-handled and human-handled conversations perform, directly in Delight Desk, without stitching two reports together.
- **Your metrics, your layout:** Combine the metrics and filters most relevant to your operation, with up to 35 metrics across 10 tabs on a single dashboard.
- **Share it or export it:** Share a dashboard with your team so everyone works from the same numbers, or download the data behind it for offline review.
Reporting in Delight Desk now starts with your questions instead of ours. Your team gets the metrics it tracks, in the layout it reads, with AI agents and human agents measured on the same dashboard.
## Agent Skills and Skill-based Routing: Send every ticket to a human agent who knows how to solve it
Source: https://delight.ai/product-release/agent-skills-and-skill-based-routing
- [Product release](/product-release)
## Agent Skills and Skill-based Routing: Send every ticket to a human agent who knows how to solve it
Sep 15, 2026
The hardest tickets get resolved by the person with the right expertise, like the agent who speaks the customer's language or knows the product inside out. [Delight Desk](https://delight.ai/desk) could already send a ticket to the right team, and now it can find the right person on that team with skill-based routing.
### What's new
- **The right person on the right team:** Tickets still go to the right team first, then to the human agent on that team whose skills match.
- **Rules that know what each ticket needs:** Define which skills a ticket requires based on details like its category or channel. A billing question from a Korean-speaking customer can require both Billing and Korean.
- **A skills profile for every agent:** Each human agent has a profile of the skills they hold, like the languages they speak or the products they know best, so admins can see every agent's expertise in one place.
The right ticket now reaches the right human agent. Customers get an answer from someone who knows the answer, and your specialists spend their time on the work they're best at.
## Time-based Rules: Tickets become more urgent the longer they wait
Source: https://delight.ai/product-release/time-based-rules
## Time-based Rules: Tickets become more urgent the longer they wait
Sep 15, 2026
Every ticket in the queue is on a clock, but no team can watch all of them by hand. Without a system tracking elapsed time, stalled tickets are easy to miss, especially ones that arrive overnight or over the weekend.
Time-based Rules watch the clock for you. Admins decide how long is too long, and [Delight Desk](https://delight.ai/desk) updates a ticket's priority automatically once that threshold passes, at any hour.
### What's new
- **Down-to-the-minute escalation:** Set thresholds in hours and minutes, so a chat that's waited 15 minutes can escalate as fast as an email that's waited a day.
- **Rules as specific as your queue:** Target a rule to a single channel, team, or priority, and combine conditions, like time since the customer's last message or time spent waiting in Pending.
- **Safe by design:** Desk checks each ticket's latest details before acting, so a rule never overwrites a more recent change from a human agent or keeps changing the same ticket over and over.
- **Built to work with SLA Management:** Use the same rules to raise a ticket's priority once it breaches its SLA target.
Tickets that have waited too long now rise in priority on their own, so nothing slips through just because no one happened to look.
## Internal Tickets: Split complex issues across teams without losing the thread
Source: https://delight.ai/product-release/internal-tickets
## Internal Tickets: Split complex issues across teams without losing the thread
Sep 15, 2026
Some customer issues need more than one team. A large refund needs finance, a bug needs engineering, and a policy question needs trust and safety. In [Delight Desk](https://delight.ai/desk), pulling in another team meant copying context into an untracked ticket or pinging someone in Slack, and the link back to the customer's request was lost.
Internal Tickets let human agents hand off part of a customer request to another agent straight from the ticket they're working on. Each internal ticket stays linked to the original, so both sides can track the work without losing the customer's context.
### What's new
- **Delegate without losing the customer:** Hand off a piece of the request to another team with a built-in link back to the original ticket, so nobody has to re-explain the context.
- **A private workspace for every handoff:** The requesting agent and the assignee work it out inside Desk, with every reply kept internal and nothing reaching the customer.
- **Every handoff in one place:** All internal tickets for a request live in an Internal tab on the original ticket, and closing one can update the original automatically.
Splitting a complex issue across teams no longer means losing track of it. Every team works its piece, and the agent who owns the customer relationship can see the whole picture from one ticket.
## Voice Handoff to Desk: Human agents pick up the call with the full story
Source: https://delight.ai/product-release/voice-handoff-to-desk
- [Product release](/product-release)
## Voice Handoff to Desk: Human agents pick up the call with the full story
Aug 11, 2026
The handoff from an AI agent to a person is where many phone experiences break down. Without the context of the conversation so far, the human agent starts from zero and the caller has to explain everything again, frustrating them.
Voice Handoff connects your AI voice agent to your human team in [Delight Desk](https://delight.ai/desk). The moment a call needs a person, Desk creates a ticket and offers the call to an available agent, with everything they need to continue the conversation already in front of them.
### What's new
- **An AI-written call summary:** Every handoff ticket opens with the customer's issue, the key points of the conversation, and recommended next steps, and the full transcript is in the ticket whenever the agent needs more detail.
- **Customer history at a glance:** Links to the customer's previous support tickets show whether this is a new problem or a repeat one.
- **Answer right in the ticket:** Human agents take the call directly in the Desk ticket view in their browser, with no separate phone system, and the caller stays on the same line from the AI agent to the person.
- **Every call on record:** Replay the recorded call, see how it ended, and jump straight from the AI conversation to its Desk ticket.
Your AI agent handles the call for as long as it should, and a person takes over knowing everything the AI agent knew. Customers get one continuous conversation, even when it changes hands.
# Blog
## Expert guides on improving CX with AI agents
Source: https://delight.ai/blog
## Expert guides on improving CX with AI agents
All
- [All](/blog)
- [Product](/blog/product)
- [AI for CX](/blog/ai-for-cx)
- [AI agent](/blog/ai-agent)
- [Industry](/blog/industry)
- [AI workflow](/blog/ai-workflow)
- [Insights](/blog/insights)
### Introducing Intelligent Voice Handoff: A complete path from AI to human
[Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)
Kimberly Chiguindo Bonilla — Product marketing manager
---
### Patient engagement solutions for consumer health brands
[Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
Caroline Adamec — Content engineer
---
### The complete guide to customer experience analytics
[The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
Ishaan Bansal — AI engineer, internal operations
---
### AI agent for insurance: What it is and how modern carriers use it
[AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)
Kibeom Lee — Machine learning lead
## Most Popular
- [View more](/blog)
### 6 real MCP examples, and what people actually do with them
[6 real MCP examples, and what people actually do with them](https://delight.ai/blog/ai-agent/6-real-mcp-examples)
Ishaan Bansal — AI engineer, internal operations
---
### How to build an AI agent: 8-step tutorial
[How to build an AI agent: 8-step tutorial](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent)
Ian Heinig — Agentic AI marketer
---
### Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration
[Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration](https://delight.ai/blog/product/zendesk-to-delight-desk-migration)
Cheryl Chai — Product marketing lead
---
### AI agents vs. chatbots: Differences & similarities explained
[AI agents vs. chatbots: Differences & similarities explained](https://delight.ai/blog/ai-agent/ai-agent-vs-chatbot)
Ian Heinig — Agentic AI marketer
## Case studies
[View more](/customers)
### How BJ’s is improving members’ digital experience with help from Bev
[How BJ’s is improving members’ digital experience with help from Bev](https://delight.ai/customers/bjs)
---
### Mixpanel x delight.ai: When support knows who you are
[Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
---
### How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F
[How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services)
---
### Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen
[Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
[View more](/customers)
## Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
# Blog Posts
## The 13 best AI agent platforms for 2026
Source: https://delight.ai/blog/industry/agentic-ai-companies
- [Blog](/blog)
- [Industry](/blog/industry)
- The 13 best AI agent platforms for 2026
## The 13 best AI agent platforms for 2026
[Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 7 2026
### Key takeaways
- AI agent platforms split into two groups. One is **horizontal foundation-model and hyperscaler players** (OpenAI, Microsoft, Anthropic). The other is **specialized platforms built for one workflow**, which is what this list covers.
- Ratings, pricing, and proof points vary widely. Only **9 of the 13 vendors** below have a confirmed, public G2 rating, and only **4 have a named, dated customer result** rather than a logo wall or an aggregate stat.
- The right pick depends on **use case, technical fit, channel coverage, governance, and vendor transparency**, not just feature breadth. See the “How to pick an AI agent platform” section below.
Household names like OpenAI, Anthropic, Microsoft, and Google Cloud also get called “AI agent platforms.” They build the foundation models several vendors below run on. This list focuses specifically on **AI agent platforms built for real business workflows**, not the underlying model providers.
## At a glance
- [Delight.ai](https://delight.ai/) is best for **CX teams wanting omnichannel, memory-driven support** with transparent per-resolution pricing.
- [Cognition Labs (Devin)](https://www.cognition.ai/) is best for **engineering teams automating software delivery** end-to-end.
- [Salesforce Agentforce](https://www.salesforce.com/agentforce/) is best for **enterprises already invested in the Salesforce ecosystem**.
- [UiPath](https://www.uipath.com/) is best for **enterprises automating cross-functional workflows** at scale.
- [MavenAGI](https://www.mavenagi.com/) is best for **CX teams wanting a no-code agent** plus an internal copilot.
- [Relevance AI](https://relevanceai.com/agents) is best for **teams building multiple custom agents** without heavy engineering.
- [Lindy](https://www.lindy.ai/) is best for **individuals and small teams wanting a general-purpose personal agent**.
- [Moveworks](https://www.moveworks.com/) is best for **large, compliance-heavy enterprises standardizing employee support**.
- [Decagon](https://decagon.ai/) is best for **CX teams prioritizing fast implementation and proven deflection**.
- [Aisera](https://aisera.com/) is best for **large enterprises consolidating IT, HR, and CX support**.
- [Sierra](https://sierra.ai/) is best for **enterprises with budget for a fully bespoke conversational agent**.
- [Beam AI](https://beam.ai/) is best for **operations teams automating cross-functional business processes**.
- [AgentOps AI](https://www.agentops.ai/) is best for **engineering teams building and debugging their own agent stack**.
## The 13 AI agent platforms compared
| Company | Category | Pricing model | Best fit | G2 rating |
|---|---|---:|---|---|
| [delight.ai](https://delight.ai/) | AI concierge for customer experience | Conversation-based, $0 seat fee | CX teams wanting omnichannel, memory-driven support | [4.6/5 (124 reviews)](https://ai.g2.com/marketplace/tools/sendbird-delight-ai) |
| [Cognition Labs (Devin)](https://www.cognition.ai/) | Autonomous software engineering agent | $20-$500/mo tiers + custom | Engineering teams automating software delivery | Not on G2 (Trustpilot ~3.0/5) |
| [Salesforce Agentforce](https://www.salesforce.com/agentforce/) | Enterprise agentic AI built into Salesforce | $2/conversation + custom enterprise | Enterprises already on Salesforce | [4.3/5 (1,109 reviews)](https://www.g2.com/products/agentforce-service-formerly-salesforce-service-cloud/reviews) |
| [UiPath](https://www.uipath.com/) | Agentic automation / RPA platform | From $25/mo + custom enterprise | Enterprises automating cross-functional workflows | [4.5-4.6/5 (~7,770 reviews)](https://www.g2.com/products/uipath-agentic-automation/reviews) |
| [MavenAGI](https://www.mavenagi.com/) | AI customer support agent | Custom enterprise only | CX teams wanting a no-code agent plus copilot | [G2 badges, no aggregate score found](https://www.mavenagi.com/resources/g2-badges-spring-2026) |
| [Relevance AI](https://relevanceai.com/agents) | No-code AI agent building platform | Free-$349/mo tiers + custom | Teams building multiple custom agents | [4.8/5 (~21-50+ reviews)](https://www.g2.com/products/relevance-ai/reviews) |
| [Lindy](https://www.lindy.ai/) | General-purpose personal AI agent | $49.99-$199.99/mo tiers + custom | Individuals/small teams wanting a personal agent | [4.9/5 (170 reviews)](https://www.g2.com/products/lindy-lindy/reviews) |
| [Moveworks](https://www.moveworks.com/) | Enterprise employee support AI | Custom, ~$15-40/employee/yr | Large, compliance-heavy enterprises | [4.4-4.6/5 (51-125 reviews)](https://www.g2.com/products/moveworks/reviews) |
| [Decagon](https://decagon.ai/) | Conversational customer support AI | Per-conversation (~$0.99) or per-resolution | CX teams prioritizing fast implementation | [4.9/5](https://www.g2.com/products/decagon/reviews) |
| [Aisera](https://aisera.com/) | Enterprise service management AI | Custom, $200K-$2M+/yr | Large enterprises consolidating IT/HR/CX | [4.4/5 (147 reviews)](https://www.g2.com/products/aisera/reviews) |
| [Sierra](https://sierra.ai/) | Conversational customer service AI | Not public, est. ~$150K/yr | Enterprises with budget for a bespoke agent | [4.3/5 (15 reviews)](https://www.g2.com/products/sierra/reviews) |
| [Beam AI](https://beam.ai/) | Business-operations process automation | Not published | Operations teams automating cross-functional processes | Not on G2 |
| [AgentOps AI](https://www.agentops.ai/) | AI agent observability/dev tooling | Free-$49/mo tiers + custom | Engineering teams building/debugging their own agents | Not listed on G2 |
## How we evaluated each AI agent platform
We judged every vendor on the same five criteria below, the ones that matter most when you're actually choosing a B2B tool rather than just browsing features.
- **Use case fit (Best for):** The clearest signal of whether a platform is right for you is whether it was built for your specific problem. A tool built for engineering automation won't serve a CX team well, no matter how strong its reviews are.
- **Capabilities (Key benefits):** What a platform can actually do, not just what it claims to do, determines whether it solves your problem end to end or only partway.
- **Pricing:** Budget is usually a hard constraint set by finance or leadership before you ever get to a demo, so we list exact pricing or the clearest available estimate for every vendor, and flag the ones that don't publish pricing at all.
- **G2 rating:** Independent, aggregated customer sentiment shows what real users think after the sales process ends. Only 9 of the 13 vendors here have a public score.
- **Proof point:** A named, dated customer result is stronger evidence that a vendor delivers than a logo wall or an unsourced aggregate stat. Only 4 of the 13 vendors have one.
Where a vendor doesn't publish pricing, isn't listed on G2, or has no named customer result, we say so directly instead of leaving it blank.
## Review of the 13 AI agent platforms
### 1. Delight.ai
**Best for:** CX leaders and support teams that want **omnichannel, memory-driven AI agents** with transparent, per-resolution pricing.
[Delight.ai](https://delight.ai/) (formerly Sendbird) is a no-code AI agent platform for customer experience. Its agents remember every interaction across chat, SMS, WhatsApp, email, and voice, so returning customers pick up right where they left off, and the platform integrates with your existing support stack instead of replacing it.
- **Pricing:** Conversation-based usage fees, $0 seat fee.
- **G2 rating:** [4.6/5 (124 reviews)](https://ai.g2.com/marketplace/tools/sendbird-delight-ai).
- **Proof point:** Norse Atlantic Airways grew AI containment from 60% to 80% in two weeks after replacing a legacy chatbot (Alf Lim, Chief Product Officer, January 2026).
**Key benefits**
- **No-code builder** trained on your knowledge base, policies, and support playbooks
- [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) that unifies every customer interaction into one evolving profile
- [Trust OS](https://delight.ai/trust-os) governance layer for real-time monitoring and risk control
- **White-glove onboarding** from proof-of-concept through deployment
### 2. Cognition Labs (Devin)
**Best for:** Engineering teams that want to **automate software delivery end-to-end**, from planning to deployment.
[Cognition Labs](https://www.cognition.ai/)'s AI agent, Devin, plans tasks, writes code, runs tests, debugs, and deploys software with minimal human oversight. It operates in a real development environment, integrates with GitHub, Slack, and CI/CD pipelines, and can deploy in a Virtual Private Cloud for enterprise data control.
- **Pricing:** $20/month (Core), $500/month (Team, 250 Agent Compute Units), custom Enterprise.
- **G2 rating:** Not on G2 (Trustpilot ~3.0/5).
- **Proof point:** Not published. Cognition Labs hasn't released a named, dated customer result.
**Key benefits**
- **Autonomous software engineer** that plans, codes, tests, and deploys end-to-end
- **MultiDevin** manages parallel agents and large backlogs at once
- **VPC deployment** for full data and compliance control
- Custom-trainable Devins for specific engineering tasks
### 3. Salesforce Agentforce
**Best for:** Enterprises already invested in the Salesforce ecosystem that want **agentic AI built directly into their CRM**.
[Salesforce Agentforce](https://www.salesforce.com/agentforce/) embeds AI agents across Sales, Service, and Marketing Cloud (rebranded on G2 as separate Agentforce products), handling customer conversations and internal workflows using your existing CRM data.
- **Pricing:** $2 per conversation (Conversations tier), which requires Service Cloud and typically a 5-11 month setup, plus custom enterprise pricing.
- **G2 rating:** [4.3/5 (1,109 reviews)](https://www.g2.com/products/agentforce-service-formerly-salesforce-service-cloud/reviews). Salesforce's G2 presence is now split across several rebranded Agentforce product pages.
- **Proof point:** Salesforce reports 29,000+ Agentforce deals and $800M in related ARR, though under 10% of customers have scaled it and out-of-box accuracy runs around 35% (2026, an aggregate figure rather than a single named account).
**Key benefits**
- Built directly into **Salesforce Sales, Service, and Marketing Cloud**
- **Conversation-based pricing** for customer-facing agents
- Deep integration with existing **CRM data and workflows**
- Backed by Salesforce's enterprise support and partner ecosystem
### 4. UiPath
**Best for:** Enterprises that want to **automate cross-functional workflows at scale** using a mix of AI agents, bots, and humans.
[UiPath](https://www.uipath.com/)'s Agentic Automation Platform combines AI agents, RPA bots, and human input to orchestrate workflows across departments. Tools like Agent Builder and UiPath Maestro support both low-code and pro-code development, with enterprise-grade access controls.
- **Pricing:** From $25/month; median buyers pay around $370/year, scaling well beyond that for enterprise deployments.
- **G2 rating:** [4.5-4.6/5 (~7,770 reviews)](https://www.g2.com/products/uipath-agentic-automation/reviews).
- **Proof point:** Not published as a single named result. UiPath cites aggregate customer data instead.
**Key benefits**
- **Multi-agent orchestration** with human-in-the-loop controls
- Supports both **low-code and pro-code** development
- Integrates third-party AI models and enterprise apps via API
- **UiPath Maestro, Autopilot, and Healing Agent** for adaptive automation
### 5. MavenAGI
**Best for:** CX teams that want a **no-code support agent plus an internal copilot** for human reps.
[MavenAGI](https://www.mavenagi.com/)'s Agent Maven™ resolves customer tickets across SMS, chat, email, and phone, while Maven Copilot supports human agents with case summaries and suggested responses. Both are trained through a no-code interface.
- **Pricing:** Custom enterprise only; no published tiers or self-serve option.
- **G2 rating:** [G2 badges, no aggregate score published](https://www.mavenagi.com/resources/g2-badges-spring-2026).
- **Proof point:** Not published. No named, dated customer result found.
**Key benefits**
- Agent Maven™ delivers **multi-channel** support across SMS, chat, email, phone, and self-service
- Lets teams build and train agents on tone, knowledge, and actions **without writing code**
- Provides operational visibility, performance tracking, and system security for large-scale deployments
- **Maven Copilot** assists human agents with summaries and suggestions
### 6. Relevance AI
**Best for:** Teams that want to **build and deploy multiple custom AI agents** without heavy engineering.
[Relevance AI](https://relevanceai.com/agents) is a no-code platform for assembling an “AI workforce” of agents that handle sales development, support, and research tasks, integrating with your existing tech stack.
- **Pricing:** Free tier (200 actions/month), Pro at $19/month (annual), Team at $234/month (annual, roughly $349 month-to-month), custom Enterprise.
- **G2 rating:** [4.8/5](https://www.g2.com/products/relevance-ai/reviews) (review count varies by source, roughly 21-50+ reviews).
- **Proof point:** Case studies with Qualified, Send Payments, and Zembl. Zembl reports using Relevance AI as a digital AI SDR to follow up on lower-scored leads and close language and coverage gaps across APAC and AMER (2026).
**Key benefits**
- **No-code builder** for SDR, support, and research agents
- Free tier available with **200 actions per month**
- Integrates with existing tech stacks for **lead scoring and follow-up**
- Named case studies across sales and support use cases
### 7. Lindy
**Best for:** Individuals and small teams that want a **general-purpose personal AI agent** for email, scheduling, and research.
[Lindy](https://www.lindy.ai/) is a general-purpose AI agent platform that automates email, calendar management, and web research, with a computer-use capability on higher tiers.
- **Pricing:** Plus at $49.99/month (2 inboxes), Pro at $99.99/month (3x usage, computer use), Max at $199.99/month (7x usage, 5 inboxes), custom Enterprise. The free tier was discontinued in early 2026.
- **G2 rating:** [4.9/5 (170 reviews)](https://www.g2.com/products/lindy-lindy/reviews), though Trustpilot sits far lower at 1.7/5, with complaints about billing and support.
- **Proof point:** Not published. No named, dated customer result found.
**Key benefits**
- **General-purpose agent** for email, calendar, and web research
- **Computer use** capability on Pro and higher tiers
- Strong G2 sentiment on ease of use
- Credit-based usage across **Plus, Pro, and Max** tiers
### 8. Moveworks
**Best for:** Large, **compliance-heavy enterprises** standardizing employee support.
[Moveworks](https://www.moveworks.com/) is an enterprise employee-support platform that classifies, routes, and resolves tickets using agentic RAG across hundreds of connected systems. It's the only agentic AI vendor listed in the FedRAMP REG Marketplace.
- **Pricing:** Custom enterprise; disclosed figures suggest roughly $15-40 per employee per year.
- **G2 rating:** [4.4-4.6/5 (51-125 reviews)](https://www.g2.com/products/moveworks/reviews).
- **Proof point:** Not published as a single named result. Moveworks cites aggregate customer data instead.
**Key benefits**
- **Agentic RAG** across systems, formats, and domains
- **100+ integrations** and support for multiple LLMs
- Low-code **Agentic Studio** for scalable agent development
- **FedRAMP REG authorized** for regulated industries
### 9. Decagon
**Best for:** CX teams that want **fast implementation and proven deflection** results.
[Decagon](https://decagon.ai/) builds conversational AI agents that resolve customer issues across chat, email, and phone using Agent Operating Procedures instead of rigid scripts. Its AI copilot also supports human agents and improves through continuous learning.
- **Pricing:** Roughly $0.99 per conversation, or per-resolution pricing; annual contracts estimated at $95K-$590K+.
- **G2 rating:** [4.9/5](https://www.g2.com/products/decagon/reviews).
- **Proof point:** Chime reports a 70% combined resolution rate; Duolingo reports 80% deflection; ClassPass reports a 10x increase in deflection; Hunter Douglas reports $1M in AI-handled revenue.
**Key benefits**
- **Agent Operating Procedures** for fast, scalable agent-building
- Resolves issues across **chat, email, and phone**
- AI copilot for human agents that improves over time
- AI-powered insights surface patterns and anomalies
### 10. Aisera
**Best for:** Large enterprises **consolidating IT, HR, and CX support** in one platform.
[Aisera](https://aisera.com/) embeds AI agents directly into Salesforce, ServiceNow, Zendesk, and Jira to automate workflows and assist human agents with context-aware responses and summaries. Its Intelligent Swarming feature routes complex issues to the right experts automatically.
- **Pricing:** Custom enterprise, roughly $200K-$2M+ per year.
- **G2 rating:** [4.4/5 (147 reviews)](https://www.g2.com/products/aisera/reviews).
- **Proof point:** Named a Visionary in Gartner's 2025 Magic Quadrant for AI Applications in ITSM. No single named customer result was found beyond a logo wall.
**Key benefits**
- **Agent Assist** generates summaries and next-best actions using domain-specific LLMs
- Embedded directly into **Salesforce, ServiceNow, and Zendesk**
- **Intelligent Swarming** routes issues to cross-functional experts
- Automated wrap-ups and knowledge article generation
### 11. Sierra
**Best for:** Enterprises with budget for a **fully bespoke conversational agent**.
[Sierra](https://sierra.ai/) builds custom conversational AI agents that handle complex, multi-step tasks like returns and subscription changes across voice, chat, email, and SMS. Its Knowledge Engine grounds responses in your FAQs, policies, and documentation to minimize hallucinations.
- **Pricing:** Not publicly disclosed; estimated around $150K per year depending on setup complexity.
- **G2 rating:** [4.3/5 (15 reviews)](https://www.g2.com/products/sierra/reviews).
- **Proof point:** SiriusXM, Sonos, and ADT are cited as customers in 2026 case studies.
**Key benefits**
- Handles **complex, multi-step workflows** end-to-end
- Omnichannel across **voice, chat, SMS, and email**
- **Guardrails** for brand-aligned workflow design
- Knowledge Engine reduces hallucinations with grounded context
### 12. Beam AI
**Best for:** Operations teams **automating cross-functional business processes**.
[Beam](https://beam.ai/) deploys self-learning AI agents that work together to optimize workflows across support, supply chain, finance, and healthcare. Pre-configured agent libraries speed up deployment for common business functions.
- **Pricing:** Not publicly disclosed.
- **G2 rating:** Not on G2.
- **Proof point:** Not published. No named, dated customer result found.
**Key benefits**
- **Self-learning agents** that continuously optimize execution
- **Multi-agent coordination** for cross-functional automation
- Pre-configured agent library across multiple business functions
- Real-time adaptation to shifting operational demands
### 13. AgentOps AI
**Best for:** Engineering teams **building and debugging their own agent stack**.
[AgentOps AI](https://www.agentops.ai/) is a developer-first observability platform for AI agents and LLM applications, with time-travel debugging, real-time logging, and cost tracking across 400+ LLMs and frameworks like OpenAI SDK, CrewAI, and LangChain.
- **Pricing:** Free tier (50,000 events/month); $49/month (Pro, 500,000 events); custom Enterprise.
- **G2 rating:** Not listed on G2.
- **Proof point:** Not published. No named, dated customer result found.
**Key benefits**
- **Time-travel debugging** and real-time observability
- Tracks every **token, LLM call, and tool invocation**
- Integrates with **OpenAI SDK, CrewAI, AutoGen, and LangChain**
- **Full audit logs** and prompt-injection protection
## How to pick an AI agent platform for your business
Choosing the right AI agent platform is about more than comparing features. Work through these six factors before you commit.
### Step 1 - Define your use case
Get clear on what problem you want AI agents to solve before you take a single demo. Customer service automation, internal operations, agentic commerce, and engineering tasks call for different platforms entirely, so a defined use case is what lets you narrow the field to the vendors above worth a closer look.
### Step 2 - Assess technical fit
AI agent platforms range from no-code tools built for non-technical teams to engineering-heavy platforms that require API integration and custom workflow configuration. Match that range to your team's actual bandwidth. If you don't have engineering time to spare, prioritize the no-code [AI agent builders](https://delight.ai/builder) in the table above.
### Step 3 - Look for scalability
Most teams start with one or two narrow use cases, but a platform that can't support multi-agent systems, centralized control, and role-based permissions will hit a ceiling fast once agentic AI proves its value and you want to expand it to other departments.
### Step 4 - Check channel support
Confirm the platform covers the channels your customers actually use, whether that's in-app chat, web, email, SMS, or voice. For the most seamless experience, look for [omnipresent AI agents](https://delight.ai/omnipresent) that retain context and memory as customers move between channels.
### Step 5 - Evaluate governance, security, and compliance
Autonomous agents without tech-enforced guardrails are a liability, especially in healthcare, finance, or regulated enterprise IT. Look for audit logs, data governance controls, compliance with standards like SOC 2, HIPAA, or GDPR, and clear control over agent tone, guardrails, and the knowledge it draws from.
### Step 6 - Consider strategic support
The best AI agent platforms pair the technology with real onboarding support, help defining success metrics, and ongoing guidance for fine-tuning your systems, not just a self-serve product and a support ticket queue.
## What sets the best agentic AI companies apart
All the leading AI agent platforms in 2026 make it easy to build and manage agents. What separates the best from the rest is trust. Delight.ai's own 2026 consumer research found that 71% of consumers already use AI customer service, but only 57% say they like it, a 14-point gap that's become the real competitive battleground (Delight AI Index, 2026). Prioritize vendors whose agents are well-governed, secure, compliant, and flexible enough to fit your team, not just the ones with the longest feature list.
Delight.ai's agentic AI platform for CX and no-code AI agent builder enables your team to deploy intelligent, scalable [AI agents for customer service](https://delight.ai/ai-customer-service) without the need for heavy engineering resources.
[Contact our team](https://delight.ai/contact-sales) of AI experts to learn more.
- [The 13 AI agent platforms compared](#the-13-ai-agent-platforms-compared)
- [How we evaluated each AI agent platform](#how-we-evaluated-each-ai-agent-platform)
- [Review of the 13 AI agent platforms](#review-of-the-13-ai-agent-platforms)
- [How to pick an AI agent platform for your business](#how-to-pick-an-ai-agent-platform-for-your-business)
- [What sets the best agentic AI companies apart](#what-sets-the-best-agentic-ai-companies-apart)
## Frequently asked questions about agentic AI
**What is agentic AI?**
Agentic AI refers to AI systems that can take purposeful action toward a goal with limited human input. Unlike traditional AI models that react to prompts, agentic AI can reason through problems, plan steps, and use tools to complete complex tasks across systems. For example, AI customer service agents can resolve tickets, process returns, or update account info without human help.
**What is the difference between traditional AI and AI agents?**
The difference between traditional AI and AI agents comes down to action. Traditional AI models are usually designed to solve a specific problem when prompted, like generating text or images. AI agents, on the other hand, are built to take action. They can reason through a task, use tools, and work across systems to achieve a goal. Instead of waiting for instructions, AI agents help move work forward with minimal human input.
**What is the difference between conversational AI and agentic AI?**
The difference between conversational AI and agentic AI comes down to whether the system just talks or actually acts. Conversational AI is built to understand and respond to human input, usually through chat or voice. It's great for answering questions or handling simple interactions. Agentic AI goes a step further. It can take action toward a goal, follow workflows, and complete multi-step tasks.
**What is the difference between generative AI (Gen AI) and agentic AI?**
The difference between generative AI and agentic AI is that one creates and the other acts. Generative AI (Gen AI) tools like ChatGPT create content (e.g., text and images) when you prompt them. Agentic AI builds on that by taking steps to act on the output, such as following workflows or triggering actions to accomplish AI customer care. For example, while a Gen AI tool might write a response to a customer question, an AI customer service agent can send that response across channels, log the interaction, resolve the customer issue, and update the support ticket.
**What should I look for when choosing an agentic AI vendor?**
The most important things to look for when choosing an agentic AI vendor are use case fit, technical fit, scalability, channel coverage, governance, and vendor transparency. Use case fit comes first, since customer-facing support, internal employee support, and engineering automation call for different platforms entirely. From there, weigh technical fit (no-code versus engineering-heavy), scalability across departments, and channel coverage (chat, voice, SMS, email). Governance and compliance matter too, especially SOC 2, HIPAA, and GDPR. Finally, factor in how much strategic support the vendor offers beyond the software itself, plus whether it publishes reviews, transparent pricing, and named customer results. Several agentic AI companies still don't publish any of the three.
**Which companies are leading the agentic AI market in 2026?**
Foundation-model providers like OpenAI, Anthropic, Microsoft, and Google Cloud lead the broader agentic AI category, and several vendors in this list build on their models. Among agentic AI companies built specifically for customer experience and support, delight.ai, Decagon, Sierra, and Moveworks lead the pack, each with a different specialty. Delight.ai leads on omnichannel, memory-driven CX. Decagon leads on fast implementation and proven deflection. Sierra leads on bespoke, enterprise-only deployments. Moveworks leads on employee-facing support at compliance-heavy enterprises.
## Keep reading
[View more](/blog "View more")
[
The 8 best AI tools for ecommerce in 2026](/blog/ai-for-cx/best-ai-tools-for-ecommerce)[
Customer service MCP for ecommerce](/blog/ai-for-cx/customer-service-mcp-ecommerce)[
Who is responsible when AI makes a mistake?](/blog/ai-agent/who-is-responsible-ai-mistakes)[
How does real-time customer engagement impact your business?](/blog/ai-for-cx/real-time-customer-engagement)
[View more](/blog "View more")
### Ready to unleash the power of
AI customer service agents?
[ Contact sales](/contact-sales)
## AI in retail: 13 trends and examples to guide you in 2026
Source: https://delight.ai/blog/industry/ai-in-retail
- [Blog](/blog)
- [Industry](/blog/industry)
- AI in retail: 13 trends and examples to guide you in 2026
## AI in retail: 13 trends and examples to guide you in 2026
[](https://delight.ai/author/ian-heinig)
Ian Heinig
Agentic AI marketer
March 31 2026
In 2026, AI in retail is fast-becoming a competitive necessity. According to KPMG, AI adoption in the retail industry will [grow from 33% to 85% by 2027](https://assets.kpmg.com/content/dam/kpmgsites/xx/pdf/2026/01/ai-in-retail-report.pdf), marking one of the fastest adoption curves of any sector in the global economy.
Five core forces are converging in 2026 that every retailer needs to understand, as AI is being used across online and physical stores to:
- [Change consumer behavior and shopping](https://delight.ai/blog/industry/ai-in-retail#section-anchor-id-2)
- [Enhance customer experience (CX) and marketing](https://delight.ai/blog/industry/ai-in-retail#section-anchor-id-3)
- [Restructure retail operations](https://delight.ai/blog/industry/ai-in-retail#section-anchor-id-4)
- [Improve margins and cost controls](https://delight.ai/blog/industry/ai-in-retail#section-anchor-id-5)
- [Optimize the supply chain](https://delight.ai/blog/industry/ai-in-retail#section-anchor-id-6)
The AI in the retail market is maturing as it transforms each aspect of retail. 2025–2026 are considered "AI pivot years" where adoption shifts from experimental pilots to necessary infrastructure as AI in retail evolves away from isolated tools into orchestrated systems that start to do more than narrow work, but do entire jobs on behalf of customers and retailers in concert with other systems.
Each of these aspects is reshaping the competitive landscape—and together, they define what it means to be an AI-ready retailer in 2026. This article helps CMOs, CX, and Ops leaders understand the trends and prioritize investments as they navigate a rapidly evolving landscape.
### What is AI in retail in 2026?
AI in retail use machine learning (ML), data analytics, natural language processing (NLP), and automation to optimize operations and deliver highly personalized shopping experiences. It now spans three generations of AI technology that work in harmony when integrated effectively.
**Predictive analytics** analyzes historical data to identify patterns and make predictions. It turns vast datasets into actionable insights that power AI-powered demand forecasting, inventory optimization, segmentation, and personalized product recommendations.
**Generative AI** enables conversational customer engagement through natural language via retail chatbots and AI assistants. It can also dynamically feed retail systems with “just in time” assets, automatically creating product descriptions, marketing copy, and personalized outreach.
**Agentic AI** is the frontier—autonomous systems that perceive, reason, and act across retail systems in real time without human intervention. AI agents enable retailers to replace siloes with unified customer data and isolated tools with a coordinated ecosystem where specialist AI agents simulate human roles, coordinate across customer service, inventory, pricing, and supply chain in real time to tackle complex tasks.
With [68% of retailers expecting to deploy agentic AI within the next year](https://deloitte.wsj.com/cmo/when-ai-becomes-the-buyer-how-agentic-commerce-is-reshaping-retail-1b5095d5#:~:text=A%20large%20majority%20of%20surveyed,An%20error%20occurred.), retail operations are shifting from reactive automation to proactive orchestration. Rather than isolated tools solving singular problems, AI agents mark a shift to scaling unified intelligence that enhances customer experience and optimizes operations not as separate initiatives, but as a single compounding motion as they continuously act, learn, and improve together.
### 13 trends shaping AI in retail in 2026 (with examples)
AI is becoming omnipresent and omnipotent in retail because it offers a scalable solution to many of the industry’s core challenges. Here are some emerging trends to watch as you chart a course for AI adoption.
### AI is changing consumer behavior
#### 1. The rise of agentic commerce (AI shopping)
AI is fundamentally changing how consumers find, compare, and purchase products. Instead of browsing websites or using traditional search, consumers increasingly prompt AI agents to find, compare, and purchase products for them. This shift, known as agentic commerce or AI shopping, is well underway. Nearly [60% of consumers already use AI to shop](https://news.darden.virginia.edu/2025/06/17/nearly-60-use-ai-to-shop-heres-what-that-means-for-brands-and-buyers/) for its faster, more personalized shopping experience.
This presents a challenge for retailers. When AI agents mediate the shopping journey, they bypass traditional retail interfaces entirely. For example, referral traffic from ChatGPT now accounts for [20% of Walmart's total referral traffic](https://digiday.com/media/chatgpt-is-now-20-of-walmarts-referral-traffic-while-amazon-wards-off-ai-shopping-agents/). To remain visible and competitive, retailers must evolve from search engine optimization (SEO) to generative engine optimization (GEO)—ensuring product catalogs, pricing, and fulfillment data are accurate, API-accessible, and optimized for AI discoverability—or risk becoming invisible in the era of AI-ed shopping.
#### 2. Hyper-personalization: The “segment of one”
Hyper-personalization goes beyond traditional methods by analyzing real-time signals such as behavior, context, and preferences to deliver recommendations tailored to a true “segment of one.” Instead of solely analyzing past activity, AI anticipates shoppers' needs and dynamically adapts each interaction to the moment by determining the “next best action” on that channel. According to Deloitte, [67% of retail executives](https://www.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html) expect to deploy AI-driven personalization within the next year, more targeted campaigns, tailored experiences, and loyalty programs that truly align with each individual.
For instance, consumers increasingly use AI agents powered by large language models as shopping assistants. [Nordstrom’s AI concierge](https://www.customerexperiencedive.com/news/ai-nordstrom-dedication-human-care/804769/) acts as a personal stylist—offering GenAI-powered trend reports and recommendations tailored to individuals’ unique tastes and context, even local events or weather. Using hyper-personalized conversational commerce, retailers can meet customers one-to-one at scale, driving higher engagement and conversion.
#### 3. Autonomous 24/7 customer service
Customer expectations for fast, personalized, consistent support are always evolving—and agentic AI enables retailers to simultaneously improve customer service and operational efficiency. Unlike traditional chatbots that simply retrieve answers, AI agents resolve issues end-to-end without human intervention. They handle the high-volume inquiries—such as order status, returns, refunds, and product FAQs—that overwhelm retail service teams, especially in peak periods, freeing human staff to focus on high-value customer interactions.
Gartner predicts that AI agents will [automate up to 80% of frontline customer service interactions](https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290) by 2029. Delight.ai’s [AI concierge](https://delight.ai/) takes this a step further. By remembering each customer in its long-term memory, it delivers tailored context-aware resolutions across all channels—eliminating wait times and the friction of repeating information. Because the AI agent lets customers seamlessly pick up where they left off, it effectively meets their expectations for consistent, high-quality service on every channel.
### AI is enhancing customer experience (CX) and marketing
#### 4. Omnichannel experiences
As [consumers become more value-focused and price-sensitive](https://www.simon-kucher.com/en/insights/us-consumer-affordability-2026-insights-our-tariffs-and-price-increase-pulse-study#:~:text=As%202025%20progressed%2C%20concern%20about,consumers%20to%20stay%20on%20board.) in 2026 and beyond, the retail winners will likely be those investing in AI-ready infrastructure that empowers brands to simultaneously improve their service quality, manage costs, and deliver personalized experiences that feel worth the price.
Agentic AI makes this possible. Inherently omnichannel, agentic architecture unifies retail data into a single, evolving 360-degree customer view for use across pricing, inventory, and supply chain systems—while scaling real-time, context-driven CX personalization across mobile, desktop, in-store, and post-sale touchpoints. Unsurprisingly, Deloitte research shows that an [enhanced omnichannel experience is the top growth lever](https://www.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html) for retail executives in 2026.
#### 5. Generative AI integration
Generative AI is moving beyond retail chatbots into the backbone of retail operations, and investment is accelerating to match. [Over 80% of retailers are increasing their investment](https://www.eposnow.com/us/resources/how-generative-ai-in-retail-is-boosting-sales/#:~:text=Streamlined%20Supply%20Chains%20and%20Inventory,better%20anticipate%20variations%20in%20demand.) in AI in the next 12-24 months, pursuing the 10-30% reduction in costs and meaningful sales growth it can deliver when implemented effectively.
Applications range from dynamically creating “just in time” marketing content to guiding conversational search experiences with voice-enabled AI. More advanced is AR/VR-enabled virtual fitting rooms for apparel and cosmetics brands. All these retail use cases can improve conversion among the [71% of consumers](https://www.capgemini.com/us-en/news/press-releases/71-of-consumers-want-generative-ai-integrated-into-their-shopping-experiences/) who say they want GenAI integrated in their shopping journeys.
#### 6. Agentic marketing
Agentic AI enables retail marketers to shift from reactive, manual campaign management to proactive, autonomous optimization. AI agents continuously adjust ad spend across channels, tailor outreach at the individual level, and surface the right offer at the right moment to improve ROI through autonomous, hyper-personalized shopping. Retailers’ confidence in their AI capabilities is high, with [94% ](https://www.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html)expecting to bring more marketing activities in-house in 2026.
As generative AI makes creative tools more accessible, competitive advantage will accrue to the retailers who most effectively blend creativity, data, and AI-driven insight to deliver unique brand experiences in a market that’s increasingly crowded and full of “AI slop.”
#### 7. Phygital experiences
AI is moving into physical stores to enhance the customer experience for shoppers, provide insights to associates, and improve margins. The emerging model is "phygital"—environments where digital and physical experiences are seamless, with AI working invisibly across both.
Walmart, for example, announced plans to [remodel over 650 locations](https://www.abc10.com/article/news/nation-world/walmart-remodel-stores-list-2025/507-98eaab2a-9d7a-4aa0-a810-b3197f3ff901) to its “Stores of the Future” concept. These locations focus on a seamless omnichannel experience, expanded product selections, and enhanced pick-up and delivery options that integrate through QR codes with social commerce platforms like TikTok Shop. More advanced deployments will include autonomous checkout, AI-powered shelf monitoring, and dynamic pricing, as well as AI tools and kiosks that surface real-time inventory and customer context to in-store associates, who can guide shoppers without leaving the floor.
### AI is reshaping retail structures
#### 8. Retail merchandising
Agentic AI is reshaping merchandising, shifting work from manual execution to strategic oversight and goal setting. According to McKinsey, agentic AI for retail can [reduce manual tasks of retail analysts by up to 60%](https://www.mckinsey.com/industries/retail/our-insights/merchants-unleashed-how-agentic-ai-transforms-retail-merchandising)—automating or standardizing the once-manual tasks of pricing analyses, assortment diagnostics, vendor materials, and performance reporting.
Rather than waiting for weekly planning cycles, AI agents continuously monitor real-time signals and act directly, reducing task times from weeks to hours. In the process of adjusting prices, flagging replenishment exceptions, and running promotional tests, AI also feeds these results back into machine learning models, driving continuous improvement and providing insights for retail systems.
#### 9. Workforce augmentation
As AI moves from isolated tool to interconnected digital workforce, the most effective retailers aren't replacing human teams—they're redefining their roles and workflows with the speed and precision of AI. Research shows that [30–35% of activities across consumer functions could be automated by AI by 2030](https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/from-blueprint-to-breakthrough-how-ai-and-automation-can-transform-the-consumer-enterprise), with the biggest gains in frontline and commercial roles. However, over-automation carries a real risk: removing human judgment from sensitive touchpoints can erode trust and alienate customers.
With [AI-powered customer service](https://delight.ai/ai-customer-service), especially, augmentation is better than replacement. BJ's Wholesale Club, for example, uses its AI concierges to both handle routine support inquiries and assist its human agents by surfacing key context to resolve complex issues faster. The result is a support operation that scales through peak seasons without added headcount, and delivers better experiences where it matters most.
### AI is improving margins and cost controls
#### 10. Dynamic pricing
With [costs expected to rise](https://www.forbes.com/sites/pamdanziger/2026/03/11/retail-prices-are-now-the-deciding-factor-as-consumer-uncertainty-intensifies/#:~:text=%E2%80%9CThe%20longer%20this%20lasts%2C%20the,grow%20revenue%20and%20maintain%20margins.) in 2026 due to surging energy prices, tariffs, and higher wages, AI-powered pricing strategies are increasingly popular. Dynamic pricing continuously analyzes competitor pricing, demand signals, and inventory levels to find the optimal price for each item in each context, protecting profitability without defaulting to blanket discounting.
Walmart's AI-integrated digital shelf labels (DLS) are a prime example. They enable real-time competitive repositioning and automated markdowns on perishables as expiration dates approach. This has helped the Bentonville behemoth to reduce[ operational costs by up to 30%](https://finance.yahoo.com/news/walmart-bets-digital-shelf-labels-232027895.html) through automated markdowns and inventory management.
#### 11. Inventory management
As AI moves into core operations, machine learning models are synthesizing historical sales data with real-time signals—such as weather, social trends, trade conditions—to predict demand with precision and trigger proactive replenishment before problems occur, preventing waste and lowering turnover costs.
In-store, autonomous mobile robots now handle continuous stock counting and shelf monitoring, while existing security cameras use computer vision to detect stockouts in real time, marking a shift to zero-touch operations that eliminate manual inventory counting entirely. Physical stores are also being reimagined as micro-fulfillment centers that support online shoppers through curbside pickup and BOPIS, maintained by tight synchronization of warehouse-to-store inventory. Aside from lowering logistics costs and reducing overstock, this leads to fewer stockouts and reduced support tickets; a win-win.
### AI is optimizing the retail supply chain
#### 12. Autonomous supply chain optimization
In an era of shifting trade policies, labor shortages, and demand volatility, AI is emerging as a scalable solution for supply chain resilience. AI-driven demand planning, real-time inventory management, and dynamic pricing all combine to give retailers a truly responsive supply chain—one that reroutes shipments on the fly, rebalances inventory across locations, and reduces lead times without human effort. Per Deloitte, [30% of retailers currently use AI for supply chain visibility](https://www.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html), and this figure is expected to reach 41% by next year.
No longer reactive, AI now lets retailers go beyond visibility and engage in continuous, proactive optimization: from detecting demand signals earlier to adjusting inventory positions before stockouts occur to responding to disruptions in minutes, not days.
#### 13. Logistics and last-mile delivery
Fulfillment and last-mile delivery represent some of retail's highest and fastest-growing costs, and multi-agent orchestration attacks both directly. This involves a network of specialized AI agents that coordinate in real time: an inventory agent repositions stock closer to demand, a logistics agent adjusts routing on the fly, and a fulfillment agent prioritizes shipments—all without human handoffs.
The result is a supply chain that resolves operational complexity on its own, compressing response times and managing cost pressures with a real-time AI workforce. For example, Alibaba r[educed delivery errors by 40%](https://digitaldefynd.com/IQ/ways-alibaba-using-ai-case-study/#:~:text=accuracy%20and%20efficiency.-,Results,overstock%20issues%2C%20improving%20inventory%20efficiency.) using AI-powered route optimization.
### How to prepare for AI in retail in 2026
As AI moves to the heart of retail operations, the business impact is clear—but deploying it at scale and cost-effectively remains a challenge. According to Cisco's 2025 AI Readiness Index, 98% of organizations report increased urgency to deploy AI—yet [only 13% are fully prepared to realize its potential](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf). AI-ready organizations are 4x more likely to move pilots into production and 50% more likely to report measurable returns. The rest are stumbling over poor data quality, integration challenges, and shortages of AI talent—lacking a scalable foundation for AI in retail.
The retailers closing the implementation gap aren't the most urgent. They’re the most prepared. They've done more than invest in technology. They've supported this with operational redesign and cultural change.
This multi-dimensional approach to readiness turns AI adoption into a repeatable, cross-functional process that spans logistics, procurement, production, and marketing—avoiding the risks, inefficiencies, and technical debt that derail the majority of AI initiatives by effectively aligning leadership, business objectives, and capabilities.
The investment required is real on multiple fronts. Deloitte's 2026 Global Retail Outlook finds [44% of organizations say legacy systems are slowing innovation](https://www.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html), showing that agentic, agent-ready infrastructure that replaces fragmented data environments and creates a scalable foundation for agentic commerce, hyper-personalization, and autonomous operations.
Equally important are people and processes: commercial teams need training to work alongside AI agents in real time — interpreting outputs, directing systems toward strategic goals, and exercising judgment where AI cannot.
Those who build this multi-dimensional AI readiness early will be best positioned as AI agents become the primary interface for consumer transactions.
Delight.ai helps retailers get there. We offer AI concierges purpose-built for retail workflows, AI governance frameworks, and forward-deployed teams with deep expertise in helping retail customers—all helping you deploy AI safely and sustainably.
[Contact sales](https://delight.ai/contact-sales) to learn more.
- [What is AI in retail in 2026?](#section-anchor-id-0 "What is AI in retail in 2026?")
- [13 trends shaping AI in retail in 2026 (with examples)](#section-anchor-id-1 "13 trends shaping AI in retail in 2026 (with examples)")
- [AI is changing consumer behavior](#section-anchor-id-2 "AI is changing consumer behavior")
- [AI is enhancing customer experience (CX) and marketing](#section-anchor-id-3 "AI is enhancing customer experience (CX) and marketing")
- [AI is reshaping retail structures](#section-anchor-id-4 "AI is reshaping retail structures")
- [AI is improving margins and cost controls](#section-anchor-id-5 "AI is improving margins and cost controls")
- [AI is optimizing the retail supply chain](#section-anchor-id-6 "AI is optimizing the retail supply chain")
- [How to prepare for AI in retail in 2026](#section-anchor-id-7 "How to prepare for AI in retail in 2026")
### FAQs about AI in retail
#### How do retailers use AI?
Retailers use AI across the full customer journey and retail operation — from agentic customer support and personalized product recommendations to demand forecasting, inventory optimization, dynamic pricing, and visual search. The most advanced deployments use multi-agent orchestration to coordinate these functions simultaneously, enabling autonomous retail operations that improve with every customer interaction.
#### How does AI improve inventory management?
By analyzing real-time sales data alongside external signals like weather, local events, and trade conditions, AI forecasts demand with a precision no human team can match — preventing stockouts before they occur and eliminating the overstock that ties up capital. Retailers using AI-driven inventory management report forecasting error reductions of 20–50% and stockout reductions of up to 50%.
#### How does AI enhance customer experience?
AI delivers personalized recommendations, proactive support, and seamless omnichannel experiences by building a continuous, evolving view of each customer across every touchpoint. delight.ai's Agent Memory carries context across sessions and channels — so customers never repeat themselves, and every interaction reflects who they are, not just what they last clicked. Applications range from AI shopping assistants and virtual try-ons for fashion and beauty, to proactive shipment updates that turn a potential churn moment into a loyalty one.
#### What are the key benefits of AI in retail?
The compounding benefits include higher revenue through personalization and agentic commerce, lower operational costs through support automation and workforce optimization, smarter inventory management, faster checkout experiences, and stronger customer loyalty. McKinsey estimates personalization alone delivers a 10–15% revenue lift and up to 30% improvement in marketing efficiency.
#### What are the biggest challenges for retailers adopting AI?
The primary barriers are AI readiness gaps rather than technology gaps: poor data infrastructure, absent governance frameworks, fragmented legacy systems, and insufficient cultural and operational alignment. Cisco's AI Readiness Index finds only 13% of organizations are fully prepared to realize AI's potential — and the retailers that succeed treat readiness as a cross-functional, repeatable process rather than a one-time technical deployment.
#### How can retailers start with AI?
The right starting point is the use case that delivers immediate ROI while building the data infrastructure everything else depends on — typically autonomous customer support, which handles high-volume interactions end-to-end while generating the unified customer intelligence that powers personalization, forecasting, and agentic commerce downstream. The right partner brings not just technology, but readiness frameworks and forward-deployed expertise to help sequence the investment correctly from day one.
### Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## How agentic AI is reshaping retail
Source: https://delight.ai/blog/industry/agentic-ai-in-retail
## How agentic AI is reshaping retail
[
Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 9 2026
From recommendation engines to chatbots, AI has been enhancing retail operations for years. The next leap forward is agentic AI—autonomous systems that act on behalf of customers or businesses. Unlike earlier AI tools that require prompting or rigid rules, agentic AI can execute tasks from start to finish without human intervention.
Agentic AI has a degree of autonomy, and this signals a major paradigm shift. For retailers, it means more data-driven, efficient, and profitable operations. For customers, it means a more personalized experience where a trusted digital concierge is available at every touchpoint, 24/7.
Agentic AI also unifies siloed retail systems into a single connected ecosystem. Capable of operating seamlessly across platforms, it syncs and processes data in real time—including live customer behavior and operational inputs. This enables truly data-driven decision-making and automation across functions, driving gains in everything from productivity to personalization at scale.
For retailers faced with rising costs, higher customer expectations, and operational complexity, agentic AI is a scalable solution to many of the industry's most persistent challenges. Unsurprisingly, a full [75% of surveyed retailers say AI agents will be essential by 2026](https://www.salesforce.com/resources/research-reports/connected-shoppers-report/), according to Salesforce’s Connected Shopper Report.
Read on for a breakdown of how [agentic AI in retail](https://delight.ai/industries/retail) is shifting retail operations from reactive to proactive, offering a new standard in intelligent automation, personalization, and profitability.
### What is agentic AI in retail?
Agentic AI in retail refers to the use of autonomous AI systems—often called AI agents—that reason, make decisions, direct their own processes and tool usage, and act on behalf of users or retail businesses to achieve the goals set for them. In retail, this could mean an [AI concierge](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder) that handles transactions, returns, and WISMO from start to finish for customers—freeing support teams to focus on high-value, customer-centric tasks like clienteling and brand storytelling.
Agentic AI can operate seamlessly across systems, using APIs to automate and optimize processes with real-time data. For example, an online retailer might deploy an agentic AI system that monitors competitor prices, product engagement, and social media activity, and continuously adjusts its pricing, merchandising, and promotions in the moment to drive sales. Or a grocery retailer could use agentic AI to manage daily and seasonal staffing levels, considering both historical patterns and real-time foot traffic to predict needs with greater accuracy.
Whether used to scale hyper-personalized experiences, optimize merchandising, or improve B2B marketing, agentic AI can reduce costs and inefficiencies for retailers while delivering more timely, relevant experiences that elevate customer experience (CX) and satisfaction.
### Why is agentic AI in retail important?
Agentic AI represents a step change in how retail decisions are made and executed. Unlike traditional automation or reactive generative AI, agentic AI can act with autonomy. It perceives its environment, interprets context, processes diverse data, and adjusts accordingly in real time—all within a continuous cycle of observation, reasoning, and action.
This combination of real-time data and process control brings a new level of adaptability to retail operations. Finally, retailers can shift from reactive to dynamic systems that adjust on the fly to changing conditions, whether in the supply chain or the customer experience, for a new generation of intelligent process automation.
Agentic AI in retail goes far beyond automating routine tasks and data-driven processes to improve efficiency. It also transforms support interactions into a growth engine. Rather than simply resolving "Where's my order?" questions on their own, AI agents can also recognize and capitalize on conversion moments. For example, [ecommerce AI agents](https://delight.ai/industries/retail) can make tailored product recommendations, nudge membership sign-ups, or trigger targeted promotions at just the right moment—all without human intervention.
Agentic AI fills a different role in each area of retail operations:
- **For customers:** Acts as a 24/7 [omnipresent AI concierge](https://delight.ai/omnipresent), guiding product discovery and tailoring experiences across every channel and touchpoint, while gathering interaction data into one unified, evolving customer profile.
- **For retail CX teams:** Acts as a virtual collaborator, automating routine tasks and surfacing insights so teams can focus on delivering the best and most cost-effective CX.
- **For retailers:** Offers a scalable solution that simultaneously unites siloed systems, increases efficiency, drives data-driven decision-making, and boosts profitability.
Next, let’s explore how agentic AI differs from other forms of AI in retail—and where it’s already making an impact.
### What’s the difference between agentic AI in retail and other AI?
Agentic AI is the latest breakthrough in a set of evolving and complementary AI technologies for retailers. If you’re new to [AI in retail](https://delight.ai/industries/retail), it can be difficult to know the difference between these, so here’s a quick breakdown of how each works and its growing role in retail operations:
#### Core types of AI in retail
- **Predictive AI:** Predicts, classifies, and anticipates customer needs based on historical data. For example, Amazon’s recommendation engine analyzes user data for patterns to suggest relevant products.
- **Generative AI:** Produces content, recommendations, and experiences based on patterns it’s learned, such as marketing copy, chatbot responses, or personalized product descriptions. For example, Amazon’s Rufus chatbot helps shoppers find and compare products via conversational interactions.
- **Agentic AI:** Acts on behalf of customers or businesses toward a goal using tools and real-time data. For example, Alibaba’s [AI concierge](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder) handles [70% of support interactions](https://www.sdcexec.com/software-technology/ai-ar/news/22947891/alibaba-alibaba-releases-worlds-first-ai-agent-for-global-trade#:~:text=Accio%20Agent%20compresses%20tasks%20that,into%20workflows%20completed%20in%20minutes.&text=%C2%B7-,Accio%20Agent%20automates%2070%25%20of%20traditionally%20manual%20workflows%2C%20compressing%20fragmented,seamless%2C%20AI-powered%20cycle.&text=Users%20start%20by%20inputting%20a,the%20right%20choice%20of%20supplier.&text=Accio%20Agent%20leverages%20advanced%20language,cost%20or%20innovation%20versus%20compliance.), including returns and WISMO workflows from start to finish to elevate CX and streamline operations.
To recap: predictive AI is for insights, generative AI is for content, and agentic AI is for acting in the moment—whether to hyper-personalize experiences, make instantaneous decisions, or scale intelligent automation.
### Use cases of agentic AI in retail
Agentic AI in retail offers a solution to many of the industry’s most persistent challenges. In fact, according to Salesforce’s 2025 Connected Shoppers Report, [43% of retailers are currently piloting autonomous AI solutions](https://www.salesforce.com/resources/research-reports/connected-shoppers-report/), while another 53% are actively evaluating the potential uses of autonomous AI.
Use cases for agentic AI in retail vary from fast-to-deploy wins like CX agents to deep, systemic transformations, and these examples show the versatility and impact of agentic AI in retail:
#### 1. Autonomous 24/7 customer support
Traditional chatbots can’t adapt to unexpected issues or resolve complex cases without escalation, which can frustrate customers and erode brand trust. [AI concierges](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy26-q4-glbl-content-ebook-finance-playbook&utm_content=autoresponder), however, can anticipate customer needs based on their profile, tailor interactions in the moment based on context, and resolve issues from start to finish—even make context-rich handoffs if needed.
**Example:** The AI agent can detect when an order will be delayed, then proactively reach out to the customer with an apology and a discount code. This enhances the customer experience while freeing human reps to focus on nuanced, high-value cases.
*AI agents for retail can handle multi-step tasks like returns, WISMO, and more*
#### 2. Dynamic pricing and promotion
Static pricing strategies often leave margin on the table or price customers out due to an inability to account for real-time data. Agentic AI, however, enables retailers to continuously adjust pricing and promotions based on signals such as demand, inventory, competitor pricing, and live customer behavior, helping to capture margin while remaining competitive.
**Example:** Agentic AI in retail can price up high-demand items at peak times to capture value, while discounting slow-moving inventory to clear stock profitably, or updating promotion items at opportune times to preserve margins.
#### 3. Hyper-personalized experiences at scale
One-size-fits-all marketing can miss conversions, but agentic AI in retail can help. By tailoring messages and offers based on a shopper’s profile and current behavior, location, or session context, agentic AI can deliver the most relevant, actionable experiences at each touchpoint. From product discovery to optimized marketing, ecommerce AI agents can increase conversions, engagement, and loyalty for retailers by making their CX more intuitive and rewarding at every touchpoint.
**Example:** A loyalty member shopper visits a store and lingers in front of running shoes, so the [AI agent for retail](https://delight.ai/industries/retail) sends a push notification with a discount on a best-selling model in their size, plus matching socks for cross-sell. And if the cart gets abandoned, the agent proactively triggers a 10% off promotion before the shopper leaves the cart to help and close the sale.
*AI agent proactively triggers a discount in checkout to curb cart abandonment in real time*
#### 4. Manage seasonal surges
Support teams can get overwhelmed by Black Friday, holidays, and viral product launches. Without scalable systems, tickets pile up, SLAs slip, and customer frustration mounts. Agentic AI agents scale instantly to handle high volumes when built with the right agent-friendly infrastructure. And with localized AI agents, brands can ensure a consistent tone, accuracy, and resolution quality even across different languages, contexts, or product lines. This keeps retailers agile during peak periods without relying on temporary staff.
**Example:** A fashion brand drops a limited-edition release that drives a sudden surge of order inquiries. The AI agent handles 80% of them—verifying shipping, resolving payment issues, and explaining return policies—as human teams focus on keeping CSAT scores high throughout.
#### 5. Inventory and supply chain optimization
Stockouts, overstocks, and supply disruptions can reduce margins and erode customer trust. Agentic AI can forecast demand across locations with greater accuracy, identify supply chain risks early, and dynamically rebalance inventory to raise margins—bringing new levels of predictability and precision in inventory and supply chain management.
**Example:** A grocer’s supply chain management system reroutes shipments for fresh produce around a storm delay, then updates in-store displays based on expected sell-through rates to ensure availability and reduce waste.
#### 6. Autonomous merchandising
Merchandising often relies on historical data or manual updates, but agentic AI enhances these decisions with live data on inventory, current events, and customer behavior. This helps to create more profitable mixes and maximize revenue.
**Example:** A fashion retailer’s AI agent swaps its homepage banners mid-day to promote products trending on social media, while reconfiguring in-store endcaps with similar in-demand items to drive sales.
#### 7. Real-time omnichannel CX orchestration
Shopping journeys are historically fragmented across apps, websites, and in-store touchpoints—but agentic AI in retail can deliver a single, consistent, context-driven experience across platforms and channels. By analyzing user data and current context against business goals while operating seamlessly across channels, AI agents for retail make omnichannel experiences possible.
**Example:** A retailer integrates an [ecommerce AI agent](https://delight.ai/industries/retail) into its site, mobile app, and in-store kiosks. A customer browsing for hiking boots gets a push notification about nearby store availability. When they enter the store, an in-store AI-powered robot guides them to the boots and suggests related gear. If they leave without buying, the AI emails them an offer for free shipping, providing a more convenient and efficient experience that leads to greater conversion.
*Agentic AI operates across channels and platforms, enabling a truly seamless retail journey for customers*
### Benefits of agentic AI in retail
From better CX to greater profitability, agentic AI delivers a variety of strategic advantages for retailers. By integrating [retail AI agents](https://delight.ai/industries/retail) into core functions, businesses can unlock significant benefits like:
#### 1. Context-driven personalization at scale
Retailers have long aspired to deliver 1:1 personalization, but siloed systems and data delays made it impossible to act in real time, resulting in generic or irrelevant recommendations.
With [omnichannel AI agents](https://delight.ai/omnipresent), retailers can tailor recommendations and content with unified, real-time customer profiles and live context, delivering more timely, relevant shopping experiences that boost conversion, average order value (AOV), and loyalty.
#### 2. Faster, smarter decision-making
Retail teams often rely on guesswork and lagging data to make sales and marketing decisions, which hinders their ability to adapt and preserve margins.
By processing vast and diverse datasets in near real-time, agentic AI empowers retail teams (and connected systems) to make faster, more informed decisions around pricing, promotions, and inventory optimization to lift profitability and protect margins.
#### 3. Improved efficiency and cost reduction
Retail operations often incur costly inefficiencies, whether from overstaffing in slow times, missed demand spikes, or unnecessary markdowns.
Agentic AI brings data-driven precision to retail processes. By continuously adapting to shifting inputs like demand shifts, competitor moves, and traffic patterns, it helps retailers to optimize resource allocation and protect margins without adding to headcount.
#### 4. Enhanced customer experience
Retail customer journeys have long been fragmented across apps, websites, and stores—leading to disjointed interactions that feel generic and often fail to convert.
Agentic AI changes this by enabling tailored concierge-style experiences across every touchpoint. Acting like a personal shopper, AI agents for retail remember each customer’s preferences, history, and context to deliver the most appropriate experiences at every stage. The result is a new level of convenience, continuity, and competitiveness for retailers that reduces friction, drives sales and cross-sells, and strengthens loyalty with a standout CX.
#### 5. Intelligent process automation
Manual interventions in retail logistics, staffing, and marketing are slow and error-prone, leaving retailers vulnerable to delays, inefficiencies, and rising costs.
Agentic AI can recognize impending issues and inefficiencies based on evolving factors like weather, customer activity, or social media mentions, then correct them instantly. Whether acting in the moment to reroute shipments, rebalance staff schedules, or reallocate budget in response to live data, AI helps retailers to minimize disruption and loss.
#### 6. Greater profitability
The competing pressures of margin, customer satisfaction, and operational costs make it challenging for retailers to sustain profitability.
With agentic AI, retailers can strike the right balance by simultaneously optimizing personalization, efficiency, and decision-making with accurate data across functions. This helps to unlock both top-line growth and bottom-line gains. In fact, Bain & Company estimates that AI-driven personalization with AI agents can [increase retail revenues by 5–10%](https://www.bain.com/about/media-center/press-releases/2024/one-year-into-their-gen-ai-era-retailers-must-scale-early-investments-as-shoppers-adopt-ai-into-their-daily-lives--bain--company/).
### Best practices of agentic AI in retail
Even with a trusted AI framework, agentic AI projects can stumble without a clear roadmap. To set your initiative up for success, here are some best practices to keep in mind:
#### 1. Start with clear goals
Agentic AI works best when given a well-defined goal. Maybe you want to reduce cart abandonment, optimize marketing, or reduce resolution times. Clear objectives give your AI agent a framework to operate in and help you measure success.
#### 2. Prioritize data quality
AI is only as good as the data it runs on. Make sure you establish processes to ensure data is accurate, comprehensive, secure, and compliant. Without robust data management, your AI risks making irrelevant or wrong decisions that erode trust.
#### 3. Pick your best use cases
Begin with high-impact use cases that deliver real value to customers and your business. Think about the repetitive customer questions you’d love to automate or the operational headaches you’d rather not deal with manually. By piloting agentic AI in these areas, you’ll build trust, prove ROI quickly, and supply AI with sufficient data to perform and improve effectively.
#### 4. Balance autonomy with oversight
AI agents can perform tasks from start to finish, meaning guardrails are needed to ensure they don’t veer off brand or policy. For example, you might set approval thresholds for discounts or have a human approve AI’s suggestion for staffing reallocation. By defining limits on what AI agents do, and where humans are needed, retailers can unlock speed and scale while ensuring AI trust and accountability.
#### 5. Integrate across channels
Customers don’t think in channels; they just want a fast and seamless experience. Deploy agentic AI with the mindset that it should work consistently across web, mobile, in-store, and social. This way, no matter where shoppers engage, the experience feels unified, relevant, and personalized—a true concierge experience.
#### 6. Commit to continuous improvement
Agentic AI isn’t a ‘set it and forget it’ technology. To ensure AI performance and safety, retailers should test and evaluate AI agents, updating them with the latest data to refine them over time. Run A/B tests on specific agentic AI workflows, such as product recommendations, and track KPIs (like conversion or support resolution time) to ensure AI stays aligned with your goals and customer expectations.
#### 7. Prioritize trust and transparency
Agentic AI must feel fair and transparent to customers, or it will be hard to maintain their trust. For instance, this might mean explaining how AI recommends products to put customers at ease (e.g., "You got this discount because you’re a loyalty member"), provide opt-outs for personalization where appropriate, and respect their data privacy to ensure ethical use of customer data through compliant, responsible AI frameworks.
### Getting started with agentic AI in retail
Agentic AI promises to unlock a new standard in operational efficiency, personalization, and customer satisfaction for retailers. By shifting retail systems from reactive to proactive, agentic AI in retail leads to lower costs, higher profits, and an interconnected retail ecosystem that adapts to customers in the moment.
For retailers facing rising customer expectations and operational complexity, agentic AI isn’t just a nice-to-have innovation. It’s fast-becoming the foundation for the next generation of competitive advantage—where every shopping journey feels personal, every decision is data-driven, and every process runs on proactive intelligence that operates across systems and channels.
If your team is struggling with outdated ticketing systems, repetitive requests, and poor customer experiences, consider implementing delight.ai's [AI agent for retail](https://delight.ai/industries/retail).
- [What is agentic AI in retail?](#section-anchor-id-0 "What is agentic AI in retail?")
- [Why is agentic AI in retail important?](#section-anchor-id-1 "Why is agentic AI in retail important?")
- [What’s the difference between agentic AI in retail and other AI?](#section-anchor-id-2 "What’s the difference between agentic AI in retail and other AI?")
- [Use cases of agentic AI in retail](#section-anchor-id-3 "Use cases of agentic AI in retail")
- [Benefits of agentic AI in retail](#section-anchor-id-4 "Benefits of agentic AI in retail")
- [Best practices of agentic AI in retail](#section-anchor-id-5 "Best practices of agentic AI in retail")
- [Getting started with agentic AI in retail](#section-anchor-id-6 "Getting started with agentic AI in retail")
## New era of voice AI agents: Faster, smarter, and built with human-like empathy
Source: https://delight.ai/blog/product/introducing-voice-ai-agent
## New era of voice AI agents: Faster, smarter, and built with human-like empathy
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
March 16 2026
When customers pick up the phone, they’re not just asking for answers; they’re asking for resolutions. And on the busiest days, when volume spikes and patience runs low, their expectations are higher than ever.
Today, we’re introducing the next evolution of [voice AI agents](https://delight.ai/voice): rebuilt for speed, engineered for natural conversations, and integrated with the full power of our [omnipresent AI agent platform](https://delight.ai/omnipresent). It’s not just about adding voice capabilities. It’s about making voice better — faster, clearer, more capable, and more trusted than ever.
## Voice is still the most human channel
*From Boomers to Gen Z, voice is still the most popular support channel*
Voice is the original customer service channel, and it remains the one people reach for when the issue is urgent or the stakes are high. It is still the gold standard when trust, clarity, and resolution matter. But while customers still rely on phone calls, traditional voice automation hasn’t kept up. Slow, robotic, hard-to-understand voice bots have become a source of frustration rather than relief. Voice AI agents change that.
Voice isn’t just a channel. It’s a reflection of your brand. That’s why we’ve made every detail customizable. From personality and speaking speed to tone, language, and branded behavior, voice should sound like your business, not like a bot.
*Choose the tone of voice and personality of your AI agent*
With nearly a decade of experience powering customer communication for the world’s most trusted brands, we understand the value of every single “hello.” That first second sets the tone for the entire conversation. So, we set a higher bar for speed, for clarity, and for human-like understanding.
> “I think voice is the ultimate unlock for AI for the general population — the day-to-day workers, the elderly, the non-digital natives. Instead of Grandma asking her son to come and fix the internet on her behalf, now she can talk to an AI agent that can walk her through step by step.”
>
> — John Kim, CEO of delight.ai
## Designed for the chaos of real conversations
### Fast
Every second matters in a voice interaction. A long pause or awkward gap can break the flow and make it obvious you are talking to a robot. Delight's [voice AI agents](https://delight.ai/voice) are built to respond instantly, keeping conversations natural and fluid.
### Cuts through the noise
Real-world conversations are messy. People mumble, interrupt, change course mid-sentence, or speak in noisy environments. They have accents or regional dialects. Our voice AI handles it all — accents, cutoffs, background noise, and interruptions — without missing a beat.
### Accurate
Our voice AI agents don't just transcribe. They listen for intent, adapt to emotion, and drive resolution with precision. And unlike many models that are only tested in production, ours are pre-tested against real-world scenarios with [Trust OS](https://delight.ai/trust-os), our responsible AI evaluation framework. The result: consistent performance, verified outcomes, and a voice experience you can trust.
## Voice is most powerful when it’s part of something bigger
Delight's voice AI agents are not a standalone solution. They’re part of a unified, omnichannel AI platform that seamlessly blends phone, chat, SMS, email, and more. That means your AI agent can send a confirmation text during a call, deliver a recap email immediately after, or switch channels mid-conversation, without losing context.
And when human help is needed, the handoff is seamless. Agents pick up right where AI left off, with full memory and context making sure every customer feels like a VIP. And it’s all powered by one AI brain that stays with your customer across channels, so there’s never a need to repeat, restart, or reset.
*AI agents are powerful across all the channels your customers use*
This upgrade is just the beginning. As we continue to build the most complete AI agent platform, we’re setting a new standard for customer experience: fast, human, and truly omnipresent. The future of customer service isn’t just automated. It’s personalized, proactive, and present on every channel, especially voice.
- [Voice is still the most human channel](#voice-is-still-the-most-human-channel)
- [Designed for the chaos of real conversations](#designed-for-the-chaos-of-real-conversations)
- [Voice is most powerful when it’s part of something bigger](#voice-is-most-powerful-when-its-part-of-something-bigger)
## Keep reading
[View more](/blog "View more")
[](https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird)
[Delight.ai — The next chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)
[](https://delight.ai/blog/product/for-you-conversations)
[How For You Conversations make every interaction feel personal](/blog/product/for-you-conversations)
[](https://delight.ai/blog/product/introducing-agent-memory-platform)
[Introducing AMP: The Agent Memory Platform](/blog/product/introducing-agent-memory-platform)
[](https://delight.ai/blog/product/connection-starts-with-omnipresence)
[The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[View more](/blog "View more")
## Delight.ai — The Next Chapter of Sendbird
Source: https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird
## Delight.ai — The Next Chapter of Sendbird
[
John S. Kim
CEO & Co-founder](https://delight.ai/author/john-s-kim)
March 31 2026
For over a decade, Sendbird has powered the conversations that connect the world. Every month, billions of messages move through our platform, between customers and brands, sellers and buyers, patients and providers. Wee helped businesses make connections instant, reliable, and global.
While digital communications became more universally accessible, the customer experience itself stagnated, and we felt our sense of purpose begin to fade.
In the age of AI, one question keeps surfacing: if technology has advanced so much, why hasnt customer experience truly changed in the past decades?
Wee built smarter bots, faster systems, and more channels. Messages got faster. Responses are even more precise. But the feeling of being understood, of being truly seen and cared for, quietly disappeared.
The quantity of conversations grew by orders of magnitude, but the quality of interactions hardly advanced beyond the early days of chat.
The irony is that technology meant to bring brands closer to their customers often pushed them further apart due to ever increasing volume. We solved for speed and cost, but not for care.
**Delight.ai exists to bring that feeling of connection and care back to conversations.** To live up to the real promise of AI: customer experiences that make every customer feel seen, heard, and valued.
### Why we built delight.ai
---
The world doesnt need another AI company. It needs one that knows what makes experiences unforgettable.
Customer experience has stood still for decades, not because companies dont care, but because the system to make caring scale never existed.
Delight.ai was built to change that
to deliver experiences that feel connected, consistent, and deeply human. The kind of experience that never feels automated, even when it is.
The kind of experience that:
- **Always listens and is deeply observant,** catching the little signals you never expected anyone to notice.
- **Always remembers,** when most people forget.
- **Stays a step ahead,** surprising you in ways you didnt even know you needed.
- **Moves on your time**, adapting to your schedule, never theirs.
- **Makes you feel like youre the only one** in the room.
This is what delight.ai stands for
the branded AI concierge for every customer.
### The foundation wee earned
---
Delight.ai stands on more than a decade of work building the worlds most trusted communication infrastructure.
At Sendbird, wee powered billions of conversations between people and brands across every industry and corner of the world. Those conversations taught us something simple but profound: connection isnt just about speed or scale. Its about understanding the individual. Every exchange carries memory, it carries a trace of what came before and a clue to what should come next.
Thats the foundation delight.ai is built on. The same reliability, security, and global reach that powered billions of conversations now power an intelligent system designed for personalization, presence, and trust.
### What makes delight.ai different
---
At the heart of delight.ai are three breakthroughs that define what a truly delightful customer experience feels like
personalized, present, and deeply human.
#### AMP: The Agent Memory Platform
At the core of Delight's intelligence is AMP
the Agent Memory Platform
an evolving layer that connects what your AI knows with what your business needs to achieve. AMP is built on two powerful engines: **Memory** and **Business Intent.**
#### Memory: A living understanding of every customer
Memory begins with what you already know about your customers, their profiles, purchase history, and preferences, and builds from there. Every interaction creates new memories, enriching your understanding with every question, click, and conversation. Over time, these moments form a living, evolving profile that helps delight.ai recognize intent, recall context, and immediately respond as if it has known your customer all along.
#### Business Intent: Connect to your business outcomes
Understanding alone isnt enough. Agent Memory Platform connects memory with your business goals. Business Intent is the framework that translates customer context into meaningful action. It encodes what matters most to your business
whether thats conversion, retention, efficiency, or lifetime value
directly into AIs reasoning.
By combining what the agent *remembers* with what the business *prioritizes*, AMP empowers [delight.ai](https://delight.ai/) to act with purpose, it ensures every interaction drives the right outcome: the next best offer, the fastest resolution, the moment that deepens loyalty.
#### For You Conversations (FYC): Hyper-personalization in ways you never thought possible
Inspired by the "For You" concept wee come to love on social platforms, Delight brings that same sense of relevance and recognition to customer experience. Every interaction feels tailor-made, shaped by who your customer is, what they care about, and even the small details they have shared, like being a dog owner or training for their first marathon.
#### Memory + FYC: A compounding cycle
More conversations create more memories. More memories make every conversation more personal. Together, they form a compounding cycle of connection and understanding that grows stronger with every exchange. Each moment builds on the last, turning ordinary interactions into experiences that feel deeply individual and impossible to forget.
Over time, this continuous cycle creates customers who dont just return, but stay loyal and become brand-obsessed.
#### Omnipresence: Connection that never breaks across channels
Delight.ai keeps every conversation alive, no matter where it starts or where it goes next. A chat that begins on your website or over a phone call can continue in your app, over text, or through email
always with the same memory and context intact.
Customer journeys are never linear. They pause, they shift, they resume. Delight.ai keeps the thread alive through it all, so every return feels effortless and familiar. It is one continuous dialogue that moves with your customer. This is what true omnipresence means, when your customer never feels the distance between channels, and your brand never misses a moment to reconnect and deepen the relationship.
#### Trust OS
All of this is powered by the foundation of Trust OS. [Trust OS](https://delight.ai/trust-os) is the first system designed to give enterprises complete confidence in how AI behaves. It brings observability, control, oversight, and scale to every conversation, ensuring Delight operates with transparency, accountability, and confidence at enterprise level.
Every interaction is governed by rigorous standards for privacy, security, and performance
so personalization never risks compliance, and automation never loses accountability.
Trust OS is how Delight turns innovation into something dependable, a system where every delightful experience is grounded in trust.
### The future of connection: Branded AI concierge
Delight.ai was built for the next era of customer experience, one that listens, remembers, and moves with your customers. An experience that feels personal, present, and deeply human.
And when every interaction feels this effortless, it stops being service.
It becomes a delight.
- [Why we built delight.ai](#section-anchor-id-0 "Why we built delight.ai")
- [The foundation wee earned](#section-anchor-id-1 "The foundation we\u2019ve earned")
- [What makes delight.ai different](#section-anchor-id-2 "What makes delight.ai different")
- [The future of connection: Branded AI concierge](#section-anchor-id-3 "The future of connection: Branded AI concierge")
Keep reading
---
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## The future of customer connection starts with Omnipresence
Source: https://delight.ai/blog/product/connection-starts-with-omnipresence
- [Blog](/blog)\n- [Product](/blog/product)\n\n## The future of customer connection starts with Omnipresence\n\n[](https://delight.ai/author/leighanne-manwiller)\n\n[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)\n\nProduct marketing manager\n\n\n\nMarch 31 2026\n\nWe’ve all been there. A chat drops, a call ends, or you switch from web to text and have to start the whole story over again. For customers, it’s frustrating. For brands, it’s costly. Every restart means lost context, lost momentum, and too often, a lost sale.\n\nEven as AI becomes more advanced, most assistants still struggle to follow customers across channels. They’re fast but forgetful, treating every interaction like it’s the first.\n\nAt **delight.ai**, we believe conversations should flow as naturally as they do in person. That’s why we built **Omnipresence**. Whether a customer starts on your website, moves to SMS, or jumps on a call, Omnipresence keeps the thread alive. Context carries over, [memory stays intact](https://delight.ai/blog/product/introducing-agent-memory-platform), and the experience feels seamless.\n\nBecause great connections shouldn’t take effort. It should just happen.\n\n### The AI agent that moves with your customer\n\nOmnipresence redefines what it means to stay connected. It follows your customers wherever they go. No restarts. No silos. No “what can I help you with today?” on repeat. Every conversation moves smoothly from one channel to the next, as if your customer is speaking to the same trusted concierge.\n\n\n\nPicture a customer on the phone about a damaged order. While still talking, they email a photo, and within seconds the AI processes the image, updates the case, and confirms next steps. No transfers to a different department and no confusion.\n\nOr imagine a chat that begins on your website, pauses when the customer steps away, and picks up later through email or text. Omnipresence remembers the details and resumes naturally, without repetition or lost context. Even if a call ends unexpectedly, it can follow up to re-engage the customer and keep the dialogue alive.\n\nThis is true cross-channel intelligence. Every message, call, and touchpoint is synchronized, creating one continuous story between your brand and your customer. In the dashboard, every interaction appears as part of a single, ongoing conversation.\n\n\n\n### The technology behind Omnipresence\n\nBehind the scenes, Omnipresence is powered by **Memory**, **Actionbooks**, and **Trust OS**—the foundation that makes every **delight.ai** experience personal, present, and accountable. Memory ensures that context carries across every interaction. Actionbooks and triggers coordinate transitions between channels, whether they happen mid-conversation or after a conversation ends.\n\nUnlike traditional multichannel systems that simply enable different channels, Omnipresence synchronizes intent and conversations. It’s the difference between resuming a conversation and starting a new one. That [continuity builds familiarity](https://delight.ai/blog/product/for-you-conversations), and familiarity builds trust, the real measure of great customer experience.\n\n\n\n### Why Omnipresence matters\n\nCustomer journeys are rarely linear. People pause, switch devices, or need to double-check something before they commit. But while they take time to decide, your AI agent shouldn’t lose its place. Omnipresence turns those pauses into opportunities to re-engage, recover, and move the conversation forward.\n\nFor businesses, that means fewer drop-offs, higher conversions, and stronger relationships. For customers, it means an experience that feels natural and human. It’s technology that finally adapts to the way people communicate, not the other way around.\n\nAt **delight.ai**, we hold ourselves to a simple promise: every customer deserves a concierge—[one who remembers, listens, and stays present](https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird).\n\n### Built on Trust OS\n\nOmnipresence runs on [**Trust OS**](https://delight.ai/trust-os), the enterprise foundation that ensures every interaction is reliable, observable, and secure. Trust OS gives businesses the visibility and control to deploy AI with confidence. It ensures that every action is transparent, compliant, and consistent with your brand’s voice.\n\nThe most connected experiences are built on the most dependable foundation. Trust OS is where every delight.ai experience begins.\n\n\n\n**Omnipresence** isn’t just another product release. It’s a leap forward for AI-powered customer experience. Built by **delight.ai** and backed by Sendbird’s decade of communication expertise, Omnipresence introduces a new standard for connection—always on, always aware, always personal.\n\nYour customers shouldn’t have to start over. Now, they never will.\n\n- [The AI agent that moves with your customer](#section-anchor-id-0 "The AI agent that moves with your customer")\n- [The technology behind Omnipresence](#section-anchor-id-1 "The technology behind Omnipresence")\n- [Why Omnipresence matters](#section-anchor-id-2 "Why Omnipresence matters")\n- [Built on Trust OS](#section-anchor-id-3 "Built on Trust OS")\n\nKeep reading\n\n[View more](/blog "View more")\n\n[](/blog/product/introducing-trust-os-v2)\n\n[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)\n\n[](/blog/product/low-csat-analysis)\n\n[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)\n\n[](/blog/product/introducing-agent-steward)\n\n[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)\n\n[](/blog/product/voice-v2)\n\n[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)\n\n[View more](/blog "View more")
## How For You Conversations Make Every Interaction Feel Personal
Source: https://delight.ai/blog/product/for-you-conversations
- [Blog](/blog)
- [Product](/blog/product)
- How For You Conversations Make Every Interaction Feel Personal
## How For You Conversations Make Every Interaction Feel Personal
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
March 31 2026
For years, brands have promised personalization, but most experiences still feel automated. Customers are greeted by name, yet the conversations that follow feel generic and disconnected. Every interaction looks the same, no matter who’s on the other side.
Customers notice. They notice when they hear their own names echoed back but still feel like strangers in every new chat. They notice when conversations [restart every time they change channels](https://delight.ai/blog/product/connection-starts-with-omnipresence). And they notice when brands that “know” them still don’t seem to *remember* them.
**For You Conversations** from *delight.ai* changes that. It’s how brands create conversations that truly feel for you — exchanges that understand who someone is, what they care about, and what they might need next. [It’s not just personalization by name, but by need, moment, and intention](https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird), turning understanding into moments.
With For You Conversations, every interaction feels familiar. Every message feels intentional. And every customer feels recognized.
### How conversations become “For You”
Every meaningful experience begins with the [Agent Memory Platform](https://delight.ai/blog/product/introducing-agent-memory-platform) and the simple act of being known. When an AI agent can remember what matters to each customer, it stops feeling like software and starts feeling like service. **For You Conversations** builds on that foundation of connection.
Inspired by the “For You” experiences we love on social media platforms, For You Conversations brings the same sense of relevance to brand interactions. Every message feels like it was crafted specifically for you because it was.
A frequent traveler might see aisle seats for domestic flights and window seats for international ones. A florist could send a reminder when an anniversary is coming up. A returning shopper might receive a recommendation that feels almost telepathic because it’s based on what they’ve shared before.
Each interaction feels intentional, not scripted. Every exchange reminds customers that they’re seen and understood. A For You Conversation adjusts tone, timing, and recommendations in real time based on who the customer is and what they care about. It’s not just personalization, it’s recognition.
### The flywheel of personalization
The best conversations build on what came before. Each exchange adds another layer of understanding — a small detail remembered, a preference noted, a promise kept. **For You Conversations** turns those details into lasting relationships.
Over time, this creates a compounding effect where every interaction deepens understanding and strengthens the relationship. The next time a customer reaches out, the response feels more relevant, the tone more familiar, and the experience more natural. What starts as personalization becomes something deeper. It becomes recognition.
This flywheel of personalization transforms ordinary interactions into lasting relationships. It turns reactive service into proactive care, and customers into advocates who feel truly understood. It’s how **delight.ai** turns ordinary service into a memorable experience that stays with people long after the chat ends.
### Built on trust, ready for the enterprise
Every delight.ai experience is built on [**Trust OS**](https://delight.ai/trust-os), Sendbird’s foundation for safe, accountable AI.
Trust OS gives enterprises visibility into what the agent remembers, when it recalls certain details, and how it applies them in conversation. That means brands can deploy AI agents that personalize responsibly, with full transparency and control. Trust isn’t an add-on for delight.ai, it’s built in.
Loyalty isn’t built through transactions. It’s built through moments that make people feel recognized and valued. Customers return to brands that understand them, not just ones that respond quickly.
With **For You Conversations**, delight.ai helps companies turn that principle into practice. It listens, adapts, and responds in ways that make every interaction feel personal and intentional. It’s how brands deliver experiences that are consistent, intelligent, and deeply human.
This is what the next era of customer experience looks like. Service that feels personal, present, and truly connects.
Because the best AI agents don't replace empathy. They scale it.
- [How conversations become “For You”](#section-anchor-id-0)
- [The flywheel of personalization](#section-anchor-id-1)
- [Built on trust, ready for the enterprise](#section-anchor-id-2)
### Keep reading
[View more](/blog)
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog)
## Introducing AMP: The Agent Memory Platform
Source: https://delight.ai/blog/product/introducing-agent-memory-platform
- [Blog](/blog)
- [Product](/blog/product)
- Introducing AMP: The Agent Memory Platform
## Introducing AMP: The Agent Memory Platform
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
March 31 2026
Scroll through any “For You” feed on social media today and you’ll see something remarkable: algorithms that *know you*. They anticipate what you’ll click, what you’ll linger on, even what you didn’t know you wanted.
But when you message a brand? The experience feels nothing like that. The agent greets you like a stranger, asks for your order number again, and forgets everything the moment the chat ends.
It’s time customer experience caught up with the rest of the internet.
### AMP — the Agent Memory Platform of delight.ai
**AMP** (Agent Memory Platform) is delight.ai’s new intelligence layer that gives your AI agents **a living memory** and **a business brain**. It connects what your agent *remembers* about every customer with what your business *wants to achieve.*
### Memory: A living understanding of every customer
Every business already has **structured data**, think about names, purchase history, ticket IDs neatly stored in a CRM. That’s useful, but it’s static. It tells you what happened, not why.
In every conversation, customers leave traces – the subtle clues about who they are and what’s changing in their lives. *“I just moved, can you update my address?”* or *“My dog chewed through the cable again.”* They might hint at frustration, *“This is the second time this has happened.”* Or anticipation, *“I’m upgrading before the holidays.”*
Each of these moments carries unstructured data, the context that reveals needs, emotions, and opportunities. But most systems lose it the moment the chat window ends.
**Delight.ai’s Memory** captures all of it. It fuses structured data with the unstructured intelligence hidden in conversations, building a living, evolving understanding of each customer. In short, your CRM records the past. Memory understands the present, and predicts the future.
### Business Intent: Connect memory to your business outcomes
Understanding alone isn’t enough. AMP’s **Business Intent** gives that understanding a purpose.
AMP doesn’t just know your customers, it knows what your business is trying to achieve. Whether it’s **reducing churn**, **increasing cart size**, **driving upgrades**, or **accelerating resolution time**, Business Intent turns company goals into live instructions for your AI.
These intents act like a compass and guide how your agent responds, recommends, and resolves in every moment. If a customer sounds at risk of leaving, the agent shifts into retention mode. If they’re browsing frequently but hesitating to purchase, it leans into conversion. If sentiment drops mid-chat, it pivots to empathy and recovery.
### Memory + Business Intent forms the Agent Memory Platform
Together, **Memory** and **Business Intent** form **AMP — the Agent Memory Platform**, the intelligence layer powering **delight.ai, the branded AI concierge.**
Memory gives your AI agents a continuously evolving view of every customer, turning conversations into data and data into insight. Business Intent ensures that insight drives the right actions, whether it’s reducing churn, increasing conversion, growing average order value, or improving CSAT.
With both working in sync, AMP becomes the operational brain behind your AI agents, translating understanding into impact at scale.
Leading brands in retail and travel are already using Agent Memory Platform to personalize interactions, anticipate customer needs, and deliver service that feels both human and on-brand — all while driving measurable business results. Because real advantage comes from intelligence that understands your customers and acts on what matters to your business.
- [AMP — the Agent Memory Platform of delight.ai](#section-anchor-id-0 "AMP — the Agent Memory Platform of delight.ai")
- [Memory: A living understanding of every customer](#section-anchor-id-1 "Memory: A living understanding of every customer")
- [Business Intent: Connect memory to your business outcomes](#section-anchor-id-2 "Business Intent: Connect memory to your business outcomes")
- [Memory + Business Intent forms the Agent Memory Platform](#section-anchor-id-3 "Memory + Business Intent forms the Agent Memory Platform")
### Keep reading
[View more](/blog "View more")
[](https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird)
[Delight.ai — The next chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)
[](https://delight.ai/blog/product/for-you-conversations)
[How For You Conversations make every interaction feel personal](/blog/product/for-you-conversations)
[](https://delight.ai/blog/product/connection-starts-with-omnipresence)
[The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[](https://delight.ai/blog/industry/ai-in-retail)
[AI in retail: 13 trends and examples to guide you in 2026](/blog/industry/ai-in-retail)
[View more](/blog "View more")
## Introducing Trust OS: The foundation for responsible AI agents
Source: https://delight.ai/blog/product/introducing-trust-os-for-ai-agents
- [Blog](/blog)
- [Product](/blog/product)
[](https://delight.ai/author/john-s-kim)
[John S. Kim — CEO & Co-founder](https://delight.ai/author/john-s-kim)
March 16 2026
When it comes to AI, we’ve all heard the same promises: “enterprise-grade,” “secure by design,” “reliable.” But too often, those promises ring hollow.
In today’s AI arms race, most companies want their AI agents to resolve tickets, take real action, and move the business forward. They want AI to be a driver of outcomes, not just a chatbot. But here’s the uncomfortable truth: The more agency you give your AI, the more [trust](https://delight.ai/blog/insights/generational-differences-ai-trust) it demands. And most enterprise AI agent platforms aren’t ready for that tradeoff.
At delight.ai, we power over 7 billion conversations every month, serving more than 4,000 customers around the world—including some of the most complex and demanding brands. For our customers, trust isn’t a luxury. It’s [the foundation everything else is built on](https://delight.ai/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation).
Imagine bringing on a junior employee and asking them to handle your most sensitive customer escalations on day one. You give them access to internal tools, customer data, and the authority to take real action, like processing refunds or resetting passwords. But you provide no training, no supervision, and no way to monitor what they’re doing. There’s no audit trail, no oversight, no way to reverse a mistake.
You would never do that with a human agent. But that’s how most companies are deploying AI agents today. They’re delegating responsibility to systems they can’t fully see, test, or control. Real trust doesn’t come from hope or optimism. It comes from having the systems in place to evaluate, govern, and improve. It comes from **accountability**.
## Meet Trust OS: The industry’s first system for AI accountability
Today, I’m proud to introduce [**Trust OS**](https://delight.ai/trust-os), a foundational system built to make AI agents accountable, responsible, and ready for enterprise scale. It’s everything you’d expect from a mission-critical platform: testability, traceability, governance, human oversight, and global scale.
It’s built on four core pillars:
1. **Observability** – See what your AI is doing, and why
2. **Control** – Guardrails to deploy with confidence
3. **Oversight** – Great AI still needs a manager
4. **Scale-proven infrastructure** – Global, multilingual, enterprise-ready
## Observability: See what your AI is doing, and why
You can’t trust what you can’t see. That’s why Trust OS gives you visibility into every AI decision, before and after deployment.
Without clear insight into how your AI agent makes decisions, you’re flying blind. Observability is the first pillar of Trust OS, and it’s designed to bring [full transparency to every interaction](https://delight.ai/blog/ai-for-cx/why-dont-people-trust-ai-customer-service). With Conversation Benchmark Testing, teams can simulate multi-turn conversations and compare AI responses against expected outcomes before an AI agent talks to a customer. These tests can be scheduled to run continuously in the background, helping you identify regressions or blind spots before they impact customers.
But AI observability goes deeper than testing. Every AI interaction is recorded with detailed activity logs, showing exactly which Actionbooks were triggered, which API calls were made, and what knowledge sources were used to inform a response. Audit logs help you trace every change across your deployment. Together, these capabilities form a diagnostic layer that enables proactive quality assurance—so you’re not just reacting to AI mistakes, you’re preventing them.
*Run test conversations to monitor your AI agent*
## Control: Guardrails to deploy with confidence
Enterprise teams move fast, but trust requires safety at every step. Trust OS gives you the controls to move quickly without compromising integrity.
AI governance frameworks aren't about slowing down; they’re about moving fast without breaking trust. Control is the second pillar of Trust OS, giving your team a robust framework for managing changes with precision. Version control allows you to track prompt and configuration history across your AI agents, compare changes side-by-side, and roll back when needed. Development and production environments are separated, ensuring that updates can be thoroughly tested before being deployed to end users.
*Side-by-side comparison of changes to your AI agent’s operating procedures*
When you’re ready to launch, you can choose exactly which updates to deploy. It’s granular, intentional, and enterprise-safe. Role-based access control (RBAC) lets you define who can do what across the system, so developers, prompt engineers, and support leads each operate within the boundaries of their responsibilities.
*Control who on your team can do what with role-based access control (RBAC)*
## Oversight: Great AI still needs a manager
Even the most advanced AI agents need a human in the loop. That’s why the third pillar of Trust OS is oversight, built with multiple safety nets to catch, flag, and fix problems before they escalate. With AI Agent Scorecards, teams can evaluate conversations across key dimensions like tone, clarity, relevance, and solution effectiveness. This makes it easy to identify weak spots in AI performance and continuously improve through direct human feedback.
*Use a human-in-the-loop scorecard for AI evaluation*
When something goes off-script, Trust OS flags it automatically. AI monitoring means hallucinations, harmful content, context injection, banned phrases, and other policy violations are surfaced before they become customer issues. And if a serious problem arises, you can instantly deactivate the AI agent. This kind of oversight transforms your AI from a black box into a supervised system that is always learning, always improving, and always accountable.
## Scale-proven infrastructure: Global, multilingual, enterprise-ready
This isn’t our first platform. We power over 7 billion conversations every month across 4,000+ global customers. Our systems are trusted by companies with the highest compliance, privacy, and availability requirements. That same foundation now powers the next generation of AI agents with support for multilingual conversations, global deployment environments, and centralized workspace assets.
*AI agents are multilingual and conversations can be instantly translated*
Trust OS adapts to your operating model. Whether you're managing one agent or orchestrating dozens across regions and business lines, our platform gives you the confidence to scale AI without compromising on safety or control.
## Trust is the bridge to AI maturity
AI maturity isn’t about flashy demos or vanity metric wins. It’s about confidently giving your AI agent real responsibility—the ability to take action, resolve issues, and carry the weight of customer experience. That level of agency is where true business value lies. But agency can’t be granted blindly. It has to be earned. And the only way to earn it is through [trust](https://delight.ai/blog/insights/do-millennials-trust-ai-more-than-gen-z).
*Trust is the bridge between what your AI can do and what you're willing to let it do*
Without that bridge, even the most capable AI remains confined to low-risk, low-reward tasks. It becomes another chatbot that’s scripted, shallow, and ultimately ineffective. If AI is going to take on real responsibility, it needs real systems behind it. When you have the framework to see what AI is doing, to control how it behaves, and to intervene when it matters, AI becomes a multiplier. That is when real responsibility is earned.
Just as engineering teams wouldn't ship code without testing, reviewing, and monitoring, AI agents should never be deployed without rigorous accountability. Without it, they’re not “enterprise-ready,” they’re a liability.
At delight.ai, we believe that trust is not a marketing claim—it’s an operating system. One built on four critical foundations: Observability, control, oversight, and scale-proven infrastructure. That’s what Trust OS delivers.
Ready to put trust into production? [Request a demo](https://delight.ai/contact-sales) and see how Trust OS can transform your AI from a black box into a trusted agent.
- [Meet Trust OS: The industry’s first system for AI accountability](#section-anchor-id-0 "Meet Trust OS: The industry’s first system for AI accountability")
- [Observability: See what your AI is doing, and why](#section-anchor-id-1 "Observability: See what your AI is doing, and why")
- [Control: Guardrails to deploy with confidence](#section-anchor-id-2 "Control: Guardrails to deploy with confidence")
- [Oversight: Great AI still needs a manager](#section-anchor-id-3 "Oversight: Great AI still needs a manager")
- [Scale-proven infrastructure: Global, multilingual, enterprise-ready](#section-anchor-id-4 "Scale-proven infrastructure: Global, multilingual, enterprise-ready")
- [Trust is the bridge to AI maturity](#section-anchor-id-5 "Trust is the bridge to AI maturity")
Keep reading
[View more](/blog "View more")
[
Why don't people trust AI customer service?](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
[
Does AI reflect on your brand reputation?](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
[
Generational differences in AI trust](/blog/insights/generational-differences-ai-trust)
[
Do millennials trust AI more than Gen Z?](/blog/insights/do-millennials-trust-ai-more-than-gen-z)
[View more](/blog "View more")
## From carts to care: How retail AI agents turn every interaction into loyalty
Source: https://delight.ai/blog/industry/retail-ai-agents
## From carts to care: How retail AI agents turn every interaction into loyalty
[
LeighAnne Manwiller
Product marketing manager](https://delight.ai/author/leighanne-manwiller)
March 9 2026
It’s Black Friday. Shoppers are refreshing tracking links, typing promo codes into checkout boxes, and abandoning carts after waiting just a few seconds too long. Multiply that scene across thousands of customers and dozens of channels, and you see the reality of modern retail. Every second counts and every touchpoint can drive loyalty.
Of course, this isn’t just about Black Friday. Holiday rushes, viral TikTok campaigns, and seasonal product drops put constant pressure on customer support teams. At the same time, everyday questions like “Where’s my order?” pile up by the thousands. And while most of those questions are simple, the stakes are not. Each one is a potential sale saved or a customer lost.
*Share product recommendations with your customers to increase Average Order Value (AOV)*
## Keeping up with the modern shopper
Delight.ai's AI agents are built for this exact reality: the volume, velocity, and emotional stakes of retail. Instead of just answering questions, our agents resolve them instantly, accurately, and while staying on brand. A customer asking about their order status doesn’t just get a generic reply. The agent checks the system, pulls the right information, and delivers the update in your tone of voice. If there’s an issue, the agent can take action, like issuing a refund, updating an address, or offering a proactive cart recovery nudge.
*Proactive discounts and cart recovery reminders encourage customers to complete their purchase*
Shoppers aren’t confined to a single channel and your support shouldn’t be either. Today’s customers bounce from app to SMS, from WhatsApp to email, from a chatbot on your website to a live call with your team. Delight AI agents move with them, unifying the conversation across every touchpoint. On Black Friday, that means a shopper who starts with a text asking about their delivery can seamlessly continue the conversation in-app without ever repeating themselves.
At peak demand, reliability matters most and that’s where our AI agents excel. They scale instantly to meet surges in demand, eliminating the need to spin up BPO or scramble to onboard seasonal hires. Instead of feeling the chaos of peak season, your customers experience the same fast, empathetic support they’d expect on any other day.
## Trust OS, engineered into every interaction
Speed and scale only matter if customers can rely on the answers they get. That’s why every Delight AI agent is built on [**Trust OS**](https://delight.ai/trust-os), our foundational system to observe, control, and oversee your AI. Trust shouldn’t be an afterthought, it should be engineered into every interaction.
When your AI agent is fielding thousands of questions in the middle of a holiday rush, Trust OS gives you confidence that every answer is accurate, compliant, and unmistakably on brand. You can see how the AI makes decisions, monitor performance, and fine-tune behavior with ease. In moments when customer loyalty hangs in the balance, this level of control is non-negotiable.
## The next era of customer support
[AI agents in retail](https://delight.ai/industries/retail) aren’t just about helping human agents keep up, they’re about fundamentally rethinking how support is delivered. They handle the repetitive, high-volume questions that overwhelm human teams, and empower your human agents to focus on higher-value interactions. For retailers, this means more than just cost savings. It’s faster resolutions, consistent brand voice at scale, and the ability to deliver empathetic, reliable support to every customer, no matter how busy the season gets.
With features like Actionbooks, your AI Agents don’t stop at answering questions, they follow the same step-by-step workflows your human agents do, resolving issues directly. Safeguards prevent misuse, like fraudulent promo code requests or edge-case return attempts. And with our Build, Test, Evaluate framework, you can validate how an agent performs before it ever engages with a customer.
*Actionbooks give your AI agent operating procedures to follow*
Picture your support team on Black Friday. Instead of scrambling to change delivery addresses or answer promo code questions, they’re free to focus on escalations and building meaningful relationships because your AI agent is taking care of the rest.
## Turning chaos into opportunity
The impact is clear. Customers get answers faster, support teams spend less time on repetitive tickets, and businesses save on costs while boosting satisfaction. But the real difference shows up when the pressure is highest. In the middle of the Black Friday rush, when every second feels like chaos, shoppers who feel taken care of don’t just complete their purchase, they remember the experience. They come back, spend more, and stay loyal.
*Retail AI agents help your customers find what they are looking for*
This is how support transforms from a cost center into a loyalty engine. With delight.ai's AI agents, powered by [Trust OS](https://delight.ai/trust-os), you don’t just survive the surges of modern retail, you turn them into opportunities to deepen customer relationships and grow revenue.
Black Friday may be the most visible stress test for retail support, but the truth is that retail moves fast every day of the year. With delight.ai, your customer experience moves just as fast.
- [Keeping up with the modern shopper](#section-anchor-id-0 "Keeping up with the modern shopper")
- [Trust OS, engineered into every interaction](#section-anchor-id-1 "Trust OS, engineered into every interaction")
- [The next era of customer support](#section-anchor-id-2 "The next era of customer support")
- [Turning chaos into opportunity](#section-anchor-id-3 "Turning chaos into opportunity")
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## What is agentic commerce? Overview and how to prepare
Source: https://delight.ai/blog/industry/agentic-commerce
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 25 2026
**Key takeaways**
- **Agentic commerce is shopping run by AI agents** that research products, compare options, and complete or guide purchases on someone's behalf, inside a conversation instead of a website.
- **It has 2 sides**, with buy-side agents working for the shopper, like ChatGPT or Meta's Muse, and sell-side agents working for the business, like Lowe's Mylow or BJ's Wholesale Club's Bev.
- **Retailers can't control the shopper's agent**, but they can own the sell-side agent that answers it, along with the customer relationship that follows every sale.
## What is agentic commerce?
Agentic commerce is a model of online shopping, powered by [agentic AI](https://delight.ai/glossary/agentic-ai), in which AI agents research products, compare options, and complete purchases on behalf of a shopper or a business. Instead of clicking through product pages, a shopper says what they need in plain language, and an agent handles the search, the comparison, and the checkout.
Delight.ai builds [the retailer's side of that conversation](https://delight.ai/agentic-commerce). Its AI agent answers shoppers, and the AI agents shopping for them, with the retailer's own products, offers, and rules, then helps complete the purchase in the same chat. [Book a demo to see it on your catalog](https://delight.ai/contact-sales).
## How fast is agentic commerce growing?
In July 2026, AI-driven traffic to retail sites grew 62% year over year, and AI-referred visitors converted at a rate 60% higher than non-AI traffic, according to [Adobe's analysis of US retail site visits](https://business.adobe.com/assets/pdfs/resources/sdk/ai-traffic-trends-report-august-2026/ai-traffic-trends-report-2026-08-19.pdf). By 2030, agentic commerce could orchestrate $900 billion to $1 trillion in US B2C retail revenue and $3 trillion to $5 trillion globally, according to [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchants).
For retailers, the bigger change sits behind the screen. [AI agents](https://delight.ai/glossary/ai-agent) don't browse storefronts or respond to brand storytelling, so they query APIs, compare price and availability in real time, and read reviews before they decide. **In agentic commerce, your backend becomes your storefront.**
## What's the difference between buy-side and sell-side agentic commerce?
Buy-side agentic commerce is a shopper's own AI agent finding and buying products for them. Sell-side agentic commerce is a retailer's own AI agent helping that shopper, or the shopper's agent, choose the right product and complete the purchase, which is what [Delight.ai agentic commerce](https://delight.ai/agentic-commerce) is built for.
### What does buy-side vs sell-side look like in practice?
Take one shopper who needs trail running shoes in a wide fit, under $150, delivered before a race on Saturday.
- **On the buy side**, she asks ChatGPT. Her assistant searches several retailers' product feeds, filters for wide sizes and Saturday delivery, compares prices and reviews, and can check out after she confirms if the merchant supports it. The retailer that wins the sale gets an order, but not the conversation about her fit or her race.
- **On the sell side**, she asks an outdoor retailer's own Delight.ai agent on its website, or her assistant asks it for her. Because Delight.ai's [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) carries her purchase history into the conversation, the agent knows she bought a size 9 wide last spring, checks which wide-fit models are in stock near her, applies her member discount, and helps her complete the purchase in the same chat. The retailer keeps the whole conversation, including the race, so next month it can suggest recovery gear she's likely to want.
Retailers are seeing results on the sell side. Lowe's AI home improvement advisor, Mylow, is already driving double the conversion rate for online shoppers compared with those who don't use it, according to [Modern Retail](https://www.modernretail.co/technology/lowes-wants-to-do-more-with-ai-shopping-in-2026/). BJ's Wholesale Club runs its own sell-side shopping assistant, Bev, on Delight.ai.
### How do the 2 sides compare?
| Feature | Buy-side agentic commerce | Sell-side agentic commerce |
|---|---|---|
| Whose agent is it? | The shopper's, such as ChatGPT, Meta's Muse, or Perplexity | The retailer's or brand's own agent on its website, app, or messaging channels |
| Who sets the rules? | The shopper and the AI platform, within limits set by payment networks | The business, through its own policies, offers, and approval rules |
| What does it need from a retailer? | Machine-readable product data, real-time inventory and pricing, and an agent-ready checkout | Connected catalog, order, and customer data, plus clear rules for what the agent can do on its own |
| How does it choose a product? | Compares offers across many retailers and picks the best fit for the shopper | Recommends from one retailer's range, using what it already knows about this shopper |
| What does the retailer keep? | Usually the order, but little of the conversation or intent behind it | The full conversation, preference signals, and a direct customer relationship |
| Examples today | Google agentic checkout, Perplexity and PayPal's Instant Buy | Lowe's Mylow, Walmart's Sparky, BJ's Wholesale Club's Bev |
### How do buy-side and sell-side agents work together?
The 2 sides increasingly meet in the middle, with a shopper's agent asking questions and the retailer's agent answering with live inventory, pricing, and offers. Agent-to-agent protocols let agents from different companies discover each other and exchange information securely, while commerce protocols such as ACP and UCP add product and checkout details on top.
## How does agentic commerce work?
Agentic commerce works by handing each step of a purchase to an AI agent, from understanding the request to placing the order. A typical buy-side purchase follows 5 steps, and a sell-side agent can step in at any of them:
1. **The shopper states a goal** in plain language, such as a budget, a use case, and a delivery date.
2. **The shopper's agent searches** product feeds, retailer APIs, and reviews to build a shortlist.
3. **The retailer's systems or agent respond** with live price, stock, delivery options, and any offer that applies.
4. **The purchase completes** through an agent-ready checkout, using tokenized payment credentials within the limits the shopper approved.
5. **The retailer follows up** on delivery, returns, and reorders, often through its own agent.
If no sell-side agent is involved, each step leaves the retailer with less control and less data. The shopper's agent sees the full conversation, while the retailer sees only the order.
A Delight.ai agent closes that gap at step 3. Instead of a bare API response, the shopper's agent gets an answer from the retailer's own agent, with its offers and policies, and the retailer keeps a record of the whole conversation.
## How is agentic commerce different from traditional ecommerce?
In traditional ecommerce, a person navigates a retailer's website or app, then browses, compares, and checks out on their own. In agentic commerce, an AI agent does that work, so the retailer's audience shifts from human visitors to AI agents reading structured data.
That makes AI the new front door of ecommerce. The comparison shows how the shopper's journey changes at each stage.
| Stage | Traditional ecommerce | Agentic commerce |
|---|---|---|
| Discovery | The shopper searches Google or a marketplace and clicks through results | The shopper asks an AI agent, which returns a short list |
| Comparison | The shopper opens tabs and reads product pages | The agent compares price, specs, reviews, and availability through data and APIs |
| Checkout | The shopper fills in a cart and payment form on the retailer's site | The agent completes checkout through an agent-ready flow, with the shopper's approval |
| Customer data | The retailer sees every visit, click, and cart | The retailer sees the order, plus the conversation only if its own agent took part |
| What wins the sale | Brand, merchandising, and search ranking | Accurate structured data, price, availability, and trust |
### Where does agentic commerce show up today?
This shift is already underway. A few agentic commerce examples:
- **Meta's Muse**, a personal AI agent that launched in the US in September 2026, can shop across the web and [check out with Stripe's Link wallet](https://stripe.com/newsroom/news/stripe-helps-meta-muse-shop-with-link) once the shopper approves the total in the chat.
- **ChatGPT accounted for about 20% of Walmart's referral traffic** and 10% of eBay's in August 2025, according to Similarweb data reported by [Digiday](https://digiday.com/media/chatgpt-is-now-20-of-walmarts-referral-traffic-while-amazon-wards-off-ai-shopping-agents/).
- **Google's agentic checkout** can track a product's price and, once it hits the shopper's budget, [buy it on the merchant's site with Google Pay](https://blog.google/products/shopping/agentic-checkout-holiday-ai-shopping/) after the shopper confirms.
- **OpenAI and Perplexity both launched in-chat checkout** in late 2025, and in March 2026 OpenAI [let merchants use their own checkout](https://openai.com/index/powering-product-discovery-in-chatgpt/) while it focuses on product discovery.
The question retailers must answer is no longer only "how do we rank on Google?" It's "who owns the customer relationship when an AI agent makes the purchase?"
In the short term, retailers without machine-readable catalogs, real-time inventory APIs, and transactional infrastructure won't appear in AI-led purchasing flows at all. Agents that rely on structured data can't evaluate or recommend what they can't read.
In the long term, retailers risk losing ownership of the customer. Without their own agent, they can slide from brand destination to inventory supplier for someone else's platform, and lose the behavioral data, preference signals, and loyalty moments that drive personalization, retention, and lifetime value.
A retailer-owned agent is how retailers keep that data. With [Delight.ai's agentic commerce agent](https://delight.ai/agentic-commerce), every conversation, whether it starts with a shopper or a shopper's agent, stays with the retailer and shapes the next recommendation.
## Which protocols power agentic commerce?
Agentic commerce runs on a small set of open protocols that let agents, merchants, and payment providers exchange product, checkout, and payment details in a standard format. Most launched in 2025 or 2026, and several were built to work together.
The table shows who is behind each protocol, what it does, and which side of the transaction uses it.
| Protocol | Who's behind it | What it does | Which side uses it |
|---|---|---|---|
| [Agentic Commerce Protocol (ACP)](https://www.agenticcommerce.dev/) | OpenAI and Stripe, open-sourced in September 2025 | An open standard for checkout and product flows between buyers, AI agents, and businesses | Both |
| [Universal Commerce Protocol (UCP)](https://developers.googleblog.com/under-the-hood-universal-commerce-protocol-ucp/) | Google, with Shopify, Etsy, Wayfair, Target, and Walmart, announced in January 2026 | Lets businesses publish their commerce capabilities so AI platforms can discover and transact with them | Both |
| [Agent Payments Protocol (AP2)](https://cloud.google.com/blog/products/ai-machine-learning/announcing-agents-to-payments-ap2-protocol) | Google and 60+ partners, September 2025 | Records what a shopper approved, such as price limits and the exact cart, in signed digital mandates | The payment step between the shopper's agent, the merchant, and payment providers |
| Model Context Protocol (MCP) | Anthropic, now part of the Linux Foundation's Agentic AI Foundation | Connects AI applications to external tools and data, such as a product catalog or checkout | Mostly merchants and tool providers exposing their systems to agents |
| Agent2Agent (A2A) | Google, now a Linux Foundation project | Lets agents from different companies discover each other and exchange information securely | Agent to agent |
| Mastercard Agent Pay and Visa Intelligent Commerce | Mastercard and Visa, April 2025 | Designed to give registered, verified agents tokenized payment credentials with shopper-set limits | The shopper's agent |
None of these protocols replaces a retailer's own agent. They define how a shopper's agent talks to a business, and a sell-side agent decides what the business says back, so the practical question for most retailers is which protocols their commerce platform and payment provider already support. Delight.ai's agent works on the retailer's side of these protocols and [connects to existing inventory, payment, and internal systems](https://delight.ai/integrations) without a migration project.
## How can retailers prepare for agentic commerce?
With [30% to 45% of US consumers](https://www.bain.com/insights/2030-forecast-how-agentic-ai-will-reshape-us-retail-snap-chart/) already using generative AI for product research and comparison, the question for retailers is no longer whether to take part. It's how to stay visible to buy-side agents while owning the sell-side conversation.
Third-party agents favor the most reliable, best-value option for each shopper, so retailers who present themselves as the cheapest, safest, and most relevant choice win the agent's recommendation.
Both sides rest on the same foundation, agentic architecture, which turns legacy retail systems built for human visitors into machine-readable, API-driven systems. That same layer creates the unified customer data behind [hyper-personalization](https://delight.ai/glossary/hyper-personalization) and many other [high-value AI use cases in retail](https://delight.ai/blog/industry/ai-use-cases-retail).
The 5 steps that follow cover both sides. Steps 1 and 4 prepare you for shoppers' agents, steps 2 and 5 build your own agent, and step 3 connects the two.
### 1. Optimize product data for AI discoverability (buy-side)
Most product data was built for search filters, not AI agents. Making it machine-readable for agents is often called generative engine optimization (GEO) or answer engine optimization (AEO).
When the data itself becomes the product, agents can find, parse, compare, and surface products when shoppers ask across traditional search, AI assistants, and [conversational AI](https://delight.ai/glossary/conversational-ai) platforms. Retailers can focus on 3 areas:
- **Real-time inventory and pricing APIs** let agents query live data directly, which makes products retrievable in AI-led search.
- **Structured data such as Schema.org markup** makes price, size, color, and materials instantly machine-readable, so agents don't have to guess from prose.
- **Fact-rich product content** with precise specifications, material sourcing, dimensions, and use cases gives agents enough detail to choose your product over a vague listing.
### 2. Retain customers with your own agent (sell-side)
Even when a purchase starts on a third-party platform, a retailer can still capture the conversation and the sale. Shoppers say they trust retailers' on-site agents 3 times more than third-party agents, for now, according to [Bain](https://www.bain.com/insights/agentic-ai-in-retail-how-autonomous-shopping-redefining-customer-journey/).
A branded AI agent works as the gateway between a shopper's agent and your catalog, and it protects brand equity. **To win in agentic commerce, your agent must prove its value** through accurate data, personalized help that acts on the shopper's behalf, and secure, verifiable identity.
Picture a shopper whose personal agent asks a home improvement retailer's branded agent whether a 5/8-inch copper pipe is in stock. A branded agent can answer with the nearest store that has it and the shopper's loyalty pricing, context a general-purpose agent doesn't have, so the transaction runs through the retailer's own logic.
Standard handshakes between referring and branded agents, using protocols like ACP, let structured purchase intent such as "I need to fix a leaky faucet" flow straight into the retailer's systems.
A Delight.ai agent gives retailers this branded layer, so the conversation and the sale run through your own catalog and rules. [See a demo with your own products](https://delight.ai/contact-sales).
### 3. Enable agent-to-agent transactions (both sides)
Avoiding disintermediation, where a retailer becomes a mere supplier for other AI, means becoming an active participant in agentic commerce's [agent-to-agent (A2A) economy](https://sendbird.com/blog/ai-agent-to-agent-economy). When your branded agent works with shoppers' agents and other retailers' agents to source products and services, competitors can become collaborators.
Customer → Personal agent → Branded agent
These 2 capabilities make this work:
- **Transactional agents** use standards like ACP to create carts, check inventory, and start payments programmatically, which turns a conversation into a completed order.
- **Scoped agent payments** through tools such as Stripe's Shared Payment Tokens or Google's AP2 let retailers accept payments from third-party agents within limits the shopper approved, so every agent-initiated payment stays within a set amount and time window.
*Example of the A2A economy for B2B and B2C agentic commerce*
### 4. Design the autonomous backend (buy-side)
To win the transaction reliably, your backend has to be as agentic as your storefront. A shopper's agent that hits an out-of-stock signal or a stale price simply moves on to a competitor.
Agent-integrated architecture moves retailers from reactive logistics to systems that correct themselves, which protects margins and keeps agent-to-agent interactions reliable. For example, **inventory agents** can watch sales velocity alongside signals like weather, local events, or social trends, then trigger reorders or shift stock between stores before a shortage shows up.
Pricing changes too, because it becomes a real-time negotiation. With **dynamic, logic-based pricing**, a retailer can offer a querying agent a bundle or loyalty-match price, which makes it the best choice in that agent's comparison without cutting prices across the whole market.
### 5. Secure data sovereignty (sell-side)
In agentic commerce, whoever owns the interaction owns the intelligence. A branded agent captures zero-party data and intent signals that third-party AI can't see, which gives the retailer a lasting data advantage and a direct customer relationship.
- **Governance and guardrails** reduce the risk of an agent misusing data or giving wrong answers, and transparent data practices build brand-specific trust that general-purpose AI can't match.
- **Proactive personalization** turns captured data into the next sale, for example a maintenance reminder and coupon for the lawnmower a shopper bought last summer, sent before they think to search.
## How does Delight.ai power agentic commerce for retailers?
Delight.ai provides the sell-side agent, the business's own AI agent for guiding a shopper from first question to completed purchase in one conversation. It works on the retailer's behalf, whether the one asking is a person or a shopper's AI agent.
Here's what that looks like in practice:
- **Business-approved rules** define what the agent can do on its own, such as recommending a product or applying an offer, and hand off to a person when a request falls outside them; Delight.ai calls these reviewed rules [Actionbooks](https://delight.ai/product-release/action-books-management).
- **Customer memory** from AMP carries a shopper's history, preferences, and current intent across sessions, so recommendations build on what the business already knows.
- **Shared context across channels** through [Omnipresence](https://delight.ai/omnipresent) covers chat, SMS, email, voice, WhatsApp, and in-app messaging, so a conversation can start on the website and finish by text.
- **Oversight and control** come from [Trust OS](https://delight.ai/trust-os), which gives teams visibility into why the agent did what it did and the controls to change it.
BJ's Wholesale Club runs this model with Bev, an AI shopping assistant powered by Delight.ai that brings product discovery, savings guidance, membership help, and member care into one assistant. Members who engaged with Bev converted at a higher rate and showed greater purchasing activity per visit than those who didn't, according to the [BJ's Wholesale Club case study](https://delight.ai/customers/bjs).
> Whether a member is browsing for a grill, looking up club hours, or asking about a renewal, they're talking to the same Bev. That's how a real member-team member relationship is supposed to feel.
>
> Scott Vandegrift, VP of Product, BJ's Wholesale Club (2026)
## Takeaway
Shoppers' agents will keep getting better at finding and buying products, and no retailer controls them. What a retailer can control is the agent on its own side of the conversation, the one that knows its catalog, its customers, and its rules.
Delight.ai builds that agent, so your brand answers AI shoppers with your own products and offers and keeps the customer relationship after the sale. [Book a personalized demo](https://delight.ai/contact-sales) to see Delight.ai agentic commerce on your own catalog. You can also [explore how Delight.ai agentic commerce works](https://delight.ai/agentic-commerce).
- [How fast is agentic commerce growing?](#section-anchor-id-0)
- [What's the difference between buy-side and sell-side agentic commerce?](#section-anchor-id-1)
- [How does agentic commerce work?](#section-anchor-id-2)
- [How is agentic commerce different from traditional ecommerce?](#section-anchor-id-3)
- [Which protocols power agentic commerce?](#section-anchor-id-4)
- [How can retailers prepare for agentic commerce?](#section-anchor-id-5)
- [How does Delight.ai power agentic commerce for retailers?](#section-anchor-id-6)
- [Takeaway](#section-anchor-id-7)
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Agentic commerce FAQ
Common questions about how agentic commerce works and what it means for retailers.
### How is agentic commerce different from traditional ecommerce?
In traditional ecommerce, a shopper browses a retailer's website or app, compares options, and checks out on their own. In agentic commerce, an AI agent does that work by querying product data and APIs, comparing price and availability in real time, and completing or guiding the purchase. The retailer's audience shifts from human visitors to AI agents, which favor accurate structured data over brand storytelling.
### Is agentic commerce safe for shoppers and retailers?
It can be, when the right controls are in place. Payment network programs are designed to give verified agents tokenized credentials instead of raw card numbers, and protocols like AP2 record exactly what a shopper approved, such as a price limit or a specific cart. On the retailer's side, a sell-side agent should follow business-approved rules and hand off to a person when a request falls outside them.
### Can an AI agent buy something without the shopper's approval?
Usually not without limits the shopper sets first. Google's agentic checkout only buys after the shopper confirms the purchase and shipping details, and Visa Intelligent Commerce is designed to let shoppers set spending limits and conditions for their agent. AP2 records the shopper's approved price limits and cart in signed digital mandates that the merchant and payment provider can check.
### What are some examples of agentic commerce?
On the buy side, Google's agentic checkout can buy a product on a merchant's site with Google Pay once its price hits the shopper's budget and the shopper confirms, and Perplexity and PayPal launched Instant Buy in late 2025. On the sell side, retailers run their own agents, such as Lowe's Mylow, Walmart's Sparky, and BJ's Wholesale Club's Bev, which answer product questions and guide shoppers toward a purchase.
### Do retailers need their own AI agent if shoppers already use ChatGPT?
Yes, if they want to keep the customer relationship. A shopper's agent can find and buy a retailer's products without ever sharing the conversation behind the order. A retailer's own sell-side agent, like the one Delight.ai builds, answers that shopper or the shopper's agent with the retailer's inventory, offers, and policies, and keeps the intent data that powers personalization, retention, and repeat purchases.
## Keep reading
[View more](/blog)
[How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[View more](/blog)
## AI in finance: 24 examples of how AI is used in finance and banking
Source: https://delight.ai/blog/industry/ai-in-finance
## AI in finance: 24 examples of how AI is used in finance and banking
[
Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 16 2026
AI is reshaping the financial sector, heralding a new era of data-driven decision-making, efficiency, security, and customer experience. Driven by promises of streamlined operations, reduced costs, and improved customer experience—investment in AI continues to surge.
According to Statista, adoption of generative AI in finance reached [52%](https://www.statista.com/statistics/1557104/generative-ai-adoption-financial-services-worldwide/) in 2024. Yet, another Statista report found that [only 33%](https://www.statista.com/statistics/1419945/main-ai-benefits-financial-services/) of organizations saw gains in efficiency or customer experience—highlighting a growing need to bypass the hype and practical outcomes.
This raises questions: how is AI used in finance? Where is it delivering real value?
This article highlights **the 24 top AI use cases in finance.** Each includes real-life examples of AI in finance from industry leaders to help you make the right strategic AI investments.
### What is artificial intelligence (AI) in finance?
AI in finance refers to the use of machine learning (ML), deep learning, and predictive analytics to analyze data, automate processes, and enhance the decision-making of financial organizations.
Capable of analyzing and leveraging vast datasets in real time, AI improves the speed and precision of financial services and operations. It can enhance everything from customer service to forecasting, risk management to fraud detection, investment strategies, and more.
### How to use AI in finance
AI is rapidly reshaping the finance industry, making it more efficient, secure, and customer-centric. By integrating AI tools into existing operations, finance organizations can reduce costs, streamline operations, and set a new standard in customer experience.
The core types of AI used in finance are currently:
#### Generative AI (GenAI)
According to Statista, [52% of financial organizations are using generative AI](https://www.statista.com/statistics/1557104/generative-ai-adoption-financial-services-worldwide/) as of 2024, up from 40% in 2023. GenAI can be used to automate much of customer communications, support, and account management. Using natural language processing (NLP), GenAI tools deliver accurate, relevant answers to customer queries at scale in the form of chatbots. These tools can meanwhile analyze customer sentiment, generate reports and summaries, surface insights, or serve as virtual assistants to employees to enhance decision-making and efficiency.
#### AI-powered predictive analytics
AI-powered predictive analytics use machine learning algorithms to analyze historical and real-time data to forecast future trends, assess risks, and optimize financial strategies. Using predictive analytics, finance teams can move beyond manual efforts and reactive decision-making into more proactive, data-driven, efficient approaches across various functions.
#### AI agents for finance
The next frontier of intelligent automation in finance is AI agents. These autonomous applications can reason, process data in real time from multiple sources, and execute complex tasks from start to finish without human intervention. They can be used to automate customer service, enhance security, streamline backend operations, and personalize financial services and operations.
### 24 AI in finance examples
With AI adoption in finance increasing, a [recent survey](https://www.oecd.org/en/topics/sub-issues/digital-finance/artificial-intelligence-in-finance.html) shows its most common AI use cases in finance are in customer relations, process automation, and fraud detection.
*AI in finance by application among business leaders in 2025 ( [Image source](https://www.oecd.org/en/topics/sub-issues/digital-finance/artificial-intelligence-in-finance.html) )*
In the following sections, we’ll explore AI in finance examples from financial leaders that align with these top use cases, including:
- Customer relations and personalized banking
- Process automation
- Fraud detection and prevention
- Data analysis and financial planning
- Risk management and compliance
- Trading and portfolio management
#### Customer experience and personal banking with AI
A full [89%](https://www.jpmorgan.com/technology/digital-banking) of customers use mobile apps for banking, so the digital experience has never been more important in finance. Meanwhile, [41%](https://www.jpmorgan.com/technology/digital-banking) of customers say they want more personalized banking experiences and information.
This section highlights five AI use cases in finance with real-life examples of how AI improves front office operations.
##### 1. Automated customer service with AI chatbots
AI chatbots for finance provide 24/7 customer support on mobile apps, websites, chat, and other digital channels. Using natural language processing (NLP), they provide contextually relevant, tailored responses for customer self-help that automate much of account management. For example, chatbots can handle account inquiries, process payments, assist with loan applications, or provide tailored financial advice.
**Example:** **Bank of America’s chatbot, Erica,** helps customers check balances, execute transactions, and get personal financial insights—even sending proactive alerts to customers when unusual transactions appear in accounts.
*Bank of America’s chatbot notifies customers of unusual transactions to aid in money management ( [Image source](https://bankautomationnews.com/allposts/retail/bofas-ai-powered-assistant-erica-pulls-in-10-million-users/) )*
##### 2. Personalized financial advice from AI advisors
AI is an effective way to scale high-quality customer interactions, and another popular use case is personalized financial advice via AI robo-advisors. AI models analyze customers' spending habits, savings, and investment behavior to identify patterns and trends and provide tailored financial recommendations 24/7 that align with current market conditions and stated financial goals.
This enhances the customer experience, adding a new layer of convenience and confidence to digital banking experiences that customers appreciate.
**Example:** **Wealthfront**, an AI-powered robo-advisor, helps customers optimize their investments based on risk tolerance and financial goals.
##### 3. Voice-enabled conversational AI
Another way to create more convenient, tailored financial experiences is with voice-enabled conversational AI. Using NLP, voice-enabled AI assistants enable customers to ask banking questions and perform a growing list of transactions—check balances, pay bills, or transfer money—all via voice command.
This gives customers a more effortless way to manage their money, upgrading CX while also freeing employees to focus on complex tasks to boost productivity. Conversational AI can also be deployed in call centers to automate workflows, tailor service interactions, and elevate CX.
**Example:** **Bank of America’s Erica** is an AI-powered voice-enabled virtual assistant that enhances customer experience and streamlines banking operations.
##### 4. Hyper-personalized offers
AI can deliver tailored offers for financial products to customers based on historical data and, thanks to AI agents, real-time context. Machine learning (ML) predicts when customers might need a loan, credit increase, or investment options based on existing data.
AI agents go a step further, monitoring current in-app behavior, chat conversations, or market conditions and proactively triggering offers based on historical data and real-time context. This automates the delivery of the most relevant offers to better drive marketing performance, sales, upsells and cross-sells.
**Example:** **American Express** uses AI to personalize credit card offers to customers.
##### 5. Seamless omnichannel experiences at scale
[AI concierges](https://delight.ai) seamlessly integrate across existing tools and AI systems, acting as the connective tissue for omnichannel engagement and support. Unlike GenAI chatbots, AI agents enable truly seamless cross-channel engagement by storing customer interaction data in memory.
This allows them to maintain context between interactions and hold one continuous conversation with customers that spans channels—creating continuity and removing friction to elevate the omnichannel customer experience (CX).
**Example:** **Capital One** integrates AI across multiple touchpoints for a smooth CX.
### Process automation with AI in finance
For all its front office upgrades, AI is equally effective at creating operational efficiencies, reducing costs, human error, and streamlining operations. For example, one study found AI can reduce the time it takes to complete tasks by up to [40%](https://daloopa.com/blog/the-role-of-ai-in-transforming-financial-services#:~:text=Boosting%20Efficiency%20and%20Productivity,our%20capabilities%20and%20service%20delivery.).
These next six AI use cases in finance show how AI enables financial organizations to do more with less.
##### 6. AI-driven loan applications
AI accelerates the loan approvals process while improving its accuracy, helping reduce loan risk by [up to 70%](https://www.automationanywhere.com/solutions/ai-solution-accelerators/loan-underwriting#:~:text=AI%20agents%20validate%20application%20data,risk%20by%20up%20to%2070%25.&text=AI%20agents%20orchestrate%20underwriting%20workflows,60%25%20with%20accuracy%20and%20compliance.). Neural networks and ML can quickly analyze an applicant’s financial history, alternative credit data, and transaction patterns to assess creditworthiness in seconds and reduce default rates. AI agentic workflows can automate loan underwriting processes while staying compliant with relevant regulations and policies to mitigate risk.
**Example: ZestFinance** uses AI to aggregate, analyze, and process data from multiple sources in real time to automate loan underwriting.
*An example of an AI-automated loan underwriting workflow ( [Image source](https://www.app0.io/blog/ai-in-lending) )*
##### 7. Automated reporting with robotic process automation
Another AI example in finance is using robotic process automation (RPA) to automate a variety of repetitive and rule-based tasks. This includes processing loan applications, handling account reconciliations, and generating regulatory and financial reports. By reducing the time and effort needed to produce financial documents, RPA both reduces costs and human error as it improves employee productivity and morale.
**Example:** **Deutsche Bank** uses RPA to automate back-office processes like compliance reporting and transaction reconciliations.
##### 8. Document processing with AI in finance
AI-powered OCR (Optical Character Recognition) technology extracts and processes structured and unstructured data from documents, then ML algorithms analyze, search, and store this data for document-intensive processes. For example: loan servicing, investment research, invoicing, mortgage lending, credit scoring, know your customer (KYC) verification, and more.
Even with human verification at the end, using AI to process financial documents helps to reduce manual data entry, errors, and accelerate financial workflows while mitigating risk.
**Example:** **JPMorgan Chase’s COIN (Contract Intelligence)** automates the review of legal documents, saving 360,000 hours of human work annually.
##### 9. Automated data reconciliation
AI automates the matching of financial transactions across systems, ensuring records are kept accurate and consistent for real-time use, and making the month-end closing process for banks more efficient. AI models can identify and flag potential errors or discrepancies in the process of cleansing, standardizing, and validating transaction data.
This helps financial institutions to improve their data accuracy, leading to improved compliance reporting, risk management, and insights into performance.
**Example:** **Xero**, a cloud-based accounting platform, uses AI to automate transaction reconciliation and improve financial accuracy.
##### 10. AI-driven image recognition
AI like computer vision and deep learning models can quickly and accurately process and derive insights from images and videos. This enables financial organizations to accelerate insurance claim processing, damage assessments, check processing—even expedite customer onboarding with KYC-compliant identity document verification.
**Example:** **HSBC** uses AI-driven image recognition to automate check processing, improving efficiency and reducing fraud.
##### 11. Speech recognition AI for call center automation
AI can quickly convert audio to text using NLP, providing a faster way to evaluate customer calls and improve services. By transcribing customer calls automatically, AI reduces the need for manual reviews, saving time for service teams while identifying patterns like common issues or customer sentiment. This helps to generate insights that improve training, quality assurance, and the service experience in a targeted way.
**Example:** **JPMorgan Chase** uses speech recognition AI to enhance internal operational efficiency and improve call center productivity and customer service.
### Fraud detection and prevention with AI in finance
Thanks to AI in finance, fraud detection and prevention has never been more accurate, effective, and efficient. By analyzing datasets and continuously monitoring transactions in real time, AI models can identify patterns and anomalies that suggest fraudulent transactions, financial crime, and trade spoofing with unparalleled speed and accuracy.
In fact, more than [7 in 10 banks](https://www.pymnts.com/news/security-and-risk/2024/seven-in-10-financial-institutions-use-ai-and-ml-to-combat-fraud/) now use AI to detect fraud, as AI algorithms reduce detection time by [90%](https://www.allaboutai.com/resources/ai-statistics/finance/) compared to traditional methods.
Here are three AI use cases in finance with real life examples of fraud detection and prevention.
##### 12. Real-time fraud detection and prevention
AI-powered fraud detection systems not only detect fraud in real time, they proactively act to stop it. ML algorithms spot unusual spending patterns, login attempts from unrecognized devices, and sudden changes in transaction behavior. Meanwhile, AI agents proactively trigger fraud prevention measures such as two-factor authentication or access controls. This enables a proactive, always-on approach to fraud prevention, halting fraudsters in advance to protect against financial loss, risk, and reputational damage.
**Example:** **PayPal** uses AI to scan millions of transactions for fraudulent activity. The system flags anomalies and prevents fraudulent transactions before they occur.
*AI agent systems offer real-time connectivity and data processing for enhanced fraud detection and prevention ( [Image source](https://www.capellasolutions.com/blog/detecting-fraud-in-real-time-how-a-financial-institution-leveraged-ai) )*
##### 13. Behavioral analysis
AI can also assess behavioral traits like typing speed, mouse movements, login times, or how users hold their smartphones to detect fraudulent activities. And if a transaction is attempted in an abnormal manner, the system can trigger additional authentication or block the transaction for the suspect user—without impacting the user experience at large.
**Example: BioCatch** uses AI-driven behavioral biometrics to detect fraud in digital banking transactions by identifying unusual patterns in user behavior.
##### 14. Anti-money laundering (AML)
AI enhances AML efforts by continuously monitoring financial transactions and identifying suspicious patterns or anomalies linked to money laundering. AI agents can pull in data from outside sources via APIs in real-time—like sanctions screenings or adverse media mentions—improving accuracy and reducing false positives compared to traditional rule-based AML systems.
**Example: HSBC** uses AI-driven AML systems to monitor billions of transactions and reduce false positives while improving fraud detection accuracy.
### Financial forecasting and data analysis with AI
If knowledge is power, AI is a kingmaker. AI models trained to recognize patterns and make calculations can process vast sets of historical data, market trends, and alternative sources in real time, enabling a deeper understanding of current market behaviors, greater agility, and more informed decision-making.
AI confers a massive informational advantage, so it’s no surprise that, according to an NVIDIA survey, [over 90%](https://www.nvidia.com/en-us/industries/finance/ai-financial-services-report/) of financial services companies are either assessing or already using AI.
Here are two AI use cases in finance for financial forecasting and data analysis with examples.
##### 15. Data analysis
Machine learning algorithms with big data analytics can analyze huge datasets and extract insights much faster and more effectively than humans. This enables financial organizations to automate much of market research and data analysis, enhancing everything from risk management to trading strategies to decision-making with deeper, more comprehensive business intelligence.
**Example:** **Goldman Sachs** uses AI-driven data analysis to pull insights from stock markets, news articles, financial reports, and more to enhance decision-making, risk management, and more.
##### 16. Financial planning and forecasting
AI models can forecast financial trends like revenue, expenses, and cash flow with unparalleled accuracy. They do this by synthesizing data from multiple sources to identify patterns, risks, and trends to inform financial plans and models. This enables predictive models to anticipate market changes, investment performance, and drive smarter decisions that minimize risk and maximize returns.
**Example:** **LendingClub** uses AI-driven predictive models to assess borrowers and mitigate risk before approving loans.
### Risk management and compliance with AI
AI’s ability to process large datasets in real time and detect patterns means it can more accurately mitigate risk in credit lending, identity verification, and cybersecurity.
Agentic AI, which integrates seamlessly across business systems, can then automatically compile data to maintain compliance across systems.
Here are four AI use cases in risk management and compliance with real examples from top financial institutions.
##### 17. Credit risk analysis
AI improves the accuracy of credit risk analysis by incorporating alternative data sources such as social media behavior, online purchase history, and utility bill payments. Unlike traditional credit scoring that relies on FICO and similar scores, AI enables lenders to assess creditworthiness for people with little to no credit history, reducing risk while driving transactions. It’s estimated that using machine learning to assess a more comprehensive range of data points will help lenders to cut losses by [23% annually](https://www.chaserhq.com/blog/how-is-artificial-intelligence-impacting-finance).
**Example:** **Upstart**, an AI lending platform, assesses borrowers based on their education, employment history, and spending habits rather than just credit scores.
*An example AI-automated workflow for financial credit scoring and risk analysis ( [Image source](https://www.app0.io/blog/ai-in-lending) )*
##### 18. Regulatory compliance automation
Financial institutions must comply with a variety of ever-changing regulations, and AI is a boon to the RegTech stack. AI automates compliance monitoring by analyzing transactions, contracts, and reports, detecting potential regulatory violations, and reducing manual auditing efforts.
**Example:** **IBM Watson** assists banks by analyzing regulatory changes and ensuring adherence to financial laws.
##### 19. Automated identity verification
AI speeds up the process of Know Your Customer (KYC) verification, using ML to analyze and authenticate identity documents, transaction history, IP addresses, and more. By automating the process of investigating if an applicant's information is being used legitimately, AI helps financial institutions prevent identity theft and fraudulent account activity without adding to workloads.
**Example:** **Onfido** uses AI-powered facial recognition and document verification to streamline digital identity checks for financial institutions.
##### 20. AI-powered cybersecurity
Agentic AI can continuously monitor network traffic and financial data to identify anomalies that suggest a cyber attack, then respond to threats in the moment. This enables real-time threat detection and response that significantly enhances the cybersecurity posture of financial institutions.
**Example:** **JPMorgan Chase** uses AI-powered cybersecurity tools to protect infrastructure from cyberattacks by monitoring network traffic for unusual patterns.
### Trading, investment, and portfolio management
In money markets where nanoseconds can be the difference between opportunity and regret, AI’s ability to turn patterns in datasets into strategic, split-second trades can lead to a competitive edge.
What’s more, AI algorithms can analyze data and execute trades far faster than human traders—even up to [90% faster](https://artsmart.ai/blog/ai-in-finance-statistics-trends/) than non-AI tools.
Here are three AI use cases in finance with examples of how it leads to faster and better trading decisions.
##### 21. Algorithmic trading
AI is a boon for stock trading, especially algorithmic and high-frequency trading. For example, AI algorithms can analyze historical and real-time market data to execute split-second trades at optimal moments, adjusting trading strategies in real-time based on customers’ financial situation, market conditions, even news events. This increase in trading speed and precision helps organizations to capitalize on fleeting opportunities and maximize returns.
**Example:** Renaissance Technologies, a hedge fund, uses AI-driven algorithmic trading to identify patterns and maximize profits.
*AI enhances the speed and accuracy of stock trading*
##### 22. Portfolio management
By analyzing a vast array of market conditions and economic indicators, AI helps investors and financial institutions to make better decisions and optimize their portfolios. It helps investors to manage portfolios by recommending asset allocations that maximize returns while minimizing risk, and adjusts portfolios automatically based on market conditions and customer goals.
**Example:** **Betterment** uses AI to help investors build and rebalance portfolios based on market changes.
##### 23. Market sentiment analysis
AI-driven sentiment analysis can identify the main emotions or opinions in a text or audio recording, adding a valuable point of data to inform decision-making, financial modeling, CX improvements, and investment strategies. By scraping various sources such as news articles, social media, or earnings reports, AI can gauge public, investor, or customer sentiment with accuracy in seconds. This enables organizations to gauge the prevailing perception in response to key events, or in key areas, and make more informed decisions going forward.
**Example:** **Dataminr** provides AI-driven real-time alerts on financial news events that could impact stock markets.
### Benefits of AI in finance
- **Automation** – AI streamlines workflows, enhances security, and personalizes banking experiences.
- **Accuracy** – Reduces manual errors in data processing, analytics, and customer interactions.
- **Efficiency** – Frees employees for strategic work by automating routine tasks like document verification.
- **Speed** – Processes vast data quickly, improving decision-making, risk modeling, trading, and compliance.
- **Availability** – Enables 24/7 financial management and support via cloud-based AI.
- **Innovation** – Unlocks new financial products and services through predictive analytics.
- **Personalization** – Delivers tailored financial insights, proactive recommendations, and seamless digital experiences to enhance customer satisfaction and engagement.
### Next steps: Using AI in finance
In finance, the next leg of the digital transformation hinges on AI. In the words of PayPal’s chief AI officer, Asim Tewary:
*“As I think of it, AI will just go away and become pervasive like an operating system.”*
For all its potential, AI is complex. It helps to create a well-considered AI strategy that accounts for factors like explainability, transparency, risk, and compliance while also defining the key applications and use cases.
If you’re looking for help with creating your AI strategy and deploying AI agents, delight.ai can help. Our team of AI experts includes machine learning engineers, data scientists, and more who are skilled and ready to help enterprises craft and deploy an effective AI strategy at scale.
Our robust [AI agent platform](https://delight.ai/ai-agent) makes it easy to [build AI agents](https://delight.ai/builder) on a foundation of enterprise-grade infrastructure that ensures optimal performance with unmatched adaptability, security, compliance, and scalability. [Contact our team](https://delight.ai/contact-sales) of friendly AI experts to get started.
- [What is artificial intelligence (AI) in finance?](#section-anchor-id-0 "What is artificial intelligence (AI) in finance?")
- [How to use AI in finance](#section-anchor-id-1 "How to use AI in finance")
- [24 AI in finance examples](#section-anchor-id-2 "24 AI in finance examples")
- [Benefits of AI in finance](#section-anchor-id-3 "Benefits of AI in finance")
- [Next steps: Using AI in finance](#section-anchor-id-4 "Next steps: Using AI in finance")
### Keep reading
[View more](/blog "View more")
[
Delight.ai — The next chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)[
How For You Conversations make every interaction feel personal](/blog/product/for-you-conversations)[
Introducing AMP: The Agent Memory Platform](/blog/product/introducing-agent-memory-platform)[
The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[View more](/blog "View more")
## 14 steps to improve your CSAT score
Source: https://delight.ai/blog/ai-for-cx/14-steps-to-improve-your-csat
## 14 steps to improve your CSAT score
[Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
Maintaining customer satisfaction is crucial for the success of any business. At a glance, here are 14 ways to improve CSAT and have a competitive advantage in the market:
1. **Measure your current CSAT and set goals**
2. **Ask the right questions**
3. **Deliver omnichannel support**
4. **Maintain your brand voice at every touchpoint**
5. **Adopt asynchronous in-app support**
6. **Maintain personalization**
7. **Listen and act on customer feedback**
8. **Build a support relationship recovery program**
9. **Weave customers into your culture**
10. **Build a QA process**
11. **Reduce response times with automation**
12. **Provide a self-service option**
13. **Satisfied employees create satisfied customers**
14. **Solutions > excuses**
The stats show that retaining customers is much more cost-effective than finding new ones — and happy customers often become devoted advocates.
For this reason, improving CSAT (customer satisfaction) scores should be a top priority for every company. But how exactly can you drive customer satisfaction in the right direction?
In our work, we have seen how brands can turn around their CSAT scores with a few key adjustments.
To help you achieve the same kind of transformation, we used our insights to create a recipe for CSAT success. Keep reading to discover the secret ingredients.
### What is CSAT score?
Before we dive into the action plan, it’s a good idea to understand the challenge here.
**CSAT is short for customer satisfaction score.** This metric helps you track how your customers feel after interacting with your business — such as making a purchase or seeking customer support.
In most industries, **the average CSAT score is somewhere in the** [**70–80% range**](https://www.fullview.io/blog/csat-benchmarks-by-industry).
*CSAT benchmarks for company size*
Achieving a score above 80% is obviously better than falling below this benchmark. But even if you have a healthy CSAT score, it’s worth trying to push it higher.
### How to calculate CSAT
In order to calculate your CSAT score, you first need to collect some data.
CSAT surveys are usually quite simple, asking customers to rate their recent experience on a scale of 1–10. Your score is the percentage of respondents who give you an overall positive rating (7–10).
This isn’t the only way to come up with a figure. You can use emoji-based surveys to break customers into three groups (happy, neutral, unhappy) or use a scale of 1–5, where ratings of 4 and 5 are regarded as positive.
### Other customer satisfaction metrics
CSAT isn’t the only method for measuring how your customers feel. Here are some other customer satisfaction metrics to pay attention to:
- **Net promoter score (NPS)** — Similar to CSAT, but focuses on long-term satisfaction with your brand or product.
- **Customer effort score (CES)** — This metric asks users to rate the level of difficulty involved with using your product or service.
- **Customer retention rate (CRR)** — In simple terms, what percentage of customers are sticking with you over a period of time?
- **Customer churn rate (CCR)** — On the flip side, how many customers are leaving?
Tracking multiple metrics and KPIs can help you build a more complete picture of satisfaction levels throughout your business.
### Why is improving customer satisfaction important for your business?
While customer satisfaction might seem like a “fluffy” topic, the truth is that CSAT scores are often closely linked to the bottom line. In fact, this metric is an excellent health indicator for your business.
Here are some of the key benefits that usually come with improved customer satisfaction scores:
#### Better customer loyalty
According to multiple [studies](https://www.mycustomer.com/customer-experience/loyalty/infographic-customer-acquisition-vs-retention-costs), the cost of acquiring new customers is five times higher than earning sales from your existing customers.
To unlock that repeat business, you need to satisfy your clients or users. [Research](https://www.arcjournals.org/pdfs/ijmsr/v3-i2/8.pdf) shows a **clear link between customer satisfaction and customer loyalty**.
Push your CSAT scores upwards, and you’re more likely to have buyers coming back for more.
Building a community is a good way of achieving this. By providing a social space where customers can interact between themselves and with your brand, you encourage a greater sense of ownership.
#### Increased customer lifetime value
A positive side-effect of improving customer retention is usually an **increase in customer lifetime value (CLV)**.
This metric measures the amount of revenue you generate from each customer, on average, over the course of your business relationship.
By keeping your customers happy, you’re likely to extend these connections over a longer period. And just as importantly, you have a better chance of selling upgrades to returning buyers.
#### Reduce customer churn
What’s the opposite of customer loyalty? Churn.
If you can consistently meet customer expectations, **you’re going to lose fewer of those customers over time**. Reduced churn usually means a more consistent flow of revenue. It also reduces the pressure on your marketing and sales departments to come up with a constant stream of new leads.
Churn is particularly important for digital businesses, because users are notoriously fickle. In a competitive environment, providing good customer service is often the best way to prevent people jumping ship.
#### Improve brand perception
The way each customer feels about your business is largely dependent on their satisfaction levels. This matters both on an individual level, and in terms of your overall brand.
Whether customers are happy or displeased, they are likely to share their feelings with others — contributing to a **narrative about your brand**. These perceptions can spread quickly, and they can be difficult to change once they set root.
The best way of ensuring that perceptions are positive is by improving your overall customer satisfaction levels.
### The 14 steps in your CSAT improvement action plan
Customer satisfaction is influenced by a range of factors — including your product and your pricing. These are fundamental pillars of your business that are difficult to change.
However, you can still improve the overall customer experience you provide by making adjustments to your support and marketing strategies.
To guide you through this process, we have created a comprehensive action plan. Here’s how to unlock better customer satisfaction in seven easy steps:
#### 1. Measure your current CSAT and set goals
Before embarking on any major project in business, it’s always a good idea to take stock.
Sending out detailed CSAT surveys will help you to gauge the mood among your customers right now, and evaluate the performance of your customer service teams. This data will provide a reference point for any future improvements.
As such, it’s worth collecting plenty of customer feedback. Be sure to ask about every step of the customer journey, from initial contact through to after-sales support.
Along with measuring your CSAT, this data can provide insights through other metrics. If you’re not already tracking **NPS (Net Promoter Score)** and **CES (Customer Effort Score)**, we highly recommend that you start doing so.
Put together, these metrics are really your key performance indicators.
Delight.ai's real-time analytics dashboard tracks resolution and CSAT trends automatically, so you're not waiting on a quarterly survey to see whether a change worked.
#### 2. Ask the right questions
Although the CSAT metric was designed to measure how your customers feel, your score can be influenced by other factors. Perhaps the most prominent one is the content of your customer satisfaction surveys.
In particular, survey questions can play a pivotal role in shaping the responses you receive.
When you’re composing your survey, try to:
- Only ask questions that serve a specific purpose
- Use clear language that everyone will understand
- Avoid asking leading questions (i.e. ask people for their opinion rather than asking them to agree with your own statement)
- Ask for ratings rather than binary answers
Stick with these principles, and you should certainly see better quality data — and possibly a higher score.
Delight.ai's AI agent captures sentiment in every conversation, giving you richer, ongoing data than a single post-interaction survey question and helping you more accurately assess customer sentiment.
#### 3. Deliver omnichannel support
Once you have baseline numbers to work with, it’s time to start making some changes. If you don’t already **offer omnichannel customer support**, this should be your first priority.
You see, how you provide support is a big factor in customer satisfaction. People want to be able to get help when they need it, and through the channels that are most convenient for them.
While pushing all support requests through a single channel might seem convenient from a business perspective, you’re unlikely to deliver a great customer service experience this way.
Instead, we would recommend offering a mix of contact channels. The blend could include:
- Live chat
- In-app support
- Email
- Social media
- Phone
**Delight.ai's** [**AI copilot**](https://delight.ai/desk/ai-copilot) **lets your support team handle every channel from one shared inbox, in real time.** Add AI agents on top, and self-service resolves the routine requests before a human ever sees them, cutting response times toward zero.
#### 4. Maintain your brand voice at every touch point
When customer interactions happen across multiple channels, the overall experience can end up feeling a bit disjointed. That’s why your next action point should be **setting a consistent brand voice**.
Your brand voice is essentially the character of your business. It could be fun and light-hearted, or very serious and professional. But what really matters most is that you’re consistent. You want your brand to come across as likable and trustworthy, not weird and unpredictable.
To make this happen, we recommend working on three key areas:
- **Communication with customers** — If your contact center and marketing department can maintain a consistent tone of voice, you’re more likely to deliver the kind of reliable service that customers crave
- **Design and user experience** — Maintaining a consistent look across all channels will give customers more confidence in your professionalism, and help them to navigate your support systems
- **Continuity in support** — When someone asks for help via live chat, they shouldn’t have to repeat the whole query when they later contact you via email or social media
Getting everything perfectly lined up may take some time, but you can speed up the process by building comprehensive guidelines for tone of voice and design within your company with [Trust OS](https://delight.ai/trust-os). Trust OS lets you set and enforce a consistent tone across every AI-handled conversation, so consistency doesn't depend on individual agents remembering the guidelines.
#### 5. Adopt asynchronous in-app support
If your business has a presence on mobile platforms, it’s a good idea to adopt asynchronous in-app messaging as one of your primary support channels.
Unlike live chat, asynchronous support does not require a real-time connection between the customer and your support team. Conversations continue naturally when the user has time, and when you have the bandwidth to provide assistance. When you don’t, chatbots can reduce wait times.
**Asynchronous messaging is also more familiar for most people.** After all, it’s the same style of communication we use every day in apps such as Messenger, WhatsApp, and Telegram.
Of course, you could use these third-party platforms to provide support. However, keeping users in your app allows you to maintain full control over customer data and analytics provide that continuity of service we talked about earlier.
**Delight.ai puts** [**AI agents**](https://delight.ai/builder) **directly inside your app, so customers get async support without ever leaving your branded experience.**
#### 6. Maintain personalization
As a general rule, customers love personalization. Any service that is shaped to individual needs is always going to be more convenient to use, and personalized offers tend to be more relevant.
What’s more, personalized messaging makes us feel more valued — a bit like the local barista who knows you by name. When you add these factors together, you’re almost guaranteed to see an improved CSAT score.
The stats back this up. Research by McKinsey found that [71% of consumers](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) expect companies to deliver personalized interactions, and 76% of respondents are annoyed when their experience isn’t personalized.
In other words, introducing personalization to your product should be a top priority if you care about CSAT.
If possible, you should also try to make the personalization seamless — such as being able to put down a conversation on one platform and pick up the thread elsewhere. **Delight.ai's** [**Agent Memory Platform (AMP)**](https://delight.ai/agent-memory-platform-amp) **carries that context automatically, so a customer who starts on chat and follows up on WhatsApp never has to repeat themselves.**
#### 7. Listen and act on customer feedback
While it’s worth taking some proactive steps to boost customer satisfaction, it’s simply not possible to reach a high CSAT score without **listening to your customers and acting on what you hear**.
There are many different ways to collect customer feedback — from face-to-face interviews and focus groups to digital surveys and even online review sites.
Whichever method you choose, what matters most is that you focus on the negative feedback.
Sure, some of the worst survey responses may hurt your ego. But these reviews can give you valuable insights into the mind of your unhappiest customers. If you can fix their pain points, you will be well on the way to a record CSAT score.
Delight.ai automatically flags negative-sentiment conversations for review, so the feedback that matters most doesn't get buried in a survey export.
#### 8. Build a support relationship recovery program
Unless your CSAT is close to perfect, you probably have a few less-than-satisfied customers. Some might have had a single bad experience; others may have felt their frustrations growing over time.
Either way, you need to win back their approval if you’re going to ramp up your customer satisfaction levels.
In our experience, the best way of achieving this is through a **service recovery program**. This should provide a clear action plan for re-engaging with a client when something goes wrong.
The plan should define:
- **Who gets in touch with unhappy customers** — It’s often a good idea to escalate issues and get a support manager involved
- **How they should approach the conversation** — As a general rule, you should apologize, ask questions to understand why the customer is unhappy, create an action item to fix the problem, and then follow up
- **What records you keep on these events** — Making a note of incidents helps you learn valuable lessons, and tracking the number of events over time helps you figure out whether you’re making progress
Delight.ai's [Desk](https://delight.ai/desk) keeps the full conversation history attached when a case escalates, so whoever runs your recovery outreach isn't starting from a blank page.
#### 9. Weave customers into your culture
In order to effect any major change in business, you really need to go beyond protocols and guidelines. These are merely signposts that guide your staff along your desired path. But the direction of travel comes from your culture — both in terms of the company, and your team of individuals.
As such, the businesses that really excel in customer satisfaction tend to be those with a customer-centric mindset.
Although it’s important to keep the bottom line in sharp focus, **putting an emphasis on customer needs gives you a better chance of achieving your financial goals** in the long term.
In fact, looking after loyal customers and first-time buyers alike is one of the best ways to drive new growth in your existing business.
By automating the routine requests, Delight.ai frees your team to spend more of their time on the relationship-building work that actually shapes a customer-centric culture.
#### 10. Build a QA process
A product that doesn’t work properly is always going to receive low-end CSAT scores.
In some cases, this is as a result of poor design and engineering. But even if you have a really well-designed product, bugs and defects can easily ruin the overall user experience.
For example: push notifications might not seem that important. But when users are relying on updates from your app, reliable delivery really matters.
This is why **having a quality assurance process is really important** if you want to maximize your CSAT score.
Before you release new updates, make sure to run thorough tests on all major features and your interface. If you don’t have the knowledge or resources in house, outsource the testing process to QA specialists.
Delight.ai's sandbox testing and staged deployment let you validate an AI agent against real scenarios before it ever reaches a live customer.
#### 11. Reduce response times with automation
Customer support is often one of the key battlegrounds for CSAT.
You can have the most extensive knowledge base in the world, but some of your users are going to need help. When they reach out, they don’t want to spend three hours trying to get through to a call center, or wait half a week for an email reply.
In fact, [HubSpot](https://blog.hubspot.com/sales/live-chat-go-to-market-flaw?__hstc=238111519.53ba22f3b93a94d8a1360e96c797223d.1659624006455.1659624006455.1659624006455.1&__hssc=238111519.1.1659624006456&__hsfp=868227580) found that 90% of consumers regard an immediate response to support requests as "important" or "very important."
**With Delight.ai,** [**Lotte Homeshopping**](https://delight.ai/reports/ai-index) **cut support workload by 30-40% by automating first-response and routine resolution, without adding headcount.** Creating a chatbot allows users to begin the process of seeking help, and to feel like they are making progress — even when all your agents are busy.
AI chatbots further elevate the support experience, allowing customers to engage immediately in human-like conversations while they wait for live agents.
Meanwhile, the AI chatbot can start to gather information about the customer issue. This means that by the time a member of your support team is available, they should be able to solve the issue faster.
#### 12. Provide a self-service option
While it’s important to provide live help, most users want to fix problems themselves. What’s more, [77% of customers](https://blog.vanillaforums.com/15-customer-self-service-and-experience-stats-to-know-2020) actually view brands in a more positive light when they provide self-service options.
There are several ways to supply this kind of support. Creating a thorough knowledge base is a good starting point. If you include a community element, users can even help each other with more niche issues.
For the most common support requests, like refunds and returns, **a simple self-service help center should do the trick**. Automating these tasks should massively increase the amount of time your support team has for real problems.
You could also think about setting up a chatbot. This technology allows customers to interact with your knowledge base and automated tools within a familiar, conversational interface. **With Delight.ai,** [**Hanssem's**](https://delight.ai/customers/hanssem) **resolution rate climbed from 48% to roughly 86% over five months as its AI agent took on more self-service resolution, cutting transfers to a human agent in half.**
#### 13. Satisfied employees create satisfied customers
While tactical measures can move your CSAT score in a positive direction, you really need to take a more holistic approach if you want to maximize customer satisfaction and reap the rewards.
A good place to start applying this mindset is behind the scenes, within your support team. Agents who are feeling the pressure are generally less likely to provide the kind of service that customers expect. People are perceptive, and even the smallest hint of stress in the way someone speaks can come across badly.
To avoid these issues, **try to create a good working environment for your support team**. Give them tools that work efficiently, and access to the information they need. As we mentioned before, using automation can reduce the overall burden placed on your agents.
In turn, your staff should be better placed to deliver the kind of friendly, patient service that every customer wants.
Delight.ai's AI agent absorbs the repetitive, high-volume requests, so your team spends their time on conversations that actually need human judgment.
#### 14. Solutions > excuses
As part of your holistic approach, it’s important to look at an under-par CSAT score as **an opportunity for improvement, rather than something to explain away**.
Self-reflection is the key here. What could you be doing better? If you find yourself out of ideas, try to research the reasons why people give you a specific rating. This could be through your original survey, or through a follow-up questionnaire or interview.
When you listen to the personal experiences of your customers, the path ahead becomes clear: you simply need to address any problems they mention.
### Improve customer satisfaction with Delight.ai
Whether you operate in the tech space or simply want to provide a better customer experience through your digital channels, Delight.ai can help. It’s fast, flexible, and fully configurable to your needs.
Sound good? [Talk with our sales team](https://delight.ai/contact-sales) to discover how Delight.ai can boost your CSAT in weeks.
- [What is CSAT score?](#section-anchor-id-0 "What is CSAT score?")
- [How to calculate CSAT](#section-anchor-id-1 "How to calculate CSAT")
- [Why is improving customer satisfaction important for your business?](#section-anchor-id-2 "Why is improving customer satisfaction important for your business?")
- [The 14 steps in your CSAT improvement action plan](#section-anchor-id-3 "The 14 steps in your CSAT improvement action plan")
- [Improve customer satisfaction with Delight.ai](#section-anchor-id-4 "Improve customer satisfaction with Delight.ai")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
### Keep reading
[View more](/blog "View more")
[
Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[
Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)
[
Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[
The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[View more](/blog "View more")
## 13 AI use cases in retail that will define 2026 (+ examples)
Source: https://delight.ai/blog/industry/ai-use-cases-retail
- [Blog](/blog)
- [Industry](/blog/industry)
- 13 AI use cases in retail that will define 2026 (+ examples)
## 13 AI use cases in retail that will define 2026 (+ examples)
[
Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 25 2026
Imagine a customer contacts support asking where her order is. The AI agent checks the CRM, spots a carrier delay, and resolves the issue proactively—but not before issuing a next-purchase discount that turns her frustration into appreciation.
The next day, she returns to browse the website, and the same [AI agent](https://delight.ai/learn/ai-agent) switches roles instantly. Now a shopping assistant, it offers personalized product recommendations based on her history, session context, and the local forecast. When she purchases, it triggers a swarm of activity behind the scenes: an inventory agent flags the item as trending, a pricing agent adjusts positioning, and a logistics agent reroutes stock from the nearest warehouse.
This is artificial intelligence (AI ) in retail in 2026. No longer a fragmented set of tools, but a coordinated ecosystem of specialist AI agents, each simulating human roles to meet rising customer expectations and optimize retail operations in real time.
According to KPMG, AI adoption in the retail industry will grow [from 33% to 85% by 2027](https://kpmg.com/cn/en/media/press-releases/2025/10/ai-adoption-in-retail-and-sustainability-redefine-consumer-spending-habits-across-hk-and-the-gba.html). It’s a new era—so where do you start?
Read on to learn the 13 top AI use cases in retail with examples that are redefining how organizations sell, operate, and support customers in 2026.
## The new frontier of retail: Agentic AI
Before we highlight key use cases, it’s important to understand how agentic AI is reshaping retail at every level. What began with [predictive analytics](https://delight.ai/learn/predictive-analytics) and [generative AI](https://delight.ai/learn/generative-ai) tools has evolved into [agentic AI](https://delight.ai/learn/agentic-ai)—autonomous AI agent systems that perceive, reason, and act on behalf of customers and retail processes without human intervention.
AI agents mark a shift from reactive automation to proactive intelligent orchestration—replacing disjointed platforms with a scalable, system-level solution to retailers’ biggest challenges. Reports show that between [80-95% of retailers](https://www.emarketer.com/content/5-key-stats-on-rise-of-agentic-ai-retail#:~:text=Key%20stat:%2075%25%20of%20retailers,could%20become%20a%20major%20barrier.) are piloting or experimenting with agentic AI use cases.
Three aspects to consider:
- **Agentic architecture is the foundation.** It replaces siloed systems with a single ecosystem that runs on unified customer data. This evolving, 360-degree customer view powers continuous improvement over time.
- **Agentic experiences are the expectation.** A unified customer view enables retailers to scale delivery of consistent, hyper-personalized, omnichannel experiences that increase customer loyalty and conversion.
- **Agentic commerce is the frontier.** Instead of customers browsing storefronts, AI agents increasingly handle product research, comparison, and purchasing on their behalf. McKinsey estimates [agentic commerce](https://delight.ai/blog/industry/agentic-commerce) will [generate $3–5 trillion](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-automation-curve-in-agentic-commerce) in global value by 2030. Retailers who don't optimize their systems for AI agents risk becoming invisible, bypassed by platforms that do.
In short, as adoption surges, AI is becoming a competitive necessity in retail. By offering a new operating system to power truly unified omnichannel personalization, agentic AI is ushering in an AI-first future of retail operations that simultaneously improves the customer experience (CX) and operational efficiency.
Here are some top ways retailers are investing in AI capabilities.
## 13 AI use cases in retail that will define 2026
### 1. Autonomous 24/7 customer support
AI can automate the high-volume, repetitive tasks that overwhelm retail customer service teams (order tracking, returns, product FAQs) and eat into margins. Predicted by Gartner to [handle 80% of frontline interactions](https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290) by 2029, AI agents free retail staff to focus on high-sensitivity interactions and maintaining customer loyalty.
Say a customer requests a refund on Black Friday. Unlike traditional chatbots that just retrieve answers, [delight.ai's AI Concierge](https://delight.ai/) remembers the customer, queries the CRM, validates the status with logistics, executes the refund via API call, and then updates the customer record. If the case exceeds its scope, the AI agent escalates with full context. Given its scalability across service channels, retailers using agentic AI in customer-facing roles report [66% productivity gains and 57% cost savings](https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-agent-survey.html).
*AI customer service agents handle routine inquiries like returns and WISMO from start to finish*
**Key AI technology:** AI agent platforms, agentic frameworks, AI-ready APIs for CRM/ERP integration.
### 2. AI search and product discovery
AI search is transforming how shoppers find and buy products, shifting from keyword matching into intent-based discovery. Using natural language understanding (NLU), AI interprets the query’s intent and context to synthesize a unique set of personalized product recommendations. For instance, instead of typing "blue dress," a customer enters "elegant attire for a garden wedding in June”—and receives a curated set of results based on their history, preferences, and live browsing context.
The same AI agent that powers support can drive this experience, evolving search boxes and shopping assistants into conversational discovery engines that reduce friction and [improve conversion significantly](https://www.rankscience.com/blog/ai-search-vs-google-traffic-data). According to McKinsey, [40–55% of consumers](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search#:~:text=The%20AI-powered%20consumer%20decision,of%20this%20shift%20are%20clear.) now use AI search to make buying decisions, a clear signal that the shift to [agentic commerce](https://delight.ai/blog/industry/agentic-commerce) is already underway.
*AI search lets customers browse products by intent and context using* [*AI shopping concierges*](https://delight.ai/industries/retail)
**Key AI technology:** Vector databases for semantic search, multi-modal embeddings (searching via images + text), and neural re-ranking algorithms.
### 3. Agentic commerce
After AI search finds the perfect product for a customer, agentic commerce comes next. On the customer’s behalf, AI agents autonomously verify stock via retailer APIs, compare options, and complete the purchase using the customer's authorized payment method. This shifts the retail journey from a shopper interacting with a website to a brand's AI agent interacting with a customer's AI agent, also known as [the agent-to-agent (A2A) economy](https://sendbird.com/blog/ai-agent-to-agent-economy).
This is both an opportunity and a risk. Traffic to US retail sites from generative AI browsers surged 4,700% year-over-year in 2025, and these visitors [spent 32% more time on site](https://business.adobe.com/blog/generative-ai-powered-shopping-rises-with-traffic-to-retail-sites), browsed 10% more pages, and bounced 27% less. But retailers who don't build their own agentic layer (agent-ready architecture) risk losing ownership of customers, becoming mere inventory suppliers to someone else's platform.
**Key AI technology:** Autonomous transaction protocols, secure digital wallets for AI Agents, "headless" commerce APIs for machine-to-machine checkout.
### 4. Conversational commerce and voice AI shopping
Conversational commerce is the crossroads of messaging apps, AI assistants, and faster shopping experiences. It enables customers and brands to interact through text or voice-based conversations across channels like WhatsApp, smart assistants (e.g, Alexa), or AI embedded in branded apps. Conversational commerce is becoming a norm in retail due to its increased convenience, personalization, and accessibility, with [22% of consumers already preferring voice-enabled AI to typing](https://keywordseverywhere.com/blog/voice-search-stats/#:~:text=12.,and%2087%25%20believe%20it's%20faster.&text=13.,say%20they%20use%20voice%20assistants.&text=17.,out%20at%20home%20with%20friends.).
Say a customer asks a retail AI chatbot: "Need a gift for a runner. Under $100. Arrives by Friday." The AI interprets the query, checks inventory and delivery windows, and completes the purchase. [Voice AI](https://delight.ai/learn/voice-ai) enhances this, enabling hands-free interaction for mobile users, and helping retailers scale peak-season support without extra hiring.
**Key AI technology:** Latency-optimized [small language models](https://delight.ai/learn/small-language-models) (SLMs) for edge devices, natural language understanding (NLU), WebSocket-based real-time voice streaming.
### 5. Hyper-personalization and predictive engagement
[Hyper-personalization](https://delight.ai/learn/hyper-personalization) uses machine learning (ML) to target a "segment of one"—delivering the most contextually relevant recommendation or offer for that customer on that specific channel in that moment. Powered by a unified data layer and [AI memory](https://delight.ai/learn/ai-memory), it identifies the “next best action” for each individual based on their historical data *and* live context. More precise targeting and more relevant interactions at scale can drive a [10–15% revenue lift](https://businesschief.com/technology-and-ai/mckinsey-prioritise-personalisation-for-10-15-revenue-lift#:~:text=Research%20shows%20that%20personalisation%20most,market%2C%20it's%20how%20they%20operate.) and up to 30% improvement in marketing efficiency.
Predictive engagement goes further by anticipating behavior. Target [tracks subtle shifts in purchases](https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/) of unscented lotion and supplements to identify second-trimester pregnancies among customers, then delivers personalized offers for strollers and diapers before customers even modify their purchasing. This level of foresight is now table stakes for personalization leaders.
*Our* [*AI agent for retail*](https://delight.ai/industries/retail) *provides hyper-personalized recommendations based on user intent and long-term AI memory*
**Key AI technology:** Real-time feature stores, transformer-based sequence models (for predicting the "next best action"), and federated learning for privacy-compliant data processing.
### 6. AI-enhanced omnichannel experiences
Today's consumers move fluidly between digital and in-store experiences, and agentic AI moves with them. By unifying customer data into one evolving source of truth, AI agents bridge formerly fragmented channels and platforms—enabling consistent, real-time personalization across mobile, desktop, in-store, and post-sale touchpoints without losing context between sessions.
For example, BJ's Wholesale Club deployed an AI agent across its mobile app, website, and in-store kiosks to streamline online and in-store shopping. As it guides shoppers to the right aisles and product pages, the agent delivers location- and context-aware promotions and loyalty program offers—boosting membership signups and digital engagement while improving CX.
**Key AI technology:** Unified customer data platform (CDP), cross-channel orchestrators, edge computing.
### 7. Generative AI content and experiences
Generative AI allows retailers to create product descriptions, marketing copy, personalized emails, and social assets in real time—all tailored to specific segments without adding to headcount. It enables a retailer with 50,000 SKUs to instantly convert its manufacturer specs into compelling, platform-optimized product descriptions. Or, marketing teams can iterate 10x more campaign variations—feeding these “just in time” generative outputs directly into AI-agent-optimized advertising to improve ROI.
Virtual try-ons, AI-generated product visualization, and dynamically generated landing pages can place these generative outputs directly in conversion points. Retailers using generative AI report up to [40% reductions in content production time](https://www.rankscience.com/blog/the-ai-content-trust-gap-solution#:~:text=Why%20does%20AI-assisted%20content,compared%20to%20unedited%20AI%20content.) alongside measurable improvements in engagement.
**Key AI technology:** Multi-agent content orchestrators, [retrieval augmented generation (RAG)](https://sendbird.com/blog/what-is-an-ai-agent/rag), latent diffusion models (LDMs).
### 8. Cart abandonment recovery
Roughly [70% of online shopping carts](https://baymard.com/lists/cart-abandonment-rate) get abandoned in checkout, costing retailers an estimated $18 billion annually. By detecting hesitation signals like mouse movement and slow scrolling in real-time, AI systems can intervene with tailored incentives via retail chatbots, offering free shipping or payment plans to close the sale.
Beyond proactive engagement, generative AI can draft “just in time” follow-ups tailored to individual shoppers. Predictive analytics also scores and segments carts to determine the best course of action to reduce abandonment rates and increase AOV.
*Ecommerce AI agent proactively triggers a discount in checkout to curb cart abandonment*
**Key AI technology:** Generative dynamic creative (that builds unique offers in real-time), intent-based exit prediction ML models, and identify resolution graphs.
### 9. Dynamic pricing and competitive intelligence
Dynamic pricing uses [AI algorithms](https://delight.ai/learn/ai-algorithms) to adjust product prices in real time based on demand signals, competitor pricing, inventory levels, and customer behavior. This more effectively optimizes prices over static methods, helping retailers stay competitive and maximize profits with comprehensive data analytics.
For example, Amazon's pricing algorithm updates millions of prices every 10 minutes, using a multi-agent system of specialized AI agents that track numerous factors to optimize its “Buy Box” price.
**Key AI technology:** [Reinforcement learning (RL)](https://delight.ai/learn/reinforcement-learning) for price elasticity modeling, high-frequency web scrapers, and Bayesian inference models for demand forecasting.
### 10. Demand forecasting and inventory management
Inaccurate demand forecasting is one of retail's most expensive problems; too much of this, not enough of that. [Machine learning](https://delight.ai/learn/machine-learning) models analyze historical sales alongside real-time signals—weather, local events, global trade conditions—to predict what to stock, in what quantity, and where with a precision humans can’t match. McKinsey estimates AI [reduces forecasting errors by 20–50%](https://www.oracle.com/scm/ai-demand-forecasting/) and cuts excess inventory costs by a similar margin.
The result is proactive replenishment that prevents stockouts, reduces overstock, and improves vendor relationships. For instance, Amazon's Supply Chain Optimization Technology (SCOT) AI processes 400 million+ items across 270 time spans, repositioning inventory autonomously when demand shifts to [improve long-term forecasts by 20% regionally](https://www.aboutamazon.com/news/operations/amazon-ai-innovations-delivery-forecasting-robotics#:~:text=Amazon's%20supply%20chain%20has%20a,with%20future%20expansion%20coming%20soon.).
**Key AI technology:** Digital twin simulation of supply chain, causal AI, transformer-based time series models.
### 11. Supply chain optimization and last-mile delivery
In a world where demand shifts overnight and trade conditions change by the week, AI replaces reactive supply chain management with continuously self-optimizing operations. Predictive analytics anticipate disruptions before they occur. Multi-agent systems reroute inventory automatically. Generative AI solves last-mile precision problems that traditional mapping tools struggle with.
Amazon optimizes the last mile with three specialized AI agents. SCOT handles demand forecasting, Wellspring's generative AI shows drivers' exact drop-off points, while a third agentic framework enables warehouse robots to project visual cues to human workers, sorting packages to the correct vehicle faster.
**Key AI technology:** Digital twins simulation, graph neural networks (GNNs) for route optimization, and [computer vision](https://delight.ai/learn/computer-vision) for automated warehouse auditing.
### 12. Visual search and image recognition
AI visual search lets shoppers upload a photo to find matching items. Powered by computer vision and ML, this enhances discovery for products that customers see and want, but struggle to describe. It also shortens the path from inspiration to purchase in categories like fashion, home décor, and accessories. For example, Nordstrom offers a “shop the look” feature in its mobile app, which identifies items in an image by texture, color, or fabric, then suggests complementary items or complete outfits to reduce search abandonment.
Computer vision also extends into physical stores—using image recognition to enable AI-powered shelf monitoring, automatic out-of-stock detection, and grab-and-go checkout that drive operational efficiency.
**Key AI technology:** Visual transformers (ViT), vector embeddings, fine-grained attribute recognition.
### 13. Sustainability and waste reduction
AI helps retailers reduce waste across the entire operation. Demand forecasting prevents overproduction. Route optimization cuts fuel consumption. Packaging AI recommends right-sized materials per order. In-store computer vision triggers automated markdowns on perishables before they expire rather than after. Retailers integrating AI into sustainability operations report [15–20% reductions in waste-related costs](https://community.ibm.com/community/user/discussion/how-ai-powered-waste-reduction-software-is-revolutionizing-retail#:~:text=Retailers%20using%20artificial%20intelligence%20have,mid-to-large%20retailers.), turning operational efficiency into environmental performance.
**Key AI technology:** Product lifecycle analytics, AI-optimized reverse logistics (returns management), transparency tracking.
## Benefits of AI in retail
When effectively integrated, these AI use cases offer retailers a suite of benefits in the near-term while positioning them to lead in the AI-driven future:
- **Better customer experience:** AI scales personalized interactions using real-time data, delivering 24/7 support and better product recommendations that can [boost customer satisfaction by up to 25%](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/next-best-experience-how-ai-can-power-every-customer-interaction).
- **Improved retention and loyalty:** AI makes loyalty feel more personal and sticky, from hyper-personalized loyalty offers to proactive outreach that catches at-risk customers.
- **Greater operational efficiency:** Retailers integrating AI into retail operations report an [average 25% increase in operational efficiency](https://www.martinnewman.co.uk/articles/ai-technology-for-retailers#:~:text=35%25%20reduction%20in%20overall%20logistics,45%25%20improvement%20in%20operational%20efficiency.) from in-store to the warehouse.
- **Enhanced decision-making:** AI turns first- and third-party data into actionable intelligence for human and AI system decision-making.
- **Cost savings:** From support automation to employee agent assist to operational efficiency, [57% of retailers](https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-agent-survey.html#:~:text=If%20your%20company%20is%20like,facing%20cases%20are%20rising%20fast.) adopting AI agents report significant cost savings from lower cost-to-serve.
- **Stronger fraud prevention:** Retailers using AI for fraud detection [reduce fraud losses by up to 30%](https://www.unisco.com/returns/returns-fraud-detection-and-prevention#:~:text=In%20the%20logistics%20sector%2C%20fraudulent,per%20year%20in%20potential%20losses.), without the false positives that disrupt legitimate customers.
- **Higher conversion rates:** From hyper-personalization to AI shopping assistants like Amazon Rufus that [drive 5–25% more revenue](https://www.envive.ai/post/ai-agent-engagement-rate-statistics), AI’s faster, intent-driven journeys drive growth.
- **Margin improvement:** Smarter inventory management, optimized pricing strategies, reduced waste, and lower support costs all directly improve the bottom line.
## Challenges of AI in retail
While [66% of retailers](https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-agent-survey.html) say they are piloting or have already implemented agentic AI, most are stumbling along the path to value. Critical challenges include:
**The readiness gap.** According to the Cisco AI Readiness Index, 98% of organizations report increased pressure to deploy AI, but [only 14% are truly prepared to realize its potential](https://investor.cisco.com/news/news-details/2023/Cisco-Launches-New-Research-Highlighting-Seismic-Gap-in-Companies-Preparedness-for-AI/default.aspx). The lesson: urgency and readiness aren't the same thing.
Most retailers are accumulating technical debt through fragmented deployments, incurring risk from lacking governance, and getting bogged down in inefficiencies from a lack of cultural and operational alignment around technical investments. Those pulling ahead have made readiness a cross-functional, repeatable process that sidesteps the pitfalls of failed AI deployments.
**The agentic architecture investment.** For retailers to both capture the immediate value of agentic AI and position themselves for an AI-first future, they must make a significant investment in data infrastructure, system integration, and AI governance from the jump.
In the age of agentic commerce, deep product catalog integration, real-time inventory awareness, unified customer history, and embedded guardrails are prerequisites. Retailers who skip this foundational work face a compounding risk of stalled deployments as well as potential loss of customer ownership as agentic commerce shifts purchasing decisions away from retail interfaces toward AI agents that don't favor unprepared brands.
- [The new frontier of retail: Agentic AI](#section-anchor-id-0 "The new frontier of retail: Agentic AI")
- [13 AI use cases in retail that will define 2026](#section-anchor-id-1 "13 AI use cases in retail that will define 2026")
- [Benefits of AI in retail](#section-anchor-id-2 "Benefits of AI in retail")
- [Challenges of AI in retail](#section-anchor-id-3 "Challenges of AI in retail")
## FAQs about AI in retail use cases
1. What is agentic AI in retail?
Agentic AI refers to autonomous systems (AI agents) that can perceive, reason, and act to achieve a goal set for them without constant human intervention. Unlike traditional chatbots that simply retrieve information, Agentic AI in retail can execute tasks such as processing a refund, negotiating a discount based on inventory levels, or rerouting a delayed shipment by communicating directly with logistics APIs.
2. How is AI being used in retail in 2026?
In 2026, AI has shifted from fragmented tools to a unified agentic ecosystem. Key uses include agentic commerce (where AI agents shop on behalf of consumers), hyper-personalization (predicting the "next best action" for a segment of one), and autonomous inventory orchestration (using Digital Twins and Causal AI to manage supply chains in real-time). The focus is no longer just on automation, but on proactive, intelligent orchestration of the entire retail value chain.
3. How does agentic commerce change the customer journey?
Agentic Commerce shifts the retail landscape from "human-to-website" to "agent-to-agent." Instead of a customer manually browsing and comparing products, their personal AI agent interacts with a brand's AI agent to verify stock, compare features, and execute the purchase via secure APIs. This requires retailers to optimize their digital presence for AI-readiness, ensuring their product data is accessible and "readable" by autonomous shopping agents.
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Introducing role-based access control (RBAC) for AI agents
Source: https://delight.ai/blog/ai-agent/ai-agent-role-based-access-control
## Introducing role-based access control (RBAC) for AI agents
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
## Why does AI agent RBAC matter?
AI agent role-based access control (RBAC) is a permissioning system that lets enterprises assign granular access, such as knowledge-base editing, workflow deployment, or read-only reporting, to specific roles across AI agent operations. As AI agents move beyond experimentation into real customer interactions, more teams get involved—product, support, engineering, operations—and each brings their own needs and risks.
Without AI role-based access control (AI RBAC), anyone can edit your AI agent’s knowledge base or agentic workflow (AI SOP), potentially leading to inconsistent behavior, off-brand experiences, or compliance violations.
AI RBAC provides a structured way to manage access so the right people can do their part, and nothing more. By allowing you to scale AI agents without the chaos, bottlenecks, or finger-pointing, RBAC helps teams collaborate effectively to maximize the quality, safety, and accountability of AI systems.
## The challenge: Agentic AI governance in enterprise operations
AI agents make decisions autonomously, touch sensitive data, and serve as the frontline for a growing number of enterprise functions—from customer service to fraud detection to employee support.
Effectively governing these agentic capabilities requires collaboration across multiple teams, from design to training to deployment. As a result, agents often evolve rapidly through continuous iteration.
Without a scalable permissioning structure for AI agents, enterprises face not just chaos but real risk from:
- **Unauthorized access** to sensitive AI agent logic, AI model parameters, and training content
- **Untracked changes** to agent knowledge and tools
- **Risky AI deployments** that bypass review
- **Inefficient workflows** and bottlenecks
- **Confusion across teams** over who owns what
These risks aren't theoretical: 53% of organizations report their AI agents have exceeded intended permissions, according to an [April 2026 Cloud Security Alliance survey](https://cloudsecurityalliance.org/press-releases/2026/04/16/more-than-half-of-organizations-experience-ai-agent-scope-violations-cloud-security-alliance-study-finds).
AI governance is often overlooked by fast-moving teams. However, access controls are essential to turning agentic AI projects into successful, sustainable AI customer service operations. For example:
- **Regional support teams** are assigned to specific agents but can’t access others
- **Product teams** have full access to development environments, but only team leads can promote changes to production
- **Ops teams** can review flagged messages for hallucinations and compliance, but can’t edit AI SOPs (actionbooks)
- **Compliance teams** get read-only access to AI agent performance metrics and outputs
To scale AI operations without sacrificing oversight and control of AI agents, **enterprises need a scalable way to manage role-based access for everyone involved at a targeted level.**
## Powering safe and scalable AI collaboration with AI RBAC
Delight.ai launched role-based access controls and permissions for [AI support agents](https://delight.ai/ai-customer-service). This way, **multiple teams can work in parallel without confusion, bottlenecks, and avoidable risks**.
From inside the AI agent dashboard, you can now **define exactly who can access what** to support effective AI governance, down to specific agents, features, and functions.
This allows teams to:
- **Assign custom roles** for different team types (e.g. support lead vs admin)
- **Grant specific permission sets** across your digital workforce
- **Safely delegate work** while preserving platform, model, and data integrity
- **Restrict access to specific features** (knowledge sources, AI SOPs, or testing centers)
- **Separate development and production environments**
- **Grant access by region or use case**
Example: A permission set for a “Compliance Reviewer” might allow review of flagged messages and read-only access to AI agent conversations, but block access to editing tools and the deployment environment.
Designed to support multi-team AI operations, AI agent role-based access control makes it fast and easy for team leads and stakeholders to manage access at a detailed level. When **everyone works efficiently within clearly defined guardrails, you never have to choose between speed and safety as you scale AI operations**.
## How it works: Define access in one of two ways
Delight's AI agent RBAC separates access into two different layers:
| Layer | Definition | Scope of access |
|---|---|---|
| Role | A predefined label or grouping that represents a job function or user type | Broad — may cover access across multiple areas or systems |
| Permission set | The specific actions a user is allowed to perform within a system | Granular — defines what a user can do with specific AI agents and specific features |
Each member is assigned a role that defines their level of access to Delight AI agents and other products (like [AI copilot](https://delight.ai/desk/ai-copilot)). For more exact control over AI agent features, you can create a permission set that manages access to specific agents and their specific functionalities.
Permissions are the specific tools a person can use. A role is the blueprint that says which tools are needed for which job. Assign someone the role, and you’ve automatically handed them the right tools for the tasks they’re responsible for.
Here’s an example of a permission set and the custom roles that can be associated with it:
### Permission set:
- View data on AI agent performance
- Edit knowledge base
- Deploy actionbook workflows (AI SOPs)
- Access the AI agent test center
### Role:
- **AI Admin** → all permissions
- **AI QA analyst** → view data and test only
- **AI support team lead** → View performance data, edit knowledge base, deploy AI SOPs, access test center
## How do you set up roles and permissions in the AI agent dashboard?
We recommend you start by creating custom permission sets, and then create roles based on those sets. This way, permission sets are modular, reusable, and easy to manage at scale, and can be assigned to certain roles, which align with your broader organizational designations.
### Step 1: Create a permission set
Inside the AI agent dashboard, navigate to Organization Settings > Roles > Permission Set for AI Agent, then click Create Permission Set +. From here, you can define what specific actions users can take in the AI agent environment.
To create a new permission set, fill out the following fields, then click **Save**:
- **Name:** Provide a unique title for the permission set.
- **Description:** Write a short sentence explaining what this permission set permits.
- **Permissions:** Select functionalities accessible with this permission set
- **AI agent access:** Select the AI agent(s) that can be managed with this set
By creating a custom permission set, you customize which members can do what under what conditions. Next, you’ll create one or more roles that use that permission set.
### Step 2: Create a role, and assign a permission set to it
Once you've created a permission set for AI agents, you’ll create a new role to assign the set to. In the dashboard, go to Organization Settings > Roles > Create role+.
To create a new role, fill out the following fields, then hit **Save:**
- **Name:** Provide a unique title for the role.
- **Description:** Write a short sentence explaining what this role permits.
- **Permissions:** Select products and their functionalities accessible in this role.
- **AI agent permission set:** Select permission sets granted to a member with this role.
With both roles and permissions defined, you have a tailored access model that reflects your operational structure. You can now assign a custom role to any dashboard member, making it easy to **give everyone the exact right access and permissions they need to collaborate safely and effectively.**
## Ready to strengthen AI governance and collaboration with AI RBAC?
With role-based access control, AI leaders can define precise access for each contributor, ensuring everyone has exactly the access they need, and nothing they don’t.
Role-based access control gives teams the freedom to move quickly with clear guardrails. Support, product, QA, engineering, and compliance teams can all collaborate without stepping on each other, while AI agents stay accurate, secure, and on-brand.
This is effective AI governance in action. AI agent RBAC is now live on delight.ai's [AI agent platform](https://delight.ai/ai-agent), and part of our AI [Trust OS](https://delight.ai/trust-os)—a robust suite of trust features for ensuring AI control, AI transparency, and AI responsibility.
- [Why does AI agent RBAC matter?](#section-anchor-id-0 "Why does AI agent RBAC matter?")
- [The challenge: Agentic AI governance in enterprise operations](#section-anchor-id-1 "The challenge: Agentic AI governance in enterprise operations")
- [Powering safe and scalable AI collaboration with AI RBAC](#section-anchor-id-2 "Powering safe and scalable AI collaboration with AI RBAC")
- [How it works: Define access in one of two ways](#section-anchor-id-3 "How it works: Define access in one of two ways")
- [How do you set up roles and permissions in the AI agent dashboard?](#section-anchor-id-4 "How do you set up roles and permissions in the AI agent dashboard?")
- [Ready to strengthen AI governance and collaboration with AI RBAC?](#section-anchor-id-5 "Ready to strengthen AI governance and collaboration with AI RBAC?")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Frequently asked questions
Common questions about AI agent role-based access control (RBAC).
**What is AI agent RBAC?**
AI agent role-based access control (RBAC) is a permissioning system that lets enterprises assign granular access, such as knowledge-base editing, workflow deployment, or read-only reporting, to specific roles across AI agent operations.
**How is AI agent RBAC different from standard team permissions?**
Standard permissions usually apply one access level across an entire tool. AI agent RBAC goes further: it scopes access to specific AI agents, specific functions within them (editing a knowledge base vs. deploying a workflow), and specific environments (development vs. production), so different teams can work in parallel safely.
**Does Delight.ai support region-based or agent-specific access control?**
Delight.ai's AI agent RBAC supports both region-based and agent-specific access control. Permission sets can be scoped to specific AI agent(s), and access can be granted by region or use case, so a team can be assigned to manage one agent without touching others.
## Keep reading
[View more](/blog "View more")
[ Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[ Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)
[ Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[ The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[View more](/blog "View more")
## 15 AI risks to your business and how to address them
Source: https://delight.ai/blog/ai-agent/ai-risks-for-businesses
## 15 AI risks to your business and how to address them
[](https://delight.ai/author/ian-heinig)
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
AI promises a wealth of opportunities for businesses, but realizing its transformative benefits also means addressing its potential risks.
Today, [72%](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) of organizations are using AI in some form, yet [only 20%](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year) have a formal AI risk strategy in place. As a result, stories of AI risks and costly missteps—from data privacy breaches to ethical lapses—are increasingly in the news. Without proper safeguards for AI, businesses face an increased risk of cybersecurity threats, regulatory violations, legal liability, and a loss of public trust.
For IT leaders and stakeholders, understanding these risks is vital to turning AI projects into safe, successful, and scalable solutions. It means tackling questions like:
- How do you systematically identify and mitigate the risks associated with AI?
- What guardrails can ensure your AI systems remain safe, ethical, and compliant over time?
This article explores **the 15 greatest risks of AI to businesses and how to address them** using effective AI risk management frameworks, AI governance, and AI risk management tools.
Specifically, we’ll cover:
- [Ethical and legal risks of AI](#ethical-and-legal-risks-of-ai)
- [Data risks of AI](#data-risks-of-ai)
- [Operational risks of AI](#operational-risks-of-ai)
- [Top AI risk management frameworks](#3-top-ai-risk-management-frameworks)
- [Leading AI risk management tools](#the-top-ai-risk-management-tools)
## Ethical and legal risks of AI
The ethical and legal risks of AI are among the most pressing for enterprises. With AI set to become the new IT bedrock for enterprises, it must be safe and legal—both for customers and companies to use—or the risks may outweigh the rewards.
While [most executives](https://ibm.co/ai-ethics) consider AI ethics important, [only 20%](https://ibm.co/ai-ethics) say their organization’s practices on ethical AI currently match these stated principles and values.
Here are five major pitfalls around AI ethics and legality and how to avoid them.
### 1. Bias
AI algorithms—or the rules AI systems use to make decisions—can inherit biases from the data they were trained on. These learned biases can be replicated across the AI lifecycle, leading not only to inaccurate outputs, but also outcomes that are potentially harmful, unfair, or discriminatory.
For example, iTutorGroup, which offers English language classes to Chinese students, [recently settled an age discrimination hiring lawsuit](https://www.reuters.com/legal/tutoring-firm-settles-us-agencys-first-bias-lawsuit-involving-ai-software-2023-08-10/) after its AI-driven tutor software rejected female applicants over the age of 55. This makes mitigating AI bias a key step in avoiding financial loss, ensuring regulatory compliance, avoiding reputational damage, and maintaining user trust.
*How to address:*
- Establish an AI governance framework that addresses bias mitigation as part of a comprehensive AI strategy.
- Mitigate bias proactively by using representative datasets, evaluating fairness metrics, and building diverse AI teams.
- Identify biased outputs by continuously monitoring AI model outputs, performing bias audits on data, post-deployment impact assessments, and model retraining.
- Use bias detection tools like [AI Fairness 360 (IBM)](https://research.ibm.com/blog/ai-fairness-360), [Fairlearn (Microsoft)](https://fairlearn.org/), or [Google’s What-If Tool](https://pair-code.github.io/what-if-tool/).
### 2. Lack of transparency and explainability
AI algorithms and models often function as “black boxes,” making it difficult to understand their reasoning and how they arrived at a certain output. This lack of transparency and explainability—or the ability to justify AI’s decisions—complicates efforts to take proactive measures that mitigate risk.
Businesses that fail to be transparent or accountable for their AI systems are at risk of losing public trust, especially in high-stakes areas like [AI in healthcare](https://delight.ai/industries/healthcare) or [AI in finance](https://delight.ai/industries/financial-services). For example, public scrutiny over platform algorithms can lead to bad press and regulatory investigations.
*How to address:*
- Adopt [explainable AI techniques](https://medium.com/@dallanoce.fd/explainable-ai-a-complete-summary-of-the-main-methods-a28f9ab132f7) such as continuous model evaluation.
- Assess the interpretability of AI results by auditing and reviewing AI outputs based on explainability standards within a clear AI governance framework.
- Use explainable AI tools like [LIME](https://c3.ai/glossary/data-science/lime-local-interpretable-model-agnostic-explanations/), [SHAP](https://shap.readthedocs.io/en/latest/), or [AI Explainability 360](https://aix360.res.ibm.com/) (IBM).
**Learn more:** [How to make AI agents more transparent by reviewing their logs](https://www.linkedin.com/posts/wonbaesuh_ai-agents-observability-ugcPost-7298123850913853440-4NS4?utm_medium=ios_app&rcm=ACoAABWgG7gBtkS1ZZwEnCVbcwLj35FGJS3lZc0&utm_source=social_share_send&utm_campaign=copy_link)
### 3. Lack of accountability
As AI systems increasingly influence decision-making and real-life, it begs the question—who's responsible when AI systems make mistakes? Who's accountable when AI decisions cause harm?
Such questions are at the heart of issues with self-driving cars and facial recognition technologies. Currently, regulations around these questions are getting worked out. But in the meantime, at risk of legal disputes, loss of trust, and reputational damage, businesses must ensure there’s someone responsible for how these decisions are made and their knock-on effects.
*How to address:*
- Maintain detailed logs and audit trails to facilitate reviews of AI’s behavior and decisions.
- Implement [role-based access controls for AI](https://delight.ai/blog/ai-agent/ai-agent-role-based-access-control) to ensure accountability.
- Document decisions during AI design, deployment, and testing for inspection if needed.
- Build accountability into AI using risk frameworks such as: [EU Ethics Guidelines for Trustworthy AI](https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai), [OECD AI Principles](https://oecd.ai/en/ai-principles), or [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework).
### 4. Regulatory non-compliance
Businesses face steep fines and legal penalties should they fail to comply with government regulations such as GDPR or sector-specific guidelines. AI regulations are evolving fast and vary by region, requiring organizations to stay informed and compliant.
*How to address:*
- Assign a legal team to track, interpret, and adapt to the evolving compliance landscape.
- Align your AI governance strategy with the above frameworks.
- Maintain standardized documentation and audit trails across teams.
### 5. Intellectual property (IP) infringement
Generative AI can produce art, music, text, and code that closely mimics the work of individual people. Because these AI systems are often trained on large datasets scraped from the web, they may ingest copyrighted content without proper licensing or attribution.
IP issues around AI-generated content are still evolving. This legal grey area leads to potential liabilities for enterprises. For example, [Getty Images sued the AI art generator Stable Diffusion](https://www.theverge.com/2023/2/6/23587393/ai-art-copyright-lawsuit-getty-images-stable-diffusion) for copying millions of images from its database to train models.
*How to address:*
- Implement checks that monitor AI training data for copyrighted content.
- Be cautious about inputting sensitive IP into AI systems.
- Audit AI model outputs to ensure compliance with IP regulations and avoid risk exposure.
## Data risks of AI
The datasets that AI systems rely on can be vulnerable to cyberattacks, tampering, and data breaches. One study found that [96%](https://www.ibm.com/thought-leadership/institute-business-value/report/securing-generative-ai) of business leaders believe that adopting generative AI makes a security breach more likely, yet [only 24% have secured their current generative AI projects](https://www.ibm.com/thought-leadership/institute-business-value/report/securing-generative-ai).
As with other technology, businesses can mitigate the data risks of AI by protecting data integrity, security and privacy throughout the AI lifecycle.
Here are the three biggest risks around AI and data, and how to address them.
### 6. Data security
By breaching the datasets that power AI technologies, bad actors can launch cyberattacks on enterprises. This can result in unauthorized access, compromised confidentiality, data loss, and reputational damage—especially in regulated industries such as [AI-driven healthcare](https://delight.ai/resources/mindmakers-podcast-pelu-tran).
AI tools can also be manipulated to clone voices, create fake identities, and launch phishing emails that put traditional security measures to the test. Since 2022, there’s been a [967% increase in credible phishing attacks](https://www.cnbc.com/2023/11/28/ai-like-chatgpt-is-creating-huge-increase-in-malicious-phishing-email.html) that use LLMs to create convincing phishing emails that bypass traditional filters.
*How to address:*
- Establish an AI security and safety strategy.
- Conduct threat modeling and risk assessments to find security gaps in AI environments.
- Assess vulnerabilities in AI models using adversarial testing.
- Adopt a secure-by-design approach for the AI lifecycle to safeguard AI training data.
- Consider vertical AI agents for cybersecurity that enable continuous monitoring and threat-response.
### 7. Data privacy
AI development environments can leak sensitive data if not secured properly. Large language models (LLMs) are trained on massive volumes of data, often sourced by scraping websites without users’ consent.
If training data is exposed and contains sensitive or personally identifiable information (PII), enterprises could face regulatory penalties, user backlash, and loss of public trust. AI systems that handle customers’ personal data can be vulnerable to privacy breaches, leading to legal issues.
*How to address:*
- Inform users how data is collected, stored, and used.
- Provide opt-out mechanisms.
- Consider using synthetic data to preserve privacy.
- Use [data minimization](https://www.kiteworks.com/risk-compliance-glossary/data-minimization/) and [data anonymization](https://www.k2view.com/what-is-data-anonymization/) techniques to mask PII.
### 8. Data integrity
AI models and systems are only as reliable as the data they’re trained on. Using outdated, unclean, corrupted, or incomplete data can lead to inaccurate outputs, false positives, or poor decision-making that puts the enterprise at risk of legal or financial loss.
Zillow, for instance, [used an AI model to predict home values](https://www.cnn.com/2021/11/09/tech/zillow-ibuying-home-zestimate/index.html#:~:text=For%20Zillow%2C%20one%20of%20the,property%20records%2C%20homeowner-submitted%20details) in its iBuying program. The model’s training data didn’t account for sudden market shifts, overestimating prices and resulting in a $500 million loss.
*How to address:*
- Vet third-party data sources for quality and documentation.
- Audit AI models after deployment to assess data quality and impact.
- Regularly retrain models using the latest data and reevaluate model performance for accuracy.
## Operational risks of AI
Like any technology, AI models and systems are susceptible to operational risks. If left unaddressed, these risks can lead to system failures, cybersecurity breaches, and costly decisions.
Here are the seven major operational AI risks, and how to address them.
### 9. Misinformation and manipulation
Bad actors can exploit AI technologies to spread misinformation, create deepfakes, and impersonate individuals. For example, a finance worker was [tricked into wiring $25 million to scammers](https://www.cnn.com/2024/05/16/tech/arup-deepfake-scam-loss-hong-kong-intl-hnk/index.html#:~:text=Hong%20Kong%20police%20said%20in,to%20be%20deepfake%20re-creations.) after joining a Zoom call with what he thought were real executives—but all were deepfake AI-generated replicas.
AI hallucinations—or when AI confidently posits error as truth—also open the door to misinformation and manipulation. If spread across social media, AI-generated falsehoods can harass individuals, create liabilities, and damage the business's reputation.
*How to address:*
- Train users and employees how to spot misinformation and disinformation.
- Validate and fact-check AI outputs before taking action.
- Prioritize high-quality data and rigorous model testing to ensure accurate outputs.
- Use human oversight to detect hallucinations and errors.
- Follow latest research in deepfake detection and media authentication.
### 10. Model risks
Bad actors can target AI models, compromising the model’s integrity by manipulating architecture, weights, LLM parameters, or other core components that determine performance and behavior. For example, hackers could tamper with how an AI model classifies objects, causing unsafe outcomes in self-driving systems.
*How to address:*
- Monitor models for unexpected changes in performance using machine learning observability tools.
- Safeguard models at rest with containerization and isolated environments.
- Conduct adversarial testing to check for model vulnerabilities.
### 11. Model drift
AI model performance can degrade over time as the relationship between its training data and the real world changes. This process, known as model drift, results in outputs that are potentially less accurate, less relevant, or even harmful.
Two main types:
- **Data drift:** The input data distribution changes, but the task stays the same (e.g., phrasing of search queries changes over time).
- **Concept drift:** The relationship between inputs and outcomes changes (e.g., customer value signals change).
*How to address:*
- Continuously track model performance for drift using AI drift detection and observability tools.
- Retrain models regularly to ensure accurate outputs based on fresh data.
- Document model drift incidents to inform team learning, model updates, and maintain AI transparency.
### 12. Sustainability and scalability
AI systems are new and complex, so moving from experimentation to operational AI that delivers long-term business value is challenging. In a Boston Consulting Group study, [74%](https://www.bcg.com/publications/2024/wheres-value-in-ai) of respondents said they struggle to achieve and scale value from AI initiatives.
If scalability and sustainability aren’t part of a long-term AI strategy, enterprises risk inconsistent AI performance, elevated operating costs, and lack of leadership buy-in.
*How to address:*
- Invest strategically in a few high-priority AI opportunities then scale to maximize AI’s value.
- Focus on integrating AI into cost reduction and revenue generation efforts to offset costs.
- Reshape core business processes and support functions as part of a dedicated AI transformation.
### 13. Integration challenges
Integrating AI with existing infrastructure is complex and resource-intensive. According to Deloitte, [47% of AI adopters](https://www.deloitte.com/global/en/about/press-room/gen-ai-survey.html) cite integration with existing systems as a top barrier.
Common issues include compatibility, data silos, and system interoperability, which can lead to disruptions and increased maintenance.
*How to address:*
- Invest in scalable, interoperable architecture for AI systems.
- Develop AI in cross-functional teams that include data scientists and machine learning engineers.
- Avoid vendor lock-in by using open-source, modular AI when possible.
### 14. Scarcity of AI talent
There's a shortage of skilled personnel to design, deploy, and manage AI systems at scale. Gartner found [56%](https://www.gartner.com/en/newsroom/press-releases/2021-09-13-gartner-survey-reveals-talent-shortages-as-biggest-barrier-to-emerging-technologies-adoption) of organizations cited a lack of AI talent as a major barrier.
This can lead to stalled projects, wasted investments, and reliance on costly third-party vendors.
*How to address:*
- Upskill and reskill employees with training programs on AI, machine learning, and data science.
- Recruit AI talent with competitive compensation, flexible work, and clear career paths.
- Partner with AI experts and consultants to accelerate projects and transfer knowledge before bringing maintenance in-house.
### 15. Lack of adoption
Even well-integrated AI systems will fail if users don’t understand or trust them. Employees may fear job displacement, misinterpret AI-driven roles, or distrust AI decisions, undermining adoption and ROI.
AI is more than a tech shift—it’s a people shift. Without change management, organizations risk deploying tools teams don’t use or trust.
*How to address:*
- Involve stakeholders early, framing AI tools as a way to enhance roles instead of replacing them.
- Explain the vision for AI, emphasizing opportunities for upskilling to diminish resistance.
- Invest in training and enablement to help employees adapt and realize the value of AI workflows.
## What is AI risk management?
AI risk management is the process of systematically identifying, assessing, and mitigating the potential risks associated with AI technology to ensure safe and ethical use. It aims to minimize AI's negative impacts and maximize its benefits while fostering trust among customers, stakeholders, and regulators.
Key aspects:
- **Proactive approach:** Anticipating and preventing AI risks rather than reacting.
- **Systematic process:** Structured identification, analysis, and remediation of risks across the AI lifecycle.
- **Focus on trustworthiness:** Ensuring AI systems are safe, reliable, fair, and accountable.
- **Comprehensive:** Accounting for ethical, security, and operational risks.
Addressing AI risks early helps foster a culture of innovation while minimizing costly missteps and ensuring AI systems are reliable and competitive.
## 3 top AI risk management frameworks
AI risk frameworks are playbooks for managing risks across the AI lifecycle. The most trusted frameworks include:
- **NIST AI Risk Management Framework:** The National Institute of Standards and Technology’s [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) (benchmark standard, four-stage approach).
- **The EU AI Act:** [The EU Artificial Intelligence Act](https://artificialintelligenceact.eu/), a regulatory law governing AI in the European Union.
- **ISO/IEC standards:** ISO and IEC standards for various AI risk management aspects ([ISO standard overview](https://www.iso.org/standard/77304.html)).
## The top AI risk management tools
If you’re ready to mitigate AI risks, a growing set of tools can help identify, assess, and mitigate issues:
- Risk assessment & compliance: [RiskWatch](https://www.riskwatch.com/), [LogicGate](https://www.logicgate.com/)
- Cybersecurity & vulnerability: [RiskSense](https://www.ivanti.com/company/history/risksense), [Darktrace](https://www.darktrace.com/)
- Third-party & supply chain: [CyberGRX](https://cloud.google.com/security/compliance/cybergrx), [Prewave](https://www.prewave.com/)
- Financial risk & fraud: [Previse](https://previse.co/en-us/), [Quantifind](https://www.quantifind.com/)
- Incident management & compliance reporting: [Resolver](https://www.resolver.com/), [Compliance.ai](http://compliance.ai)
## AI risks: Inevitable but not intractable
As adoption accelerates, so does the need for thoughtful governance, ethical safeguards, and proactive risk management. By recognizing AI risks early, business leaders can build robust AI risk strategies—allowing them to harness AI’s potential while minimizing liabilities.
If you’re looking for help with creating your AI risk strategy, delight.ai can help. Our team of AI experts includes machine learning engineers and data scientists ready to help enterprises craft and deploy an effective AI risk strategy at scale.
Our [AI agent platform](https://delight.ai/ai-agent) makes it easy to [build AI agents](https://delight.ai/builder) for cybersecurity and risk on a foundation of [enterprise-grade infrastructure](https://sendbird.com/blog/messaging-infrastructure-ai-agents) that ensures performance, security, compliance, and scalability.
[Contact our team](https://delight.ai/contact-sales) of AI experts to get started.
- [Ethical and legal risks of AI](#ethical-and-legal-risks-of-ai "Ethical and legal risks of AI")
- [Data risks of AI](#data-risks-of-ai "Data risks of AI")
- [Operational risks of AI](#operational-risks-of-ai "Operational risks of AI")
- [What is AI risk management?](#what-is-ai-risk-management "What is AI risk management?")
- [3 top AI risk management frameworks](#3-top-ai-risk-management-frameworks "3 top AI risk management frameworks")
- [The top AI risk management tools](#the-top-ai-risk-management-tools "The top AI risk management tools")
- [AI risks: Inevitable but not intractable](#ai-risks-inevitable-but-not-intractable "AI risks: Inevitable but not intractable")
## Keep reading
[View more](/blog "View more")
[](https://delight.ai/blog/product/delight-ai-the-next-chapter-of-sendbird) [Delight.ai — The next chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)
[](https://delight.ai/blog/product/for-you-conversations) [How For You Conversations make every interaction feel personal](/blog/product/for-you-conversations)
[](https://delight.ai/blog/product/introducing-agent-memory-platform) [Introducing AMP: The Agent Memory Platform](/blog/product/introducing-agent-memory-platform)
[](https://delight.ai/blog/product/connection-starts-with-omnipresence) [The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[View more](/blog "View more")
## Introducing the localized AI agent by delight.ai
Source: https://delight.ai/blog/ai-agent/localized-ai-agents-for-multilingual-customer-service
## Introducing the localized AI agent by delight.ai
[
Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
### Can you serve all global customers with a single AI agent?
For global enterprises that want to scale AI agents across markets, one challenge looms large:
**How do you maintain fast, accurate, and personalized AI support across dozens of languages, countries, and contexts—without multiplying agent complexity?**
Many AI agents on the market treat localization as a simple translation layer—just switch the language and call it done. But enterprises know better.
Operating across countries means navigating a web of support policies, regulations, customer expectations, and workflows—the complexity of which stops most agents short. These complexities impact more than the user experience—they also shape how support teams create, manage, and scale [AI customer service](https://delight.ai/ai-customer-service) across regions.
Delight.ai is solving this AI agent challenge with a **new localized AI agent.** Our AI agent for customer service now **dynamically adapts to any language, country, and context** using an advanced multi-locale AI framework. This flexibility enables a new level of agent performance and control from right inside your AI agent dashboard.
### Multilingual AI customer service: What’s holding enterprises back?
For global enterprises, scalable multilingual support is a must-have. While some companies opt to [build an AI agent](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent) for each locale, this creates a costly, siloed support structure where each agent must be configured, evaluated, and monitored independently—which doesn’t streamline operations or cut costs.
The problem isn’t translation. Most AI agent architectures aren’t built to handle the complexity of global support: multiple languages, fragmented knowledge sources, and locale-specific workflows.
If agents can’t dynamically retrieve the right content for the right context, teams end up dealing with issues like:
- Inconsistent answers and user experiences
- Ballooning AI agent maintenance costs
- Duplicate work across agents for different locales
- Slow rollout for new markets or policies
### Introducing multi-locale, multi-language support for delight.ai AI agents
Delight.ai has developed a powerful new AI agent architecture built for multilingual, multi-regional support. This architecture enables a single agent to access and act on the most appropriate language and knowledge for every country, language, and user context—no matter how complex your support landscape.
#### How does it work?
#### Configurable, context-aware AI agent design
The key to this seamlessly localized experience is our new **context object and configuration system**, which powers the behavior of our [AI concierges](https://delight.ai). It allows users to define language preference orders by country, configure country-specific AI SOPs, and reuse global content—all from the delight.ai AI agent dashboard.
Thanks to this configuration system, the AI agent can now select the most appropriate knowledge base and actionbook (AI SOP) based on the user’s country, language, or user context. To enable the agent’s selection, every knowledge source or AI agent actionbook can be labeled as:
- **Global:** Used universally across markets
- **Country-specific:** Assigned to one or more regions
*Delight.ai customer service AI agent dashboard settings for languages*
The agent can now dynamically select the right knowledge and workflows based on the setCountry metadata passed from the client. With this automatic routing process in place, there’s no need to manually manage logic across agents. Most importantly, every customer will receive a support experience in their native tongue from the start.
*Example: If a user’s country is set to Korea, the agent will automatically refer to global resources as well as Korea-specific resources to generate its responses.*
#### What if the user’s language changes during conversation?
Conversations can shift languages in real life. This is why Delight's AI agent can detect these language changes in real time to continue the conversation in any new language—while retaining memory, context, and tone from before. This flexibility helps ensure a customer experience that’s seamless, satisfying, and CSAT-friendly from start to finish.
#### How do you ensure agent performance?
Localized support hinges on delivering the right information to the right user in the right language. That’s why delight.ai's AI agent architecture also uses retrieval-augmented generation (RAG) with country- and language-aware filters to ensure it performs with accuracy, even in multi-market environments where coverage is incomplete or evolving.
#### How does it work?
When you assign a knowledge source to specific locales or languages in the agent dashboard, you’re creating a filter that guides the agent’s retrieval process. This way, if the agent runs into data retrieval issues (like it can’t determine the right knowledge base), **Delight's fallback logic** kicks:
- The agent **prioritizes** fully localized content for the user's language and country.
- If unavailable, it **falls back** to a default language (e.g., English) while maintaining contextual relevance.
- It deliberately **avoids retrieving** content from unrelated markets or unsupported locales.
With RAG serving as a fallback, your localized AI agent only pulls the most region-optimized content. The result is a reliable localized experience, even if there’s an issue at the data layer.
### The customer service benefits of localized AI support agents
The Delight [AI agent platform](https://delight.ai/ai-agent) offers a fluid, natural multilingual support experience to each customer—no human intervention needed. Our multi-locale AI support agent helps you:
- **Lower operational overhead:** Manage fewer agents more easily across your reusable knowledge sources and agentic AI workflows.
- **Expand faster:** Enter new markets quickly by layering country- or language-specific knowledge on top of existing [AI concierges](https://delight.ai).
- **Ensure consistency:** Maintain shared agent SOPs globally while allowing for local nuance and adjustment.
- **Boost accuracy:** Prevent agents from using the wrong playbooks or answering in the wrong language to boost agent performance and CSAT.
With delight.ai, **you only need to deploy a single localized AI agent to deliver an authentic, human-like customer experience in every market.** Our AI agents now function as configurable parts of one unified AI agent platform with enterprise-grade controls and customizability.
### Omnichannel AI support, localized
The best customer experiences are both global and local. Delight.ai's localized [AI customer service agent](https://delight.ai/ai-customer-service) adapts to each user’s location, language, and assigned content coverage dynamically, ensuring **every interaction is locally accurate, globally consistent, and operationally scalable.**
**TL;DR:** Look for AI agents that have the intelligence *and* the flexibility to operate up to brand standards in any market or context imaginable. Since your business doesn’t operate in one language, one policy, or one workflow, your AI support shouldn’t either.
Features like delight.ai's multi-locale support are part of an evolving suite of user-friendly controls for [AI customer care](https://delight.ai/ai-customer-service) that also ensure enterprise-grade AI transparency, AI security, and AI compliance—from right within the Delight AI agent dashboard.
Localized AI agents are now available in the delight.ai AI agent dashboard.
[Contact sales](https://delight.ai/contact-sales) or your CSM to learn more.
- [Can you serve all global customers with a single AI agent?](#section-anchor-id-0 "Can you serve all global customers with a single AI agent?")
- [Multilingual AI customer service: What’s holding enterprises back?](#section-anchor-id-1 "Multilingual AI customer service: What’s holding enterprises back?")
- [Introducing multi-locale, multi-language support for delight.ai AI agents](#section-anchor-id-2 "Introducing multi-locale, multi-language support for delight.ai AI agents")
- [The customer service benefits of localized AI support agents](#section-anchor-id-3 "The customer service benefits of localized AI support agents")
- [Omnichannel AI support, localized](#section-anchor-id-4 "Omnichannel AI support, localized")
### Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Chatbot vs conversational AI: What is the difference?
Source: https://delight.ai/blog/ai-agent/difference-between-chatbot-and-conversational-ai
## Chatbot vs conversational AI: What is the difference?
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai](https://delight.ai/author/cheryl-chai)
March 23 2026
### Overview
In today's rapidly evolving technological landscape, chatbots and conversational AI platforms have become increasingly prevalent. These solutions enhance customer service experiences and streamline communications, yet many people are confused about the difference between chatbots and conversational AI. This article defines both, explores key differences, examples, and guidance for choosing the right approach.
### Understanding the basics: Defining chatbots and conversational AI
Chatbots (chatterbots or bots) are computer programs that simulate human conversation. Traditional chatbots use pre-programmed responses keyed to specific keywords or phrases. Early systems were rule-based with limited inputs; modern chatbots can be more sophisticated but often remain focused on scripted interactions.
Conversational AI platforms encompass chatbots but go further: they leverage natural language processing (NLP) and machine learning to understand intent, context, and sentiment, and to generate human-like responses. Machine learning enables systems to learn from interactions and improve over time.
With NLU/NLG and machine learning, conversational AI delivers more human-like experiences, personalizing responses and handling complex queries. Applications span customer service, healthcare, fintech, education, and more — for example, appointment scheduling in healthcare or co-pilot analytics in finance.
### What is conversational AI?
Conversational AI transforms machine–human interaction using two core components: natural language understanding (NLU) and natural language generation (NLG). NLU interprets user input; NLG constructs fluent, context-aware replies.
Key traits:
- Dynamic learning: systems improve from interaction data using machine learning.
- Context awareness: conversations consider prior context, user preferences, and sentiment.
- Personalization: responses can be tailored by location, time, and user history.
Conversational AI is widely applicable across sectors and continues to evolve, promising more intuitive and contextually relevant interactions.
### What is a chatbot?
Chatbots automate customer interactions to provide instant answers to common queries. They are commonly deployed on websites, messaging apps, and social channels for 24/7 engagement.
Strengths:
- Fast, automated responses for routine tasks
- Reliable handling of high volumes of repetitive inquiries
- Often lower initial investment than full conversational AI platforms
Limitations:
- May rely on predefined scripts and keyword matching
- Can struggle with ambiguous, complex, or multi-turn conversations
Modern AI chatbots blend rule-based flows with machine learning components for richer interactions, including product recommendations, support, and 24/7 availability.
### What is an example of a conversational AI bot?
Here are prominent examples of conversational AI in action.
#### 1. The Familiar Voice: Siri, Alexa, and Google Assistant
These voice assistants are widely used conversational AI agents on smartphones, smart speakers, and other devices. They perform tasks like setting alarms, answering questions, controlling smart home devices, and more.
#### 2. Conversational AI applications beyond chatbots
Educational bots (e.g., Duolingo's bots) help users practice languages. Mental-health chatbots provide emotional support and triage. Across industries, conversational AI supports learning, therapy, and transactional assistance.
### Chatbot vs conversational AI: Which one is more effective for customer service?
Effectiveness depends on goals and complexity:
- Chatbots: best for immediate responses, basic information retrieval, and high-volume routine tasks. They are cost-effective for simple automation.
- Conversational AI: better for personalized, context-rich interactions and complex queries requiring understanding of intent and sentiment. It often yields more natural, satisfying customer experiences.
Choose conversational AI when prioritizing advanced customer experience and handling nuanced queries; choose chatbots for straightforward automation and cost-sensitive deployments.
### Choosing the right solution: Factors to consider when implementing chatbots or conversational AI
Key considerations:
1. Objectives: Define whether you need immediate responses, automation of repetitive tasks, or personalized experiences.
2. Customer profiles: Understand your users and common query types to match solution complexity to needs.
3. Scalability: Assess expected interaction volume and complexity; conversational AI scales better for complex, large-volume scenarios.
4. Resources: Evaluate technology, budget, and expertise. Chatbots often require less initial investment; conversational AI requires more development and maintenance.
Align these factors with business goals (support, sales concierge, etc.) to choose the appropriate technology.
### Conclusion
Chatbots and conversational AI both transform customer interactions. Chatbots efficiently automate routine tasks; conversational AI provides deeper understanding, personalization, and richer conversational experiences. The right choice depends on objectives, audience, scalability, and resources.
### Anchors
- [Chatbot vs conversational AI: What is the difference?](#)
- [Understanding the basics: Defining chatbots and conversational AI](#)
- [What is conversational AI?](#)
- [What is a chatbot?](#)
- [What is an example of a conversational AI bot?](#)
- [Chatbot vs conversational AI: Which one is more effective for customer service?](#)
- [Choosing the right solution: Factors to consider when implementing chatbots or conversational AI](#)
- [Conclusion](#)
### Keep reading
[View more](/blog)
- [Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
- [Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
- [Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
- [Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog)
## Answering Reddit’s top questions about AI agents
Source: https://delight.ai/blog/ai-agent/reddits-ai-agent-questions-answered
- [Blog](/blog)\n- [AI agent](/blog/ai-agent)\n- Answering Reddit’s top questions about AI agents\n\n
[Agentic AI marketer](https://delight.ai/author/ian-heinig)\n\n\n\nMarch 23 2026\n\n## Answering Reddit’s top questions about AI agents\n\nAI agents—software systems that act autonomously on behalf of users—are as promising as they are polarizing. Agents have taken the world by storm: investment is surging, speculation is rampant, and people everywhere are asking what to make of them, or how best to harness them.\n\nSince the rise of AI agents, delight.ai has been hard at work combining our award-winning, enterprise-grade communication APIs with agentic AI. The result, not for lack of rigor or humility, is an [AI agent platform](https://delight.ai/ai-agent) that helps enterprises to connect with customers anytime, anywhere, without limits.\n\nIn this article, our in-house AI experts and product team share their perspectives on Redditor’s biggest questions about AI agents—helping you cut through the hype and understand the reality on the ground.\n\n### Question 1: Are AI agents just hype?\n\n\n\n[*Reddit asks*](https://www.reddit.com/r/ArtificialInteligence/comments/1lredx9/are_ai_agents_just_hype/) if AI agents are the future or just marketing spin.\n\n#### Short answer: AI agents are real and groundbreaking technology—but most aren’t yet production-ready. Overhype is common.\n\n##### Why the hype exists:\n\n- **Breakthrough potential:** Unlike previous automation, AI agents can reason, make decisions, use tools and external data, and complete tasks end-to-end in pursuit of the goals set for them.\n- **Rapid progress:** AI agents can handle a wider range of tasks than previous AI and even set its own goals, creating excitement for the future of work. The technologies underlying agents (e.g., generative AI, machine learning) are also advancing.\n- **Buzz & investment:** Companies eager to capitalize on the AI trend are spuriously labeling products as AI agents in attempts to attract customers and investors.\n\n##### Why doubt remains:\n\n- **“Agent washing”:** Many products marketed as “AI agents” are just automation with a new label to appeal to the AI trend.\n- **Limitations vs ambitions:** Currently, agents can misfire, hallucinate, or loop (pathway explosion), especially in complex workflows.\n- **High failure rates:** A 2025 RAND study found [80–90% of AI projects never leave the pilot phase](https://www.rand.org/pubs/research_reports/RRA2680-1.html); Gartner expects 40% of agent projects to be scrapped by 2027. There’s more enthusiasm than execution at present.\n\n**Reality check:** AI agents aren’t magic—nor are they vaporware. Like the transformative cloud computing before it, agents need strong safety, governance, and transparency layers to earn enterprise trust and adoption. Until safety layers are standard, hype will outpace results.\n\n##### How to make agents work today:\n\n- **Start small and specific:** Agents are more likely to deliver returns in narrow, enduring use cases (e.g., handling ecommerce returns, automating outreach) than broad, general-purpose tasks.\n- **Balance human + AI:** For instance, Klarna’s “all-in” approach to AI customer service received [customer blowback](https://www.forbes.com/sites/quickerbettertech/2025/05/18/business-tech-news-klarna-reverses-on-ai-says-customers-like-talking-to-people/) until it reintroduced human agents in the mix.\n- **Go slow to go fast:** Early AI adopters see value when they pair realistic expectations with strong guardrails.\n- **Governance = trust:** Deployments need guardrails and frameworks to scale without compromising trust, compliance, and security. [AI agent platforms](https://delight.ai/ai-agent) like delight.ai, for example, address the transparency, fragility, and trust problem with enterprise-grade APIs with AI governance frameworks that make agents resilient, safe, and responsible.\n\n### Question 2: Do you know any real-world examples of using AI agents?\n\n\n\n[*Reddit asks*](https://www.reddit.com/r/AI_Agents/comments/1k68jn7/do_you_guys_know_some_real_world_examples_of/) about real-world use cases and examples of AI agents.\n\n#### Short answer: Yes—AI agents are already powering tasks across finance, healthcare, retail, and daily life. Most success so far comes from narrow, well-defined use cases.\n\n##### Examples from everyday life:\n\n- **Virtual assistants:** Siri, Alexa, and Google Assistant can set reminders, manage schedules, and control smart home appliances using agentic AI capabilities.\n- **Recommendation engines:** Netflix, Spotify, and Amazon use AI agents to analyze user behavior and then personalize content, product recommendations, and more.\n- **Smart devices:** Consumer devices like smart thermostats, smart security systems, or robot vacuum cleaners now embed AI agents for enhanced capabilities.\n- **Self-driving cars:** Autonomous vehicles from companies like Waymo and Tesla use multiple AI agents to perceive the environment, make decisions, and execute actions.\n\n##### In business and industry:\n\n- **Finance:** Mastercard’s AI agent scores 100B+ transactions annually for fraud risk in milliseconds; JPMorgan’s COiN analyzes legal docs; high-frequency trading relies on AI agents for speed.\n- **E-commerce:** Amazon uses AI agents for dynamic pricing and personalized recommendations; Sephora’s Virtual Artist enables virtual try-ons.\n- **Customer service:** Beyond scripted bots, AI agents handle multi-step tasks like refunds, transactions, and subscription renewals with user context. Klarna, for example, leaned too heavily into [AI customer service](https://delight.ai/ai-customer-service) before rebalancing with human support.\n- **Healthcare:** AI agents enhance diagnosis, drug discovery, and robotic surgery, while their autonomous, goal-oriented behavior frees up providers to deliver better care and outcomes.\n- **Workplace tools:** Microsoft Copilot agent summarizes documents, drafts responses, and automates workflows in real time.\n\n**Reality check:** AI agents are being applied to a widening set of use cases, but still falter in overly broad or ambitious workflows they’re not yet ready for. This is why agentic frameworks and orchestration standards like MCP (Model Context Protocol) are emerging: they help agents coordinate their reasoning engines, APIs, and knowledge sources, improving performance and reliability across a growing range of tasks.\n\n##### How to make AI agents work today:\n\n- **Blend autonomy with human oversight**, especially in sensitive domains or regulated industries like healthcare or finance.\n- **Focus agents on high-volume, low-margin tasks** (fraud checks, order tracking, recommendations). Here’s a 601-item list of [examples from Google](https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders).\n- **Layer in strong governance and transparency** from the start to ensure reliable, responsible operations. AI agent platforms like delight.ai come with built-in AI observability tools and governance frameworks to scale agents with minimal risk.\n\n### Question 3: Developers building AI agents—what are your biggest challenges?\n\n\n\n[*Reddit discusses*](https://www.reddit.com/r/AI_Agents/comments/1kf4qgx/developers_building_ai_agents_what_are_your/) what makes building AI agents so difficult.\n\n#### Short answer: Developers face hurdles at every layer, from data quality to performance bottlenecks, ensuring transparent operations to managing compute costs. A technology defined by novel autonomous operations and decision-making, AI agents present myriad challenges, even to experts.\n\n##### Technical & architectural challenges:\n\n- **Data quality & availability:** Agents need access to high-quality, structured data to function, but often encounter scattered, noisy, or siloed systems, which undermine decision-making.\n- **Memory & context:** Managing agents’ long-term memory and session data is difficult, as APIs can only carry so much context before computing costs and latency spike.\n- **Multi-agent system complexity:** Coordinating multiple agents to interact around complex tasks presents challenges in communication, decision-making, and dependencies that can derail workflows.\n- **Scalability:** Scaling agents to handle bigger datasets, user volumes, and more complex tasks can lead to performance bottlenecks, unwieldy architectures, and ballooning costs.\n- **Reliability & error handling:** Agents can behave inconsistently, while API failures or edge cases can stall an entire workflow if monitoring and fallback rules aren’t in place.\n- **Integration:** Plugging agents into existing enterprise and legacy systems is often more difficult than [building the AI agent](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent) itself.\n\n##### Operational & financial challenges:\n\n- **Computational costs:** Running large-scale AI agents requires a lot of compute power, making cost control a constant concern and a major barrier for some.\n- **Framework complexity:** Overly complex agent frameworks can make simple tasks hard to implement. Developers must balance modular design with usability, and matching the right architecture and models to the appropriate use cases is a key decision point.\n\n##### Ethical & control challenges:\n\n- **Autonomy vs control:** Agents acting autonomously can be unpredictable. Balancing their autonomy with human oversight and control is a recurring obstacle.\n- **Bias & safety:** Agents trained on biased data can reproduce these in production, resulting in unethical, irresponsible, or unsafe outputs without monitoring and guardrails.\n- **Transparency & explainability:** Agent decision-making is like a “black-box,” making it often difficult to comprehend, identify, and correct errors without proper observability tools.\n\n**Reality check:** Even the best-designed and governed AI agents struggle when the expectations outpace their current capabilities—and it’s still early days. Much of what delight.ai does is manage pie-in-the-sky ideas of what agents are presently capable of.\n\nAdmittedly, developers who find that what’s marketed as an autonomous “agent” feels like a glorified workflow aren’t necessarily wrong. Agents are new, evolving, and their full potential remains untapped. Their frameworks feel complex now because they’re built to support capabilities that will only matter as agent use cases get more sophisticated, which is a function of time.\n\nIn the meantime, [AI agent builders](https://delight.ai/builder) like delight.ai help developers and organizations to hurdle these myriad challenges with robust APIs, observability features, and AI governance frameworks that help keep agents' evolving workflows both manageable and reliable at scale.\n\n- [Question 1: Are AI agents just hype?](#section-anchor-id-0 "Question 1: Are AI agents just hype?")\n- [Question 2: Do you know any real-world examples of using AI agents?](#section-anchor-id-1 "Question 2: Do you know any real-world examples of using AI agents?")\n- [Question 3: Developers building AI agents—what are your biggest challenges?](#section-anchor-id-2 "Question 3: Developers building AI agents—what are your biggest challenges?")\n\n### See Delight.ai in action.\n\n[View demo](https://delight.ai/demo-request)\n\n### Keep reading\n\n[View more](/blog "View more")\n\n[
Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)\n\n[
The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)\n\n[
AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)\n\n[
What is a plumbing answering service?](/blog/industry/plumbing-answering-service)\n\n[View more](/blog "View more")\n
## How to build effective real-time AI voice agents: Principles and best practices
Source: https://delight.ai/blog/ai-agent/how-to-build-ai-voice-agents
Ian Heinig
[Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
## How to build effective real-time AI voice agents: Principles and best practices
As businesses seek to scale their customer support with greater efficiency, real-time [AI voice agents](https://delight.ai/voice) have emerged as a promising solution. These systems, powered by advancements in large language models (LLMs) and AI speech technologies, can automate audio conversations that previously required human agents.
However, delivering a practical and trustworthy AI voice experience requires more than just deploying speech recognition and generative AI. It calls for deliberate design choices, a clear scope, and operational readiness. This article outlines **key principles and practices for building real-time voice AI agents** that serve customers reliably and responsibly.
### Why real-time voice AI agents?
Voice remains one of the most natural and accessible modes of communication. Unlike text-based interfaces, voice does not require a screen, typing, or literacy. This makes it especially useful in customer service contexts like phone customer support, appointment scheduling, or delivery coordination.
Modern AI voice agents understand natural speech, manage turn-taking in conversation, and provide intelligent responses—increasingly achievable functions thanks to improvements in AI speech-to-text, AI text-to-speech, and large language models. But building one that consistently works in real-world scenarios requires more than technical integration.
*Delight.ai voice AI agent makes a call*
### Focus on a narrow and valuable AI voice agent use case
One of the most important starting points when designing a voice agent is to **define a narrow scope**. Voice agents are not general-purpose assistants; they perform best when they are optimized for a specific, high-value task.
For example, handling order status inquiries, booking appointments, or resetting passwords are all use cases with:
- Predictable conversational patterns
- Clear business rules
- High enough volume to justify automation
Keeping the scope focused makes it easier to design, test, and monitor the AI agent’s performance, while avoiding unexpected AI failures that might occur in broader, less structured tasks.
### Optimize for real-time human-to-AI interactions
Unlike text-based interfaces, voice is synchronous. Users expect rapid, natural responses. To meet these expectations, voice agents need to be built with **real-time interaction as a core design principle**.
*Global infrastructure communication cloud supports low-latency AI voice calls*
A well-functioning AI voice agent should:
- Begin speaking as soon as it has a confident partial understanding of the user’s input
- Know when the user is pausing vs. when they are finished
- Interrupt itself or wait appropriately depending on user behavior
Achieving this level of interactivity requires carefully managing communication latency and designing systems that can respond before the user feels a delay. It is critical to choose a platform familiar with managing the volume and quality of real-time audio calls.
### Treat AI voice agents like operational systems
Voice agents are not static deployments. They must be monitored, evaluated, and tested regularly. This includes:
- Logging real conversations (with appropriate privacy measures)
- Analyzing where users drop off or where handoffs to human agents occur
- Reviewing failure cases to improve future handling
As with any live system, AI tooling and processes need to be in place to test updates before deployment, track AI performance metrics in production, and rapidly fix regressions.
Additionally, it’s important to prepare fallback strategies: what happens if the [voice AI agent](https://delight.ai/voice) doesn’t understand the user or cannot complete the task? Designing clear AI handoff protocols is essential to maintaining a reliable experience.
### Build trust in AI voice agents through transparency and control
Trust in voice AI systems is not automatic. It must be earned through predictable, explainable behavior. This means designing AI voice agents that:
- Clearly communicate what they can and cannot do
- Avoid over-promising or pretending to be human
- Give users a way to exit or escalate when needed
For organizations to build safe voice AI agents, teams need AI agent platform capabilities to manage observability, governance, safety and compliance, testing and evaluation, and scalability.
AI trust should not be treated as a feature but as an outcome of how the system is designed, deployed, and maintained. Choosing a [top-rated AI agent builder](https://delight.ai/builder) can provide these features for trust, control, and governance in a single, simplified platform.
### Augment customer experience AI voice customer service
Voice agents represent an incredible opportunity to enhance the customer support experience and extend service availability without burdening the team. However, their success depends on more than the underlying technology. The most effective voice AI agents are:
- Designed for a clear, valuable purpose
- Built for real-time responsiveness
- Treated as ongoing operational systems
- Designed to earn operational teams' and customers' trust
For organizations exploring [voice AI](https://delight.ai/voice), starting small, monitoring closely, and iterating based on real-world usage are essential to delivering value without compromising the customer experience.
If you're ready to invest in [AI voice customer service](https://delight.ai/voice), let's talk.
Our team will quickly guide you from proof of concept to production. And our proven real-time communication platform—trusted by global leaders like Hinge and DoorDash—will scale your [AI-powered support calls](https://delight.ai/voice) instantly, anywhere in the world.
[Contact us today](https://delight.ai/contact-sales).
### Sections
- [Why real-time voice AI agents?](#section-anchor-id-0 "Why real-time voice AI agents?")
- [Focus on a narrow and valuable AI voice agent use case](#section-anchor-id-1 "Focus on a narrow and valuable AI voice agent use case")
- [Optimize for real-time human-to-AI interactions](#section-anchor-id-2 "Optimize for real-time human-to-AI interactions")
- [Treat AI voice agents like operational systems](#section-anchor-id-3 "Treat AI voice agents like operational systems")
- [Build trust in AI voice agents through transparency and control](#section-anchor-id-4 "Build trust in AI voice agents through transparency and control")
- [Augment customer experience AI voice customer service](#section-anchor-id-5 "Augment customer experience AI voice customer service")
### Keep reading
[View more](/blog "View more")
[Delight.ai — The next chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)
[How For You Conversations make every interaction feel personal](/blog/product/for-you-conversations)
[Introducing AMP: The Agent Memory Platform](/blog/product/introducing-agent-memory-platform)
[The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[View more](/blog "View more")
## Introducing sentiment analysis for AI agents
Source: https://delight.ai/blog/ai-agent/ai-agents-for-sentiment-analysis
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- Introducing sentiment analysis for AI agents
## Introducing sentiment analysis for AI agents
[](https://delight.ai/author/ian-heinig)
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
## Improving AI customer service with built-in sentiment analysis
AI agents are uniquely positioned to handle many frontline enterprise tasks, from customer conversations to transaction processing to product upsells. But how do your customers actually feel about these interactions?
For support leaders trying to scale an amazing AI support experience, knowing what the AI agent says isn’t enough. Improving service quality while achieving AI transparency requires a knowledge of how frustration, satisfaction, confusion, and gratitude arise—and having the right data to fix it.
This is why delight.ai is introducing **end-user sentiment analysis for AI agents.** Every AI-led conversation now comes with sentiment analysis and scoring, trend tracking, and actionable metadata for the human handoff.
This means your delight.ai AI agents can actually tell you how your customers feel, at scale or per conversation. With this visibility, support leaders can take targeted action to improve processes and outcomes with clear insights located right in their AI agent dashboard.
## Why does sentiment visibility matter in AI conversations?
AI agents are fast, scalable, and omnipresent—but if they lack robust analytics and controls, they can leave support teams in the dark on key questions like:
- Was the user happy with the resolution?
- Did the customer feel heard or ignored?
- Were there signs of frustration before the handoff?
- *Did their tone change?*
- *What set them off?*
- *How was it fixed?*
Without sentiment data, human agents can receive escalations without full (and vital) context, support leaders may miss patterns and opportunities, and worst of all, a portion of customers are silently left wanting more.
## Sentiment scoring, trend insights, and smarter handoffs
The delight.ai AI agent sentiment analysis feature **classifies every AI conversation into one of three categories** that can be reviewed in the [AI agent builder](https://delight.ai/builder):
Each classification includes an explanation of why a sentiment was assigned, drawn from an analysis of the conversation summary.
*See real-time user sentiment rates in the AI agent dashboard.*
With precise sentiment data at hand, support managers have real-time visibility into customer sentiment at scale. In the AI agent dashboard, they can also drill down to pinpoint exactly where and why experiences break down to start making improvements.
## How is sentiment analysis built into the AI agent platform?
Sentiment analysis is integrated across the delight.ai [AI agent platform](https://delight.ai/ai-agent) in the following ways:
### Dashboard insights
Inside your **AI Agent Dashboard > Evaluate > Conversation**, you’ll now find:
- **Sentiment scores** per conversation summary
- **Filters** by sentiment type that isolate specific experience patterns
- **Trend visualizations** over time
*See real-time sentiment metrics like revisit rate, abandoned conversation rate, and other sentiment metrics in the AI agent dashboard.*
**Use case:** A support manager tracks a spike in negative sentiment over the past week. In the dashboard, they see the reason given for the negative classification is inaccurate responses (probably arising from a gap in the AI agent’s knowledge base).
### Sentiment metadata for smarter human handoffs
Every time a conversation gets escalated, human agents now receive **sentiment metadata** alongside the message. This helps them to:
- Know if the user is coming in calm or upset
- Adjust tone and response strategy accordingly
- Reduce resolution time with context-aware responses
**Use case:** A frustrated user gets escalated. The agent sees: Sentiment: Negative – User confused about billing. No need to ask, How can I help you today?
### Sentiment API and webhook support
Engineering teams can tap into sentiment data programmatically with:
- **API endpoints** that fetch sentiment scores
- **Webhook events** that trigger when sentiment thresholds are met (e.g. post-conversation negative sentiment)
**Use case:** Set up alerts or trigger automated agentic workflows when negative sentiment crosses a predefined threshold.
## How can sentiment analysis help your teams?
Different teams rely on sentiment data to improve performance, visibility, and decision-making:
- **Support leaders** use sentiment trends to catch surges in frustration, spot broken handoffs, or detect knowledge gaps before they affect CSAT.
- **Product managers** correlate spikes in negative sentiment with feature rollouts, UI changes, or bot responses to identify experience friction.
- **Engineers** use structured sentiment data to automate alerts, trigger routing logic, or feed quality monitoring systems.
## Why is sentiment analysis key to better AI customer service?
AI support doesn’t fail often because it’s inaccurate, but because it provides a poor emotional experience. This happens when customers feel stuck or misunderstood by AI agents.
Sentiment analysis helps close this gap—bringing emotional intelligence to your AI customer experience—while deepening your understanding of customers.
More than a KPI, customer sentiment is a powerful signal that helps you:
- Improve the accuracy and empathy of your [AI customer service](https://delight.ai/ai-customer-service)
- Enhance human agents with key emotional context
- Spot patterns before they escalate or spiral out of control
- Make good decisions more easily with clear data in your AI agent dashboard
In addition to sentiment analysis, delight.ai offers a suite of features to help teams optimize AI agent performance with full visibility:
- AI agent scorecards for AI agent evaluation
- Activity trails for ensuring AI transparency
- Multi-turn conversations for AI agent testing
## Ready to close the empathy gap in AI support with delight.ai's sentiment analysis?
Customer sentiment is a key indicator of support health and customer experience—and that’s why it’s now a core feature of delight.ai's AI agent platform. Knowing how customers feel in detail is vital to scaling [AI customer service](https://delight.ai/ai-customer-service) that’s not only reliable but also elevates the experience to new heights.
Whether you deploy proactive, omnichannel [AI concierges](https://delight.ai) for customer service across email, SMS, WhatsApp, in-app, or your website—sentiment analysis helps AI-powered support teams deliver a more empathic, satisfying experience at scale.
Ready to learn more? Sentiment detection, analysis, and scoring is available now to all of our AI agent customers.
[Contact sales](https://delight.ai/contact-sales) or your CSM to learn more.
- [Improving AI customer service with built-in sentiment analysis](#section-anchor-id-0 "Improving AI customer service with built-in sentiment analysis")
- [Why does sentiment visibility matter in AI conversations?](#section-anchor-id-1 "Why does sentiment visibility matter in AI conversations?")
- [Sentiment scoring, trend insights, and smarter handoffs](#section-anchor-id-2 "Sentiment scoring, trend insights, and smarter handoffs")
- [How is sentiment analysis built into the AI agent platform?](#section-anchor-id-3 "How is sentiment analysis built into the AI agent platform?")
- [How can sentiment analysis help your teams?](#section-anchor-id-4 "How can sentiment analysis help your teams?")
- [Why is sentiment analysis key to better AI customer service?](#section-anchor-id-5 "Why is sentiment analysis key to better AI customer service?")
- [Ready to close the empathy gap in AI support with delight.ai's sentiment analysis?](#section-anchor-id-6 "Ready to close the empathy gap in AI support with delight.ai's sentiment analysis?")
## Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## AI agents vs. chatbots: Differences & similarities explained
Source: https://delight.ai/blog/ai-agent/ai-agent-vs-chatbot
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- AI agents vs. chatbots: Differences & similarities explained
## AI agents vs. chatbots: Differences & similarities explained
[Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
Whether it's ChatGPT, Siri, or a popup widget that suggests products on an ecommerce site—you’ve probably interacted with a chatbot recently.
These digital assistants can answer customer questions and automate simple tasks based on a specific set of data. However, since the arrival of AI agents, these generative AI tools can feel a bit limited.
An AI agent, by contrast, is like a digital assistant on steroids. It can make decisions, plan actions, and even learn from experiences—all in pursuit of the goals set for it.
Instead of simply responding to users, AI agents can solve multi-step problems on their own, and thereby automate far more complex use cases.
This article provides a full breakdown of the differences between AI agents and chatbots to help you determine which is the right choice for your business.
## What is a chatbot?
A traditional chatbot is a computer program that relies on pre-defined rules, decision trees, and scripted responses to interact with users. First created in 1964, chatbots are primarily used to handle basic interactions, retrieve information, and answer common customer support questions. These days, the best chatbots use AI—but they serve basically the same purpose.
An AI chatbot is a software tool designed to simulate human conversation with users through voice or text interactions. It uses conversational AI techniques like natural language processing (NLP) to understand user queries and automate relevant responses to them.
AI chatbots are most effective when trained on a specific dataset and programmed with responses to certain business-critical questions. This enables the chatbot to generate accurate and relevant answers to user queries while enhancing routine interactions with human-like conversation.
But because they use a less advanced form of AI, chatbots cannot understand context beyond their training or make decisions on their own. As a result, they struggle to respond accurately to user inputs that fall outside the scope of their knowledge base, which is typically derived from a large language model (LLM), plus any specific data they’ve been trained on.
## What is an AI agent?
An AI agent is a more advanced artificial intelligence system that has a degree of autonomy and can act on its own to achieve the goals set for it. Unlike AI chatbots that can only respond to user inputs, AI agents can autonomously make decisions, execute multi-step plans, and even integrate outside data and tools to solve complex problems.
Built on large language models (LLMs), AI agents use sophisticated machine learning and natural language processing to understand and interact with their environment. Unlike simpler systems, AI agents can understand context, learn from interactions, and adapt their strategies over time to a specific goal. Given their autonomy and advanced capabilities, AI agents are capable of handling a broader range of challenging and open-ended tasks.
## What are the differences between AI chatbots and AI agents?
These two AI technologies are often confused because they both interact with users through natural language. However, AI agents and AI chatbots differ in many important ways, specifically:
### Problem-solving capabilities
AI chatbots are designed to handle specific and contained tasks, such as answering FAQs, executing basic transactions, and gathering information. To generate a user response, chatbots use natural language processing to interpret an input and match it to the most appropriate output in their structured knowledge base. But they lack the ability to reason or solve multi-step problems.
AI agents, on the other hand, are capable of autonomous decision-making. By combining goal-oriented behavior and machine learning, AI agents can tackle multi-step objectives in novel ways within the boundaries set for it. If given a complex objective, the agent will break down the task into a series of smaller steps. For example, if tasked with [building an AI agent](https://delight.ai/builder) to launch a website, the AI agent could develop the site structure, generate content for each page, design visuals, debug issues—all from a single command.
### Scope of knowledge
AI chatbots operate within a narrow domain of knowledge, limited to what’s in their static knowledge base and available from integrations with tools like CRMs, calendars, and knowledge bases. To provide users with accurate and relevant outputs, they must be trained on a business’s unique data. The most advanced AI chatbots can retrieve data from specific external sources using APIs in a process called function calling, but only by user request. Nor can they synthesize this information on the fly, or autonomously add it to their knowledge base.
AI agents, in contrast, can retrieve and integrate data from their environment in real time to achieve their goals. Agents base their action on what they perceive in their environment, and can fill in the gaps as needed by connecting to other digital systems. For example, an AI agent can call on external data sources and tools—web searches, LLMs, databases, even other agents—then synthesize the new information to tackle a wider range of objectives.
### Learning and adaptation
AI chatbots can improve but this typically requires human intervention. For example, users can indicate whether the bot responses were helpful or not, providing manual feedback to refine its outputs. Regular updates or retraining using techniques like retrieval augmented generation (RAG) are the primary way to improve accuracy and performance. The most advanced AI chatbots may use machine learning to improve response selection. However, the chatbot will still struggle to handle novel interactions that fall outside its training data.
By contrast, AI agents can continuously learn from interactions, improving and adapting by integrating interaction data into their knowledge base. Using techniques like reinforcement learning, AI agents can adjust their approach to better align with user preferences and business objectives. By analyzing and storing data from interactions in memory, then moving data in memory to its knowledge base, the AI agent creates a data feedback loop to autonomously self-improve over time.
### Memory and context retention
AI chatbots can only retain context from a single session. Since they don’t hold memory, they can’t learn from unsatisfactory responses or mistakes without manual human input. This lack of memory also limits the ability to personalize interactions based on user preferences, past interactions, and relevant data. Once the session ends, the chatbot is back to square one.
AI agents, by comparison, maintain context of past interactions by storing and retrieving data from past interactions in their memory. This enables AI agents to adapt to user expectations and provide a more personalized experience with better responses.
For example, an [ecommerce AI agent](https://delight.ai/industries/retail) could refer to past interactions in memory, purchase history in CRM, and the current conversation to deliver personalized recommendations and cart abandonment offers based on all the relevant context. Importantly, the AI agent will analyze its past actions and outcomes in memory, then add this data to its knowledge base to adapt its strategies over time.
## How to choose: AI agent vs AI chatbot
Even though AI agents offer more advanced capabilities and can handle complex tasks, they aren’t the right choice for every business. As you evaluate your specific needs, resources, and goals to determine the best AI solution for you, here are the key factors to consider:
- **Cost:** AI agents have higher costs because they are part of complex agentic AI systems that require large datasets, complex integrations, and continuous monitoring to mitigate risk and ensure compliance. AI chatbots, by contrast, are more cost-effective to deploy and maintain. A custom AI chatbot can still provide great value without the significant resources required for advanced AI agents.
- **Use case complexity:** If your use case involves decision-making across domains, multi-step workflows, and multiple integrations, you likely want an AI agent. However, if you want to answer FAQs, handle repetitive interactions, or guide users through processes like onboarding, then a chatbot should work.
- **Development and maintenance resources:** AI agents require more technical capabilities and time for ongoing development. These sophisticated agentic AI systems typically require advanced skills in system integrations and machine learning as well as resources for oversights and monitoring. AI chatbots are less powerful but require far less specialized expertise to implement, and are easier to update.
- **Data privacy and security:** AI agents, with their ability to access other systems and data, typically require more robust security measures than AI chatbots. This is especially important if your use case involves sensitive user data or regulatory compliance. While chatbots are less versatile due to their limited scope, they are also easier to secure and audit.
- **Scalability:** AI agents are designed for a more dynamic range of use cases, so they may be better-suited for evolving user needs as your business grows. A business could even develop a team of AI agents, each with its specialized own use cases. By contrast, AI chatbots are suited to handling high volumes of user interactions, but they’re much less scalable to diverse needs.
## AI chatbot and agent examples
### AI chatbot example: IT support
A business could use an AI chatbot as the first point of contact for employee IT issues. Using natural language, the chatbot can ask a sequence of basic questions to identify the issues and troubleshoot common problems like connectivity issues or password resets. If the solutions offered from its knowledge base don’t work, the chatbot will escalate to a human agent.
### AI agent example: IT support
Returning to the prior example of IT support, how would an AI agent handle a support request differently? Unlike a chatbot, the AI agent could resolve a more complex troubleshooting issue all on its own. It would start by breaking down a complex objective into a series of subtasks, then use external tools and knowledge sources to achieve a solution.
For example, the AI agent could:
1. Query the company's database for service history and device logs
2. Request real-time diagnostics on devices and systems via an API (application programming interface) call
3. Analyze the collected data to identify potential causes and suggest solutions
## AI chatbot and AI agent use cases
### AI chatbot use cases
AI chatbots are commonly used to automate customer interactions and streamline business processes. Well-suited for handling high volumes of routine customer service queries and routine interactions, they're a cost-effective solution to customer service automation.
Here are some common AI chatbot use cases:
- **Customer support:** An ecommerce site could implement an AI chatbot to answer FAQs about its shipping and returns policies and recommend products. The ecommerce AI chatbot interprets the customer queries and generates the most relevant response based on its training, providing immediate answers 24/7 and reducing the workload for customer service reps.
- **Automated scheduling:** A hospital could deploy an AI chatbot on its app and website to allow patients to book and manage appointments independently. The AI chatbot for healthcare is integrated with the scheduling software, helping to gather patient information and streamline patient intake to increase efficiency.
- **Lead generation:** A real estate company could launch an AI chatbot on its website that captures and qualifies leads as it answers FAQs and suggests property listings. The real estate AI chatbot gathers contact information, flags qualified leads based on predefined criteria, and integrates lead data to help agents close more deals.
### AI agent use cases
While it's still early days, AI agents promise to handle various tasks that require making decisions, using multiple data sources, and understanding context.
Here are some of the most exciting AI agent use cases:
- **Customer service:** CX AI agents deliver unmatched customer service for a wide range of industries, from retail to financial services, with 24/7 availability and swift handling of commonly asked questions. Plus, AI agents can analyze customer data to personalize responses, cut operating costs by reducing the need for human agents, and ensure consistent information, slashing wait times and enhancing the customer experience.
- **Business operations:** Capable of collecting and processing vast amounts of data in real time before making decisions, AI agents are set to reshape business operations. They could autonomously manage supply chains, optimize inventory levels, coordinate communications, forecast demand—all while minimizing human input on routine tasks to improve efficiency and reduce costs.
- **Cybersecurity:** A business could deploy an AI agent to serve as the tireless guardian of network security. The AI agent could continuously monitor network traffic, detect threats and anomalies, and respond in real-time without constant human oversight. Both proactive and adaptive, the AI agent could strengthen the company's defense and ensure a cybersecurity posture that’s more secure, scalable, and cost-effective.
- **Software development:** Enterprises can [build an AI agent](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent) to manage its entire development lifecycle rather than just generate code. The AI agent could design system architecture, write code, detect bugs, and more to dramatically accelerate the software development process and make it more cost-effective.
## AI agent vs AI chatbot: Pick the solution for your use case
By now, you’ve got a sense of how AI agents and chatbots differ, and how each can be used to improve business operations and customer experience.
Chatbots serve a vital function in [AI customer service](https://delight.ai/ai-customer-service) by resolving up to 80% of queries without human intervention. If you have immediate needs to improve customer service and experience while reducing customer service workloads, AI chatbots are a cost-effective solution.
AI agents, though, are better if you want to automate complex tasks and deliver a more personalized experience beyond customer service. As this paradigm-shifting advancement in AI technology continues to evolve, it will undoubtedly gain exciting new applications in the future.
For some businesses, a hybrid approach that balances using chatbots with AI agents could be the optimal choice. Leverage a leading [AI agent platform](https://delight.ai/ai-agent) like delight.ai to build AI agents to streamline your business today.
- [What is a chatbot?](#section-anchor-id-0 "What is a chatbot?")
- [What is an AI agent?](#section-anchor-id-1 "What is an AI agent?")
- [What are the differences between AI chatbots and AI agents?](#section-anchor-id-2 "What are the differences between AI chatbots and AI agents?")
- [How to choose: AI agent vs AI chatbot](#section-anchor-id-3 "How to choose: AI agent vs AI chatbot")
- [AI chatbot and agent examples](#section-anchor-id-4 "AI chatbot and agent examples")
- [AI chatbot and AI agent use cases](#section-anchor-id-5 "AI chatbot and AI agent use cases")
- [AI agent vs AI chatbot: Pick the solution for your use case](#section-anchor-id-6 "AI agent vs AI chatbot: Pick the solution for your use case")
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## AI voice agents: Everything you need to know (features, use cases, and platforms)
Source: https://delight.ai/blog/ai-agent/ai-voice-agents
## AI voice agents: Everything you need to know (features, use cases, and platforms)
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
### What is an AI voice agent?
An [AI voice agent](https://delight.ai/voice) is an intelligent software system that engages in real-time voice conversations with users, typically over phone calls, and performs tasks or resolves issues without human intervention.
Unlike legacy [AI voice assistants](https://delight.ai/voice) like [Alexa](https://alexa.amazon.com/) or [Siri](https://www.apple.com/siri/)—designed for narrow, scripted tasks—modern voice AI Agents are trained on natural conversations, integrate with backend systems, and can understand intent, handle context, ask clarifying questions, and take meaningful actions like a human agent would. They aren’t just interfaces but an autonomous AI workforce for voice-based customer service.
#### AI voice agents vs. IVR vs. voice bots
Many people confuse voice AI agents with traditional voice bots or IVR (Interactive Voice Response) systems—but they’re fundamentally different:
- **IVR (Interactive Voice Response)** is a rule-based phone system that offers menu trees (“Press 1 for sales…”). IVRs rely on rigid scripts and provide limited functionality, often frustrating users who want quick, natural interactions.
- **Voice Bots** are a step up from IVR, often using speech recognition to trigger simple automated responses. However, they typically lack deep contextual understanding and don’t adapt well to complex or dynamic conversations.
- **Voice AI Agents** are built on advanced speech and natural language processing (NLP) technologies. They understand intent, follow multi-turn conversations, integrate with backend systems, and complete real tasks. They can troubleshoot, update accounts, schedule appointments, or escalate to human agents—providing a seamless, human-like customer experience.
### Why AI voice?
Phone calls remain one of the most trusted and widely used customer service channels. According to global surveys, [68% of consumers](https://www.ruby.com/blog/customer-experience-statistics) prefer to connect with customer support via phone, surpassing live chat support, email, or chatbots. And despite the proliferation of digital channels, [more than 76% of all consumers](https://influx.com/blog/customer-service-stats-2023) choose phone calls as their primary channel to reach customer support representatives.
Yet traditional voice support is expensive, slow to scale, and difficult to monitor. Calling is still stressful for many—[71% of customers say talking to customer service is more stressful than the issue itself](https://www.prnewswire.com/news-releases/71-of-consumers-say-calling-customer-service-is-more-stressful-than-the-problem-itself-zingly-report-finds-302312161.html). Frequent problems like long wait times and dropped calls contribute to dissatisfaction.
This is where [AI voice agents](https://delight.ai/voice) offer a massive opportunity. They reduce hold times, offer quality responses, scale automatically during peak periods, and provide 24/7 availability—without agent fatigue. For enterprises facing dual pressures of improving availability and controlling costs, voice AI delivers a rare combination: scalable automation with consistent, branded interactions that actually improve customer satisfaction.
### How does an AI voice agent work?
An AI voice agent operates through a layered stack of natural language processing (NLP) and speech technologies that enable real-time, human-like conversations over voice channels:
- **Automatic Speech Recognition (ASR):** Accurately converts spoken input into text, even in noisy environments or with diverse accents.
- **Natural Language Understanding (NLU):** Analyzes the transcribed text to detect user intent and extract key entities (such as names, dates, or order numbers).
*A flowchart of how AI voice agents work*
- **Dialogue Management:** Maintains conversation state and context across multiple turns, enabling coherent back-and-forth interactions.
- **Action Layer:** Connects to enterprise systems like CRMs, ERPs, or proprietary APIs to retrieve data, trigger AI workflows, or update records in real time.
- **Text-to-Speech (TTS):** Delivers responses in natural-sounding speech, often tailored by language, tone, or brand persona.
Modern [enterprise-grade AI agents](https://delight.ai/enterprise-grade) go further. They learn from historical call data, leverage large language models (LLMs) for more nuanced reasoning, and offer seamless escalation to human agents, preserving context and reducing friction during handoffs.
### What are the key features of an AI voice agent platform?
Some key features to look for in an AI voice agent platform include:
#### Multilingual and cultural support
Advanced voice AI agents must go beyond basic translation. They need to understand local languages, accents, and idioms while adapting to cultural norms and regulatory requirements. This regional fluency makes them ideal for global, scalable AI customer service deployments and expands accessibility to underserved markets.
#### Smart interruption handling
Detects when a user speaks over the agent and gracefully recovers.
#### Tone and sentiment analysis
Adjusts responses based on customer emotion and urgency.
#### Context awareness
Maintains memory of conversation state and user history across interactions.
*Delight AI voice agent answering a support call*
#### System integrations (CRM knowledgebase, ERP, ticketing)
Enterprise-ready AI agents must connect to backend systems to retrieve customer data, update tickets, trigger workflows, and perform actions like human agents.
#### Fallback to live agents
When necessary or requested, voice AI agents must promptly hand off the conversation to human agents with full context, ensuring continuity and avoiding frustration.
#### Rapid retraining on real data
AI agents must improve over time by using conversation data. Enterprise platforms allow rapid AI agent knowledge updates and tuning to ensure agents stay accurate and aligned with evolving business needs.
#### AI agent observability and oversight
A robust voice AI agent platform must include detailed logs and activity trails, transcripts, and analytics to track performance, understand the AI agent behavior, and optimize resolution outcomes over time.
#### Compliance and redaction tools
To meet regulatory standards (like SOC2, HIPAA, or GDPR), voice AI agent platforms must offer automatic redaction of sensitive data, secure data storage, and audit-ready reporting.
### Top use cases for voice AI agents
#### Order status inquiries
Customers can call in and instantly get real-time updates on their order—shipping status, expected delivery, delays—without waiting in a queue.
#### Account authentication
Voice AI agents can verify users using voice biometrics, PINs, or contextual info, speeding up secure access without manual ID checks.
#### Appointment scheduling or rescheduling
From healthcare to field services, customers can book, confirm, or change appointments via voice with natural conversation, 24/7.
#### Subscription management
Whether it’s upgrading a plan, pausing service, or canceling a subscription, voice AI agents can handle the process conversationally and compliantly.
#### Service activation/deactivation
Set up internet, activate new phone lines, or turn off services—all without a human agent. The AI integrates directly with backend systems to execute actions.
#### Issue triaging and call routing
The AI agent can identify the problem and route the caller to the right department or escalate to a live agent, reducing call handling time.
#### Bill explanations or payments
Voice agents can explain charges, break down invoices, and even accept payments securely, helping customers resolve billing issues faster.
#### Handling long-tail queries
AI voice agents can now handle “long tail” customer queries that are too niche or infrequent to justify scripting for traditional IVRs—like how to transfer hotel or airline rewards points internationally.
#### 24/7 Tier-1 support coverage
Companies can use AI voice agents to provide consistent, around-the-clock support across geographies, covering holidays, weekends, and off-hours without adding headcount.
#### Load balancing during call surges
Voice agents are ideal for absorbing traffic spikes during promotions, outages, or billing cycles. They reduce wait times and relieve pressure from human agents.
### Top voice AI platforms
As voice AI agents mature, a growing ecosystem of platforms is emerging—each offering different strengths in scalability, integration, and language support. While the space is evolving quickly, a few names stand out in the enterprise landscape for their performance in customer-facing deployments:
- **Delight.ai** (AI customer service) – An [enterprise-grade AI agent platform](https://delight.ai/enterprise-grade) offering high performance, multimodal (text, voice) omnichannel (in-app mobile & web chat, SMS, email), [proactive AI agents](https://delight.ai/blog/ai-for-cx/proactive-ai-customer-service) for customer service, built on a trusted real-time communication platform powering over 4000 businesses, including [DoorDash](https://careersatdoordash.com/blog/building-a-unified-chat-experience-at-doordash/), Yahoo, and [Hinge](http://sendbird.com/resources/hinge).
- **PolyAI** (call center) – Known for its natural, multilingual conversational capabilities and enterprise-grade voice support to replace phone Interactive Voice Response (IVR).
- **Kore.ai** (enterprise AI automation) – Offers a robust, no-code platform for building voice and digital agents, with deep integrations and strong analytics.
*Delight.ai is a leading enterprise-grade AI platform*
- **Verint** (contact center) – Focuses on contact center automation with AI agents that reduce live-agent load while improving customer satisfaction scores.
- **Amelia** (enterprise automation) – Combines conversational AI with RPA and backend orchestration, suited for complex, enterprise workflows.
- **SoundHound** (embedded voice) – Leverages its own speech recognition and large language models to offer branded, fully embedded voice experiences.
- **Lindy** (customer support) – Multimodal AI assistant app to automate operations and support processes.
- **Synthflow** (sales automation) – A low-code voice-first platform to build and deploy AI voice or chat agents for sales and operations.
- **Relevance AI** (AI automation) – AI workflow platform for automating enterprise data processes with custom agents.
Ultimately, the [best AI voice platform](https://delight.ai/voice) for your business depends on your current tech stack, use case complexity, regulatory needs, and deployment scale. It’s not just about picking the most advanced AI—it’s about choosing the right fit for your infrastructure and customers.
### Thinking about enterprise-grade AI voice customer service?
If you're exploring AI voice agents to scale support and modernize your customer experience, we can help. Delight.ai offers the fastest path to a production-ready, enterprise-grade voice AI agent—with the integrations, analytics, and AI safeguards your business needs.
**Let’s talk.**
[Contact us](https://delight.ai/contact-sales) to see how you can launch [AI voice customer service](https://delight.ai/ai-customer-service) with confidence.
- [What is an AI voice agent?](#)
- [Why AI voice?](#)
- [How does an AI voice agent work?](#)
- [What are the key features of an AI voice agent platform?](#)
- [Top use cases for voice AI agents](#)
- [Top voice AI platforms](#)
- [Thinking about enterprise-grade AI voice customer service?](#)
#### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
### Keep reading
[View more](/blog "View more")
[Best AI voice agents in 2026, tested firsthand](/blog/ai-agent/best-ai-voice-agents)
[Getting started with voice AI for home services](/blog/industry/voice-ai-appointment-booking-home-services)
[How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[View more](/blog "View more")
## The complete guide to AI agent evaluation
Source: https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide
### - [Blog](/blog)
- [AI agent](/blog/ai-agent)
- The complete guide to AI agent evaluation
## The complete guide to AI agent evaluation
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 23 2026
Evaluating AI agents is crucial to building effective, trustworthy AI systems. AI agents act on behalf of users and businesses with autonomy, making decisions, executing tasks, and interacting with customers. The more agency you give an AI agent, the greater the need for rigorous evaluation to ensure its safe and proper functioning.
AI agent evaluation provides visibility not just into the agent’s outputs, but also the reasoning paths and workflows behind them—enabling transparency, iterative improvement, and the confidence to scale. However, agentic workflows often span multiple tools and touchpoints, so achieving this level of AI observability can be complex—making evaluation a multifaceted challenge.
This guide will walk you through how to evaluate AI agents to ensure their performance and reliability. You'll learn everything you need to turn AI agent pilots into trusted, scalable solutions that drive measurable gains in customer experience (CX) and efficiency, without needless risk.
Specifically, we'll cover:
- [What is AI agent evaluation?](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide#section-anchor-id-0)
- [Why is AI agent evaluation important?](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide#section-anchor-id-1)
- [How does AI agent evaluation work?](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide#section-anchor-id-2)
- [AI agent evaluation metrics](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide#section-anchor-id-3)
- [AI agent evaluation example](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide#section-anchor-id-4)
- [AI agent evaluation best practices](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide#section-anchor-id-5)
## What is AI agent evaluation?
AI agent evaluation is the process of assessing the performance, reliability, and safety of an AI agent to ensure it meets the goals and ethical standards defined by an organization. The aim is to identify errors in agent logic, improve performance, and ensure the agent operates responsibly and effectively in production. Evaluating AI agents involves testing their abilities on various tasks, as in traditional software testing. However, it also involves added considerations for the behaviors unique to agentic AI.
For example, an event like a "where's my order?" query to an [AI support agent](https://delight.ai/ai-customer-service) triggers an agentic workflow. Even if the final output is a text response, it may be the result of intermediate actions—multi-step reasoning, calling an API, or querying a database with retrieval augmented generation (RAG)—each of which needs to be evaluated separately. Beyond evaluating the final output, it’s critical to assess what the agent knows, what actions it takes, and how it plans actions within its decision-making process.
Beyond assessing task performance and the steps taken to get there, AI agent evaluation must also consider factors like AI trust, safety, compliance, and security. These dimensions are critical in real-world environments, helping ensure agents avoid unsafe behavior, maintain reliability through verifiable outputs, and resist manipulation or misuse. Efficiency is also important. Without monitoring factors like computational cost, organizations risk deploying agents that perform well but drain resources.
Ultimately, an evaluation measures whether the agent is doing the right thing (effectiveness) and how well it's doing it (efficiency). It involves assessing the agent's performance based on functional factors (accuracy, reliability, task completion) and non-functional factors (safety, cost-effectiveness) through a structured process that includes automated testing, simulations, and human-in-the-loop reviews.
## Why is AI agent evaluation important?
AI agent evaluation is important because agents are powered by large language models (LLMs), which are inherently non-deterministic. This means that agents can follow unintuitive paths to achieve their goal, perhaps reaching a correct outcome via a suboptimal process, or failing in subtle ways (which makes debugging difficult).
Without rigorous evaluation, an agent might perform well in basic scenarios but collapse in edge cases or underperform in real-world conditions. AI agents often make complex decisions in dynamic environments, and organizations need to ensure they are effective, efficient, and reliable. Otherwise, organizations risk eroding trust with users and stakeholders due to unsafe, irresponsible AI.
As AI systems become more advanced and widespread, robust evaluation becomes critical for several reasons:
### Key benefits of AI agent evaluation
- **Validate performance:** Confirm that the agent performs its defined goals (like resolving a customer inquiry) with an acceptable success rate.
- **Identify weaknesses:** Surface hidden failure modes, such as an AI agent that handles customer FAQs well, but fails in nuanced cases.
- **Enable iteration:** Reliable metrics enable organizations to measure improvements after design or model changes in accuracy, latency, cost, or user experience, without relying on guesswork.
- **Compare approaches:** With new AI frameworks and models emerging constantly, consistent evaluation provides a fair way to benchmark and choose the best option for a specific task.
- **Manage costs and resources:** Measuring efficiency metrics like compute costs, time, and API usage helps with cost control. For instance, you might find that two agents might have similar accuracy, but one costs more to run at scale.
- **Ensure trust and safety:** Robust evaluation practices are key to turning agentic AI pilots into reliable, production-ready systems that deliver lasting value.
By systematically evaluating AI agents across their lifecycle, organizations can ensure an AI agent is both doing the right things (effectiveness) and doing them well (efficiency and reliability). This enables teams to optimize AI agents, advance their AI capabilities, and minimize the risk associated with unsafe, unreliable, or biased agentic AI.
## How does AI agent evaluation work?
Evaluating an AI agent requires a structured approach within a broader formal AI agent framework that combines process observability with output validation. Evaluation methods vary, but typically include the following steps:
### 1. Define goals and success metrics
The first step is to establish what "success" looks like for your AI agent. Each of the functions, skills, or execution branches of the agent should be evaluated based on its purpose, real-world scenario, and desired outcomes.
For customer support agents, this would involve metrics like first-contact resolution (FCR), response accuracy, and CSAT scores. You can get as granular as you like with your AI agent testing and evaluation. For instance, you might evaluate the retrieval step of a RAG skill when retrieving customer data from a CRM.
In the next section below, we’ve outlined many of the key metrics for AI agent evaluation.
### 2. Collect data for a thorough test suite
To be effective, AI agent evaluation requires test sets that are representative of the real-world scenarios it might encounter. These datasets should include both typical interactions and edge cases that push the agent’s limits. These serve as your "ground truth," a baseline to measure performance against.
You don’t need thousands of examples—focus on coverage over quantity. For example, if you’re building an [AI agent for customer service](https://delight.ai/ai-customer-service), you might assemble datasets from customer inquiries and chat logs to create test cases for:
- **Routine inquiries** like "Where’s my order?"
- **Complex multi-step workflows** like updating a subscription with exceptions
- **Edge cases** like off-topic or ambiguously phrased requests
- **Stress tests** for high volumes of inquiries in peak periods
By preparing a comprehensive range of test cases that reflect the gamut of possible scenarios, you can evaluate not just whether the agent gets the "right" answer, but how consistently it performs across varied conditions. Over time, you can expand or modify this set as new usage patterns and behaviors emerge.
### 3. Outline the AI agent’s workflow
Next, you’ll break down every potential step in an agent’s workflow. By mapping each step in the agent’s logic—whether it’s calling an API, making a decision, or generating a response to a customer—you can evaluate each step individually and more easily pinpoint issues.
### 4. Pick the right evaluation methods
Once you’ve mapped out the steps in your agent’s decision path, you can decide how best to evaluate each of them. There are two main strategies:
- **Compare to expected outcomes:** If you can specify the result you want in advance—say, a particular answer or piece of data—you can measure the agent’s output against this known value. This rule-based approach shows where outputs are off and delivers the best results for reproducibility when trust and safety are in question.
- **LLM-as-a-judge or manual review:** If there’s no definitive "correct" output, or you want qualitative feedback (like how natural an answer sounds), you can involve another language model or a human reviewer. This semantic approach to evaluation provides nuanced insights into qualitative factors like tone or user experience.
AI agent evaluations often combine these strategies, as the end goal is to gather reliable data that guides improvements. For example, you might use automated tests to source metrics about response accuracy and efficiency, and use human reviewers for qualitative feedback on the user experience. There’s a strong emphasis on reproducibility in evaluations, so testing can be automated and scaled so long as the results remain comparable and trustworthy.
### 5. Conduct testing
AI agents need to be evaluated across varied environments with test sets that reflect every possible path the agent might take. For example, if you’re building an AI agent for [omnichannel AI customer service](https://delight.ai/ai-customer-service), the test scenarios should span all supported interactions, as well as unsupported requests that the agent must escalate appropriately.
Beyond validating individual steps, it’s equally important to assess how the agent operates across the entire workflow. This means reviewing whether the agent:
- Chooses the right function or tool for each step.
- Passes along the correct parameters and context.
- Executes workflows efficiently, avoiding loops or redundant actions.
- Delivers coherent, context-aware outcomes that align with customer expectations
### 6. Analyze results and benchmark
Finally, it’s time to analyze how well your AI agent performed. Beyond evaluating if the outcome was correct, assess whether the agent’s logic path was efficient, reliable, and scalable. Ask questions such as:
- Did the agent pick the right tool for the job?
- Did it call the correct function and pass along the right parameters?
- Was information handled in the right context?
- Did the agent produce a factually correct and useful response?
Benchmarking AI agent performance is important, since new AI agent frameworks and AI models continue to emerge (LangChain, AutoGen, OpenAI SDKs). By running the same test tasks across different models or agent setups, organizations can compare performance along key dimensions even as new processes and technologies emerge.
### 7. Optimize, iterate, and monitor in production
Now you can start to refine your AI prompts, streamline agent logic, or reconfigure agentic architectures to improve agent performance at scale, minimize risk, and optimize system efficiency for AI scalability and resource usage.
For example, use cases for customer service AI agents can be improved by:
- Accelerating response generation and task completion times.
- Spotting variations in error or AI hallucination rates in specific contexts.
- Identifying when and why handoffs to human agents happen too often.
Evaluation doesn’t stop at launch, as it's impossible to anticipate every query your agent will encounter in the real world, or all the possible outputs of an LLM. That’s why ongoing observability is critical. By feeding production data back into your development cycle, you can retrain, fine-tune, and adapt your agents as part of a truly robust system.
To help with this, the [leading AI agent builders](https://delight.ai/builder) like delight.ai provide analytics dashboards and observability features like real-time AI agent monitoring. This enables teams to detect and address performance issues in the moment, while tracking KPIs like resolution time, CSAT, cost per interaction, and tool usage to inform regular evaluations.
## AI agent evaluation metrics
Organizations must be able to determine if agents are performing as intended. The following AI agent metrics can help to quantify the various dimensions of agent performance:
### Task-specific and functional quality
- **Success rate/task completion:** % of tasks completed correctly.
- **Error rate:** % of failed or incorrect outputs.
- **Latency:** Time taken to process and return results for an individual step or entire task.
- **Cost efficiency:** Resources used per action (tokens, compute time, API costs).
- **Output quality:**
- **LLM-as-a-judge:** Evaluates output quality through semantics when no ground truth exists.
- **BLEU / ROUGE:** Compare AI-generated text against human-provided references.
### Responsible and ethical AI
- **Bias and fairness score:** Detects disparities in outputs across user groups.
- **Policy adherence rate:** % of responses aligned with internal compliance and policy rules.
- **Prompt injection resilience:** Measures success rate against adversarial prompts.
### User Interaction and customer experience (CX)
- **CSAT (Customer Satisfaction Score):** User-rated quality of responses.
- **Engagement rate:** How often users interact with the agent.
- **Conversational coherence:** Ability to maintain contextually relevant dialogue.
- **Task completion (UX):** % of user tasks completed successfully.
### Function calling and workflow reliability
- **Correct function selection:** Whether the agent chose the right function.
- **Parameter accuracy:** Correctness and completeness of inputs passed in an initiated function.
- **Execution path validity:** Avoiding unnecessary loops or repeated calls.
- **Semantic grounding:** Ensures parameter values derive from valid sources (user text, context, API defaults).
- **Unit transformation accuracy:** Proper handling of format/unit conversions (e.g., currencies, dates).
## AI agent evaluation example
Say you’re an online retailer that wants to [build an AI agent](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent) to handle the common use case of “Where’s my order?” (WISMO) queries. Before we outline how to evaluate this process, what actually happens within the agent?
1. The agent has to determine which tool or API it should call based on the user’s query. It needs to understand the customer’s intent—whether they’re asking about an existing order, a return, or something else.
2. Next, the agent might call the API for the order management system (OMS) to retrieve the shipping status, then cross-check with the logistics provider for real-time tracking updates. Along the way, it might ask questions to confirm the order number or delivery address.
3. Finally, the customer expects a friendly, accurate response that includes the current delivery status and, if effective, resolves the issue without escalation.
To evaluate this agentic AI workflow step by step, you would want to ask key questions like:
- Did the agent select the right system to query (OMS vs. returns database)?
- When it formed the API call, did it use the correct function and pass the right order details?
- Did it apply the customer’s context correctly, such as the order date or shipping method?
- Was each response correct, brand-aligned, and satisfying to the user?
Plenty can go wrong without evaluation. For instance, the agent might confuse two orders with similar IDs or fail to include real-time tracking data, leading to an incomplete answer. Worse, it might pass the wrong parameters to the logistics API, showing “delivered” when the package is still in transit.
This is why effective evaluation goes beyond just checking the final message. You need to trace how the agent makes decisions at each step: the tools it chooses, the way it interprets context, and the logic behind its reasoning.
## AI agent evaluation best practices
Measuring the right metrics is key to successful evaluation, but how you implement AI agents is equally important. To ensure your agents perform reliably and continuously improve, keep these best practices in mind:
### Balance multiple metrics
Rather than optimizing for a single KPI, a comprehensive evaluation must consider accuracy, latency, cost, CSAT, and safety together. AI agent dashboards show these metrics side-by-side so teams can evaluate tradeoffs effectively and efficiently.
### Benchmark and compare
Monitor and measure your LLM agents' performance across different regions, use cases, and versions against baselines to identify regressions and evaluate ROI—whether that’s human benchmarks, legacy automation, or previous agent versions.
### Automate evaluation when possible
Bake testing into your CI/CD or MLOps pipeline. With continuous evaluation, regressions can be caught before they reach production, saving both cost and customer frustration.
### Keep logs for visibility
Detailed AI agent activity logs—down to function calls, reasoning chains, and handoff decisions—are vital for debugging and retraining, providing transparency so teams can trace decisions back to their data sources.
### Incorporate human feedback
User feedback loops are crucial for optimizing customer-facing agents. CSAT surveys, in-line feedback, and conversation reviews provide qualitative signals to complement quantitative metrics.
### Stress test for robustness
AI agents should be evaluated not just on happy-path interactions but also on edge cases, adversarial prompts, and high-volume scenarios to ensure they’re resilient under pressure.
### Document and version everything
Track not just agent versions but also evaluation setups, test cases, and criteria to maintain accountability and consistency across iterations.
### Iterate for continuous improvement
AI agent evaluation isn’t a one-off task. By closing the loop between monitoring and retraining, organizations can steadily improve accuracy, responsiveness, and cost-efficiency.
### Use a framework instead of ad hoc tests
Frameworks like CLASSic (Cost, Latency, Accuracy, Security, Stability) or delight.ai's [Trust OS](https://delight.ai/trust-os), an agentic AI governance offering framework, offer structured approaches to scaling evaluation processes with clear guardrails and enterprise-grade AI governance and control.
## Popular AI agent evaluation tools
AI agents are evolving rapidly, and various tools are emerging to help organizations automate the complex process of testing agents and gathering insights for improvement. Some of these include:
### Libraries
Developer-friendly libraries like [DeepEval](https://github.com/confident-ai/deepeval), [Ragas](https://docs.ragas.io/en/stable/), [Promptfoo](https://www.promptfoo.dev/), and [HuggingFace Evaluate](https://huggingface.co/docs/evaluate/en/index) provide specialized functions for programmatic testing. These AI agent evaluation tools make it easier to benchmark accuracy, analyze outputs, and validate reasoning paths directly within your development workflow.
### Open-source platforms
Comprehensive platforms such as [LangSmith](https://www.langchain.com/langsmith) and [Weights & Biases](https://wandb.ai/site/) offer end-to-end observability to ensure agent performance. They log interactions, track metrics over time, and provide visualization dashboards—helping teams compare versions, identify regressions, and monitor production behavior at scale.
### Cloud-native solutions
Enterprise-focused platforms like [Databricks](https://docs.databricks.com/aws/en/generative-ai/agent-evaluation/) offer integrated pipelines for large-scale agent evaluation. These are well-suited for organizations that need to combine agent performance analysis with broader data engineering, governance, and compliance workflows.
### AI agent builders
The best AI agent builders come with a robust suite of AI agent evaluation tools to support key use cases. Delight.ai, for instance, offers built-in observability features like [**Activity Trails**](https://delight.ai/product-release/activity-trails-full-visibility-into-how-your-ai-agent-acts) that provide real-time transparency into agent logic, tool use, and what data corresponds to each output. This provides both granular visibility and enterprise-grade scalability, supporting evaluation to ensure AI agents meet expectations across CX, compliance, and business goals.
*Delight.ai provides robust AI agent evaluation and observability tools*
## AI agent evaluation with delight.ai
Evaluating your AI agents isn’t just about catching errors before they impact customers—it’s about building trust, ensuring performance, and scaling into real value. With delight.ai's [AI agent platform](https://delight.ai/ai-agent) for customer support and CX, you don’t have to start from scratch.
Our AI [Trust OS](https://delight.ai/trust-os) provides built-in features for observing and evaluating AI agents across their lifecycle. This shows you exactly how your agents reason, which tools they call, and what data they rely on—and how these processes correspond directly to outputs. This gives your teams the ability to monitor workflows step by step, retrain when necessary, and maintain accuracy at scale without guesswork.
To learn more, contact sales or [request a demo](https://delight.ai/contact-sales).
- [What is AI agent evaluation?](#section-anchor-id-0 "What is AI agent evaluation?")
- [Why is AI agent evaluation important?](#section-anchor-id-1 "Why is AI agent evaluation important?")
- [How does AI agent evaluation work?](#section-anchor-id-2 "How does AI agent evaluation work?")
- [AI agent evaluation metrics](#section-anchor-id-3 "AI agent evaluation metrics")
- [AI agent evaluation example](#section-anchor-id-4 "AI agent evaluation example")
- [AI agent evaluation best practices](#section-anchor-id-5 "AI agent evaluation best practices")
- [Popular AI agent evaluation tools](#section-anchor-id-6 "Popular AI agent evaluation tools")
- [AI agent evaluation with delight.ai](#section-anchor-id-7 "AI agent evaluation with delight.ai")
## Keep reading
[View more](/blog "View more")
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Understanding the AI customer experience in 2026
Source: https://delight.ai/blog/ai-for-cx/ai-customer-experience
## Understanding the AI customer experience in 2026
[](https://delight.ai/author/ian-heinig)
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 18 2026
Most AI customer experience (CX) tools can answer a question instantly, but forget everything the moment the conversation ends. [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) carries what a business already knows about a customer into every new conversation, so support stops starting over from zero.
That's the deeper version of a familiar tradeoff. Cut costs, and service suffers; invest in service, and margins shrink. The latest advances in artificial intelligence (AI) are finally closing that gap.
What began with [predictive analytics](https://delight.ai/glossary/predictive-analytics) and generative AI has evolved into [agentic AI](https://delight.ai/glossary/agentic-ai). [AI agents](https://delight.ai/glossary/ai-agent) now act on behalf of both the customer and the business, carrying a request through to resolution instead of just answering it.
Most organizations haven't captured these gains yet. Gartner predicts agentic AI will [autonomously resolve 80% of common customer service issues](https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290) without human intervention by 2029. Yet in an [October 2025 survey of 321 service and support leaders](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026), Gartner found that 91% face pressure from executive leadership to implement AI now.
That pressure doesn't always land well with customers. The [Delight AI Index](https://delight.ai/reports/ai-index/) found that 71% already use AI customer service, but only 57% like it.
In this article, you'll get a clear definition of AI customer experience, real-world examples of it working, and the common mistakes that derail AI CX initiatives before they start. By the end, you'll know what it takes to turn an AI pilot into results that actually improve the customer experience.
## What is AI in customer experience?
AI in customer experience (AI CX) refers to the use of artificial intelligence technologies—such as [generative AI](https://delight.ai/glossary/generative-ai), [machine learning](https://delight.ai/glossary/machine-learning), and [autonomous AI agents](https://delight.ai/glossary/autonomous-ai-agents)—to automate, enhance, and personalize interactions across the customer journey.
By analyzing vast sets of historical data alongside real-time context, AI transforms CX operations from static and reactive to dynamic and proactive. When organizations can better understand and anticipate customer needs in the moment, they’re able to deliver more relevant, satisfying journeys at scale—for a fraction of the cost.
This dynamic adaptability leads to a compounding advantage as AI systems learn and improve over time, delivering consistent, tailored interactions as they come to know individual customers.
## The state of AI in customer experience in 2026
Today’s customers expect fast, personalized, consistent service across every touch, so it’s no surprise that [96% of global CX leaders](https://sendbird.com/blog/ai-in-cx-report-2025) say generative and agentic AI are top strategic priorities. [AI-powered chatbots](https://delight.ai/glossary/ai-chatbot) have been table stakes for years, yet despite AI’s enthusiastic adoption, a sizable gap remains between AI implementation and measurable ROI.
What separates successful from stalled AI isn’t innovation; it’s precision and preparation. According to McKinsey, [the clearest ROI from generative AI](https://www.mckinsey.com/cn/our-insights/our-insights/beyond-the-hype-unlocking-value-from-the-ai-revolution) investments comes from structured support workflows—defined processes with clear inputs, outputs, and success criteria.
For CX teams, the power of AI in customer experience has emerged in three core areas:
- **Content acceleration:** AI is now the go-to engine for drafting support responses, knowledge articles, and customer messaging. Shorter production cycles let teams operate at higher velocity.
- **Faster experimentation:** AI has collapsed testing timelines for journey flows, campaign variations, and messaging optimization, enabling rapid iteration.
- **Insight-to-action automation:** AI no longer just surfaces insights; AI agents trigger actions. From lead routing to churn prediction to next-best-offer generation—this marks a shift from reactive generation to proactive closed-loop orchestration.
Crossing the implementation gap requires more than the right use cases. It requires [AI readiness](https://delight.ai/glossary/ai-readiness): a preparedness to sustainably adopt AI across technology, operations, and culture. According to Cisco’s 2025 AI Readiness Index, highly AI-ready organizations are [four times more likely](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf) to move AI pilots into production, and 50% more likely to see measurable ROI. Readiness is the differentiator because it makes innovation a repeatable, cross-functional process, enabling teams to move fast without eroding customer trust.
The lesson is clear: the era of the chatbot wrapper is over. AI-first is the new model for success. AI CX leaders are replacing isolated AI tools with embedded systems that serve as core infrastructure—embracing technical transformation matched by cultural and operational changes. Only through deep integration can organizations turn fragmented interactions into one seamless, delightful journey.
### How customers experience AI in 2026
- [67% ](https://www.demeterict.com/en/our-services-en/zendesk-en/zendesk-professional-services-en/zendesk-cx-trends-2025/)view AI CX favorably—up 10% from 2025 (Zendesk)
- [62%](https://www.zoom.com/en/blog/customer-experience-statistics/) believe AI improves personalization (Zoom)
- [61%](https://masterofcode.com/blog/ai-in-customer-service-statistics) prefer AI for AI responses to routine questions (Masters of Code)
- [39% ](https://www.demeterict.com/en/our-services-en/zendesk-en/zendesk-professional-services-en/zendesk-cx-trends-2025/)trust AI with high-stakes or urgent scenarios (Zendesk)
## The 9 best use cases for AI customer experience in 2026
Where are CX leaders focusing first? According to a 2025 survey from Sendbird and CCW Europe, [the top drivers of interest in AI CX](https://sendbird.com/blog/ai-in-cx-report-2025) are:
- Customer satisfaction (71%)
- Workforce productivity (63%)
- Cost reduction (56%)
To help you guide adoption, here are some of the top AI CX use cases in 2026.
### 1. Customer self-service
AI agents are redefining 24/7 self-service. Unlike traditional chatbots that only retrieve answers, AI agents interpret intent using [natural language processing](https://delight.ai/glossary/nlp) and take action—offering end-to-end resolutions for multi-step issues.
Say a customer requests a refund. The AI agent queries your customer relationship management (CRM), processes the return by calling the financial institution’s API, then updates the customer record—reducing resolution time and ticket volume. If the case exceeds the agent’s scope, it escalates intelligently while preserving context.
Gartner predicts AI self-service will [reduce contact center costs by up to $80 billion](https://www.cxtoday.com/workforce-engagement-management/gartner-bots-cut-agent-labor-costs-80bn/#:~:text=Gartner:%20Bots%20Will%20Cut%20Contact,BN%20In%202026%20-%20CX%20Today) by 2026, but [57% of customers](https://www.morningstar.com/news/pr-news-wire/20251217ne49327/75-of-consumers-left-frustrated-by-ai-customer-service) say they still find some AI self-service tools frustrating. This highlights the need to balance AI automation with a human touch and stay attentive to feedback.
### 2. Relationship management
AI agents can carry context for years, enabling CX systems to “know” every customer and provide predictive service that builds on past interactions. For instance, [AI agents for B2B support](https://delight.ai/industries/b2b) can recall where a customer stalled in onboarding, then suggest a helpful resource or alternative approach—serving as a proactive, personalized assistant that drives customer success and loyalty.
This is made possible by a combination of [AI memory systems](https://delight.ai/agent-memory-platform-amp)—which maintain relationship intelligence for each customer—and proactive orchestration, which maps potential scenarios to pre-defined resolution paths, then pulls past customer activity (e.g, support tickets, social media engagement) to create a satisfying, context-aware response.
### 3. Human-AI collaborative workforces
AI also makes customer service teams more productive, accurate, and efficient. AI copilots, embedded right into employee workflows, provide agents with real-time answers, summaries, and next-best actions, driving faster resolutions through text, voice, and multimodal capabilities.
For instance, AI agent assist tools like delight.ai's [Agent Desk Copilot](https://delight.ai/desk/ai-copilot) give support teams an AI-powered view of each customer's context and journey, surfacing real-time insights and coaching tips to sharpen performance. This enables a hybrid support model where AI handles high-volume interactions and frees employees to focus on empathy-driven scenarios to boost customer satisfaction.
### 4. Unified data and omnichannel CX
A single seamless experience that spans every channel has long been the holy grail of CX. AI agents make this possible, operating across once-siloed systems to deliver context-aware service wherever customers are. However, without a unified data layer acting as AI’s “source of truth,” it will produce inaccurate outputs (hallucinations) and inconsistent interactions that undermine the very experience they’re meant to enhance.
Siloed data is one of the biggest bottlenecks facing CX teams, with [41% of leaders struggling](https://www.cmswire.com/customer-experience/better-data-means-better-ai-across-the-customer-journey/) to unify data scattered across departments. By connecting CRM, knowledge bases, and [AI memory](https://delight.ai/glossary/ai-memory) systems into a foundation of relationship intelligence, organizations give AI what it needs to perform: a 360-degree customer view that enables one continuous, tailored, and highly satisfying journey.
### 5. Hyper-personalized journeys
[Customers reward personalization leaders with 40% more revenue](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) than competitors, making hyper-personalization a top CX opportunity. Unlike traditional segmentation, it combines unified data with live context to build a continuously evolving “segment of one.” Then, agentic AI determines the “next best action” (NBA) for the customer in that exact moment on that specific channel.
For example, instead of just modifying homepage content for returning visitors, the AI synthesizes their purchase history and prior channel engagement, then triggers generative AI to create unique content and offers for that specific scenario. The result is more relevant customer interactions and more precise recommendations that improve engagement, conversion, and retention.
### 6. AI search
AI search is transforming how customers find and act on information. Unlike basic keyword search, AI interprets user intent and context to synthesize a curated output. Adobe reports that traffic to U.S. retail sites from generative AI tools [surged 4,700% year-over-year in 2025](https://business.adobe.com/blog/generative-ai-powered-shopping-rises-with-traffic-to-retail-sites), reflecting enthusiastic adoption.
Imagine a customer visits an outdoor retailer's site. Instead of browsing by product, they ask the site’s AI concierge: *"Help me gear up for a camping trip in Big Sur this weekend. Budget: $500."* The AI analyzes purchase history, preferences, and the weekend forecast, generates a tailored shopping list, and completes the transaction. By saving time and reducing friction, AI search sets a new standard for digital CX: customer interfaces should understand and execute, not just retrieve.
### 7. Voice AI
[Voice AI](https://delight.ai/glossary/voice-ai) is redefining the call center, replacing rigid IVR menus with conversational AI systems that provide responsive service 24/7. Powered by LLMs, [AI speech analytics](https://delight.ai/glossary/ai-speech-analytics), and real-time [sentiment analysis](https://delight.ai/glossary/ai-sentiment-analysis), AI IVR systems handle intelligent routing and triage through natural language, eliminating hold times and resolving frontline inquiries faster.
[Voice-enabled AI agents](https://delight.ai/voice) can also handle end-to-end self-service, from seamless scheduling in healthcare to secure transaction processing with biometric authentication in financial services. Voice AI’s upside is significant, but so are its requirements. Scaling for high-volume periods demands enterprise-grade, low-latency architecture, as even slight processing delays can destroy the smooth flow of conversation.
### 8. Proactive customer service
One of the most exciting shifts in CX is the move from reactive to proactive AI. By analyzing external system data, customer information, and behavioral signals in real time, proactive AI can identify and resolve issues before they escalate. While [70% of customers say they expect proactive CX](https://www.cxtoday.com/ai-automation-in-cx/ai-predictive-cx-blueprint/#:~:text=Practical%20Steps%20to%20Reducing%20Churn,and%20grow%20their%20value%20faster.), only 30% say they receive it, making this a top differentiator.
Consider an airline's AI agent that monitors flight data and weather systems. Detecting a winter storm set to disrupt travel in Chicago, it proactively rebooks priority passengers on an earlier flight, notifies them via SMS, and applies its retention logic to offer a flight coupon—turning a potential crisis into a customer loyalty moment. Other use cases include churn prediction, hyper-personalized retention offers, and early detection of billing issues.
### 9. AI-powered QA and insights
Beyond helping businesses do more with less, AI also drives improvement. Unlike traditional methods, AI-powered QA analyzes 100% of interactions across chat, voice, email, and social. By identifying patterns and summarizing customer feedback in real time, AI delivers the actionable insights both humans and CX tools need to perform their best.
Effectively measuring AI performance requires new metrics. Because productivity isn’t a factor for AI, activity-based metrics like handle time are less relevant. What matters is AI’s decision-making, outcomes, and overall cost-to-serve. Looking at outcome-based metrics like resolution quality, accuracy, and cost per resolution offers a clearer picture of performance, helping teams tie AI’s inputs and outputs directly to core KPIs, then fine-tune accordingly.
## Benefits of AI in customer experience
When effectively integrated, AI CX solutions offer significant advantages over traditional methods:
- **Enhanced customer experience:** AI instantly delivers tailored, consistent interactions at scale, reducing friction, improving resolution rates, and raising overall service quality.
- **Improved efficiency and reduced costs:** By automating high-volume, routine interactions, AI reduces the cost-to-serve for transactions, returns, and common issues.
- **Greater productivity:** AI-assist tools free humans from repetitive tasks, providing real-time guidance, key context, and more bandwidth for high-value work.
- **Deeper insights:** AI identifies patterns, sentiment trends, and actionable intelligence that sampling-based QA and human analysis often miss entirely.
- **Competitive advantage:** From frontline to the back office, AI gathers deep insights and learns from each interaction, offering compounding advantages across the organization.
## Challenges of AI customer experience
Despite its clear value, implementing AI isn't without its challenges. For CX teams, some common AI challenges include:
### The AI readiness gap
Most organizations think they’re ready until deployment exposes brittleness in AI systems, often from poor data quality, undocumented workflows, or a lack of AI-ready infrastructure. According to Cisco, 83% of organizations plan to deploy AI agents—but [only 13% qualify as truly AI-ready](https://newsroom.cisco.com/c/r/newsroom/en/us/a/y2025/m10/cisco-ai-research-the-most-ai-ready-companies-outpace-peers-in-the-race-to-value.html#:~:text=The%20Index%20shows%2083%25%20of,networks%20as%20flexible%20or%20adaptable.).
The most AI-ready organizations don’t adopt AI technology for its own sake. They align leadership vision and AI capabilities with broader business objectives, and **evaluate AI readiness** pre- and post-deployment to mitigate risk and maintain alignment.
### Lack of AI governance
Customer-facing AI without guardrails is a liability. One hallucinated answer or unauthorized action can lead to a loss of customer trust, legal risk, and stalled developments.
Effective AI governance gives teams real-time visibility, explainability, and control over AI logic and behavior, letting them scale without sacrificing trust and safety. By defining how AI systems use tools, escalate edge cases, and handle sensitive data, governance isn’t an obstacle to AI adoption—it makes it sustainable.
### Balancing AI with a human touch
Over-automation is a real risk. For example, when [Klarna replaced much of its human workforce with AI](https://www.forbes.com/sites/quickerbettertech/2025/05/18/business-tech-news-klarna-reverses-on-ai-says-customers-like-talking-to-people/), the customer backlash was fierce, forcing a recalibration and staining the brand's perception.
Improving operations without losing the human touch takes careful consideration, especially in high-touch industries like travel and hospitality. AI CX isn't about replacing people, but about empowering them with the AI tools to scale more convenient, intuitive journeys—elevating service quality in novel ways to truly earn customer loyalty.
## Looking ahead: Creating an AI CX strategy in 2026
AI CX means organizations no longer have to choose between enhancing the customer experience and increasing efficiency—but only if they take a considered approach to AI strategy.
With AI moving from hype to reality, here’s where CX leaders can focus to drive success:
- **Invest early in AI infrastructure.** According to Bain & Company, [AI’s scaling compute needs demand foundational architectures today](https://www.bain.com/about/media-center/press-releases/20252/%242-trillion-in-new-revenue-needed-to-fund-ais-scaling-trend---bain--companys-6th-annual-global-technology-report/) or costly retrofitting later; those that prepare for the convergence of technological capabilities and evolving use-case demands are best-positioned to lead in the years ahead.
- **Evaluate AI readiness.** AI readiness frameworks offer a structured approach to AI adoption across key dimensions such as data, governance, and AI talent—guiding organizations from pilot to ROI. AI readiness assessments show where you stand, and where to focus next.
- [What is AI in customer experience?](#section-anchor-id-0 "What is AI in customer experience?")
- [The state of AI in customer experience in 2026](#section-anchor-id-1 "The state of AI in customer experience in 2026")
- [The 9 best use cases for AI customer experience in 2026](#section-anchor-id-2 "The 9 best use cases for AI customer experience in 2026")
- [Benefits of AI in customer experience](#section-anchor-id-3 "Benefits of AI in customer experience")
- [Challenges of AI customer experience](#section-anchor-id-4 "Challenges of AI customer experience")
- [Looking ahead: Creating an AI CX strategy in 2026](#section-anchor-id-5 "Looking ahead: Creating an AI CX strategy in 2026")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Keep reading
[View more](/blog "View more")
[](/blog/product/delight-ai-agent-guardrails-in-action)
[Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[](/blog/product/introducing-intelligent-voice-handoff)
[Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)
[](/blog/industry/patient-engagement-solutions)
[Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[View more](/blog "View more")
## How agentic AI improves omnichannel customer service
Source: https://delight.ai/blog/ai-for-cx/agentic-ai-omnichannel-customer-service
- [Blog](/blog)
- [AI for CX](/blog/ai-for-cx)
- How agentic AI improves omnichannel customer service
## How agentic AI improves omnichannel customer service
[](https://delight.ai/author/ian-heinig)
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 16 2026
[Omnichannel customer service](https://delight.ai/omnipresent)—which aims to deliver one seamless, continuous experience across all channels—has become the gold standard in recent years. Today, [73%](https://hbr.org/2017/01/a-study-of-46000-shoppers-shows-that-omnichannel-retailing-works) of customers switch between at least three channels in their journey, while [80%](https://www.salesforce.com/news/stories/customer-engagement-research/#:~:text=Salesforce%20Report:%20Nearly%2090%25%20Of,Much%20as%20Products%20or%20Services&text=The%20fifth%20edition%20of%20Salesforce's,State%20of%20the%20Connected%20Customer.) say the experience matters as much as the product itself. However, [only 45%](https://www.capgemini.com/wp-content/uploads/2025/03/18th-March-Customer-service-transformation-CRI_V7.pdf) say they’re “satisfied” with the quality of support they receive.
To meet rising customer expectations, companies are turning to AI-driven solutions to boost both satisfaction and operational efficiency. But despite the shift from rule-based automation to predictive analytics to generative AI in recent years, a true omnichannel experience remains out of reach for most.
Even as [generative AI chatbots become table stakes](https://www.zendesk.com/blog/ai-customer-service-statistics/), service operations still struggle with fragmented systems, lack of real-time context, and scalability challenges. And so, the vision of a seamless, unified customer experience remains just that: a vision.
Agentic AI changes this. AI agents are autonomous systems that reason, make decisions, execute tasks proactively—but also integrate seamlessly across business systems to leverage big data at scale.
More than just the next generation of chatbots, AI agents act as the bridge between formerly siloed channels and systems—enabling a cohesive, personalized, and seamless experience across channels that’s both more satisfying for customers and highly scalable for businesses.
### How agentic AI enables omnichannel customer service
Agentic AI in omnichannel customer service can automate tasks, personalize interactions, and improve efficiency by proactively addressing issues, tailoring interactions, and seamlessly managing customer journeys across multiple channels. This helps to reduce human workloads, improve customer sentiment, and enhance customer experience (CX) to improve retention.
Unlike generative AI tools that only produce content when prompted, AI agents act independently, interact with various systems, and learn from experience—all in pursuit of the goals set for them. AI agents can also integrate seamlessly with backend systems, tools, and even outside data sources in real time to solve problems.
By using and updating customer information across systems in real time, AI agents can automate multi-step workflows that require live data and deliver hyper-personalized support across channels. This helps support teams to scale fast high-quality care that improves customer satisfaction with far less manual effort.
In short, agentic AI isn’t just helping on one channel, it’s orchestrating support across all channels in real time—preserving context, personalizing experiences, and optimizing outcomes. With its modular architecture, it can operate as one big shared AI brain that runs between all channels and systems, unifying all data and interactions into one seamless, delightful customer experience.
*The modular architecture of an AI omnichannel customer service agent architecture*
### Benefits of agentic AI in omnichannel customer service
Integrating agentic AI into customer service operations allows businesses to provide seamless, personalized, and consistent customer experiences across channels 24/7—elevating customer satisfaction and loyalty at scale—without adding to headcount.
The major benefits of agentic AI for [omnichannel customer service agents](https://delight.ai/omnipresent) include:
#### Seamless channel integration
Agentic AI interoperates across all business systems and data sources—CRMs, helpdesk software, social, apps, voice—ensuring seamless transitions for a faster, more convenient, and more satisfying experience.
#### Personalized interactions at scale
AI agents independently analyze customer history, preferences, and behavior to deliver tailored responses and recommendations in real time, making customer care feel more meaningful at scale to improve customer satisfaction and retention.
#### Streamlined customer experience
By understanding and tracking individual customers’ needs, preferences, and conversations across channels, omnichannel [AI concierges](https://delight.ai) offer consistent, proactive service that’s seamless across channels, leading to improved customer satisfaction and retention.
#### Efficiency and scalability
By automating routine tasks and improving workflows, agentic AI reduces both human workload and errors. This frees human reps to focus on high value tasks, helping to reduce turnover and create a more consistent quality of customer service at scale.
#### Unified customer data
AI agents analyze and compile all interaction data into one unified profile that improves the quality of customer insights. This real time analysis and visibility leads to better decision-making and performance for support teams, while enabling AI agents to self-improve over time.
### Examples of agentic AI in omnichannel customer service
AI agents are already helping CS teams streamline operations, improve customer sentiment, and reduce costs by creating a cohesive customer experience. In fact, agentic AI was recently named Gartner’s [top strategic technology trend for 2025](https://www.gartner.com/en/articles/top-technology-trends-2025).
Here are some common use cases with examples of how [AI agent platforms](https://delight.ai/ai-agent) are helping to improve omnichannel service operations:
#### Proactive issue resolution
By monitoring customer data and connected systems, AI can trigger proactive interventions to address issues before they escalate to ticketed issues—[handling an estimated 80% of interactions](https://www.tupl.com/blog/how-ai-is-revolutionizing-first-call-resolution-fcr-in-customer-service/) at first contact (FCR).
**Example:** An AI agent detects repeated failed login attempts and proactively reaches out to the customer to assist with password recovery before frustration sets in, helping to reduce ticket volume and boost customer satisfaction.
#### Dynamic personalization
By analyzing both historical data and real-time context, AI agents can dynamically personalize each customer interaction. This creates a sense of individualized care at scale, enhancing customer satisfaction and loyalty.
**Example:** An AI agent in an ecommerce setting can suggest products or solutions based on both past interactions and current conversational context—as well as external data like seasonal trend reports—to tailor experiences to each customer’s exact needs.
#### Workflow optimization
AI agents can work autonomously or collaboratively with human agents to streamline operations. They can automate repetitive tasks or act as virtual assistants, embedded in human workflows to improve the speed, accuracy, and outcomes of support processes.
**Example:** Acting as copilots, AI agents can summarize conversations, suggest responses, surface relevant knowledge articles, and provide ongoing training to improve the performance and efficiency of human agents.
#### Continuous conversations
By storing and analyzing past interactions in their memory, AI customer service agents can pick up exactly where they previously left off. These smooth transitions enable a more consistent and convenient experience to improve customer satisfaction and retention.
**Example:** AI agents can start a conversation on one channel (like chat), continue on another (like email), then seamlessly hand off to a human agent if needed—eliminating the need for customers to re-enter information—especially helpful in [B2B customer service](https://delight.ai/industries/b2b).
### Building agentic AI omnichannel infrastructure
Agentic AI is a complementary technology that works in tandem with existing software, systems, and AI technologies.
If you’re considering the deployment of agentic AI for omnichannel customer service, it’s important to think about a few key components, all of which work together to create a scalable, secure, effective AI infrastructure:
#### 1. Agentic AI engine
The intelligent core responsible for autonomous actions, conversational intelligence, proactive customer engagements, and decision-making across channels. It can be custom built or is available from an [AI agent platform](https://delight.ai/ai-agent).
#### 2. Omnichannel communication layer
Enables seamless integration of multiple customer channels—SMS, chat, in-app, voice, email, social media, WhatsApp, etc.—through robust and scalable enterprise-grade communication APIs.
#### 3. Personalization layer
With access to large language models (LLMs), AI agents can dynamically summarize customer service interactions, auto-generate personalized responses, and anticipate next-best actions based on behavioral signals.
#### 4. Data integration and management
AI agents need access to clean, comprehensive customer data to operate effectively. This layer includes a customer data platform and scalable cloud infrastructure to enable real-time data synchronization across channels.
#### 5. Automation and workflow orchestration
This requires intelligent automation, workflow management, and event-driven AI orchestration so the AI agent can automate tasks and coordinate workflows seamlessly across channels. Recent innovations like Model Context Protocol (MCP) facilitate this.
#### 6. Human-AI collaboration layer
Human intervention is required for ticket escalation, but regular oversight is also needed to ensure the accuracy, efficiency, and quality of AI agent systems. This includes a human-in-the-loop (HITL) system to regularly correct, train, and improve AI outcomes.
#### 7. Analytics and feedback
Use insights to continuously improve AI and omnichannel customer service strategies, leveraging advanced analytics and KPIs to refine AI models and their outputs to enable the best possible customer experience.
#### 8. AI governance and compliance
For all its benefits, AI isn’t without its risks. It’s important to take proactive measures to ensure the responsible, secure, and compliant operation of AI across customer service channels in areas like data privacy and security, AI transparency and explainability, ethical AI, and AI compliance.
### Scaling the omnichannel advantage with AI agents
For the [80%](https://www.gartner.com/en/newsroom/press-releases/gartner-says-most-customer-experience-programs-are-not-deliverin) of organizations that expect to compete with each other based on CX, agentic AI doesn’t just help to meet today’s support challenges—it positions them to thrive in the experience-driven landscape of tomorrow.
By providing the connective tissue that links conversations, automates resolution, and scales service without sacrificing quality, agentic AI for customer service enables a future where customer support feels more natural, intuitive, and efficient for customers while being more cost-effective and efficient for businesses.
Built on a foundation of enterprise-grade infrastructure, Delight [AI concierges](https://delight.ai) deliver effortless customer support across every channel. Enjoy optimal performance with unmatched adaptability, security, compliance, and scalability.
[Contact our team](https://delight.ai/contact-sales) of friendly AI experts to get started.
- [How agentic AI enables omnichannel customer service](#section-anchor-id-0)
- [Benefits of agentic AI in omnichannel customer service](#section-anchor-id-1)
- [Examples of agentic AI in omnichannel customer service](#section-anchor-id-2)
- [Building agentic AI omnichannel infrastructure](#section-anchor-id-3)
- [Scaling the omnichannel advantage with AI agents](#section-anchor-id-4)
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
Keep reading
[View more](/blog "View more")
[](/blog/industry/patient-engagement-solutions) Patient engagement solutions for consumer health brands
[](/blog/ai-for-cx/guide-to-customer-experience-analytics) The complete guide to customer experience analytics
[](/blog/ai-agent/ai-agent-for-insurance) AI agent for insurance: What it is and how modern carriers use it
[](/blog/industry/plumbing-answering-service) What is a plumbing answering service?
[View more](/blog "View more")
## AI agents for ecommerce: 10 top use cases + agentic commerce
Source: https://delight.ai/blog/industry/ai-agents-ecommerce
- [Blog](/blog)
- [Industry](/blog/industry)
- AI agents for ecommerce: 10 top use cases + agentic commerce
## AI agents for ecommerce: 10 top use cases + agentic commerce
[
Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 16 2026
AI agents for ecommerce—autonomous systems that act on behalf of shoppers and retail systems—are fundamentally reshaping how consumers shop and businesses operate.
Unlike reactive chatbots of old, [AI agents](https://delight.ai/learn/ai-agent) are goal-oriented digital workers that leverage [large language models (LLMs)](https://delight.ai/learn/llm) and unified customer data to perform complex tasks in real time. Increasingly, they power everything from 24/7 AI-powered customer engagement to hyper-personalized product recommendations to agentic commerce—the new frontier of digital retail.
Are they impactful? Data shows that shoppers using Amazon's Rufus AI assistant are [roughly 60% more likely to make a purchase](https://fortune.com/2025/11/02/amazon-rufus-ai-shopping-assistant-chatbot-10-billion-sales-monetization/#:~:text=Customers%20who%20engage%20with%20Rufus%20during%20their,a%20purchase%20compared%20to%20those%20who%20don%27t.).
Read on to learn the top use cases for ecommerce AI agents in 2026, with real-life examples to guide you. By the end, you’ll understand why [agentic AI](https://delight.ai/learn/agentic-ai) is becoming the new operating system for ecommerce—and how you can use agents to enhance customer experience (CX), operational efficiency, and profitability as we enter the agentic commerce era.
## AI agents for ecommerce: 10 use cases & examples in the agentic commerce era
AI agents represent a sea change in ecommerce—moving retailers from reactive automation to proactive orchestration. According to Deloitte's 2026 Retail Outlook Report, agents are increasingly embedded in ecommerce platforms, and [68% of retailers plan to adopt agentic AI](https://www.deloitte.com/ce/en/issues/generative-ai/state-of-ai-in-enterprise.html) in the next 12-14 months.
Here are the 10 highest-value best use cases with real-life examples to inspire your investments in AI capabilities:
### 1. Agentic commerce
AI agents introduce a new model for ecommerce—[agentic commerce](https://delight.ai/blog/industry/agentic-commerce)—where shopping agents act autonomously on behalf of customers to find, compare, and purchase products. Rather than searching Google or directly navigating websites, consumers prompt AI agents on conversational platforms like ChatGPT. In return, they get a personalized shortlist of product recommendations in a fraction of the time.
This transformative trend toward AI shopping is already underway. Nearly [60% of consumers already use AI to shop](https://news.darden.virginia.edu/2025/06/17/nearly-60-use-ai-to-shop-heres-what-that-means-for-brands-and-buyers/), and [20% of Walmart's total referral traffic](https://digiday.com/media/chatgpt-is-now-20-of-walmarts-referral-traffic-while-amazon-wards-off-ai-shopping-agents/) now comes from ChatGPT.
For retailers, this presents a challenge: When AI agents mediate the retail journey, they bypass retail interfaces. Instead, they query APIs and scan real-time inventory, product, and fulfillment data to identify the ideal product to recommend. To remain competitive, retailers must optimize for AI discoverability by ensuring product data is highly accurate and machine-readable. Deploying branded [ecommerce AI agents](https://delight.ai/industries/retail) is also key, enabling retailers to retain customer intelligence and win transactions over third-party agents.
**Key AI technology:** Generative engine optimization (GEO), standardized AI-action APIs, agent-specific identity protocols
*AI agents interpret user intent, context, and history to synthesize unique product recommendations. They can even complete purchases autonomously in the chat window.*
### 2. Agentic architecture
AI agents also provide a new infrastructure for ecommerce, allowing merchants to both improve customer satisfaction and operational efficiency. By implementing agentic architecture, retailers can evolve from fragmented data and tools into one cohesive ecosystem that runs on unified customer data. This enables multi-agent systems, or teams of specialized agents, to collaborate in real time across formerly-siloed ecommerce platforms—using a 360-degree customer view to optimize operations and enhance CX at scale by dynamically adapting to each customer.
Beyond enabling autonomous agentic workflows, this structure also addresses a core risk of agentic commerce—disintermediation—where brand sites become mere inventory suppliers to third-party agents that determine what consumers see and buy. [McKinsey warns](https://www.digitalcommerce360.com/2025/10/09/ai-agents-redefine-shopping-forcing-retailers-to-compete-for-algorithmic-attention/) that those lacking an agentic layer risk losing transactions and the customer data that compound into a durable advantage.
**Key AI technology:** Multi-agent [AI frameworks](https://delight.ai/learn/ai-frameworks), [agent-to-agent (A2A)](https://sendbird.com/blog/ai-agent-to-agent-economy) connectivity protocols, universal data layer (semantic data layer)
### 3. Proactive fraud prevention
AI agents move fraud prevention in ecommerce from reactive to proactive. By continuously monitoring transactions, evaluating purchase data, and identifying subtle irregularities that static systems may overlook, backend agents detect and act in real time to stop threats before they escalate.
Consider a multi-agent system with a fraud prevention agent. When a customer asks, "Where's my order?", the customer-facing agent queries the logistics, CRM, and carrier systems to verify delivery status, which triggers the fraud-detection agent. This agent assesses risk factors and queries verification systems, perhaps identifying issues such as a new card registered in a foreign country, or repeated refund requests. It can then pause the transaction, trigger step-up verification, and alert the fraud team, reducing chargebacks and preventing account takeovers without disrupting the experience for typical shoppers.
**Key AI technology:** [Machine learning](https://delight.ai/learn/machine-learning), adaptive authentication orchestration, graph neural networks
### 4. Hyper-personalized shopping
By embedding agents in websites, branded apps, messaging apps, SMS, and email—merchants can scale tailored shopping and support experiences across the customer journey. Critically, AI agents don’t personalize; they hyper-personalize. Rather than solely considering purchases and preferences, AI shopping concierges also analyze the customer’s immediate browsing context, dynamically adapting product recommendations, experiences, and offers to that exact moment and channel.
For example, delight.ai’s [AI ecommerce agent](https://delight.ai/industries/retail) analyzes real-time behavior alongside customer details stored in AI memory to tailor interactions to a “segment of one” instead of broad audience buckets, then suggests the “next best action” for that user on that specific channel to drive higher engagement and conversion rates than traditional methods. The result is more precise, targeted offers that significantly boost conversion rates among the [71%](https://www.shopify.com/enterprise/blog/personalization-trends#:~:text=It's%20how%20you%20can%20make,companies%20that%20offer%20personalized%20experiences.) of consumers who expect consistently tailored interactions.
**Key AI technology:** Headless commerce APIs for recommendations, [reinforcement learning](https://delight.ai/learn/reinforcement-learning), and session embeddings for dynamic adaptation
### 5. Autonomous support & post-purchase
Unlike traditional ecommerce chatbots, AI agents can automate 80-90% of “Where is my order?” (WISMO) queries—and even execute complex tasks like return requests end-to-end. This level of autonomy changes the economics of support. [AI-powered customer service](https://delight.ai/ai-customer-service) can manage thousands of conversations at once, and it costs roughly the same to handle a product FAQ as a multi-step refund. By decoupling ticket volume from costs, AI enables support teams to improve operational efficiency as they also deliver a more seamless, satisfying experience.
Consider a peak-season scenario: a customer initiates a return on Black Friday via chat, then follows up later via email. With delight.ai’s [AI concierge](https://delight.ai/), the system retains context across channels and instantly picks up where the conversation left off. This eliminates both the friction of repeating information and the creation of duplicate tickets. At a time when [90% of consumers](https://www.clickpost.ai/blog/post-purchase-experiences-for-ecommerce#:~:text=Seamless%20post-purchase%20experiences%20build,than%20retaining%20an%20existing%20one.) say the post-purchase experience is as important as the product, merchants are using AI to meet customer expectations for fast, consistent, personalized service across channels—without adding headcount.
**Key AI technology:** [Agent memory platform](https://delight.ai/agent-memory-platform-amp) for state management, [AI agent governance](https://delight.ai/trust-os) for guardrails, NLU-based intent classifiers for intelligent routing
*Top ecommerce AI agents remember customer context across channels to scale faster, more convenient experiences.*
### 6. Proactive cart recovery
Cart abandonment is the scourge of ecommerce, representing a $18 billion issue for the industry—but AI agents can help. Using real-time behavioral analysis and [large language models (LLMs)](https://delight.ai/learn/llm), they engage customers in real-time using personalized two-way communication across WhatsApp, SMS, or phone calls with [voice AI](https://delight.ai/learn/voice-ai), often within minutes of abandonment.
Proactive cart recovery takes this a step further by engaging customers directly in checkout. When it detects a shopper has stalled on the cart page, the agent instantly offers a coupon for free shipping or a discount before they bounce to close the sale. Using dynamic discounting, the agent evaluates whether a coupon is necessary in the moment, aiming to stay within the merchant's margin-safe limits.
**Key AI technology:** Conversational AI orchestrators, behavioral intent engines, prescriptive discount models
*AI agents for ecommerce can anticipate and act in real time to proactively curb cart abandonment.*
### 7. Conversational commerce & support
AI agents are turning conversational commerce into one of retail’s highest-ROI channels. [Nearly 60% of consumers](https://www.thebusinessresearchcompany.com/report/voice-commerce-global-market-report#:~:text=The%20voice%20commerce%20market%20size%20has%20grown%20exponentially%20in%20recent,advancements%20in%20voice%20recognition%20technology.) use voice-enabled AI assistants like Alexa to research products and make purchases. Meanwhile, shoppers who interact with agents like Amazon’s Rufus [convert at nearly 4x the rate ](https://fortune.com/2025/11/02/amazon-rufus-ai-shopping-assistant-chatbot-10-billion-sales-monetization/)of those who don't. [Conversational AI](https://delight.ai/learn/conversational-ai) agents are becoming a primary interface for discovery, purchasing, and support.
Using [natural language processing (NLP)](https://delight.ai/learn/nlp) and voice recognition, agents with [voice AI](https://delight.ai/learn/voice-ai) can engage customers in real-time conversations across chat, voice, and messaging apps—offering seamless guidance from discovery to purchase to post-purchase. For example, [Stitch Fix’s voice AI agent](https://www.techtarget.com/searchcustomerexperience/news/366639944/Stitch-Fixs-AI-brand-agent-with-human-help-nails-the-look) facilitates live conversations with human stylists who offer curated outfit suggestions. The same agent that acts as a shopping assistant can instantly pivot into support mode, offering 24/7 phone support for refunds or returns while gathering customer feedback to inform improvements.
**Key AI technology:** Natural language understanding (NLU), [retrieval augmented generation (RAG)](https://sendbird.com/blog/what-is-an-ai-agent/rag), [AI sentiment analysis](https://delight.ai/learn/ai-sentiment-analysis)
*Conversational AI ecommerce agents can handle refunds, exchanges, or returns, or purchases by voice or text without human intervention.*
### 8. Optimized operations
Multi-agent systems are reshaping how ecommerce businesses manage logistics, inventory, and fulfillment to increase profitability. By replacing workflows that once required manual coordination with networks of specialized AI agents that collaborate autonomously across systems, merchants can better predict demand, optimize inventory, and ensure timely delivery in ways no human team can match.
Amazon offers a clear example of this multi-agent orchestration: one agent forecasts demand, another manages inventory, and a third optimizes delivery routing. Critically, a manager agent—Amazon Q—acts as the reasoning layer across these [verticalized agents](https://delight.ai/learn/vertical-ai), acting as an orchestrator by analyzing supply chain data, surfacing insights, and answering urgent operational questions in real time. Using this multi-agent system, Amazon [increased its same-day deliveries by 30%](https://www.aboutamazon.com/news/retail/amazon-prime-same-day-next-day-delivery-2025#:~:text=Key%20takeaways,and%20money%20year%20after%20year.) in 2025, while lowering its cost-to-serve for the third consecutive year.
**Key AI technology:** [Agentic frameworks](https://delight.ai/learn/ai-frameworks) for the orchestration layer, digital twin simulation for analysis, API-first tool use (function calling)
*Multi-agent ecommerce systems coordinate teams of specialized agents to effectively handle complex, multi-step tasks around logistics, pricing, and more.*
### 9. Loyalty program engagement
Loyalty programs drive strong revenue for brands, but only when customers choose to engage with their programs over competitors'. With AI agents for ecommerce, merchants can make their programs feel more tailored and unique to customers so they stand out from the rest.
Instead of sending the same offers to each member tailored by historical activity, AI agents use real-time behavioral signals—browsing patterns, churn propensity scores, redemption likelihood—to deliver the right incentive to each customer at the right time. For example, a customer with a high likelihood of churning gets a proactive retention offer, while a loyal member approaching a tier threshold gets a personalized nudge to close the gap.
**Key AI technology:** Uplift modeling (causal AI), next-best-action decision engines, [generative AI](https://delight.ai/learn/generative-ai) for hyper-personalized asset creation
### 10. Dynamic pricing & inventory management
When powered by ML [AI models](https://delight.ai/learn/ai-model), AI agents can replace manual pricing and reactive stock management with continuous autonomous optimization. Pricing agents monitor competitor feeds in real time, adjust prices based on demand and inventory levels, and apply personalized discounts at the cart level—predicting price elasticity with far greater accuracy to maximize both conversion and margin.
Meanwhile, inventory agents forecast demand at the SKU-and-location level, automatically trigger replenishment orders, and rebalance stock across warehouses and channels without human input. The result is a retail operation that responds to market conditions in minutes rather than days, helping merchants to reduce stockouts, clear slow-moving inventory, and protect margins at scale. Both of these dynamic approaches require guardrails, or minimum margin thresholds, to prevent AI algorithms from making mistakes that erode rather than protect margins and inventory balance.
**Key AI technology:** [Reinforcement learning](https://delight.ai/learn/reinforcement-learning) for autonomous pricing, [deep learning](https://delight.ai/learn/deep-learning) for demand forecasting, agentic workflow orchestration for execution and guardrails
### Bonus: Customer insights and optimization
By embedding AI agents across the retail funnel, merchants can gather the precise insights needed for continuous optimization. At the core of this approach is [AI memory](https://delight.ai/learn/ai-memory), a persistent system storing cross-channel behavioral signals, issues, and preferences. These insights feed into ecommerce systems to power everything from hyper-personalized marketing and dynamic pricing. The outcomes are then fed back into ML models, creating a cycle of continuous improvement that elevates decision-making for both human teams and AI systems.
This is also where the moat for agentic commerce is built. As third-party AI shopping agents increasingly mediate purchase decisions for consumers, the retailers with the richest, most structured customer intelligence are the ones whose products get recommended, whose personalized offers get surfaced, and whose loyalty programs factor into the agent's evaluation.
**Key AI technology:** Vector databases for [AI memory systems](https://delight.ai/agent-memory-platform-amp), [predictive analytics](https://delight.ai/learn/predictive-analytics), customer knowledge graphs
## Benefits of AI agents for ecommerce
AI agents offer a variety of advantages to both consumers and businesses compared to traditional automation and previous AI tools. Some of the major advantages include:
### Hyper-personalization at scale
By using real-time context, memory, and data spanning systems and customer history, AI agents deliver a new level of tailored experiences to each customer to enhance brand loyalty and drive growth.
### Seamless omnichannel engagement
Because agents operate across platforms using live data, they can provide a consistent CX across channels, for a smoother customer journey, fewer support queries, and better engagement.
### Operational efficiency and cost reduction
By automating routine tasks like inventory checks and handling support queries instantly, agents free human teams to focus on high-value work, reducing costs and errors.
### Data-driven decisions and insights
By tapping into live data from internal systems, third-party platforms, and market activity, AI agents bring real-time insights to drive analysis and decision-making, giving retailers a competitive advantage.
### Scalability for all seasons and regions
When support tickets or customer queries surge, localized, multilingual AI agents can scale seamlessly to support product launches, seasonal traffic spikes, and global operations.
## The future with AI agents for ecommerce
As the industry continues to evolve alongside agent capabilities, AI agents enable ecommerce merchants to pursue the highest-value use cases today, plus position themselves for leadership tomorrow.
By harnessing predictive analytics, generative AI, and customer-facing AI tools into a single harmonious, agentic ecosystem, AI agents are becoming essential to competitive operations in today's agentic commerce era—and whatever comes next.
To learn how delight.ai can help you [build AI agents](https://delight.ai/builder) for ecommerce and retail, [request and demo](https://delight.ai/contact-sales) or [contact sales](https://delight.ai/contact-sales).
- [AI agents for ecommerce: 10 use cases & examples in the agentic commerce era](#section-anchor-id-0 "AI agents for ecommerce: 10 use cases & examples in the agentic commerce era")
- [Benefits of AI agents for ecommerce](#section-anchor-id-1 "Benefits of AI agents for ecommerce")
- [The future with AI agents for ecommerce](#section-anchor-id-2 "The future with AI agents for ecommerce")
## FAQs about AI agents for ecommerce
**Are AI agents good for ecommerce?**
Yes. AI agents are transforming ecommerce by automating customer interactions, personalizing shopping experiences, and driving conversions. They help retailers increase sales, reduce cart abandonment, and improve customer satisfaction while lowering support costs.
**How do AI agents increase ecommerce sales?**
AI agents boost ecommerce revenue by engaging customers throughout the buying journey. They can:
- **Personalize recommendations** using browsing, purchase, and behavioral data.
- **Recover abandoned carts** through timely, conversational nudges.
- **Upsell and cross-sell** complementary products during checkout.
- **Provide instant support** for order issues, returns, and FAQs.
- **Guide purchasing decisions** by comparing options, highlighting offers, or summarizing reviews.
Together, these capabilities make the path to purchase smoother—and more profitable.
**What is the difference between AI agents and chatbots?**
While both chatbots and AI agents interact with customers through conversation, they differ in intelligence, autonomy, and capability.
Chatbots are rule-based systems that follow prewritten scripts. They can answer FAQs, handle simple workflows, and route requests—but they can’t adapt beyond what they’ve been programmed to do.
AI agents are goal-driven and autonomous—they’re the next generation of chatbots. They understand intent, reason through context, and take actions—such as updating an order, applying a discount, or checking inventory—without human intervention. Unlike chatbots, AI agents can plan multi-step tasks, learn from results, and improve over time.
**How much do ecommerce AI agents cost?**
AI agent pricing depends on scope, integrations, and level of autonomy. It’s common for pricing of [AI agent builder platforms](https://delight.ai/builder) to break down in the following ways:
- **Basic plans** (chat automation, FAQs): from $100–$500/month
- **Mid-tier plans** (multi-channel, goal-based agents): $500–$2,000/month
- **Enterprise plans** (custom logic, analytics, APIs): custom pricing.
Beyond licensing, consider factors like setup costs (training data, integrations) and ROI (increased sales, reduced support costs).
This blog has more about industry trends in [AI agent pricing](https://delight.ai/ai-agent/ai-agent-pricing).
**What exactly makes an agent “agentic”?**
An AI agent is “agentic” when it can **act independently toward goals**—not just respond. Its core components include:
- **Role:** Defines its purpose (e.g., sales assistant, support rep, product advisor).
- **Data:** The knowledge it draws on—catalogs, user profiles, order histories, FAQs, etc.
- **Actions:** The tasks it can perform, from recommending items to initiating refunds or updating carts.
- **Guardrails:** Policies that keep it aligned with business and brand values—controlling tone, data privacy, and allowed actions.
- **Channels:** The surfaces it operates on—web chat, mobile app, social DMs, SMS, or email—ensuring a consistent omnichannel experience.
Be sure to go with [trusted agentic AI companies](https://delight.ai/blog/industry/agentic-ai-companies), as agent washing—or when companies present non-agentic technology as agentic—is not uncommon.
**What are some different ways to build AI agents for ecommerce?**
There are several design philosophies for [ecommerce and retail AI agents](https://delight.ai/industries/retail), each suited to different goals:
- **Goal-based agents:** Act step-by-step toward a defined objective (e.g., complete purchase or resolve a return), evaluating actions by whether they progress the goal.
- **Utility-based agents:** Weigh trade-offs (price, delivery speed, margin, CX) and choose the action that maximizes overall value for business and customer.
- **Planning-based agents:** Build and execute multi-step plans (discover → compare → checkout → follow-up), adapting the sequence as conditions change.
- **Learning-based agents:** Improve decisions over time from feedback and outcomes (e.g., reinforcement signals, A/B results, agent scorecards), increasing accuracy and ROI.
Importantly, organizations can build multi-agent systems that use teams of specialized agents to collaborate around complex tasks, each agent doing its own part to advance a broader strategic goal.
To learn the basic steps, check out our guide on [how to build an AI agent](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent).
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2) [
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis) [
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward) [
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## AI agent pricing guide: Evolving from cost center to growth driver
Source: https://delight.ai/blog/ai-agent/ai-agent-pricing
## AI agent pricing guide: Evolving from cost center to growth driver
[](https://delight.ai/author/ian-heinig)
[Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 16 2026
Agentic AI is here and operational. From automated [AI customer care](https://delight.ai/ai-customer-service) to revenue recovery, AI agents are increasingly embedded across customer journeys and back-office processes. However, as the adoption of these agents grows, pricing becomes a top concern for technology and operations leaders.
CIOs, CTOs, and CX leaders are asking an increasingly urgent question:
*How should AI agents be priced in a way that reflects the value they create—without introducing unpredictability or inefficiency?*
While there’s no one-size-fits-all answer, the market is clearly evolving from static models rooted in legacy software economics to more dynamic, performance-linked approaches.
Let’s review the different types of pricing models for AI agents you'll find among the [best AI agent builders](https://delight.ai/builder).
## What are the different pricing model types for AI agents?
Understanding today’s AI pricing landscape requires revisiting the broader software pricing journey. Over the past two decades, we’ve seen a shift from fixed licensing models to flexible, metered usage—and now, toward pricing mechanisms that attempt to measure actual outcomes, which is especially relevant for AI agents.
### Quick pros and cons of AI agent pricing models
Each pricing model has trade-offs. While consumption models offer flexibility over traditional models, they may still reward volume over effectiveness. Unlike static software or raw computing, AI agents require pricing that reflects **business value**, not just usage. See FAQs for more pricing model details.
*Figure 1. Evolution of software pricing model*
## The emergence of outcome-based pricing for AI agents
Outcome-based pricing has gained attention for its promise of alignment: pay only when the AI agent or software achieves a meaningful, measurable result. That might be a resolved ticket, a completed transaction, or a retained subscriber.
Advocates of this model argue it offers benefits such as:
- Closer alignment between vendors and customers
- Reduction of waste from underperforming automation
- Clearer ROI attribution for budgeting and forecasting
Yet, despite its conceptual appeal, adoption has been cautious. **Defining and agreeing on outcomes is complex**, especially across functions or industries with ambiguous or multi-step success metrics.
## Examples of AI agent pricing models in the market
Several pricing frameworks are now in play across the AI vendor landscape. Each reflects different priorities—whether it's predictability, accountability, scalability, or simplicity.
What’s notable is not just the variety but also how these models represent **philosophies** about AI agents. Are they tools, teammates, infrastructure, or labor replacements? Pricing is, in many ways, a reflection of that stance.
## The challenge of outcome-based pricing for AI agents
One of the most significant constraints on outcome-based pricing is clarity around what constitutes a “successful” result. This becomes especially complex in enterprise environments with multi-touch workflows or partial contributions from AI systems.
Some recurring questions include:
- Is success binary (done/not done) or partial (assistive)?
- Who gets credit for collaborative outcomes involving AI and humans?
- Can success metrics be audited and verified by both sides?
Sophisticated vendors often co-define outcomes with their customers, building shared frameworks for measurement and governance of their AI agents. But not every organization has the internal analytics maturity—or cross-functional alignment—to support this level of nuance.
*Figure 2. AI agent outcome-based pricing charge diagram.*
## Common customer concerns about outcome-based pricing
Despite growing interest, outcome-based pricing still prompts fair skepticism. Here are a few concerns heard in executive conversations:
Outcome-based pricing isn’t inherently “better.” Like any model, it succeeds when **aligned with a clear use case, strong metrics, and operational trust.**
## Avoiding AI shelfware: A lesson from SaaS
In the early days of SaaS, companies overbought software licenses “just in case.” This led to the rise of “shelfware”—unused seats that quietly drained budgets.
AI agents, if priced without accountability, risk a similar fate. Fixed-seat models for a use case like [AI for customer service](https://delight.ai/ai-customer-service) can overstate adoption; token-based models can obscure efficiency. Without visibility into **how much value each agent interaction creates**, organizations may be left with flashy automation that fails to deliver.
Some enterprises now prioritize pilots or performance-linked contracts before scaling agent use across workflows.
## Practical next steps for AI agent pricing evaluation
Whether you’re in procurement, operations, or product, the right pricing model depends on the clarity of goals and maturity of implementation. Consider the following steps:
## What does the future hold for AI agent pricing?
AI agent pricing is not yet standardized—and that’s a good thing. Enterprises are experimenting, learning, and adapting pricing to match their **risk appetite** and **business maturity**.
Some will continue to prefer the simplicity of usage-based contracts. Others will invest in performance-linked pricing to extract maximum value. Many will blend the two.
The only constant? **Value is being redefined—and pricing must evolve with it.**
- [What are the different pricing model types for AI agents?](#what-are-the-different-pricing-model-types-for-ai-agents)
- [The emergence of outcome-based pricing for AI agents](#the-emergence-of-outcome-based-pricing-for-ai-agents)
- [Examples of AI agent pricing models in the market](#examples-of-ai-agent-pricing-models-in-the-market)
- [The challenge of outcome-based pricing for AI agents](#the-challenge-of-outcome-based-pricing-for-ai-agents)
- [Common customer concerns about outcome-based pricing](#common-customer-concerns-about-outcome-based-pricing)
- [Avoiding AI shelfware: A lesson from SaaS](#avoiding-ai-shelfware-a-lesson-from-saas)
- [Practical next steps for AI agent pricing evaluation](#practical-next-steps-for-ai-agent-pricing-evaluation)
- [What does the future hold for AI agent pricing?](#what-does-the-future-hold-for-ai-agent-pricing)
## FAQs
### What is consumption-based pricing?
Consumption-based pricing is a model in which you're charged based on the amount of resources or usage. In the context of AI agents, this could include charges per API call, token, message, or compute time. It’s similar to a utility bill—you pay for what you use, regardless of the outcome.
### What is outcome-based pricing?
Outcome-based pricing means you only pay when a specific, agreed-upon result is achieved. This could be a resolved customer support issue, a completed transaction, or a subscription retention. It aligns cost directly with value delivered rather than usage.
### What are the pros and cons of AI agent pricing models?
**Consumption-based**
**Pros**
- Simple to meter and forecast
- Pay for exactly what you use
**Cons**
- Rewards volume rather than outcomes
- Inefficient agents can create runaway costs
**Per-conversation**
**Pros**
- Easy to understand and budget
- Works well for predictable or episodic workloads
**Cons**
- You pay even for unresolved or low-value conversations
- No direct incentive for performance improvement
**Labor replacement**
**Pros**
- Easy to benchmark against existing human roles
- Familiar pricing structure for finance teams
**Cons**
- Oversimplifies agent capabilities
- Ignores multitasking or varying agent performance
**Outcome-based**
**Pros**
- Strong alignment with business value
- Encourages vendor performance
- Lower risk of wasted spend
**Cons**
- Requires clear and agreed-upon success definitions
- Attribution can be complex in shared workflows
**Agentic seat**
**Pros**
- Familiar SaaS pricing structure
- Predictable costs
- Easy to manage at scale
**Cons**
- Risk of underutilization ("AI shelfware")
- Rigid structure may limit experimentation
**Blended / hybrid**
**Pros**
- Flexible pricing aligned with different use cases
- Ideal for phased adoption or multi-function deployments
**Cons**
- More complex billing and management
- Requires strong documentation and vendor-buyer alignment
## Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## 13 Ways to improve support with an AI customer service agent
Source: https://delight.ai/blog/ai-for-cx/ai-customer-service-agent
## 13 Ways to improve support with an AI customer service agent
Ian Heinig
Agentic AI marketer
March 16 2026
**Key takeaways**
- **AI customer service agents automate repetitive requests** and complex workflows, and hand off to a human when needed.
- Building one takes 5 steps: **a knowledge base, Actionbooks, memory, staged testing, and continuous optimization.**
- Norse Atlantic Airways **grew containment from 60% to 80%** in two weeks, and customers trust AI more when they can undo its actions.
Today’s customers expect fast and accurate support 24/7. AI concierges use NLP to generate responses, route inquiries, and automate repetitive tasks, freeing human agents for high-touch work. Generative AI is becoming table stakes—Gartner expects widespread adoption by 2025. The next frontier is agentic AI: autonomous systems that perceive, reason, and use real-time data to handle multi-step tasks end to end.
This post explores 13 ways AI customer service agents help teams do more with less, and how they reshape support.
## What is an AI customer service agent?
An AI customer service agent is an advanced AI-powered digital assistant that handles customer interactions like a human agent. Powered by LLMs and NLP, it understands context, intent, and sentiment and is trained on company policies, resolved tickets, and systems.
Agentic AI goes further: agents are autonomous, adaptive, and interoperable, working across channels and systems to deliver personalized support and solve multi-step issues with minimal human intervention.
Benefits include:
- **Operational efficiency:** Automates routine tasks to reduce human workload and cost.
- **Enhanced CX:** Fast, tailored interactions that improve resolution time and satisfaction.
- **Scalability:** Handle high volumes and reduce wait times.
## 13 use cases for an AI customer service agent
AI agents combine conversational intelligence with autonomous task execution. Examples include:
### 1. Automate repetitive inquiries
Up to 70% of inquiries are simple questions like order tracking or password resets. AI agents can resolve an estimated 80% of inquiries autonomously at first contact by pulling live data and knowledge-base answers. Norse Atlantic Airways increased containment from 60% to 80% after replacing a legacy chatbot with an AI agent.
### 2. Automate complex workflows
Agents break multi-step tasks into sub-tasks and execute them across backend systems via APIs—e.g., refunds, billing updates, troubleshooting—reducing ticket volume and improving resolution rates. Hanssem raised resolution from 48% to ~86% over five months.
### 3. Reduce resolution times with AI-human handoffs
Agents detect when escalation is required (sensitive, complex, low-confidence or when customer requests a human), gather context, and summarize for human reps, improving handoff speed and reducing transfers. Lyft reduced resolution time substantially with this approach.
### 4. Proactive issue resolution
Connected to backend systems, agents can detect patterns and proactively reach out (e.g., failed logins) to address issues before customers submit tickets, reducing volume and improving retention.
### 5. Seamless omnichannel support
Agentic AI is interoperable across web, email, SMS, chat, in-app, voice, and messengers. Agents preserve context across channels so customers don't repeat themselves.
### 6. Scale localized, specialized support
Deploy multilingual, region-specific agents trained on local knowledge to scale support cost-effectively and preserve cultural nuance.
### 7. Gather customer feedback
Agents can trigger post-interaction surveys, analyze sentiment, and follow up on negative feedback—routing or escalating when needed and surfacing trends in dashboards.
### 8. Automate post-support workflows
After interactions, agents can sync CRMs, send summaries and confirmations, schedule check-ins, and trigger internal workflows like refunds or cancellations.
The delight.ai agent builder demonstrates creating workflow logic that automates multi-step AI workflows.
## Benefits of AI customer service agents
Deploying agents delivers organization-wide benefits beyond specific use cases.
### 1. Improve service quality
- Personalized support at scale using customer profiles and history.
- Consistent, accurate responses from verified sources.
- 24/7 availability and faster end-to-end resolutions.
Mixpanel eliminated roughly 10 minutes of manual verification per interaction by auto-authenticating users and pulling account context.
### 2. Greater access and quality of insights
Agents generate and integrate real-time insights (resolution time, escalation frequency, sentiment). Omnichannel agents create a unified customer view, improving KPI tracking and operational decisions.
### 3. Proactively guide human agents
Agents act as copilots: recommending responses, surfacing relevant content, summarizing interactions, and guiding next steps to speed and improve human responses.
### 4. Enhance training and onboarding
Agents provide in-the-moment assistance for new hires, recommend coaching based on identified knowledge gaps, and suggest tone adjustments for de-escalation.
### 5. Improve your help center
Agents surface and are trained on help center content, identify content gaps, recommend updates, and can draft or revise articles to keep documentation aligned with customer needs.
### 6. Scale localized, specialized support
AI agents can be specialized by region or product line to deliver accurate, culturally-aware experiences without massive human hiring.
### 7. Gather customer feedback
Agents capture feedback non-disruptively, analyze sentiment, trigger remediation when needed, and integrate trends into dashboards.
### 8. Automate post-support workflows
Agents automate follow-up tasks (CRM updates, confirmations, notifications), freeing human teams for higher-value work.
## How to build an AI customer service agent
Effective agentic AI typically rests on these building blocks:
### 1. Unified customer data
Agents need clean, centralized customer data (interactions, preferences, purchases, signals) to deliver context-aware responses.
### 2. Turn resolved tickets into Actionbooks
Actionbooks are executable SOPs the agent uses to act (book, fix, order, update, resolve). Delight.ai synthesizes Actionbooks from resolved tickets so agents start trained, not blank. Operations teams author and review Actionbooks; they don’t auto-deploy without human review.
### 3. Memory and adaptation
A memory module lets agents remember conversations and outcomes, tailoring future interactions and avoiding repetition to improve over time.
### 4. Test in stages, then deploy with graduated autonomy
Use a Test Center to run outcome-based tests (intent and action completion). Employ dev, staging, and production gates, selective deployments, and graduated autonomy (e.g., Agent Steward) with A/B testing and gradual rollouts.
### 5. Continuous optimization
Operational pipelines for training, tuning, monitoring, and feedback loops are required. Delight.ai offers Zero-Touch Improvement where agents propose Actionbook fixes from real failures with approval gates for sensitive changes.
Learn more: https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent
## Give your AI agent an undo button
Accuracy alone doesn't build trust—reversibility does. Delight.ai's 2026 AI Index found 57% of consumers trust AI more when they can undo it.
Trust OS features include:
- Deactivate AI: emergency off-switch to pause automated responses without deleting configuration.
- Version Control: compare and revert versions instantly.
- Selective Deployment: visual diffs before changes go live.
These governance tools make giving agents real responsibility safer.
## Getting started with AI customer service agents
Agentic AI shifts support from reactive to proactive. Gartner predicts agentic AI will autonomously resolve 80% of common customer issues by 2029. Delight.ai provides tools to build, train, deploy, and scale AI agents across channels with enterprise-grade infrastructure.
Explore: https://delight.ai/ai-customer-service
## Frequently Asked Questions
**What is an AI customer service agent, and how is it different from a chatbot?**
An AI customer service agent understands natural language, reasons through multi-step actions, and takes autonomous action across systems. Chatbots follow scripts and answer one question at a time.
**How much does an AI customer service agent cost?**
Pricing models vary: per-seat or pay-per-resolution. Delight.ai uses pay-per-successful-AI-resolution so cost scales with real usage.
**Can AI agents fully replace human customer service reps?**
No. Agents handle routine, high-volume work, while humans focus on sensitive, complex, or emotional cases. Best deployments combine AI and humans with smooth handoffs.
**What are the biggest challenges in deploying a customer service AI agent?**
Common challenges: unifying customer data, preventing incorrect or fabricated answers, and deciding escalation triggers. Actionbooks and Trust OS features (low-confidence flagging, deactivate) mitigate these risks.
**How do AI agents hand off to human support reps?**
Agents monitor sentiment and confidence and escalate when needed. They summarize the conversation before handoff so humans don't ask customers to repeat themselves.
## Keep reading
[View more](/blog)
[ The 8 best AI tools for ecommerce in 2026](/blog/ai-for-cx/best-ai-tools-for-ecommerce-in-2026)
[ Customer service MCP for ecommerce](/blog/ai-for-cx/customer-service-mcp-ecommerce)
[ Who is responsible when AI makes a mistake?](/blog/ai-agent/who-is-responsible-ai-mistakes)
[ How does real-time customer engagement impact your business?](/blog/ai-for-cx/real-time-customer-engagement)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## The 9 best AI chatbots for ecommerce in 2026 (+examples)
Source: https://delight.ai/blog/industry/best-ai-chatbot-ecommerce
## The 9 best AI chatbots for ecommerce in 2026 (+examples)
**Ian Heinig — Agentic AI marketer**
March 16 2026
**Quick answer:** For enterprise retailers, Delight.ai is the top AI chatbot for ecommerce in 2026, built on omnichannel memory (AMP) and built-in AI governance (Trust OS). This guide compares all 9 leading options side-by-side on price and core strength.
Key takeaways:
- **Delight.ai** tops this list for enterprise retailers that need omnichannel memory and built-in AI governance (Trust OS).
- AI chatbots handle personalized product recommendations, cart-abandonment recovery, order status, and 24/7 support, freeing teams to focus on complex cases.
- Pricing varies widely: budget tools start around $20–$30/month, mid-market $100–$300/month, and enterprise platforms are usually quote-based.
Chatbot overview (short):
| Chatbot | Best for | Core strength | Starting price |
|---|---:|---|---:|
| Delight.ai | Enterprise retailers | Omnichannel memory (AMP) + AI governance (Trust OS) | Consumption-based (contact sales) |
| Text (Chatbot) | Small–mid stores | No-code drag-and-drop bot builder | $19/user/month |
| Ada | Enterprise brands | End-to-end resolution across channels | Custom (reports ~ $30k+/yr) |
| Chatfuel | Social-first DTC | WhatsApp/Instagram/Facebook automation | $20/month |
| Tidio | Growing Shopify/WooCommerce stores | Lyro AI agent + real-time order actions | $29/month (Lyro add-on from $39) |
| Alhena AI | DTC & enterprise | Conversational product discovery + revenue attribution | Free 25 convs, then custom |
| Tolstoy AI Shopper | Apparel/fit-sensitive | Virtual try-on & sizing guidance | Contact sales |
| Botpress | Developers/enterprises | Open-source/customizable | Free, paid from $499/month |
| ManyChat | Social commerce | Visual no-code builder for social | Free, paid from $15/month |
## What is an AI chatbot for ecommerce?
An AI chatbot for ecommerce simulates conversations with online customers and automates retail workflows using machine learning (ML) and natural language processing (NLP). Embedded on websites, apps, messaging apps (WhatsApp, SMS), and email, modern AI chatbots are LLM-powered and can interpret intent, maintain context, and improve from past interactions.
## The 9 best AI chatbots for ecommerce in 2026
AI chatbots are increasingly embedded into ecommerce platforms. Below are the top options, summarized with key features, pricing, and best-fit use cases.
### 1. Delight.ai
Delight.ai (formerly Sendbird) is an omnichannel AI chatbot and CX platform built to turn customer interactions into growth moments. The no-code AI agent builder enables teams to train bots on knowledge bases, product catalogs, and support policies quickly. Dynamic reasoning via LLMs allows the agent to automate complex workflows and act in business systems.
What sets Delight.ai apart: the Agent Memory Platform (AMP) that unifies a 360-degree customer profile across channels, and a built-in governance layer (Trust OS) for visibility and control over AI behavior.
#### Key features:
- Proactive omnichannel capabilities across web, in-app, WhatsApp, SMS, email, and voice
- For-You Conversations (FYC) for hyper-personalized service
- Desk AI copilot for context-aware live handoffs
- Integrations with Shopify, Salesforce, Zendesk, CRMs
- Sandbox testing, staged deployment, governance, monitoring
- Real-time AI analytics, enterprise-grade security, multilingual support (80+)
#### Pricing:
Consumption-based pricing with volume discounts. Contact sales; try the ROI calculator.
#### Best for:
Enterprise retailers and ecommerce support teams that need omnichannel, governed AI at scale.
**G2 rating:** 4.6/5 (124 reviews)
### 2. Chatbot (Text)
Chatbot by Text is a no-code platform with a drag-and-drop builder and ecommerce templates for small to mid-sized stores. It trains directly on product catalogs and help articles to answer order, shipping, and return queries and detect buying signals.
#### Key features:
- No-code builder, pre-built ecommerce templates
- Real-time order/shipping/returns responses
- Buying-signal detection and proactive engagement
- Analytics, AI chat summaries, integrations (Shopify, Zendesk, Zapier, Slack)
#### Pricing:
Paid plans start at $19/user/month; Growth plan $79/month; Enterprise pricing available.
#### Best for:
Small to mid-sized ecommerce businesses wanting an accessible, affordable chatbot without coding.
**G2 rating:** 4.5/5 (26 reviews)
### 3. Ada
Ada is an enterprise AI-first chatbot platform for high-volume customer service. It uses multiple LLMs to retrieve knowledge and act across channels (chat, email, voice, social), executing tasks like updates or refunds in a single interaction.
#### Key features:
- LLM-powered conversational AI (OpenAI, Gemini)
- Automated omnichannel resolution, Playbook SOPs, CRM integrations (Zendesk, Salesforce, Shopify)
- Real-time analytics, AI coaching, multilingual support, enterprise security (SOC 2, ISO 27001)
#### Pricing:
Custom, quote-based; implementations often start ~ $30,000/year and can scale to six figures.
#### Best for:
Enterprise ecommerce brands with large inquiry volumes and complex workflows.
**G2 rating:** 4.6/5 (173 reviews)
### 4. Chatfuel
Chatfuel is a no-code platform for social commerce on Meta channels and TikTok DMs. It focuses on conversational commerce, abandoned cart recovery, post-purchase messaging, and ad retargeting within chatflows.
#### Key features:
- GPT-powered conversational flows for social channels
- Abandoned cart recovery, retargeting, segmentation
- Drag-and-drop flow builder, integration with Shopify, Stripe, Zapier
- Live chat support and CRM-style contact management
#### Pricing:
Business plans from $20/month for Facebook/Instagram; WhatsApp from ~$49.49/month; Enterprise from ~$300/month.
#### Best for:
DTC and social-first brands focused on Meta/TikTok conversational commerce.
**G2 rating:** 4.4/5 (45 reviews)
### 5. Tidio
Tidio combines live chat, AI chatbots, and automation. Its Lyro AI agent (built on Anthropic's Claude) can be trained on support content and perform Lyro Actions to check orders in Shopify and act in real time.
#### Key features:
- Lyro AI chatbot and Lyro Actions connected to Shopify/WooCommerce
- Visual Flows builder for cart recovery and lead gen
- Omnichannel support (chat, email, Instagram, WhatsApp)
- Agent handoff, AI Copilot suggestions, multilingual support (45+)
#### Pricing:
Free plan (50 lifetime conversations). Paid plans from $29/month; Lyro add-on from $39/month. Typical real-world cost $100–$250/month for AI+automation.
#### Best for:
Small to mid-sized ecommerce stores wanting an accessible, all-in-one AI chat + live chat platform.
**G2 rating:** 4.6/5 (1,950 reviews)
### 6. Alhena AI
Alhena AI is a revenue-focused shopping assistant that ingests product catalogs and connects to Shopify/WooCommerce. It attributes revenue to conversations and includes vertical tools (skin analyzer, fit analyzer) and proactive upsells.
#### Key features:
- Real-time integration with ecommerce platforms
- Conversational product search, voice shopping
- Revenue attribution and AOV tracking
- Vertical AI tools for beauty, fashion, home
#### Pricing:
Free for the first 25 conversations, then custom pricing.
#### Best for:
DTC and enterprise brands that want chat to drive measurable revenue.
**G2 rating:** 4.9/5 (39 reviews)
### 7. Tolstoy AI Shopper
Tolstoy's AI Shopper is built for ecommerce and Shopify brands to answer product questions, recommend items, and close sales in one conversation. It includes virtual try-on and measurement-based sizing, and auto-generated "top questions" on product pages.
#### Key features:
- AI product search and recommendations
- Virtual try-on and sizing guidance
- Auto-generated top questions on product pages
- In-chat add-to-cart, integrations with Shopify, Klaviyo, Gorgias
#### Pricing:
Contact sales.
#### Best for:
Apparel and fit-sensitive brands aiming to reduce returns and sizing friction.
**G2 rating:** 4.7/5 (17 reviews)
### 8. Botpress
Botpress is an open-source chatbot platform for developers and enterprises that need deep customization, extensible APIs, and multichannel support. It enables full control over bot behavior and integrations.
#### Key features:
- Multilingual support, drag-and-drop builder
- Maximum customization and extensibility
- Extensive integrations and developer community
#### Pricing:
Free plan available; paid plans from $499/month.
#### Best for:
Technical teams and enterprises that require bespoke, self-hosted chatbot solutions.
**G2 rating:** 4.5/5 (508 reviews)
### 9. ManyChat
ManyChat is a no-code chatbot platform optimized for social media messaging (Facebook Messenger, Instagram). It supports marketing campaigns, promotions, order management, and lead qualification with Shopify integration.
#### Key features:
- Automation for support and lead generation
- Visual chatbot builder and analytics
- Shopify integration, broadcast messaging capabilities
#### Pricing:
Free plan available; paid plans start at $15/month.
#### Best for:
Brands focused on social media ecommerce and conversational marketing.
**G2 rating:** 4.5/5 (165 reviews)
## Top use cases for AI chatbots for ecommerce
AI chatbots extend beyond FAQs: they drive discovery, leads, revenue, and support.
### 1. Make personalized recommendations
Chatbots can act as personal shopping assistants, asking targeted questions and recommending products based on behavior and history to increase conversions and AOV.
### 2. Generate leads
Connected to your CRM, chatbots capture contact details, qualify leads, and sync data for follow-up and marketing automation.
### 3. Prevent cart abandonment
Proactive engagement (prompts, discounts, reminders) and cross-channel recovery (email, push) reduce abandonment and recover revenue.
### 4. Provide instant customer support
24/7 availability handles orders, payments, FAQs, and hands off to humans when needed—improving speed and allowing agents to focus on complex issues.
### 5. Collect customer feedback
Surveys, ratings, and conversational feedback capture insights during or after purchase to inform product and support improvements.
### 6. Improve the customer experience
Personalized, automated interactions make the buying journey faster and more tailored, fostering loyalty and repeat purchases.
## Key features of AI chatbots for ecommerce
### CRM integrations
Seamless syncing with CRM, Shopify, databases, and third-party tools lets bots update profiles, trigger messages, and feed analytics back into your stack.
### Omnichannel support
Deliver consistent experiences across web, mobile, SMS, WhatsApp, social, and voice; enable cross-channel follow-up for leads and abandoned carts.
### The latest AI technology
Top chatbots use LLMs (ChatGPT, Claude, Gemini, Llama) to improve intent understanding and contextual reasoning.
### AI governance and trust
Look for governance/observability: low-confidence flagging, citation trails, audit logs, and human handoff rules (example: Delight.ai Trust OS).
### Advanced analytics
Track automated resolution rates, sentiment, CRM fetch frequency, and offer performance to optimize ROI.
## Best practices for ecommerce AI chatbots
- Enable real-time data syncing with product availability, pricing, and promotions.
- Train your chatbot on company-specific content before launch (specs, policies, FAQs).
- Customize tone and appearance to match your brand.
- Preset answers for business-critical questions to ensure consistency.
- Monitor performance with KPIs and iteratively optimize.
## Implementation note
AI chatbots and agents are now a standard expectation for modern ecommerce. If you want to add AI agents to your ecommerce experience, Delight.ai offers integrations and a no-code builder to streamline deployment.
[View demo](https://delight.ai/demo-request) | [Contact sales](https://delight.ai/contact-sales)
## Frequently Asked Questions
**How much does an AI chatbot for ecommerce cost?**
Pricing varies by vendor. Budget tools often start $20–$30/month, mid-market $100–$300/month, and enterprise is quote-based (often tens of thousands annually). Some vendors price by consumption rather than seats.
**How do you prevent an AI chatbot from giving wrong answers?**
Choose vendors with safeguards: automatic low-confidence flags, citation trails, audit logs, and clear human handoff rules. Governance/observability layers make these safeguards auditable.
**What's the difference between containment, resolution, and deflection?**
Deflection = customers diverted from contacting support; containment/resolution = the AI actually solved the issue. Ask vendors for resolution/containment metrics, not just deflection.
**Can small ecommerce businesses use AI chatbots?**
Yes. Tools like Tidio and ManyChat target small stores; Ada and Delight.ai target enterprise. Fit depends on ticket volume and coverage needed.
**Do these chatbots work across SMS, voice, and WhatsApp?**
Some (Chatfuel, ManyChat) focus on social messaging; others (Delight.ai, Ada) support omnichannel coverage including web, voice, SMS, WhatsApp, and email. Confirm channel coverage against customer behavior.
---
[View more](/blog)
Related posts:
- [Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
- [Introducing Intelligent Voice Handoff](/blog/product/introducing-intelligent-voice-handoff)
- [Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
- [The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
## A practical guide to AI in hospitality
Source: https://delight.ai/blog/industry/ai-in-hospitality
- [Blog](/blog)
- [Industry](/blog/industry)
- A practical guide to AI in hospitality
## A practical guide to AI in hospitality
[](https://delight.ai/author/ian-heinig)
[Ian Heinig — Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 16 2026
Imagine arriving at your hotel and skipping check-in entirely. Your phone unlocks the door, the AI-powered smart room adjusts to your temperature preferences instantly, and your favorite snacks arrive shortly after via a room service bot. This scenario is no longer limited to sci-fi: it’s the approaching reality of AI in hospitality.
Beyond the futuristic front-of-house applications, AI is also transforming back-of-house operations for hotels: forecasting demand, automating housekeeping schedules, and unifying data to make decision-making, marketing, and personalization go further.
But while [73 % of hoteliers](https://www.prnewswire.com/news-releases/new-study-finds-73-of-hoteliers-believe-ai-will-transform-hospitality-302352375.html?utm_source=chatgpt.com) say that AI will transform the industry, AI adoption in hospitality lags behind other sectors at [around 41%](https://www.hevs.ch/en/news/how-ai-is-and-isnt-transforming-european-hospitality-212299). Despite its promise, AI in hospitality has its challenges. Namely, the high cost of implementation and potential impacts on the guest experience. In an industry built on personal touches, excessive automation risks eroding the warmth and trust that define great hospitality—and invite customer loyalty.
Read on to learn how AI is redefining guest service, improving revenue management and efficiency—even creating new service models. Learn how to secure the benefits, address the challenges, and implement AI in hospitality effectively with a strategic framework.
- [What is AI in hospitality?](https://delight.ai/blog/industry/ai-in-hospitality#section-anchor-id-0)
- [Understanding AI in hospitality](https://delight.ai/blog/industry/ai-in-hospitality#section-anchor-id-1)
- [Core types of AI in hospitality](https://delight.ai/blog/industry/ai-in-hospitality#section-anchor-id-2)
- [Business benefits of AI in hospitality](https://delight.ai/blog/industry/ai-in-hospitality#section-anchor-id-3)
- [8 best AI use cases in hospitality (with real-life examples)](https://delight.ai/blog/industry/ai-in-hospitality#section-anchor-id-4)
- [A roadmap to value with AI in hospitality](https://delight.ai/blog/industry/ai-in-hospitality#section-anchor-id-5)
> **Quick answer:** Delight.ai gives hotels and resorts an AI agent platform built on persistent guest memory, so a guest's preferences, past requests, and stay history carry across every channel and every visit. Built on enterprise-grade infrastructure that already powers more than 7 billion conversations, it lets hospitality brands automate routine service without losing the human warmth guests expect.
## What is AI in hospitality?
AI in hospitality refers to the use of various artificial intelligence (AI) technologies to improve guest experiences, operational efficiency, and profitability for hotels, resorts, and travel services. By analyzing vast amounts of data in real-time, AI enables organizations to deliver guest services that feel more personal while making hotel operations run more smoothly and profitably. The most advanced implementations rely on persistent guest memory that carries preferences, past requests, and stay history across visits and properties. This is the approach Delight.ai uses to let a returning guest be recognized instantly rather than re-explaining themselves at every touchpoint.
Overall, AI is transforming the hospitality industry on two fronts:
### 1. Front of house: Enhancing the guest experience
Hoteliers use AI to deliver service that’s more personalized, seamless, and responsive to guests' needs, without necessarily losing the human touch. For example, virtual AI concierges and chatbots respond instantly to guest inquiries, while smart recommendation engines tailor room settings, dining suggestions, or omnichannel marketing offers to individual preferences and behaviors.
By automating routine and transactional tasks—check-ins, booking confirmations, or service requests—AI frees up frontline teams to focus on providing the best guest experience.
### 2. Back of house: Driving profitability and efficiency
AI algorithms power analytics that help hotels to anticipate demand, reduce costs, and operate more efficiently based on a dynamic mix of real-time, historical, and external data. Predictive analytics can forecast occupancy rates or staffing needs, optimize pricing and revenue management, and reveal guest or market insights to increase revenue. It also helps automate processes like scheduling, procurement, and predictive maintenance.
Agentic AI, by unifying and putting data to work intelligently, brings a new level of data-driven insight, targeting, visibility, and automation across properties to boost performance. Generative AI helps teams craft and deliver more tailored experiences in a fraction of the time, helping to drive customer satisfaction, productivity, and growth.
## Understanding AI in hospitality
AI now touches every part of the hospitality ecosystem, emerging as a core technology layer for hotels in the future. Once limited to website chatbots and recommendation engines, the global AI in hospitality market is expected to surge from $90 million in 2023 to more than [$8 billion by 2033](https://www.netsuite.com/portal/resource/articles/business-strategy/ai-hospitality.shtml#:~:text=AI%20adoption%20and%20investment%20in,is%20helping%20hotels%20improve%20security.).
For hotels, AI’s ability to analyze and apply data intelligently at scale brings a new level of precision and agility to operations. Insights from predictive AI analytics can power everything from revenue management to dynamic pricing, automated staffing to predictive maintenance—creating efficiencies that improve decision-making and net operating income (NOI).
For guests, AI now spans the travel journey, from booking to post-stay engagement. For example, AI-powered search and booking assistants—also known as agentic commerce—enable travelers to discover, plan, and reserve hotels directly from AI tools like ChatGPT or AI agents, without relying on OTAs.
According to the *2025 State of Hotel Guest Technology Report*, [58% of guests say AI improves their booking and stay](https://hoteltechreport.com/news/ai-in-hospitality), while 65% want hotel technology to be more advanced than their own. This contrast between rising expectations and growing acceptance highlights the industry’s biggest AI opportunity and challenge. Guests are attracted to new conveniences and experiences, but AI can undermine their trust and brand reputation if deployed without discernment and backed by effective AI monitoring and safeguards.
The question for most hotels is no longer *whether* to adopt AI, but *how* to build it and deploy it responsibly.
## Core types of AI in hospitality
There is no single AI tool for the hotel industry to adopt. Instead, AI is embedded across every business layer, from the front desk to the back office. These complementary AI technologies fall into three main categories, each serving a unique role in transforming how hospitality works and feels.
### Predictive AI (analytics)
Uses machine learning (ML) to analyze vast amounts of data for patterns, then recommend actions and surface insights to connected systems and users.
*Use cases:* *Demand forecasting, dynamic pricing, inventory management, guest service task automation, marketing optimization, resource management, and personalization strategies.*
### Generative AI
Enables hotel systems to understand and produce accurate, contextually relevant content using natural language processing (NLP).
*Use cases: Customer service automation (conversational AI), back-office automation, personalization, marketing content creation, code creation, summarization, and analysis.*
### Agentic AI
AI agents are intelligent systems that can perceive, reason, make decisions, and act autonomously in real time to achieve defined goals—all without human intervention. Capable of operating across connected systems, accessing external data, and unifying data sources—they can monitor situations, understand context, and take multi-step action across hotel systems in real time—effectively turning live data into autonomous, human-like action at scale.
*Use cases: Proactive problem solving, omnichannel customer service, real-time operations management, intelligent pricing and revenue management, and staff support.*
## Business benefits of AI in hospitality
AI in hospitality is poised to reshape the traditional way hotels do business. Here are some of the many ways that AI helps with hospitality operations and boosts guest services:
- **Enhanced customer service**: [AI-powered customer service for hospitality](https://delight.ai/industries/travel-and-hospitality) can elevate guest experiences by accelerating interactions, personalizing service offerings, and maintaining a consistently high quality of service from booking to check-out. This is vital for the hospitality firms, which rely on loyalty, as per a recent industry report, which showed loyalty members accounted for [52.8%](https://www.cbre.com/insights/articles/hotel-loyalty-programs-continue-to-prove-their-value-key-findings-from-675-million-members) of occupied rooms in 2024, as membership across major chains surged by 14.5%.
- **Personalized guest experiences**: From tailored loyalty rewards to smart rooms that set the guest's preferred lights and music, personalization is a key driver of guest satisfaction, spending, and loyalty. AI algorithms can analyze historical guest data and behavior in real time to tailor interactions, creating bespoke experiences that help hospitality companies boost occupancy rates and repeat stays.
- **Operational efficiency**: By automating front office processes such as reservations, inventory management, and reporting, as well as guest relations tasks like answering basic queries, AI helps hotels to create new efficiencies. Per a recent industry survey, increased efficiency is [the top way (39%) hospitality leaders say AI will reshape operations](https://www.researchgate.net/publication/385653120_Digital_Transformation_in_the_Hospitality_Industry_Improving_Efficiency_and_Guest_Experience).
- **Enhanced revenue management**: When supplied with adequate financial data and historical business intelligence, AI-driven analytics transform revenue management. By analyzing data, demand signals, and market conditions, AI systems can make real-time pricing adjustments to dynamically optimize occupancy rates, cash flow, and profitability—yielding gains in net operating income (NOI) and omnichannel sales for hotels across seasons.
- **Data-driven insights**: AI’s ability to analyze large, complex datasets makes it a top innovation area for business analytics today and into the future. By uncovering trends in guest behavior, seasonal demand, and operational costs, hotels can forecast more accurately, allocate resources strategically, enhance segmentation and targeting, and power automation with confidence.
- **Precision marketing**: Hospitality marketing teams use AI to craft hyper-targeted campaigns, optimize social engagement, and respond to guest feedback in real time. GenAI-created copy and visuals accelerate creative productivity, while predictive analytics improve personalization and targeting. Agentic AI goes further, analyzing historical data *and* live context to trigger the right offer in the moment to boost conversion and loyalty.
- **Security and monitoring**: A recent survey shows [68% of hospitality IT professionals](https://www.netsuite.com/portal/resource/articles/business-strategy/ai-hospitality.shtml) cite improving the safety of guests and staff as a top technology priority. AI enhances surveillance and building access systems, enabling faster anomaly detection and real-time alerts for suspicious activity or potential threats.
- **Sustainability efforts**: AI plays a pivotal role in smart-building technologies, helping hotels track and reduce energy and water consumption. By analyzing usage patterns and optimizing HVAC, lighting, and waste systems, AI supports sustainability goals while lowering utility costs and carbon footprint.
- **Crisis management:** AI systems can detect emerging risks faster than traditional hospitality systems, from severe weather to supply disruptions—then support real-time responses. For example, generative AI helps craft personalized alerts and guidance for guests, enabling hotels to manage crises proactively while maintaining guest safety and trust.
## 8 best AI use cases in hospitality (with real-life examples)
AI use cases in hospitality are expanding rapidly, creating new possibilities for guest service and new business models for the industry. Here are some of the most impactful:
### 1. Contactless check-in and checkout
Faster, more convenient check-ins and checkout are becoming a guest expectation in the hospitality industry, and AI can help. According to Amadeus Research's Traveler Tribes Report 2033, [64% of guests want to check in using their mobile devices](https://amadeus.com/documents/en/traveler-tribes/reports/tribes-report-final-en.pdf), while 53% prioritize hotels that offer contactless payment options.
With AI-enabled mobile apps or kiosks, hotels can automate check-in and checkout for guests. This improves guest satisfaction and reduces front-desk congestion, freeing hotel staff to focus on customer interactions that drive loyalty and repeat stays.
**Property spotlight:** Major hotel brands, such as [Hilton](https://www.hilton.com/en/p/hilton-honors-mobile-app/), Hyatt, and Marriott, now offer mobile check-in and keyless entry via their mobile apps. Perks include allowing guests to pre-select special rooms and check in early.
### 2. AI concierge
Chatbots are perhaps the most widespread form of AI in hospitality, and these virtual assistants have evolved from FAQ bots into all-purpose concierges. They use natural language understanding (NLU) to provide instant, personalized, multilingual support across websites, apps, and in-room devices around the clock. For example, they pull from the hotel's internal policies to handle routine questions about pool hours, restaurant recommendations, or extra bedding requests.
AI agents are the new generation of chatbots, able to retrieve external data and act on behalf of staff or guests in real time to create a more enjoyable stay. For instance, an [AI agent for hospitality](https://delight.ai/industries/travel-and-hospitality) could offer guests the local weather forecasts, research and suggest nightlife activities, even recommend then book local dining experiences—all without human intervention.
A recent Statista survey found that [50% travelers](https://www.statista.com/statistics/1398144/guest-interest-hotels-automated-messaging-chatbot-worldwide/) are interested in hotels that offer AI concierge services. Beyond attracting guests, agentic systems enable staff to prioritize meaningful guest interactions, boosting productivity without compromising guest satisfaction.
**Property spotlight:** Many major hotel brands offer chatbots. Beyond this, [Beverly Wilshire](https://www.hoteldive.com/news/tech-in-hologram-concierge/714711/), A Four Seasons Hotel in California, features a holographic concierge in select suites.
### 3. Enhanced guest experiences
AI is transforming personalization in the hospitality industry from a scattered, reactive approach to a proactive, unified understanding of who guests are and what matters to them. By analyzing stays, preferences, feedback, and booking patterns, AI-driven hotel systems can create dynamic guest profiles that tailor every aspect of a stay—from surprising welcome amenities to local suggestions to tailored post-stay offers.
According to EHL Hospitality Insights, [61% of hotel guests would pay more for customized experiences](https://hospitalityinsights.ehl.edu/ai-in-hospitality), yet only 23% received high personalization on their last stay. Agentic AI enables this by understanding and acting on live context signals *and* historical guest data, making interactions more timely and relevant. By connecting across formerly siloed systems, AI agents enable the seamless, scalable delivery of enhanced guest experiences that make each stay unique.
**Property spotlight:** [Marriott's Renaissance Hotels](https://www.tredence.com/blog/agentic-ai-travel-hospitality#:~:text=Source:%20Expedia%20Group-,3.,updated%20and%20vetted%20for%20quality.&text=These%20real-world%20examples%20show,built%20on%20service%20and%20connection.) offers guests curated recommendations for local dining and activities through its AI-powered agent concierge that pulls data from ChatGPT and employee favorite lists to enhance guest stays.
### 4. Personalized marketing offers
AI enables hospitality marketers to be more precise and data-driven, shifting from generic promotions to real-time, guest-specific experiences that convert better. Deloitte’s 2025 Travel Trends highlights that [timely, targeted deals drive incremental spending](https://www.deloitte.com/us/en/insights/industry/transportation/corporate-business-travel-survey.html) among hotel guests.
AI algorithms can analyze guest behavior, booking history, and spend sensitivity, while AI agents proactively trigger the delivery across all integrated channels and platforms of more timely, relevant offers. AI-driven segmentation can cluster customers by behavior, intent, and historical activity to improve targeting and marketing ROI. According to McKinsey, AI-driven personalized campaigns now drive about [40% of revenue](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) for top hospitality brands.
**Property spotlight**: [The Accor Group](https://skift.com/2025/05/27/accor-digital-strategy-ai-loyalty-hospitality/) delivers hyper-personalized offers based on past bookings and behaviors. By anticipating guest needs, the company demonstrates knowledge of guest preferences while leaving room for choice, helping to drive direct bookings and lasting loyalty.
### 5. Smart rooms with energy optimization
With guest expectations rising and operational costs tightening, AI-powered smart rooms offer hotels a way to achieve greater personalization and greater efficiency. By integrating IoT sensors and voice recognition AI technology, these rooms automatically adjust lighting, temperature, entertainment, and other settings to guests’ preferences.
The result is a more comfortable, tailored stay that's also more energy efficient. These rooms support sustainability efforts, reducing energy and water consumption by automatically powering off room lights when guests are away.
**Property spotlight:** [Marriott Hotels](https://advhtech.com/blog/how-smart-hotel-rooms-are-transforming-the-hospitality-industry/#:~:text=The%20thermostats%20can%20adjust%20heating,personalized%20hotel%20experience%20for%20guests.) rolled out [AI voice-activated assistants](https://delight.ai/voice) in select properties, allowing guests to control lights, thermostats, and entertainment systems using voice commands to enhance guest comfort while reducing energy use.
### 6. Conversational AI support
Conversational AI goes far beyond the clunky IVRs and call center scripts of traditional hospitality, using NLU to deliver tailored support that saves time for both hotel guests and staff. AI enables hospitality systems to engage in fluid, human-like dialogue while understanding guest context, tone, and emotion using sentiment analysis.
Delight.ai's [conversational AI for hospitality](https://delight.ai/industries/travel-and-hospitality) autonomously manages reservations, handles guest inquiries, and scales high-quality support across every channel without losing context between conversations. Its [agentic AI integrations](https://delight.ai/integrations) give hotel systems persistent guest memory, so a returning guest's preferences and past requests carry forward automatically instead of resetting with every new interaction.
**Property spotlight:** Delight.ai powers the always-on support layer for [Hanssem](https://delight.ai/customers/hanssem), a major home goods retailer, where the resolution rate climbed from 48% to 86% over five months as the AI agent took on more of the routine service load. The same conversational AI approach lets hospitality brands scale guest support without adding headcount.
### 7. Operational efficiency
AI is transforming hotel operations by automating tasks, optimizing resource allocation, and predicting maintenance needs before they disrupt service. AI systems can dynamically adjust staffing levels, monitor equipment performance, and optimize housekeeping schedules using real-time occupancy data and more.
This shift from reactive to proactive, AI-driven operations reduces downtime, cuts costs, and enables consistently high service standards across every property.
**Property spotlight:** [Hilton](https://www.renascence.io/journal/how-hilton-enhances-customer-experience-cx-with-digital-innovations-in-hospitality) uses AI to forecast housekeeping demand and optimize cleaning routes, lowering labor costs while maintaining cleanliness standards.
### 8. Revenue management & forecasting
Unlike traditional methods that rely on human input and intuition, AI-powered revenue management is automated, dynamic, and data-driven. ML models analyze vast datasets—from competitor pricing and booking patterns to weather forecasts and local events—to recommend optimal hotel rates and promotions in real time.
The result is more accurate forecasting, faster pricing adjustments, and greater agility in response to market shifts, helping hotels to optimize revenue across channels. According to 2025 research, hotels using AI-driven revenue management systems report an average [15–20% increase in RevPAR](https://journal.jabian.com/the-ai-driven-hotel-how-data-is-redefining-hospitality/) (revenue per available room) and improved forecasting accuracy. This enables operators to balance occupancy and profitability, increasing both precision and productivity.
**Property spotlight:** [InterContinental Hotels Group (IHG)](https://www.epic-rev.com/post/case-studies-ai-in-revenue-management-at-leading-hotel-chains?utm_source=chatgpt.com) rolled out an AI revenue management system across 5,000 hotels, boosting RevPAR through smarter segmentation and offer bundling.
## A roadmap to value with AI in hospitality
To unlock AI’s full potential, hospitality leaders must embed AI into their core business strategy. Like previous digital transformations, getting value from AI requires more than experimentation, but strategic investment, well-structured governance, and cultural alignment. According to [McKinsey](http://mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#:~:text=to%20capture%20value-,The%20state%20of%20AI:%20How%20organizations%20are%20rewiring%20to%20capture,at%20least%20one%20business%20function.), companies that redesign existing workflows end-to-end, rather than simply bolting AI onto existing processes, are far more likely to drive significant ROI from AI investments.
This enables leaders to overcome the real challenges of AI integration, which range from the high cost of implementation to integration complexity to the need to constantly balance the presence of automation with the human touch that defines great hospitality.
The following framework outlines a strategic approach for successfully implementing AI in hospitality:
### 1. Reimagine the business around AI
Begin by establishing a dedicated AI strategy team or innovation hub to drive adoption across the organization. Their mission: identify the use cases where AI can deliver the most value, whether in guest experience, loyalty programs, or back-office efficiency.
It’s important to have a clear understanding of what AI requires to be effective, including:
- **Clean, well-structured data** that AI systems rely on to operate effectively
- **Clearly defined workflows** and measurable objectives that enable AI to run reliably
- **Executive sponsorship** to guide employee adoption and maintain vision
- **Robust AI governance** to ensure safe, responsible, scalable operations
### 2. Build infrastructure for the future
Modernizing legacy systems is critical to scalable AI adoption. For the hospitality industry, a future-ready AI ecosystem hinges on four critical layers:
- **Data governance**: Ensures the quality, accessibility, and compliance of both structured and unstructured data for AI use across global operations. This includes meticulous data collection, cleansing, preprocessing, and annotation.
- **AI models**: The predictive engines that power AI analysis, insights, and decision-making. Various models exist; choosing the right one for your target use case is made easier with websites like [Huggingface](https://huggingface.co/collections/open-llm-leaderboard/open-llm-leaderboard-best-models-652d6c7965a4619fb5c27a03).
- **AI-enabled applications**: The interfaces that translate intelligence into real-world guest and staff experiences. For example: web or in-app chatbots for customers, and copilots for employees.
- **AI platforms**: Various tools are emerging that simplify the process of building, training, and deploying AI systems, from dev-focused frameworks to no-code builders. For example, delight.ai’s [AI agent builder](https://delight.ai/builder) helps non-technical teams to quickly build, manage, and scale custom AI agents for support and CX.
### 3. Encourage employee uptake
Even the best AI can stumble if organizational enthusiasm lags behind ambition. In fact, [31% of employees are actively resisting AI initiatives in their companies](https://www.expressnews.com/business/article/ai-middle-managers-training-21048161.php?utm_source=chatgpt.com)—often citing fears of job displacement, lack of training, or uncertainty about how AI will impact their role.
The goal for AI leaders should be to demonstrate how this new technology can simplify their daily tasks and improve overall efficiency. This approach helps staff to feel that AI complements their skills rather than replaces them. To enhance uptake and overall satisfaction, consider implementing user-friendly interfaces, guided assistance, and intuitive workflows to make the transition more manageable and efficient.
### 4. Build AI you can trust at scale
Trust in AI is paramount in the hospitality industry. AI systems traffic in sensitive customer data that, if breached, risks a loss of customer trust, reputational damage, and noncompliance. To safeguard guest trust and organizational integrity, leaders must:
- Implement a comprehensive AI governance framework such as delight.ai's [Trust OS](https://delight.ai/trust-os) to define your AI policies, oversight, and accountability practices.
- Enforce robust AI cybersecurity controls to prevent model manipulation, data leakage, prompt injection attacks, and other emerging threats.
- Build every AI system and workflow for reliability, privacy, fairness, and transparency from the start.
By following these steps, hospitality companies can position themselves to capitalize on the transformative capabilities of AI, driving innovation and securing a competitive edge in an industry that increasingly runs on AI.
## AI in hospitality: Balancing automation with the human touch
To unlock AI’s full potential without running the risk of alienating guests, hotels must make trust the foundation of AI innovation. That requires deploying observable, explainable, and well-integrated AI systems built on enterprise-grade infrastructure with robust compliance and security safeguards. Together, these capabilities ensure consistent performance across every guest touchpoint—protecting brand reputation while providing real-time visibility into customer sentiment and giving teams the control to optimize AI behavior before it undermines guest experience.
## How does Delight.ai help?
With over a decade of experience powering secure, enterprise-grade communications for more than seven billion conversations worldwide, Delight's [AI agent platform](https://delight.ai/ai-agent) delivers the infrastructure and APIs that enable safe, scalable, and personalized guest experiences across every channel. From real-time messaging and omnichannel support to AI observability and built-in safeguards, delight.ai helps hospitality brands deploy AI that’s not only powerful—but responsible, compliant, and built to preserve the human connection at the heart of hospitality.
Curious how our [omnichannel AI agents](https://delight.ai/omnipresent) for hospitality deliver the best voice-enabled support and CX engagement across channels?
[Contact sales](https://delight.ai/contact-sales) today.
- [What is AI in hospitality?](#section-anchor-id-0 "What is AI in hospitality?")
- [Understanding AI in hospitality](#section-anchor-id-1 "Understanding AI in hospitality")
- [Core types of AI in hospitality](#section-anchor-id-2 "Core types of AI in hospitality")
- [Business benefits of AI in hospitality](#section-anchor-id-3 "Business benefits of AI in hospitality")
- [8 best AI use cases in hospitality (with real-life examples)](#section-anchor-id-4 "8 best AI use cases in hospitality (with real-life examples)")
- [A roadmap to value with AI in hospitality](#section-anchor-id-5 "A roadmap to value with AI in hospitality")
- [AI in hospitality: Balancing automation with the human touch](#section-anchor-id-6 "AI in hospitality: Balancing automation with the human touch")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## FAQs about AI in hospitality
**What is the future of AI in hospitality?**
The future of AI in hospitality lies in creating more connected, intelligent ecosystems that unify guest data, automate operations, and personalize the guest experience at scale. From predictive maintenance and dynamic pricing to AI-driven concierge services, hotels are moving toward end-to-end automation that enhances service quality without losing the human touch.
**Will AI replace hospitality jobs?**
No—AI will augment, not replace, the human touch in hospitality. While it automates repetitive tasks like check-ins, bookings, and inquiries, it also frees staff to focus on high-value interactions that build trust and loyalty. The most successful hotels use AI to empower teams, not eliminate them.
**How big is the AI in the hospitality market?**
The AI in hospitality market was valued at **$90 million in 2023** and is expected to grow by more than **60% annually**, surpassing **$8 billion by 2033** (Source: *Oracle Hospitality / Statista, 2024*). This growth is driven by rising demand for personalization, automation, and intelligent revenue management across the travel and lodging sectors.
**What are the biggest challenges of AI in hospitality?**
Adopting AI comes with challenges for the hospitality industry, including:
- **Data privacy and compliance**: Ensuring guest data aligns with GDPR, CCPA, and local regulations.
- **Legacy system integration**: Older IT infrastructures often make AI adoption complex and costly.
- **Balancing automation and authenticity**: Too much automation can erode the warmth and human connection that define great hospitality.
**What is the best AI use case for hospitality?**
Some of the most effective uses of AI in hospitality today include:
- **Conversational AI and chatbots** for 24/7 guest support and concierge services.
- **Revenue management tools** for real-time dynamic pricing and demand forecasting.
- **Smart rooms** that personalize lighting, temperature, and entertainment based on guest preferences.
- **Predictive maintenance systems** that prevent downtime by identifying issues before they occur.
**What are some real-world examples of AI in hospitality?**
- **Marriott Bonvoy** uses AI-powered natural language search to match travelers with 140,000 luxury home rentals.
- **Universal Orlando Resort** employs facial recognition for secure, contactless park entry.
- **Radisson Hotel Group** leverages AI-powered virtual event visualization tools for sales and planning.
- **InnVest Hotels** deploys AI-enhanced delivery robots to handle guest deliveries and improve service efficiency.
- **Hilton** uses AI-driven forecasting to optimize housekeeping schedules and resource allocation.
**Why does trust matter in hospitality AI?**
AI’s success in hospitality depends on trust, both from guests and hotel staff. Transparent AI practices, strong governance, and real-time safeguards can protect against data misuse and ensure that AI-powered automation enhances, rather than undermines, the personalized customer service that defines hospitality.
## Keep reading
[View more](/blog "View more")
[](/blog/product/delight-ai-agent-guardrails-in-action) Meet Harper: Delight.ai's AI agent guardrails in action
[](/blog/product/introducing-intelligent-voice-handoff) Introducing Intelligent Voice Handoff: A complete path from AI to human
[](/blog/industry/patient-engagement-solutions) Patient engagement solutions for consumer health brands
[](/blog/ai-for-cx/guide-to-customer-experience-analytics) The complete guide to customer experience analytics
[View more](/blog "View more")
## 13 real-world AI in healthcare examples
Source: https://delight.ai/blog/industry/ai-healthcare-example
- [Blog](/blog)
- [Industry](/blog/industry)
- 13 real-world AI in healthcare examples
## 13 real-world AI in healthcare examples
[Ameer Drane](https://delight.ai/author/ameer-drane)
Agentic AI marketer
March 16 2026
Artificial intelligence (AI) in healthcare is no longer a futuristic idea—it’s already reshaping how care is delivered, managed, and experienced. From mapping the genome to automating administrative tasks, AI enables software to perform tasks traditionally handled by humans—only faster, with greater precision, and at a fraction of the cost.
With its ability to analyze complex datasets, understand natural language, and even act autonomously, AI is unlocking more than just a new standard in healthcare operations. It's paving the way to smarter diagnostics, more personalized care, and better outcomes for patients and providers.
In this article, we dive into 13 real-world examples of how AI is making an impact in healthcare today. Along the way, we’ll look at the benefits these technologies bring—as well as the challenges organizations face when adopting them.
- [13 AI in healthcare examples](https://delight.ai/blog/industry/ai-healthcare-example#section-anchor-id-1)
- [Pros and cons of AI in healthcare](https://delight.ai/blog/industry/ai-healthcare-example#section-anchor-id-2)
- [What types of AI are used in healthcare?](https://delight.ai/blog/industry/ai-healthcare-example#section-anchor-id-3)
### What is AI in healthcare?
AI in healthcare refers to the use of machine learning, natural language processing, computer vision, and other AI technologies to enhance healthcare operations, outcomes, and experiences for both patients and caregivers.
AI’s capabilities for data processing, prediction, and autonomous action make it effective in using data intelligently across systems. This enables healthcare professionals to better manage resources, make more accurate decisions, and deliver more timely, personalized care that results in better outcomes.
Rather than replacing healthcare workers, AI is being used to augment these roles, assisting with a growing range of tasks to ensure more precise, efficient, and effective care—often at a fraction of the cost.
### 13 AI in healthcare examples
To give you a clear sense of this rapidly evolving field, we rounded up the following real-world AI in healthcare examples:
#### AI in medical diagnosis
Every year, inaccurate medical diagnoses result in around [400,000 patient hospitalizations](https://www.ncbi.nlm.nih.gov/books/NBK592394/#:~:text=The%20significance%20of%20medical%20errors,promotes%20understanding%20of%20surgical%20errors.&text=Negligence%20is%20medical%20care%20that,training%2C%20knowledge%2C%20and%20experience.&text=A%20near%20miss%20is%20an,protocols%20before%20patient%20harm%20occurs.) and 100,000 deaths. Largely to blame are clinician error, large caseloads, and incomplete medical histories. AI is not only immune to these human variables, it’s been shown to identify and [diagnose disease with greater accuracy](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285156/) and efficiency than humans in many cases.
For example, UK researchers used an AI tool to successfully detect [64% of epilepsy brain lesions previously missed by radiologists](https://jamanetwork.com/journals/jamaneurology/fullarticle/2830410?guestAccessKey=e833d51e-69c6-4a26-bee3-66b84e3e9b0c&utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_content=tfl&utm_term=022425). Trained on the MRI scans of over 1,100 adults and children, the AI tool was able to spot lesions more quickly than a doctor, and also discover tiny or obscured lesions that evaded human sight. AI has also been used to analyze medical images such as X-rays and MRI scans, diagnosing conditions like cancer, pneumonia, cardiovascular diseases, and more.
*AI-powered cancer screening tools have shown* [*a 21% increase in detection rates*](https://deephealth.com/insights/ai-is-transforming-cancer-detection-whats-next/).
#### AI in preventative medicine
AI models can process large and complex datasets to identify signs of disease before symptoms appear. For example, using machine learning, one model is capable of [predicting Alzheimer's disease](https://www.ucsf.edu/news/2024/02/427131/how-ai-can-help-spot-early-risk-factors-alzheimers-disease#:~:text=UC%20San%20Francisco%20scientists%20have,the%20bone-weakening%20disease%20osteoporosis.) up to seven years before symptoms manifest. The model’s leading predictive factors of Alzheimer’s were high cholesterol in men and osteoporosis in women.
By detecting the subtle biological markers correlated with diseases, AI can help clinicians to identify and treat maladies that often go overlooked until it's too late. This approach enables a shift from reactive to proactive healthcare, allowing providers to intervene earlier, optimize screening plans, and improve health outcomes—all while reducing the costs associated with long term care.
#### AI in drug discovery
Drug discovery is time- and resource-intensive, costing an average of [$1.3 billion](https://www.cbinsights.com/research/clinical-trials-ai-tech-disruption/) and achieving just a ~10% success rate. AI accelerates the process, helping to generate new drug molecules, optimize lead compounds, and predict side effects to bring novel treatments to market faster.
For example, the [first AI-designed drug candidate](https://www.drugtargetreview.com/news/157365/first-ai-designed-drug-rentosertib-named-by-usan/) progressed to clinical trials in just 18 months. Researchers at MIT used deep learning to [identify a novel antibiotic known as abaucin](https://en.wikipedia.org/wiki/Abaucin?utm_source=chatgpt.com), which is effective against a WHO-designated critical priority pathogen.
#### AI in patient care and engagement
Chatbots and virtual assistants use natural language processing to provide guidance and support 24/7, scaling patient engagement and making digital care more accessible and personalized. AI also enables remote patient monitoring via wearables and apps, offering real-time feedback, reminders, and early issue detection.
*AI agent for healthcare integrates with patient data to deliver personalized care with high levels of accuracy.*
#### AI in personalized medicine
AI can analyze genetic, clinical, and lifestyle data to create personalized treatment plans—aka precision medicine. This helps guide treatment, minimize adverse reactions, and optimize dosages. For many chronic conditions, AI-driven personalization can lead to earlier diagnosis, prevention, and improved outcomes.
*AI promises to advance personalized medicine in many medical domains.* ([Adapted from source](https://hyscaler.com/insights/ai-powered-personalized-medicine/))
#### AI in patient experience
AI automates personalized communications—virtual assistants, appointment scheduling, and tailored messaging—addressing patient frustrations over poor communication and ensuring timely, convenient interactions that improve satisfaction and adherence.
#### AI in smart hospital rooms
Smart rooms combine IoT and AI: voice-assisted devices (e.g., Alexa), computer vision for continuous monitoring, and intelligent alerts to prevent adverse events like falls or medication errors. These systems improve safety and promote treatment adherence.
#### AI in healthcare administration
Administrative automation via AI reduces time spent on scheduling, billing, and documentation. Tools like Microsoft’s Dragon Copilot automatically listen to consultations and create clinical notes using generative AI—freeing clinicians for patient care and reducing human error.
*AI virtual assistants embedded in healthcare workflows help to streamline tasks, reduce errors, and elevate care standards.* ([Adapted from source](https://cdn-dynmedia-1.microsoft.com/is/image/microsoftcorp/msft-dragon-copilot-overview_tbmnl_en-us?resMode=sharp2&op_usm=1.5,0.65,15,0&wid=960&hei=539&qlt=100&fit=constrain))
#### AI in genomics
AI analyzes DNA sequences to identify patterns and mutations, speeding genome sequencing, reducing costs, and enabling prediction of disease risk and drug response—key enablers of precision medicine.
#### AI in healthcare data management
Using NLP and machine learning, AI converts clinical notes, imaging, wearable signals, and other sources into structured, usable data. This enables integrated patient profiles, streamlined billing and coding, and better care coordination. Examples include NVIDIA solutions to standardize and integrate health data across sources.
#### AI in robotic surgery
AI enhances robotic surgery by interpreting imaging, mapping surgical plans, guiding robotic arms, and continuously adjusting motion for precision—resulting in fewer complications, less pain, and faster recovery.
#### AI in medical education and training
AI supports learning at multiple levels: learner-focused personalization, instructor support, institution planning, and curriculum optimization. Applications include adaptive learning platforms, AI-driven simulations, and VR environments for safe procedural practice.
#### AI in mental health
AI chatbots and platforms provide 24/7 support, analyze language and speech patterns to assess mood, and surface early warning signs. Tools like Woebot can detect language shifts associated with mood disorders and prompt follow-up or clinical evaluation.
### The pros and cons of AI in healthcare
For all its wide-ranging applications, AI in healthcare isn’t without risks. Implementation must include human oversight, governance, transparency, security, and ethics.
#### Pros of AI in healthcare
- Improved diagnostics through large-scale data analysis.
- Streamlined workflows and reduced administrative burden.
- Personalized treatment plans informed by genetics and lifestyle.
- Accelerated drug discovery.
- Improved quality of care and access.
- Cost reduction via operational efficiency (projected savings: [$150 billion annually by 2026](https://www.accenture.com/us-en/industries/health/digital-health)).
- Enhanced access through chatbots and remote monitoring.
- Reduced physician stress by automating repetitive tasks.
#### Cons of AI in healthcare
- Data privacy and security concerns around sensitive health information.
- Bias from training data causing unequal outcomes.
- Model errors or "hallucinations" that can mislead clinicians.
- Risk of over-reliance eroding clinical skills.
- Ethical questions about consent, fairness, and accountability.
- High implementation costs for smaller practices.
- Human error and misinterpretation during early adoption.
- Resistance or fear of job displacement.
- Continued need for human expertise and oversight.
### What types of AI are being used in healthcare?
Several AI types are used across clinical care, operations, and research. Below are the main categories and top use cases:
#### Machine learning (ML)
Use case: Predictive analytics, diagnostics, image analysis. ML learns from historical data to make predictions or detect patterns.
#### Natural language processing (NLP)
Use case: Clinical documentation, decision support, EHR analysis. NLP extracts insights from unstructured text and powers chatbots and AI agents.
#### Expert systems
Use case: Clinical decision support, symptom checkers. These rely on predefined rules to offer recommendations.
#### Robotic process automation (RPA)
Use case: Billing, scheduling, claims. RPA automates repetitive, rule-based tasks.
#### Computer vision
Use case: Medical imaging interpretation (X-rays, MRIs, slides). Models analyze visual data to identify abnormalities.
#### Generative AI (Gen AI)
Use case: Content creation, summarizing clinical notes, patient education. LLMs generate human-like text and support documentation and patient communication.
#### Agentic AI (AI agents)
Use case: Autonomous care management, clinical decision-making, operational optimization. Agentic AI acts autonomously to achieve goals—monitoring patient data, initiating actions, and adapting with minimal human input.
If you’re looking to [build AI agents](https://delight.ai/builder) tailored to your healthcare organization’s workflows and requirements, delight.ai can help.
Our advanced [AI agent platform](https://delight.ai/ai-agent) enables healthcare providers to build, customize, and optimize AI agents on secure, enterprise-grade infrastructure that ensures scalability, compliance, and efficiency.
- [What is AI in healthcare?](#section-anchor-id-0 "What is AI in healthcare?")
- [13 AI in healthcare examples](#section-anchor-id-1 "13 AI in healthcare examples")
- [The pros and cons of AI in healthcare](#section-anchor-id-2 "The pros and cons of AI in healthcare")
- [What types of AI are being used in healthcare?](#section-anchor-id-3 "What types of AI are being used in healthcare?")
### FAQs for AI in healthcare
What is AI in healthcare?
AI in healthcare refers to the use of machine learning, natural language processing, computer vision, and other AI technologies to enhance healthcare operations, outcomes, and experiences for both patients and caregivers.
AI’s capabilities for data processing, prediction, and autonomous action make it effective in using data intelligently across systems. This enables healthcare professionals to better manage resources, make more accurate decisions, and deliver more timely, personalized care that results in better outcomes.
Rather than replacing healthcare workers, AI is being used to augment these roles, assisting with a growing range of tasks to ensure more precise, efficient, and effective care—often at a fraction of the cost.
How is AI used in healthcare?
AI enhances clinical care, streamlines operations, and expands patient access. It improves diagnosis from imaging and patient data, enables personalized treatment plans, automates administrative workflows (RPA, NLP), powers remote monitoring, accelerates drug discovery, and supports patient engagement through chatbots and virtual assistants. Agentic AI can optimize staffing, predict surges, and allocate resources.
How does AI reduce costs in healthcare?
AI reduces costs by automating administrative workflows, optimizing resource allocation with predictive analytics, preventing unnecessary tests and readmissions, and improving supply chain management.
Will AI replace doctors?
Not likely. AI is intended to support clinicians—augmenting workflows, increasing efficiency, and enabling more personalized care—rather than replacing healthcare professionals.
What are the ethical considerations of using AI in healthcare?
AI requires safeguards for data privacy, bias mitigation, accountability, and informed consent. Human oversight is essential to prevent errors and ensure AI-driven decisions align with medical ethics and patient well-being.
Why is AI important in healthcare?
AI addresses pressing challenges—improving outcomes, reducing costs, and expanding access. With growing medical data volumes, AI processes complex information quickly to support faster diagnoses and informed clinical decisions.
What is the role of generative AI in healthcare?
Generative AI (LLMs and advanced neural networks) creates new outputs—text, images, code, or recommendations—based on existing data. It helps summarize records, draft clinical notes, generate patient materials, power chatbots, and assist communication.
### Keep reading
[View more](/blog "View more")
[](/blog/product/delight-ai-the-next-chapter-of-sendbird)
Delight.ai — The next chapter of Sendbird
[](/blog/product/for-you-conversations)
How For You Conversations make every interaction feel personal
[](/blog/product/introducing-agent-memory-platform)
Introducing AMP: The Agent Memory Platform
[](/blog/product/connection-starts-with-omnipresence)
The future of customer connection starts with Omnipresence
[View more](/blog "View more")
## AI in ecommerce: Use cases, costs, and where to start
Source: https://delight.ai/blog/industry/ai-use-cases-ecommerce
[Ian Heinig
Agentic AI marketer](https://delight.ai/author/ian-heinig)
March 16 2026
Imagine a world where your customer experience becomes more personalized and seamless every day while operations become more data-driven, streamlined, and profitable. This is the power of [AI in ecommerce](https://delight.ai/industries/retail).
From predictive recommendation systems to generative AI chatbots, AI is fast becoming the new engine for online retail success. In 2024, adoption of Gen AI jumped [from 55% to 75%](https://blogs.microsoft.com/blog/2024/11/12/idcs-2024-ai-opportunity-study-top-five-ai-trends-to-watch/#:~:text=Generative%20AI%20usage%20jumped%20from,realizing%20value%20within%2013%20months.) among business leaders, signaling its growing importance in competitive operations. The next frontier is AI agents—autonomous entities that use real-time data and execute tasks from start to finish.
An [AI concierge](https://delight.ai) allows retailers to scale hyper-personalized experiences and automate complex processes with contextual understanding, setting a new standard for operational efficiency, business intelligence, and customer experience (CX).
This article explores **15 key AI use cases in ecommerce**, highlighting the **latest AI tools that B2C and B2B retailers need** to stay competitive and win customers for life.
## Understanding AI in ecommerce
With over [33%](https://www.sellerscommerce.com/blog/ecommerce-statistics/) of the world’s population now shopping online, there’s increasing demand for flexible, convenient, and seamless shopping on a global scale. Faced with rising customer expectations, costs, and operational complexity, retailers must deliver exceptional experiences to cut through the noise and win loyal customers. This is where AI shines.
AI allows online retailers to analyze and leverage vast amounts of data in real-time to improve operational efficiency, increase sales, make data-driven decisions, and gain a deeper understanding of customers—all while delivering a more personalized, seamless shopping experience across channels.
It's a win-win for shoppers and retailers, so it’s no surprise that over [70%](https://www.prnewswire.com/news-releases/harvard-business-review-analytic-services-report-70-of-survey-respondents-say-ai-is-mission-critical-for-e-commerce-success-302305152.html) of ecommerce businesses are now using AI in some form. These technologies are complementary when integrated effectively, and fall into three main buckets:
### Core types of AI in ecommerce
- **Predictive AI:** Analyzes historical data like customer behavior, preferences, and purchase history using machine learning (ML) algorithms to identify patterns and make predictions.
- Use cases: Demand forecasting, inventory optimization, dynamic pricing, audience segmentation, personalized product recommendations
- **Generative AI:** Interprets customer queries and automatically creates a contextually relevant output in any language using natural language processing (NLP) and ML.
- Use cases: [AI customer support](https://delight.ai/ai-customer-service) chatbots, automated website content, marketing campaigns, and automated product descriptions
- **Agentic AI:** AI agents are proactive systems that perceive their environment and leverage real-time data via APIs and tools to solve problems on their own. They expand AI's scope of information and functionality to use both historical and real-time data in dynamic ways.
- Use cases: All the above. AI agents enable a new generation of context-driven personalization, process automation, AI customer service, and actionable business intelligence—known as agentic commerce.
By integrating these intelligent technologies into existing functions, retailers can create more contextually appropriate and relevant buying experiences, automate complex tasks, and optimize operations to drive growth at scale.
## 15 key AI use cases in ecommerce
How do you use [AI in ecommerce](https://delight.ai/industries/retail)? Research shows that [AI customer care](https://delight.ai/ai-customer-service) can boost customer satisfaction, revenue, and cost savings by up to [25%](https://www.gartner.com/smarterwithgartner/top-10-trends-in-digital-commerce), but knowing where to start can be challenging because the technology is evolving rapidly.
Here are the most impactful AI use cases in ecommerce:
### 1. Hyper-personalized product recommendations
Personalization is a key driver of customer engagement and loyalty, as a full [81%](https://www.forbes.com/sites/shephyken/2024/04/14/the-personalized-customer-experience-customers-want-you-to-know-them/) of shoppers favor brands that tailor their experience. AI-driven recommendation systems use ML and predictive analytics to track behavior, analyze preferences, and tailor shopping experiences. Generative chatbots use NLP to recommend products based on business data they’ve been pre-trained on.
[AI customer service ](https://delight.ai/ai-customer-service) agents go further, tailoring CX using past and present data to deliver hyper-personalized recommendations. For example, a website shopping assistant agent can recommend products based on purchase history and current conversation context, user behavior, and prior interactions stored in memory—improving conversion likelihood.
**Key AI tech:** Recommendation engines like Qubit, Klevu, Amazon Recommendations AI; [AI agent builders](https://delight.ai/builder).
### 2. Proactively curb cart abandonment
Cart abandonment hurts revenue, but AI support agents can help. Context-aware, goal-oriented AI agents can detect stalled checkouts and trigger personalized offers (free shipping, discounts) in the split second before a shopper bounces.
If a customer does leave, omnichannel AI agents can follow up via chat, email, or social media with relevant cart reminders and promotions to salvage the sale.
**Key AI tech:** [AI agent platform](https://delight.ai/ai-agent).
### 3. Automated customer service with AI agents
Ecommerce AI agents can handle a large portion of customer interactions autonomously. They use NLP to understand inquiries and respond conversationally—available 24/7 across channels to answer FAQs, recommend products, and execute tasks like transaction processing.
Unlike basic chatbots, AI agents can remember past interactions and maintain continuous, cross-channel conversations, reducing friction and freeing human agents for complex issues.
**Key AI tech:** [AI agent builders](https://delight.ai/builder).
### 4. Dynamic pricing optimization
AI-driven pricing analyzes internal and external factors—availability, demand, costs, competitor pricing—to set optimal prices. Retailers can react in near real-time to market changes, improving margins and sales.
**Key AI tech:** Tools like Price2Spy, Repricer, Pricefy.
### 5. Conversational commerce with AI voice-assisted shopping
Voice-enabled shopping and search (Alexa, Google Assistant) are growing. Integrating NLP and voice tech into sites and support channels enables shoppers to complete purchases via natural conversation. Agentic AI preserves context across channel switches for seamless experiences.
**Key AI tech:** Voice recognition, NLP, AI agents or chatbots.
### 6. Smarter search capabilities
AI enhances discovery through:
- **NLP:** Understands intent and handles typos, synonyms, slang.
- **AI visual search:** Lets customers search by image using image recognition.
- **Context-based search:** Factors in trends, seasonality, and time of day to surface relevant results.
**Key AI tech:** NLP, AI-driven image recognition, context-based search.
### 7. Improved customer segmentation and marketing
AI-driven segmentation analyzes user data to identify patterns and create accurate customer segments—sometimes revealing previously overlooked segments—enabling precise targeting and personalized campaigns.
**Key AI tech:** Platforms like Salesforce Marketing Cloud, Klaviyo, BlastPoint.
### 8. Intuitive upselling and cross-selling
AI predicts complementary products and recommends them at opportune moments ("You might also like", "Frequently bought together") to drive upsells and cross-sells.
**Key AI tech:** Recommendation engines like Amazon Recommendations AI, Qubit, Klevu.
### 9. Auto-generated content
Generative AI produces tailored copy for product descriptions, marketing content, dynamic site content, and personalized messaging based on customer profiles and behavior—reducing time and cost while improving consistency.
**Key AI tech:** ChatGPT, Copy.ai, Jasper.
### 10. Fraud detection and prevention
AI analyzes transactions, behavioral data, IP, and payment details to detect irregular patterns and flag suspicious activity. Agentic AI can require additional verification or halt transactions in real time.
**Key AI tech:** Kount, Featurespace, Darktrace; specialized AI agents for fraud detection.
### 11. Fake review detection
NLP and ML analyze text patterns, writing style, and metadata to identify and flag fake reviews, preserving trust and conversion rates.
**Key AI tech:** NLP/ML tools like FakeSpot.
### 12. Supply chain optimization
AI improves operations via:
- **Smart logistics:** IoT sensors, RFID, and AI agents monitor inventory and automate replenishment.
- **Route optimization:** Real-time routing reduces transportation costs and errors.
- **Order tracking:** Continuous visibility across the last mile improves customer satisfaction.
**Key AI tech:** Optimization algorithms, AI agents, RPA.
### 13. Sales and demand forecasting
AI analyzes sales, demographics, behavior, and external factors to predict demand, reducing stockouts and overstocking. Agentic AI adds real-time market signals to improve agility.
**Key AI tech:** Predictive analytics and tools like Cogsy, Blue Yonder, Google Cloud AI for ecommerce.
### 14. Streamlined inventory management
AI calculates optimal stock levels and uses robotics and image recognition to automate warehouse tasks, reducing headcount needs and errors.
**Key AI tech:** ML, RPA, tools like Cin7, Zoho Inventory.
### 15. Omnichannel customer engagement at scale
AI agents bridge disjointed systems and unify customer data across channels, enabling automated, personalized engagement and a 360° customer view to inform decisions and actions.
**Key AI tech:** AI agent platforms, omnichannel messaging platforms.
## Benefits of AI in ecommerce
AI enables retailers to leverage real-time data to improve operations and scale personalized experiences that increase engagement, sales, and loyalty. Major benefits include:
### 1. Better customer experience
Personalized recommendations, 24/7 AI agent support, and voice shopping improve convenience and satisfaction.
### 2. Improved retention and loyalty
Enhanced CX, faster delivery, and personalized interactions encourage repeat purchases and advocacy.
### 3. Greater operational efficiency
AI automates repetitive tasks and optimizes decisions across pricing, logistics, and support.
### 4. Enhanced decision making
Real-time analysis of large datasets yields actionable business intelligence for forecasting and planning.
### 5. Cost savings
Automation and fraud prevention reduce human error and operational costs.
### 6. Stronger fraud prevention
Real-time fraud detection reduces financial losses and maintains shopper trust.
### 7. Improved marketing campaigns
AI-powered segmentation and personalization improve campaign ROI.
### 8. Higher conversion rates
Agentic AI curbs cart abandonment and proactively delivers offers to increase conversions.
## Best practices for AI in ecommerce
If you’re implementing AI, create a strategy covering implementation, monitoring, and optimization. Key best practices:
### 1. Define your objectives
Set clear goals—CX, automation, inventory optimization—to choose the right tools and KPIs.
### 2. Focus on data
Ensure datasets are clean, accurate, and relevant; invest in data collection and preprocessing.
### 3. Consider integrations and APIs
Integrate AI with existing systems via APIs or native connectors to enable real-time data flow.
### 4. Assemble an expert team
Include data engineers and ML engineers; consider upskilling, hiring, or outsourcing to specialist platforms.
### 5. Monitor and optimize
Track performance, gather customer feedback, and iterate models and automations.
### 6. Safeguard user data and privacy
Implement robust security and compliance measures to protect sensitive customer data.
## AI in ecommerce is becoming an industry staple
With [84%](https://masterofcode.com/blog/state-of-artificial-intelligence-ai-in-ecommerce-statistics-and-deployment) of retailers saying AI is a top priority, ecommerce is rapidly transforming into an AI-centered ecosystem. Agentic AI agents will become more specialized and affordable, enabling tailored, vertical solutions.
If you’re looking to deploy AI agents for retail, [delight.ai](https://delight.ai/ai-agent) offers a platform for building enterprise-grade AI agents with security and scalability.
---
- [Understanding AI in ecommerce](#section-anchor-id-0 "Understanding AI in ecommerce")
- [15 key AI use cases in ecommerce](#section-anchor-id-1 "15 key AI use cases in ecommerce")
- [Benefits of AI in ecommerce](#section-anchor-id-2 "Benefits of AI in ecommerce")
- [Best practices for AI in ecommerce](#section-anchor-id-3 "Best practices for AI in ecommerce")
- [AI in ecommerce is becoming an industry staple](#section-anchor-id-4 "AI in ecommerce is becoming an industry staple")
## Keep reading
[View more](/blog "View more")
[
The 8 best AI tools for ecommerce in 2026](/blog/ai-for-cx/best-ai-tools-for-ecommerce-in-2026)
[
Customer service MCP for ecommerce](/blog/ai-for-cx/customer-service-mcp-ecommerce)
[
Who is responsible when AI makes a mistake?](/blog/ai-agent/who-is-responsible-ai-mistakes)
[
How does real-time customer engagement impact your business?](/blog/ai-for-cx/real-time-customer-engagement)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Beyond chatbots: Retail AI agents are the future of shopping
Source: https://delight.ai/blog/industry/ai-agents-for-retail
## Beyond chatbots: Retail AI agents are the future of shopping
[Ian Heinig](https://delight.ai/author/ian-heinig)
Agentic AI marketer
March 16 2026
## Reactive AI chatbots were the start, proactive AI agents are the future
Not long ago, generative AI chatbots were the great hope of customer service automation. Retailers rushed to deploy them, eager to combine the speed and scalability of traditional scripted chatbots with the natural, human-like conversations promised by generative AI. And for a while, it looked promising. In fact, among business leaders, generative AI adoption jumped from [55% to 75%](https://blogs.microsoft.com/blog/2024/11/12/idcs-2024-ai-opportunity-study-top-five-ai-trends-to-watch/#:~:text=Key%20findings%20from%20IDC's%20The,realizing%20an%20ROI%20of%20%2410.3.) in 2024.
These new AI chatbots excelled at answering common questions 24/7, dramatically reducing support volume and cutting wait times. Customers loved getting instant responses instead of sitting through hold music. However, some chatbot limitations became impossible to ignore.
AI chatbots, for all their generative brilliance, have no real-world awareness—which significantly limits their functional intelligence. While they can tell you a store’s return policy, they can’t process a return. They can offer a discount code, but they can’t personalize the offer based on real-time context like customer behavior, competitor pricing, or a customer’s loyalty status. And sometimes, they hallucinate, [posting falsehoods as truth, once even accidentally selling a $40,000 car for $1](https://www.upworthy.com/prankster-tricks-a-gm-dealership-chatbot-to-sell-him-a-76000-chevy-tahoe-for-ex1).
Constrained by their limited scope of information, chatbots quickly proved that **conversational AI alone wasn’t enough**. What customers and businesses needed wasn’t just chat — it was intelligent action based on real-time context. **This is where AI agents come in**. These proactive, decision-making entities take customer service to the next level, leaving retailers who stop at chatbots behind as the future moves toward agentic commerce.
## AI agents for retail: More than just conversations
So what are AI agents for retail, anyway?
AI agents for retail are software applications powered by artificial intelligence (AI) built to automate tasks and enhance customer interactions for retail businesses. AI agents can be customized to serve various functions, from acting as virtual assistants to personalizing shopping experiences to recommending products to optimizing retail operations, all without human intervention.
Unlike generative AI chatbots, AI agents for retail are fully autonomous problem solvers, capable of making real-time decisions, using real-time data and tools, and executing multi-step tasks from start to finish.
The difference is fundamental. Generative AI chatbots can only provide information when prompted by users, while **AI agents proactively take action toward the goals set for them, resolving complex tasks entirely.**
Because AI agents can get things done independently, and even collaborate with other AI agents to handle more complex tasks, they can help retailers with:
- **Solving delivery delays**
Consider a typical customer service request. A chatbot might say: *“I see your package is delayed. Would you like me to transfer you to a human agent?”*
If the customer wants, an AI agent can improve the delivery time without human assistance. Because the agent is connected to backend systems and uses APIs, it can verify the inventory in real time, reroute the order to a closer fulfillment center, and apply a shipping discount for the inconvenience—all automatically.
- **Handling returns, refunds, and order changes**
AI agents can enhance the experience and efficiency of returns, refunds, and complex order modifications. For example, a chatbot will tell you the return deadline has passed, but an AI agent analyzes sentiment and consults the purchase history and the customer’s loyalty status. With this context, the agent decides whether an exception can be made based on company guidelines and business goals. If so, it processes the return instantly to make the customer happy.
- **Solving complex problems**
Enterprise AI agents can break complex tasks into a sequence of smaller, manageable subtasks, enabling them to tackle customer service queries that are too complex for chatbots. Agents can also work in teams and collaborate in a multi-agent configuration. Imagine a sales agent interacting with a logistics agent and a pricing agent, each contributing expertise to resolve a customer issue.
## AI customer support, anytime, anywhere
**Retailers have spent years chasing the dream of a true omnichannel experience**—one where customers can seamlessly switch between a website, mobile app, text messages, and in-store interactions without losing context.
The reality for retail customer support is that the experience is still not fully automated. When support conversations shift between human agents and channels, there’s a frustrating pause—a break in the flow—while human agents scramble to catch up.
**AI agents fix this. Here's how:**
- **Seamless channel switching**
AI agents can maintain context between conversations across channels. This way, if a customer moves from web chat to WhatsApp to SMS, the conversation picks up exactly where it left off—no repeating, no friction.
Even better, if a customer is driving and can’t text, the AI agent can switch to voice for a hands-free experience.
- **Personalized, proactive, scalable engagement**
Say a high-value customer abandons their cart. The AI agent can engage via WhatsApp with a personalized offer to nudge that individual back to complete the purchase. The beauty of AI agents is that your backend can deploy them at scale, responding in realtime to customer behavior. **Think of it as high-touch, event-driven marketing.**
- **Guiding customers to the best channel**
AI agents can also steer conversations to the **most cost-effective and efficient channel.** If a customer is already active in an app, the AI agent keeps them there. If needed, it can proactively guide the interaction back into the app, where messaging is branded and secure, and insights are owned. In-app messaging provides richer insights—unlike opaque, data-limited channels like SMS—allowing for deeper personalization and more thoughtful engagement.
Beyond creating new efficiencies, AI agents act as the bridge between otherwise disjointed enterprise systems and channels, making the omnichannel customer experience an operational reality. They enable a seamless customer experience that translates to greater **customer loyalty** and turns support into a **strategic revenue-generating advantage.**
## Agentic AI for retail: The nuts and bolts for retailers’ success
**For all their benefits, AI agents aren’t just ‘set it’ and ‘forget it’ tools**—they require thoughtful deployment, training, and continuous feedback to deliver the best business impact. For retailers to maximize their potential, they must consider these three components:
- **The AI agent engine** for creation, testing, and continuous learning
- **Enterprise-grade infrastructure** for security, scalability, and compliance
- **Strategic partnerships** to guide implementation and optimization.
### The AI agent engine: Create, tool-up, train, test, and optimize
An AI agent engine or builder must let businesses:
1. Define the functionality and personality of their agents.
2. Seamlessly connect AI agents to backend tools for context.
3. Integrate with support platforms like Salesforce, Zendesk, or Intercom for human collaboration.
4. Simplify agents’ training on industry knowledge, policies, and standard operating procedures (SOPs).
5. Enforce clear AI guardrails to prevent mistakes.
6. Ensure AI transparency so that agents’ decisions are explainable and compliant with regulations.
7. Before launch, retailers must use real-world customer scenarios, with their partner's assistance, to sharpen responses and catch mistakes early.
8. And once live? AI copilots, human oversight, and performance tracking ensure that retail AI agents keep getting better. As their performance and scope increase, they can evolve into an autonomous AI workforce that will replace chatbots for good.
### AI agent rails: The enterprise-grade foundation
For AI agents to perform at scale, they must be built on a foundation of secure, high-performance infrastructure that supports millions of interactions without friction. Retail businesses need to consider building AI agents on top of:
- **A global messaging network** that seamlessly connects in-app messaging, SMS, WhatsApp, and email—without lag, lost conversations, or frustrating disconnects.
- **Enterprise-grade security & compliance** with HIPAA, SOC 2, and GDPR—essential for protecting customer data and maintaining trust.
- **AI transparency** for businesses to audit AI decisions, refine agent behaviors, and guarantee control over fully automated processes. AI agents can’t be black boxes; they must be tools that work with clear and explainable boundaries.
Even the most advanced and well-designed AI agents will struggle to deliver high-quality support without a compliant and scalable foundation.
### Choosing the right AI agent platform and partner
AI agents are only as effective as the strategy behind their deployment. Retailers need:
- **Dedicated AI experts** to guide the training, testing, and optimization of AI agents.
- **Omnichannel communication expertise** to ensure AI agents deliver seamless text, voice, and video interactions across any channel.
- **Ongoing AI capabilities development** to keep up with the rapid AI, communication, and market evolution.
Retailers that treat AI agents as a one-time setup will fall behind. Those that continuously refine and optimize their AI workforce will turn AI into a competitive advantage.
## AI agents are redefining retail customer support—are you ready?
If AI chatbots did improve customer support, AI agents are revolutionizing it. The shift from conversational AI to AI reasoning and AI action is reshaping customer support as we know it, driving further **efficiency, customer loyalty, and revenue**.
Retailers who embrace AI agents for customer service now will lead the next era of customer experience. More than [80%](https://www.mckinsey.com/capabilities/operations/our-insights/where-is-customer-care-in-2024) of organizations are already investing in GenAI or expect to do so in the coming months.
Delight.ai is positioned to help retailers capitalize on the [AI customer service](https://delight.ai/ai-customer-service?utm_medium=email&utm_source=marketo&utm_campaign=fy27-q1-glbl-content-ebook-on-demand-playbook&utm_content=autoresponder) revolution. Our omnichannel [AI customer experience platform](https://sendbird.com/) has enterprise infrastructure and offers integrations to popular support software.
Get started with [enterprise AI agents](https://delight.ai/?utm_medium=email&utm_source=marketo&utm_campaign=fy27-q1-glbl-content-ebook-on-demand-playbook&utm_content=autoresponder) today.
- [Reactive AI chatbots were the start, proactive AI agents are the future](#section-anchor-id-0)
- [AI agents for retail: More than just conversations](#section-anchor-id-1)
- [AI customer support, anytime, anywhere](#section-anchor-id-2)
- [Agentic AI for retail: The nuts and bolts for retailers’ success](#section-anchor-id-3)
- [AI agents are redefining retail customer support—are you ready?](#section-anchor-id-4)
## Keep reading
[View more](/blog "View more")
[Delight.ai — The next chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)
[How For You Conversations make every interaction feel personal](/blog/product/for-you-conversations)
[Introducing AMP: The Agent Memory Platform](/blog/product/introducing-agent-memory-platform)
[The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[View more](/blog "View more")
## How to build an AI agent: 8-step tutorial
Source: https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent
[Blog](/blog)
- [AI agent](/blog/ai-agent)
- How to build an AI agent: 8-step tutorial
## How to build an AI agent: 8-step tutorial
[](https://delight.ai/author/ian-heinig)
[Ian Heinig](https://delight.ai/author/ian-heinig)
March 16 2026
Just when you were getting the hang of generative AI, the conversation shifted to the latest must-have technology: the AI agent.
AI agents are capable of far more than simply responding to user inputs. They are set to automate many complex tasks that once required human effort. In fact, Gartner predicts that AI agents will make [15%](https://www.gartner.com/en/articles/intelligent-agent-in-ai) of all organizational decisions by 2028.
This raises the question: how do you build an AI agent? What should you expect? What does it cost?
In this article, we’ll walk you through all the main steps to create and deploy an AI agent for any use case. Specifically, we’ll cover:
- [What is an AI agent?](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-0)
- [How do they work?](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-1)
- [How to build an AI agent](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-2)
- [Architecture frameworks](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-3)
- [Types of AI agents](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-4)
- [Use cases](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-5)
- [Benefits](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-6)
- [Risks and challenges](https://delight.ai/blog/ai-agent/how-to-build-an-ai-agent#section-anchor-id-7)
## What is an AI agent?
In a basic sense, an AI agent is a computer program that can act on its own to achieve the specific goals you set for it. Unlike previous foundation models like ChatGPT, agentic AI is an autonomous system that can operate largely without human intervention. AI agents can perceive their environment, analyze data from multiple sources, use tools like APIs or integrations, and make decisions to achieve their given objectives, like [AI for customer service](https://delight.ai/ai-customer-service) — all in real time.
In addition to their proactivity and advanced reasoning capabilities, AI agents continuously learn from experiences and self-improve. They do this by reflecting on their experiences, environment, and human feedback. This adaptability, combined with dynamic real-time data use, allows them to perform tasks with high levels of precision, making them effective in high-stakes scenarios such as healthcare or finance.
## How do AI agents work?
Before building an AI agent, it’s important to have a basic understanding of how they operate. This will allow you to better assess their capabilities and determine the resources you need for the development of your own AI agent.
To illustrate, let’s imagine a salesperson using an AI agent to assist with lead generation by prioritizing the top sales prospects for the day.
## How to build an AI agent: 8 key steps
### Step 1: Define the agent’s purpose and environment
The first step is to decide what you want your AI agent to do. For example, an [AI customer service](https://delight.ai/ai-customer-service) agent could handle customer inquiries and troubleshoot issues, while an ecommerce AI agent could make personalized product recommendations.
After you’ve determined the agent’s tasks and functions, you can identify the environment it will operate within. The environment is made up of all the software systems, databases, context, and spaces the agent has been programmed to perceive and interact with in pursuit of a defined objective.
Once integrated with these various systems and components, the AI agent can use any of the available tools and knowledge sources at its disposal to autonomously achieve its objective.
[*Adapted from source*](https://www.gptbots.ai/blog/how-to-build-ai-services-with-agents)
### Step 2: Assemble a development team
Even though AI agents are autonomous in their decision-making, they still require humans to define their goals and environment, as well as train, deploy, and maintain them. You’ll want to enlist the help of an expert team that’s responsible for managing your AI agent project at every stage.
Here’s a list of roles you’ll likely need on your team:
- Machine learning engineer
- Data scientist
- Software engineer
- UI/UX engineer
- DevOps engineer
You can either hire these roles in-house or outsource the development and maintenance to an [AI agent platform](https://delight.ai/ai-agent) or service. Outsourcing can be a good option when your budget is tight, your internal team is small, or the skills needed don’t align with your existing resources. It’s crucial to recruit those skilled in the specific programming languages, platforms, and technologies in your final project plan.
### Step 3: Gather and preprocess training data
AI agents must be trained on large quantities of high-quality data to accurately understand and process user inputs. If the data is inaccurate or low quality, the agent will learn the wrong things and perform poorly. Basically, the agent’s decision-making depends on the quality of its training data.
The key is to gather any and all data that’s accurate and relevant to the agent’s specific use case(s). This data can come from several sources:
- **Internal business data:** This can include sales records, financial reports, product documentation, and customer information, such as text transcripts of chat logs or support tickets.
- **External data:** This is data that you can obtain by purchasing datasets, partnering with data providers, or using publicly available data.
- **User-generated data:** This includes social media posts, product reviews, website interactions, and other data directly from customers or users.
After collecting your data, it will need to be cleaned and prepared before it’s used for training. This involves removing any irrelevant, incomplete, or incorrect data to ensure accuracy and consistency across the dataset.
For example, cleaning data can include tasks like:
- Dealing with missing values
- Detecting outliers
- Fixing typos in text transcripts
- Labeling data with tags or metadata so the AI understands its context and purpose
### Step 4: Choose your tech stack
Every AI agent is unique, so there’s no one-size-fits-all tech stack. Depending on the agent’s use case and operating environment, you’ll select from various AI components that can include:
- **Machine learning model:** The core component of an AI agent. Machine learning determines the extent to which it can learn from data and interactions and perform tasks.
- **Natural language processing:** Enables the agent to understand and respond using human language.
- **Computer vision:** Enables the AI agent to perceive and understand the world in a visual way, such as identifying and locating objects or tracking motion.
- **Robotic process automation:** This component lets the agent automate repetitive processes in a digital or physical environment.
**How to select a machine learning model**
There are two types of machine learning models, each suited for different use cases. These include:
**Neural networks:** These models are effective at understanding and generating human language, processing large amounts of data, and pattern recognition.
- Best for: User-facing applications that require the agent to understand and generate human-like responses, like in [AI customer care](https://delight.ai/ai-customer-service), for example.
- Limitations: They require large amounts of data to train effectively. They also operate as a "black box," so it can be hard to understand how they make decisions.
**Reinforcement learning:** This model learns by trial and error, teaching the agent the best way to achieve a goal using feedback from its actions.
- Best for: AI agents that need to make decisions or improve behavior based on user interactions. This includes autonomous agents in new and complex environments.
- Limitations: Designing the complex feedback mechanisms requires time and expertise, and debugging and interpreting the behavior for optimization can be challenging.
You can also choose from pre-trained models such as a GPT (Generative Pre-trained Transformer). These can be a good starting point because they’re developed and trained by researchers (like OpenAI) on large datasets of general information. However, you’ll need to fine-tune a pre-trained model to your specific task, which involves training it on the dataset specific to your use case.
### Step 5: Design the AI agent
In this step, you’ll plan the agent’s architecture. Consider data handling, scalability, and the user experience.
#### AI agent architecture
Agentic AI systems are made up of multiple interconnected components that work together to enable the autonomous decision-making processes of AI agents. This modular architecture allows for separate specialized components to be created, combined, and adapted to different tasks that function together.
The key components of AI agent architecture include:
- **Profiling module:** This helps define the AI agent’s role and function within a specific context, and influences how the agent stores information and strategizes.
- **Memory module:** AI agents can store and recall past interactions using short-term memory for immediate tasks, and long-term memory for storing learned experiences. Memory can be stored in databases, natural language, and other formats.
- **Planning module:** This module enables the AI agent to plan actions and tackle complex tasks. The agent can plan using feedback, which adjusts its strategy based on signals from the environment, humans, or its models. It can also plan without feedback, which involves breaking complex tasks into smaller, manageable steps to achieve a goal.
- **Action module:** This translates the agent’s decisions into actions. These can be simple action targets like completing tasks, making API calls, or engaging users in conversation, or action strategies that involve memory, multi-round interactions, and adjustments based on feedback.
Another benefit of agentic architecture is that you can create and modify individual components without affecting the entire system, which simplifies the building and maintenance process.
Consider if a single agent will suffice or if multiple agents are required to coordinate tasks to achieve a more complex goal. This will require you to choose between the following architectures:
**Single-agent architecture:** This architecture involves a single AI entity tasked with managing and executing tasks. It makes it easier to achieve high levels of precision and control for well-defined tasks, but it limits the ability to handle complex tasks due to a lack of collaboration and feedback from other agents in the system.
**Multi-agent architecture:** Consists of multiple AI entities working together to achieve shared, more complex tasks. Using multiple agents with specialized tasks can better tackle challenging problems and refine their strategies by exchanging feedback, but ensuring effective coordination among agents can require more sophisticated algorithms and protocols.
**Other AI agent design considerations:**
- **User experience:** If your AI agent will interact with users, create a user interface (UI) that allows for an intuitive and seamless user experience. You might also implement feedback mechanisms that allow users to leave feedback on interactions to improve the system.
- **Scalability:** AI agents must be able to process and store data efficiently. Using cloud storage providers like AWS or Microsoft Azure, plus scalable APIs and tools, is key to scaling without performance issues. Enterprise-grade architecture with near-zero latency is also key for ensuring timely user interactions.
- **Data handling:** Define how your agent will access and use data. This involves configuring APIs and tools in the environment. For instance, you could design a chat interface where users enter inputs for the agent to process. It’s also important to define how the agent will reply to user requests, or use data based on requests (such as updating a spreadsheet).
### Step 6: Train the AI agent
Now it’s time to train your machine learning model on your prepared data. In this step, the AI begins to learn from the examples in the data provided so it can perform tasks on its own.
Here are the steps to train your AI agent.
- **Prepare the training environment:** Before you start training, you’ll set up your [machine learning](https://sendbird.com/blog/ai-machine-learning) environment. You may need to install software libraries or frameworks, as required for machine learning.
- **Load your data:** Import the cleaned and labeled data into the environment.
- **Split the data:** Divide your data into two sets—training and control—that you’ll compare after training to evaluate how well your model has learned.
- **Configure training parameters:** To guide the training process, you’ll set the learning rate, batch size, and number of epochs. These parameters dictate the tolerance for error before internal adjustments and depth of learning.
- **Train the model:** The model will use the training data to learn, adjusting its internal parameters to minimize errors.
- **Measure performance:** Tracking metrics like accuracy or loss during training will show you how well the model is learning. If it isn’t performing to standards, you can adjust the training parameters and retrain it.
### Step 7: Test the AI agent
As with any complex system, it’s crucial to test and evaluate your AI agent to ensure it performs as expected. This will allow you to identify and fix any issues or biases before deployment.
To start, you can run the agent through a series of basic tasks and queries to gauge its responses. This will allow you to assess whether the agent has learned what from the training data. Evaluate responses for accuracy and efficiency, focusing on data quality and coverage to identify flaws in the agent’s decision-making process.
Next, you can use the following tests to evaluate your AI agent:
- **Unit testing:** Test the individual modules of the agent’s code to ensure they function properly on their own.
- **A/B testing:** Compare different versions of the agent to identify the most effective configuration and inform future training.
- **User testing:** Get real users to test the AI agent in real-world scenarios to identify any usability issues that worsen the experience.
- **Functional testing:** Check the agent’s functionality by running it through positive, negative, and edge-case test scenarios to see how it handles unexpected situations.
Two of the most common challenges with training an AI agent are:
- **Overfitting:** This occurs when the AI agent performs well on its training data but cannot make predictions or draw conclusions effectively from other data. One fix is cross-validation, which involves rotating the training data and testing again to ensure the model generalizes well.
- **Underperformance:** If the AI agent isn’t performing as needed, you can revisit the training phase. You might adjust the training parameters, add more data with techniques like retrieval augmented generation (RAG), or simply retrain the model.
### 8. Deploy and monitor the AI agent
Finally, you’ll deploy the AI agent by integrating it with your existing systems and workflows. This can involve embedding code into a website, setting up AI agent APIs, or configuring the agent with your mobile app. If it will handle sensitive data, be sure to implement the appropriate security measures to protect users and prevent unauthorized access.
Once integrated, you’ll be able to see how the agent interacts with users and performs in the real world. To ensure accuracy and reliability, be sure to monitor the agent’s performance regularly after your agentic AI implementation. Track metrics like hallucination rate, response accuracy, response times, and resource usage, and monitor agent action logs and feedback loops to catch any performance issues.
## How to build an AI agent: 2 development frameworks
You can either build your AI agent from scratch or use one of the many existing frameworks. This choice will depend on factors such as your budget, timeline, required level of customization, and complexity of your use case.
Here’s a breakdown of each option to help you determine the best approach for your business:
### 1. Use an existing orchestration framework
There are a variety of AI agent frameworks that provide pre-built components for typical AI agent functionalities. These typically use large language models (LLMs) as their operating core, and include libraries and tools that simplify the creation, deployment, and management of AI agents.
Common AI agent frameworks include:
- **LangChain:** Open-source framework for building LLM-powered agents that interact with external data sources and APIs for sophisticated use cases.
- **LlamaIndex:** Enables agents to retrieve and process data from diverse sources by integrating LLMs.
- **AutoGen:** Focuses on multi-agent systems that involve conversational AI and task automation by way of agent collaboration.
- **Phidata:** Python-based framework that converts LLMs into agents and provides built-in UI for managing and deploying agents.
**Pros:**
- Faster development time
- Less technical expertise required
- More cost-effective
**Cons:**
- Dependency on third-party frameworks
- Potentially limited access to certain data sources
**Best for:**
- Businesses with limited AI expertise, tight deadlines, fewer resources for investment, or applications that don’t require high levels of customization or complexity.
### 2. Build an AI agent system from scratch
Building an AI agent from the ground up will provide you with the greatest control over its design and functionality. If you’re looking to create an AI agent for specialized tasks in your business, or highly complex use cases, this could be the way to go.
That said, building from scratch will require extensive expertise in machine learning and software engineering, plus the time and resources required for what could be a lengthy development process.
**Pros:**
- Total control of the AI agent functionality
- Perfect fit for your business needs
- Ownership of AI system and model
- Access to the widest range of data sources
**Cons:**
- Significant requirements for technical expertise
- The long and costly development cycle
- Challenges of maintaining and optimizing a novel AI system
**Best for:**
- Enterprises with a large budget for development, access to expert resources, and use cases that require custom solutions.
## Types of AI agents
There’s a variety of AI agents to choose from, from task-specific automation tools to sophisticated multi-purpose assistants that can handle entire workflows.
Here are the main types:
- **Simple reflex agents:** One of the most basic types of AI. They respond to stimuli in the environment based on pre-programmed rules and without memory or learning processes. Example: email auto-responders.
- **Model-based reflex agents:** Unlike simple reflex agents that react to inputs without context, model-based agents maintain a model of the world and refer to this internal representation before they act. Example: smart home security systems.
- **Goal-based agents:** Unlike reflex agents that act based on rules or world models, goal-based agents have an objective and actively work towards it. They can plan and weigh different approaches to reach their objective. Example: smart heating systems.
- **Learning agents:** These AI systems are capable of improving behavior over time by learning from experiences, feedback, and interacting with the broader environment. They can modify their strategies for achieving an objective. Example: [AI for customer service](https://delight.ai/ai-customer-service).
- **Utility-based agents:** These agents evaluate different options and choose the action that maximizes overall utility. By assigning numerical values to different outcomes, they can handle tradeoffs between competing goals. Example: Netflix’s recommendation engine.
## 6 AI agent use cases
AI agents are poised to automate many complex use cases that once required human effort. While it’s still early days for AI agents, here are some of their most exciting applications so far:
### 1. AI for customer service and customer experience
Many companies already use AI agents to streamline support operations, reduce costs, and enhance the customer experience (CX). Available 24/7, AI agents can answer FAQs, guide customers through multi-step processes, and provide tailored recommendations—all while automating a greater percentage of routine interactions than AI chatbots.
**New capabilities:**
- [AI concierges](https://delight.ai) provide a more personalized, seamless customer experience for B2C and [B2B customer service](https://delight.ai/industries/b2b) by retaining context between interactions in their short-term memory.
- AI for CX can proactively deliver a more personalized experience across touchpoints by combining real-time access to external data with context analysis. An example is an agent recommending products based on stored history and current session behavior across multiple channels.
*Delight AI agent for customer service*
### 2. AI agents for finance
Capable of processing huge amounts of data and making real-time decisions, [AI agents for finance](https://delight.ai/industries/financial-services) could reshape the world of personal banking, trading, and investment.
**New capabilities:**
- AI agents could automate workflows such as credit checks or loan applications, guiding customers through multi-step processes to increase human efficiency and productivity.
- AI agents can detect fraudulent activities by analyzing transaction patterns and identifying anomalies, then triggering notifications for immediate intervention.
- AI agents could analyze market trends, make instant trades, and adjust trading strategies based on customers’ real-time financial position and news events.
*Capital One’s AI agent for finance automates digital account management*
### 3. AI agents for cybersecurity
By analyzing network traffic in real-time, AI agents can identify threats, anomalies, and automate responses to cybersecurity incidents to mitigate risk. This proactive yet responsive approach reduces the need for manual intervention, while improving a business’s overall security posture.
**New capabilities:**
- AI agents can detect compromised user accounts by identifying anomalies in user activities, such as unusual login times, frequencies, or locations.
- Agents can identify and block malware attempts by analyzing file behaviors and signatures.
### 4. AI agents for ecommerce
AI agents can help online retailers enhance the customer experience and optimize operations without adding to headcount.
**New capabilities:**
- By storing user activity and preferences in memory, AI agents can make highly contextual, personalized recommendations to drive conversion and satisfaction and reduce cart abandonment.
- Agents could automate inventory management and order processing to prevent shortages and overstocking.
- AI agents can analyze seasonal trends, competitors, and user sentiment to help optimize pricing strategies, marketing, and targeting efforts.
*Delight AI agent for ecommerce*
### AI support for healthcare
AI agents are already helping healthcare organizations to streamline operations and improve health outcomes with responsive, automated care based on real-time patient data.
**New capabilities:**
- AI agents have been shown to diagnose patients with greater accuracy than human doctors, analyzing symptoms, medical records, and diagnostic patient data for a more comprehensive picture of health.
- For patients with chronic disease or ongoing treatments, AI customer care can monitor vitals through wearable devices, recommend diet and lifestyle changes, and notify caregivers if trends indicate potential issues.
*Delight AI agent for healthcare*
## AI agent benefits
AI agents are a scalable way to improve business operations and customer experience. In most cases, rather than replacing employees, these autonomous digital assistants serve to complement and optimize workflows, delivering gains in several key areas:
- **Operational efficiency:** AI agents excel at handling routine tasks that are time-consuming for employees, such as document management, data entry, and transaction processing. They can also handle multiple customer interactions at once, reducing response times to streamline operations without compromising service quality. See how [Lotte Homeshopping](https://delight.ai/customers/lotte-homeshopping) AI for customer service decreased inquiries directed to human agents by 40%.
- **Enhanced customer experience:** AI agents provide customers with personalized, high-context [AI customer service](https://delight.ai/ai-customer-service) and responses right when they want it. By storing interaction data and preferences in memory, they learn and improve over time, providing ever-more accurate and relevant experiences that elevate customer satisfaction over competitors.
- **Cost savings:** By optimizing workflows, increasing productivity, mitigating cybersecurity risk, and more—AI agents can lead to significant business improvements and cost savings. Using a combination of goal-oriented behavior and machine learning, they can tackle multi-step challenges in novel ways to discover new efficiencies, opportunities, and insights across datasets.
- **Data-driven insights:** Possessed of powerful pattern recognition and analytics capabilities, AI agents can identify trends and complex relationships in data. From predictive modeling to customer sentiment analysis, they use data in real time to make recommendations, reveal opportunities, and guide decision-making.
## The challenges of building an AI agent
For all their exciting potential, AI agents aren’t without their risks and challenges. Here are the key considerations to keep in mind as you consider how to build an AI agent for your business or choose from among the [best AI agent builder](https://delight.ai/builder) platforms:
- **Integration complexity:** AI agent systems, unlike generative AI chatbots, cannot operate in a technological silo. They must be integrated with many different systems to take action and provide accurate outputs, which can be costly and time-consuming.
- **Risk management:** AI agents can pose several risks, from data privacy and security to regulatory compliance and system failures. Continuous monitoring and guardrails are highly recommended to mitigate these concerns.
- **Explainability:** The decision-making process of AI agents can be difficult to understand at times, as their chain of logic is complex and often invisible. This can lead to questions about the accuracy of outputs and, therefore, compliance concerns.
- **Maintenance and oversight:** Ensuring that AI agents perform as intended in complex environments is key to a successful agentic AI implementation, necessitating an ongoing effort to continuously monitor, manage, and optimize their behavior over time.
## Final thoughts: How to build an AI agent
AI agents are a paradigm-shifting technology for businesses in many industries. They upgrade the largely reactive capabilities of generative AI with a powerful layer of autonomous decision-making and proactive problem-solving—moving us toward machines that can actually do the work instead of just assisting.
By adopting agentic AI, businesses can unlock new levels of operational efficiency, customer experience, and cost savings. That said, the complexities of the creation and deployment of AI agents are most easily navigated with guidance from experts.
If you’re ready to discuss how to build an AI agent for your unique needs, delight.ai can help. Our AI-experienced team includes experts in machine learning, software engineering, and data science—every competency needed to build an AI agent for any use case. Begin with [AI for customer service](https://delight.ai/ai-customer-service) by [contacting us](https://delight.ai/contact-sales) today.
## Frequently asked questions
### What’s the difference between an AI agent and an AI chatbot?
AI agents and AI chatbots are different, even though they share some overlapping AI technologies. Both use natural language processing (NLP) to understand textual inputs from users, and rely on large language models (LLMs) as their core knowledge base from which to generate responses.
The key difference is that AI agents can complete tasks autonomously. By contrast, AI chatbots can only respond to user inputs. AI agents can plan actions, make decisions, and call on data outside their AI knowledge base to solve problems. On the other hand, AI chatbots are limited to providing information and answering questions from a static knowledge base.
Think of an AI chatbot as a vending machine with a predefined set of options, and a AI agent as a chef with reasoning capabilities and flexibility to prepare any number of dishes that align with its given objective like providing AI support for a specific and complex business.
### What does it cost to build an AI agent?
From development to implementation, the cost to build an AI agent can vary between $6,000 for a basic AI agent, up to $300,000 for an advanced AI agent. In general, the cost depends on the complexity of the AI agent, its framework, and the AI components included.
If you outsource this process, the cost can vary more widely. Important considerations if looking for a partner is the cost of maintenance and updates, training, and cloud hosting. To learn more, feel free to [contact us](https://delight.ai/contact-sales) about our end-to-end [AI agent for customer service](https://delight.ai/ai-customer-service) development services.
- [What is an AI agent?](#section-anchor-id-0 "What is an AI agent?")
- [How do AI agents work?](#section-anchor-id-1 "How do AI agents work?")
- [How to build an AI agent: 8 key steps](#section-anchor-id-2 "How to build an AI agent: 8 key steps")
- [How to build an AI agent: 2 development frameworks](#section-anchor-id-3 "How to build an AI agent: 2 development frameworks")
- [Types of AI agents](#section-anchor-id-4 "Types of AI agents")
- [6 AI agent use cases](#section-anchor-id-5 "6 AI agent use cases")
- [AI agent benefits](#section-anchor-id-6 "AI agent benefits")
- [The challenges of building an AI agent](#section-anchor-id-7 "The challenges of building an AI agent")
- [Final thoughts: How to build an AI agent](#section-anchor-id-8 "Final thoughts: How to build an AI agent")
- [Frequently asked questions](#section-anchor-id-9 "Frequently asked questions")
## Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Unlocking the future of business: How Agent as a Service is revolutionizing AI solutions
Source: https://delight.ai/blog/ai-agent/how-agent-as-a-service-is-revolutionizing-ai-solutions
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- Unlocking the future of business: How Agent as a Service is revolutionizing AI solutions
## Unlocking the future of business: How Agent as a Service is revolutionizing AI solutions
[ Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
April 9 2026
In a world where efficiency and personalization are paramount, Agent as a Service (AaaS) is emerging as a game-changer for businesses looking to leverage AI technology. This article explores the transformative potential of AaaS—highlighting its benefits, applications, and how it stands out in the crowded AI landscape.
### Understanding Agent as a Service
Agent as a Service represents a significant shift in how businesses deploy artificial intelligence within their operations. At its core, AaaS provides organizations with access to pre-trained AI agents that can be customized for specific functions, enabling streamlined integration into existing workflows. This model caters to a wide array of business needs, from customer service and sales to data analysis and proactive customer engagement.
The fundamental appeal of AaaS centers on its ability to deliver on-demand AI capabilities tailored to specific organizational requirements. Companies leverage AaaS for its convenience, eliminating the need for extensive in-house expertise or substantial investment in infrastructure. By removing these barriers, AaaS promotes broader adoption of AI among enterprises of every size—from nimble startups to global corporations.
In contrast to traditional AI solutions, which often require hefty investments and complex implementations, AaaS offers a more flexible and scalable approach. Platforms like [Delight.ai's agent builder](https://delight.ai/builder) exemplify this shift—enabling teams to design, test, and launch AI agents in days rather than months, with no confusing flowcharts or coding required. The ease of use associated with modern AaaS allows businesses to focus on their core operations rather than getting bogged down by technical intricacies.
Key components of today's best AaaS platforms include intuitive communication capabilities, advanced data processing, and powerful memory systems that enhance user experience. Features such as natural language processing, sentiment analysis, omnichannel continuity, and automated workflows are integral in facilitating meaningful interactions between AI agents and customers. The versatility of these features empowers businesses to tailor their AI solutions according to specific industry needs and customer expectations.
### The rise of AI agents
The demand for AI agents is rapidly growing, driven by the need for improved customer interactions and operational efficiency. A significant majority of businesses are now planning to implement AI solutions across their operations, signaling a shifting wave of technological adoption that is transforming industries across the board.
Companies are successfully adopting AI agents to enhance their customer experience. Organizations utilizing AI-driven customer support consistently report substantial increases in satisfaction—with some seeing a 30% or greater boost in customer satisfaction rates. These agents not only handle inquiries more efficiently but also provide personalized interactions that resonate with customers, fostering long-term loyalty.
What separates the best AI agents from basic automation is their ability to remember. Traditional chatbots treat every interaction as a blank slate, but a new generation of AI concierges—such as [Delight.ai](https://delight.ai/)—are built on persistent, long-term memory. They can predict user needs, offer timely solutions, and learn from every interaction to improve future responses. This creates a seamless and engaging customer experience that is critical in retaining clients in a competitive market.
### Key benefits of Agent as a Service
The advantages of AaaS extend beyond mere convenience—they encompass critical aspects of business growth and sustainability.
**Cost efficiency and scalability.** Organizations can effectively scale their AI capabilities according to demand, allowing for growth without the burden of massive capital expenditure on traditional infrastructure. This adaptability makes AaaS particularly advantageous in fluctuating markets where customer demands shift rapidly.
**Personalized, context-aware interactions.** AI agents powered by technologies like [Delight.ai's Agent Memory Platform (AMP)](https://delight.ai/product-release/agent-memory-platform) go beyond scripted responses. AMP creates a living, evolving understanding of each customer—absorbing signals from every conversation to form a profile that goes far beyond static CRM data. The result is hyper-personalization that adapts over time, ensuring customers feel genuinely valued and understood.
**Omnichannel continuity.** Modern AaaS platforms maintain context as customers move between channels. A conversation that begins on a website can continue in-app, over SMS, through email, or via [voice](https://delight.ai/voice)—always with the same memory and context intact. Delight.ai's [omnipresent AI agent platform](https://delight.ai/omnipresent) ensures the agent is always available where customers are, proactively re-engaging when conversations stall or customers switch channels.
**Data-driven insights.** Many AaaS platforms integrate analytics features that inform strategic decision-making. By understanding customer behavior and preferences more deeply, organizations can refine their marketing efforts and product offerings—staying agile in an ever-changing marketplace.
### Real-world applications of AaaS
The applicability of Agent as a Service transcends industries, showcasing how diverse sectors leverage AI technology to create efficient, personalized workflows.
**E-commerce and retail.** AI agents are revolutionizing the shopping experience by providing tailored product recommendations based on user behavior—a key factor in converting visitors into loyal customers. With long-term memory capabilities, these agents can recall past purchases, anticipate needs, and create personalized promotions that resonate with specific customer segments. [Leading e-commerce brands](https://delight.ai/blog/industry/best-ai-chatbot-ecommerce) are deploying branded AI concierges that feel like an extension of their brand rather than a generic chatbot.
**Healthcare.** AI agents are deployed as virtual assistants to improve patient engagement—scheduling appointments, offering medical information, and answering queries in a way that frees human staff to focus on critical patient care. Enterprise-grade platforms that comply with [HIPAA, GDPR, and CCPA standards](https://delight.ai/enterprise-grade) ensure that sensitive patient data is handled securely, with conversations encrypted in transit and at rest.
**Finance and fintech.** AI agents assist in customer support, manage inquiries, and provide financial guidance. The ability to manage high volumes of transactions, provide instant support, and ensure compliance with regulations creates a significant competitive advantage. By offloading routine tasks, financial institutions can redirect human resources toward more complex client interactions.
**Voice-first experiences.** Across all these industries, [AI voice agents](https://delight.ai/voice) are emerging as a powerful channel—handling inbound and outbound calls with natural, human-like speech in any language, scheduling appointments, sending SMS updates, and maintaining full context throughout the conversation.
### Evaluating AaaS providers
Choosing the right AaaS provider is critical to reaping its benefits. Businesses should prioritize several key factors:
- **Customization and brand alignment.** Look for platforms that allow you to configure your AI agent's personality, tone, and behavior to match your brand. The best providers let you write clear, human-readable instructions rather than requiring complex flowcharts or code.
- **Omnichannel reach.** Ensure the provider supports all the channels your customers use—in-app chat, web, SMS, email, voice, WhatsApp, and social messaging—with seamless context handoff between them.
- **Memory and personalization.** Evaluate whether the platform offers persistent memory that evolves over time, rather than treating each interaction as a blank slate. This is the difference between a helpful tool and a truly intelligent concierge.
- **Enterprise-grade trust and governance.** Prioritize providers with full observability, policy enforcement, and traceability. Every response should be auditable and every action should stay within brand parameters and protect against hallucinations. Platforms built with governance frameworks—like Delight.ai's [Trust OS](https://delight.ai/enterprise-grade)—give businesses the confidence to scale AI agents safely.
- **Integration capabilities.** The provider should integrate smoothly with your existing systems—CRMs, help desks, knowledge bases—so every interaction stays consistent and insights are shared across your tools.
Additionally, understanding the provider's approach to continuous improvement can drastically impact long-term effectiveness. A structured [Build-Test-Evaluate cycle](https://delight.ai/builder) ensures your AI agents keep getting better over time rather than stagnating after initial deployment.
### Future trends in Agent as a Service
The landscape of AaaS is expected to continue evolving, influenced by emerging technologies like machine learning, natural language processing, and persistent memory architectures. These advancements will further enhance AI agents' capabilities, making them even more responsive, intuitive, and genuinely helpful.
One of the most significant trends is the shift from reactive support bots to proactive AI concierges. Rather than waiting for customers to reach out, next-generation agents will anticipate needs, re-engage after drop-offs, and guide customers through their entire lifecycle—from initial discovery through to ongoing loyalty conversations. Platforms like [Delight.ai](https://delight.ai/ai-customer-service) are already leading this shift, enabling businesses to deploy AI agents that act as true brand representatives across every touchpoint.
However, as the technology advances, businesses must also address challenges concerning data privacy and AI ethics. Ensuring ethical AI practices is crucial for maintaining customer trust as AaaS becomes more prevalent. Organizations must implement robust privacy policies, maintain full transparency in how AI decisions are made, and ensure responsible usage. By proactively addressing these considerations, businesses can distinguish themselves as trusted leaders in the AI field.
The global AI market is expected to reach $190 billion by 2025, with AaaS being a significant contributor. Businesses that embrace intelligent, memory-driven AI agents now will be best positioned to lead their industries in the years ahead.
### Ready to transform your customer experience?
Unlocking the potential of Agent as a Service is not just about embracing new technology—it's about redefining the way businesses interact with their customers and enhance their operational efficiency. The era of generic, forgetful chatbots is over. The future belongs to branded AI concierges that remember every customer, maintain context across every channel, and continuously improve with every interaction.
[Explore Delight.ai →](https://delight.ai/)
- [Understanding Agent as a Service](#section-anchor-id-0 "Understanding Agent as a Service")
- [The rise of AI agents](#section-anchor-id-1 "The rise of AI agents")
- [Key benefits of Agent as a Service](#section-anchor-id-2 "Key benefits of Agent as a Service")
- [Real-world applications of AaaS](#section-anchor-id-3 "Real-world applications of AaaS")
- [Evaluating AaaS providers](#section-anchor-id-4 "Evaluating AaaS providers")
- [Future trends in Agent as a Service](#section-anchor-id-5 "Future trends in Agent as a Service")
- [Ready to transform your customer experience?](#section-anchor-id-6 "Ready to transform your customer experience?")
Keep reading
[View more](/blog "View more")
[
Delight.ai — The Next Chapter of Sendbird](/blog/product/delight-ai-the-next-chapter-of-sendbird)
[
How For You Conversations Make Every Interaction Feel Personal](/blog/product/for-you-conversations)
[
Introducing AMP: The Agent Memory Platform](/blog/product/introducing-agent-memory-platform)
[
The future of customer connection starts with Omnipresence](/blog/product/connection-starts-with-omnipresence)
[View more](/blog "View more")
## AI readiness: What it is and why it makes or breaks your deployment
Source: https://delight.ai/blog/ai-agent/ai-readiness
## AI readiness: What it is and why it makes or breaks your deployment
[
LeighAnne Manwiller
Product marketing manager](https://delight.ai/author/leighanne-manwiller)
April 15 2026
AI agents are transforming customer experience. They resolve issues in seconds, personalize interactions using real customer data, and handle high-volume inquiries without adding headcount. For CX and customer service teams, the opportunity is substantial.
But there's a meaningful gap between onboarding a CX AI agent solution and seeing those results. That gap is AI readiness.
What we’re seeing is that most organizations haven’t built AI readiness into their planning, and it’s crippling deployment. [Cisco's 2025 AI Readiness Index](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf) surveyed 8,000+ business leaders and found that while 83% plan to deploy AI agents, only 13% have the organizational foundations to do it well.
This guide is designed to help you join the ranks of the AI-ready. Let’s start with what AI readiness encompasses.
### What is AI readiness?
AI readiness is the measure of how prepared your people, data, systems, and governance processes are to deploy AI and deliver real business outcomes.
Here’s the AI readiness framework we use with enterprise CX teams to turn AI ambitions into successful deployments:
1. Building your cross-functional AI Council
2. Aligning on what success looks like for your organization
3. Assessing the infrastructure readiness of your content, data, and integrations
4. Assessing your organizational readiness: governance, talent, and culture
5. Benchmarking where you stand today
So what does being ready actually look like? It starts with who’s in the room.
### Why AI readiness starts with a cross-functional council
Most organizations begin AI evaluation with CX and IT at the table. That’s not enough. AI agents touch every layer of your business, from compliance and engineering to data privacy and brand experience. Without cross-functional alignment, you don’t just lose speed. You create risks that surface months after you’ve onboarded an [AI customer experience platform](https://delight.ai/ai-customer-experience-platform).
We recommend assembling an AI Council that refines your AI strategy and figures out what AI solutions you’ll need. The council should be a cross-functional working group with defined decision rights, and includes 6 roles:
- **Executive sponsor:** Aligns the initiative with business priorities and removes organizational roadblocks
- **CX and operations:** Identifies high-impact use cases and defines what successful resolution looks like
- **Product:** Translates customer experience goals into scalable workflows and ensures the [AI agent](https://delight.ai/ai-agent) fits into existing journeys
- **Engineering:** Evaluates technical feasibility, data architecture, and integration complexity
- **Content management:** Ensures the knowledge base is structured, current, and machine-readable
- **Legal, compliance, and privacy:** Designs guardrails around data usage, consent, and retention from the start, not after the fact
Here’s the litmus test: if your council doesn’t include someone who can say “yes” to data access, security policies, and workflow changes, it isn’t complete. Getting these people aligned before evaluation begins means vendor conversations move faster, objections surface early, and timelines reflect reality.
Having the right people aligned is step one. But before your council can assess readiness, they need to agree on what they’re building toward.
### To assess AI readiness, you need to know what problems AI will solve for your organization
Here’s where most AI readiness frameworks fall short. They help you assess your technical stack but skip the harder question: what does success actually look like for your organization?
Your executive team approves the AI initiative. You’ve assembled your AI Council. Everyone agrees AI is the future. Then someone asks, “what does success actually look like?” One leader says “resolution rate.” Another says cost reduction. A third mentions customer satisfaction. Legal wants risk mitigation. Finance wants ROI projections. What felt like alignment reveals itself as a collection of unreconciled expectations. This is where AI initiatives stall — not from lack of technology, but from lack of clarity about what AI should achieve.
We recommend having your AI Council align on the answers to 4 key questions:
1. **What use cases will the AI handle?** Start with the customer’s problem, not the vendor’s feature list. A customer checking product compatibility needs accurate specs and a clear answer. A customer requesting a refund needs action, not information.
2. **What does “quality” mean in your context?** In healthcare, it might mean zero hallucinations. In ecommerce, it might mean handling 70% of tier-1 inquiries while gracefully escalating edge cases. Define quality based on your customers’ expectations and your brand promise.
3. **How will you measure outcomes, not just outputs?** Conversations handled is an output. Customer satisfaction, retention rates, and cost per resolution are outcomes. If your metrics can’t connect AI performance to business results, you’re tracking the wrong things.
4. **Does this support a larger initiative?** The best AI implementations tie directly to existing company-wide goals: reducing support costs to fund product innovation, differentiating customer experience, or expanding into new markets.
Not all use cases are equal. Simple FAQ retrieval is high volume but low complexity. Personalized account lookups sit in the middle, requiring API access. Autonomous actions like processing refunds or rebooking flights carry the highest value, and demand the strongest governance.
Mapping your use cases by complexity (we break this down fully in [From AI Hype to AI Readiness](https://delight.ai/resources/from-hype-to-ai-readiness)) helps you prioritize what to launch first and set realistic expectations.
With your council aligned and success defined, you can assess whether your infrastructure is ready to support the use cases you’ve chosen.
### The 3 pillars of AI infrastructure readiness
For [AI in customer experience](https://delight.ai/blog/ai-for-cx/ai-customer-experience), your agent will depend on three infrastructure pillars: content readiness, data readiness, and integration readiness. Your answers to each will look different depending on the use cases and quality bar your council defined.
#### Content readiness: Is your knowledge base AI-readable?
Your current knowledge base probably works well for your customer service team. Your human agents have been trained on the context around your business and know your policies inside and out. But AI agents are different. They need concise content with clear headings, metadata, and logical formatting to retrieve and apply information accurately.
For example, if a return policy is written as a wall of text, an AI agent will struggle to extract the time window, item conditions, and proof requirements from narrative prose. Structure that same policy into distinct fields and the AI will be able to handle it with precision. The thing is, most organizations have all the documentation. It just needs to be structured for AI.
#### Data readiness: Is your data labeled and clean?
AI agents pull customer data in real time to personalize responses. If that data is inconsistent, duplicated, or poorly labeled, the agent returns wrong account lookups, contradictory answers, or broken personalization. You don’t need perfect AI data readiness on day one. But you do need to know the current state of your data and have a plan to improve it.
A 2025 Gartner survey found that [63% of organizations either lack the right data management practices for AI or aren’t sure if they have them](https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk). If you haven’t audited your data quality recently, you’re not alone — but you can’t afford to skip it.
#### Integration readiness: Do you know which APIs exist?
Your AI agent needs to read from and act on systems across your organization: CRMs, order management platforms, billing systems, identity providers. Similar to data readiness, integration readiness doesn’t mean your APIs are perfect. It means you’ve identified the systems the agent needs, defined ownership for each API, and confirmed basic reliability for supervised use.
You don’t necessarily need to worry if your APIs are “ready” for AI. It’s more whether you can start with what you have and improve as the agent proves value. That shift turns integration readiness from a blocker into a learning process.
Platforms like [delight.ai](https://delight.ai/) use deployment as a forcing function. Real interaction data reveals exactly which APIs need refinement, which data fields need cleaning, and where integration gaps affect customer outcomes.
Infrastructure tells you whether the systems are ready, but your organization needs to be ready, too.
### The 3 pillars of organizational AI readiness
Infrastructure is about whether your systems can support AI. These next 3 pillars are about whether your *organization* can: the governance to operate it responsibly, the talent to manage it, and the culture to sustain it.
#### Governance: Your readiness to operate AI responsibly
Deploying autonomous agents without guardrails is a liability. Every customer interaction becomes a potential failure point where brand trust, loyalty, and revenue are on the line. [Gartner research identifies weak governance as a primary driver behind up to 95% of failed generative AI projects.](https://www.forbes.com/sites/andreahill/2025/08/21/why-95-of-ai-pilots-fail-and-what-business-leaders-should-do-instead/)
Governance readiness means structured testing before deployment and real-time monitoring of AI decisions in production. It also means technically enforced guardrails for tone, permissions, and escalation. Because governance can’t be bolted on after the fact, platform choice matters here more than anywhere else. It needs to sit in the architecture from day one.
Delight.ai’s [Trust OS](https://delight.ai/trust-os) provides this layer: real-time observability into every AI decision, hallucination detection, and configurable guardrails that make responsible AI operationally viable at scale.
#### Talent: Your team’s readiness to work alongside AI
Even the best AI agent will underperform if the humans behind it aren’t prepared to manage it. Talent readiness means:
- **AI literacy** across both frontline and leadership teams
- **Evolving roles:** AI managers who oversee agent performance, human evaluators who assess conversation quality, and content specialists who maintain AI-ready knowledge bases
- **Aligned incentives** tied to AI-assisted outcomes, not just traditional productivity metrics
The organizations that get this right invest in training before deployment, not after. They run workshops where frontline agents practice supervising AI conversations, reviewing AI decisions, and handling escalations the AI can’t resolve. The goal isn’t to remove humans from the loop but to make human-AI collaboration effective.
#### Culture: Your organization’s readiness for change
With AI-related cost-cutting headlines in the news every week, your team could understandably be worried that AI is a threat to their roles. This is where leadership needs to step in and frame AI adoption around customer outcomes, not headcount reduction.
Try to address the concern directly: AI handles repetitive, high-volume tasks so human agents can focus on judgment-driven work that requires empathy and nuance.
Early, visible wins help shift that narrative. When your AI agent resolves 80% of password reset requests in the first week, share that result widely — in all-hands meetings, internal newsletters, and leadership reviews. Pair it with the story of what the team was able to focus on instead. When people see measurable results early and understand how AI changes their work for the better, resistance gives way to curiosity.
Infrastructure and organizational readiness together form the foundation.
Now that you have a clear understanding of what AI readiness looks like, it’s time to take a practical, actionable look at where you are today.
### Find out how AI-ready your organization is
Want to see where you stand? Take the [delight.ai AI readiness assessment](https://delight.ai/ai-readiness-assessment): 10 questions, an immediate readiness score, and actionable next steps based on your results.
Keep in mind that AI readiness isn’t a one-time checklist but a discipline. It’s a set of organizational capabilities that determine whether your AI deployment takes 6 weeks or 9 months. And it will determine whether your AI initiative delivers real ROI or becomes an expensive experiment.
This article covers the high-level framework. The full playbook, from forming your AI Council to mapping workflows to building the business case, is in our book, [From AI Hype to AI Readiness](https://delight.ai/resources/from-hype-to-ai-readiness). We wrote it for CX leaders, product managers, and technical decision-makers navigating this process.
### FAQs
#### What are common signs of low AI readiness?
The most common indicators: no clear AI owner or strategy at the executive level, fragmented or low-quality data across systems, and weak governance and risk controls. Poor system integrations with undocumented APIs and AI pilots that fail to scale into production are also red flags. These gaps often surface months into implementation, which is why an upfront assessment matters.
#### Is there an AI readiness checklist?
Not a static one — and that’s the point. Most AI readiness checklists treat preparation as a one-time exercise, but readiness evolves as your data matures, your team builds AI literacy, and your use cases expand. The more useful approach is an AI readiness assessment that scores your current maturity across AI strategy, infrastructure, governance, talent, and culture, then provides a phased roadmap that updates as you progress.
#### Who should be involved in AI readiness planning?
AI readiness planning should be cross-functional. At minimum, you need an executive sponsor, CX and operations leaders, product, engineering, and content management. Legal and compliance round out the group. If your team doesn’t include someone who can approve data access, security policies, and workflow changes, your planning group isn’t complete.
#### How long does it take to become AI-ready?
It depends on your starting maturity stage. Organizations with strong data practices, documented workflows, and cross-functional alignment (Pacesetters) typically move from readiness to production deployment in 6 weeks. Less-prepared organizations can take 9 months or longer. This doesn’t necessarily happen because your AI tools are slow, but because the organizational foundation isn’t in place.
- [What is AI readiness?](#section-anchor-id-0)
- [Why AI readiness starts with a cross-functional council](#section-anchor-id-1)
- [To assess AI readiness, you need to know what problems AI will solve for your organization](#section-anchor-id-2)
- [The 3 pillars of AI infrastructure readiness](#section-anchor-id-3)
- [The 3 pillars of organizational AI readiness](#section-anchor-id-4)
- [Find out how AI-ready your organization is](#section-anchor-id-5)
- [FAQs](#section-anchor-id-6)
### Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Introducing Trust OS 2.0
Source: https://delight.ai/blog/product/introducing-trust-os-v2
- [Blog](/blog)
- [Product](/blog/product)
- Introducing Trust OS 2.0
## Introducing Trust OS 2.0
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
July 7 2026
Last year, we introduced Trust OS — the first governance layer built specifically for AI agents in production. The foundation for responsible, accountable AI at the enterprise level: a way to observe what your AI was doing, define the rules it operated by, and hold it to standards your organization could stand behind.
It was a start. And we heard clearly from customers what they needed next: more visibility into *why* decisions were made, faster ways to change behavior without engineering, and a path toward AI that improves on its own rather than requiring constant manual intervention.
Today, we're excited to introduce **Trust OS 2.0**. Eight new capabilities that take observability, control, and continuous improvement further than anything we've shipped before.
## Observability
### **Openbook**
**Navigate your actionbook like a map — from 1,000 feet to 10**
When your AI makes a decision — issues a refund, escalates a case, applies a policy — Openbook creates a permanent, readable record of exactly what happened. Which actionbook fired. What data the agent saw. What it considered and what it ruled out.
Zoom out for the full flow. Zoom in on a single node to see exactly what the AI was doing at that moment. Openbook is the answer to "why did the AI do that?" that doesn't require an engineer to decode.
[**Read the full post →**](https://delight.ai/blog/product/introducing-openbook)
### **Low CSAT Analysis**
**Turn bad scores into a repair agenda — automatically**
Every AI team tracks CSAT. Almost none of them know what's actually causing the low scores. Low CSAT Analysis reads every 1–2 score conversation and categorizes it into one of five failure types: **Inadequate Response, Inadequate Compensation, Escalation Refusal, Situation Frustration,** and **Others.**
[**Read the full post →**](https://delight.ai/blog/product/low-csat-analysis)
### **Thinking Messages**
**Replace the typing indicator with something actually useful**
When your AI agent is mid-task — looking up an order, checking a policy, calling a tool — what does the customer see? For most deployments: a generic typing indicator. Thinking Messages replaces that with configurable per-tool text and a shimmer animation that tells users exactly what the agent is doing in real time.
"Checking your order status..." "Reviewing your account history..." The message is tied to the specific tool call — accurate every time, not a generic fallback.
[**Read the full post →**](https://delight.ai/blog/product/thinking-messages)
## Control
### **Actionbook Editor**
**Edit your AI's rules like a doc. No engineering required.**
Actionbooks get long. Fast. A refund threshold changes. A new product launches. Legal updates the cancellation policy. Your AI needs to know — today, not next sprint.
**Actionbook Editor** opens inside Workspace Settings as a structured document — sections for behavioral rules, global actions, and conditional blocks — editable by anyone on your team without a line of code.
[**Read the full post →**](https://delight.ai/blog/product/introducing-actionbook-editor)
### **A/B Testing & Gradual Rollout**
**Stop deploying AI changes on faith. Test them on real traffic.**
**Gradual Rollout** lets you expose changes to a controlled slice of real traffic — 5%, 10%, 25% — with the ability to pause or roll back at any point. **A/B Testing** runs two versions of your agent simultaneously and measures which performs better on your KPIs. Statistical confidence, not gut feel.
[**Read the full post →**](https://delight.ai/blog/product/gradual-rollout-and-a-b-testing)
### **Staging Environments**
**The missing layer between dev and production**
Every software team knows you don't test in production. But most AI teams do exactly that — not because they want to, but because there's been nowhere else to test. Staging for AI agents mirrors your production environment exactly — same integrations, same data flows, same tool connections — but stays completely isolated from live customers.
The deployment path is enforced: **Dev → Staging → Production.** There's no route that bypasses it.
[**Read the full post →**](https://delight.ai/blog/product/staging)
## Continuous improvement
### **Zero-Touch Improvement**
**Your AI gets better while you sleep**
Most AI improvement today is manual: find the failure, diagnose the cause, write the fix, deploy, repeat. Zero-Touch Improvement closes the loop automatically — a graduated system with three levels you choose based on how much trust you've built:
[**Read the full post →**](https://delight.ai/blog/product/introducing-zero-touch-improvement)
### **Test Upgrade**
**Build test cases from real conversations. Not hypotheticals.**
A test suite is only as good as the scenarios it covers — and most test suites are built from imagined edge cases, not the real patterns your AI encounters every day. The upgraded Test tooling lets teams pull directly from real customer conversations to build test cases. Organize with labels, share sets across dev/staging/production, and when tests fail, get actionable analysis — not just "failed" but exactly why and what to change.
[**Read the full post →**](https://delight.ai/blog/product/test-upgrade)
## What's next
Trust OS 2.0 is available now for all delight.ai customers. These eight capabilities ship as part of the platform — no separate contracts, no add-on pricing.
If you're already running delight.ai, your team can start with the features most relevant to where you are today. If you're evaluating, this release is a good representation of the kind of operational depth we build into the platform.
See Trust OS 2.0 in action. Talk to our team for a walkthrough.
### [**Talk to our team →**](https://delight.ai/contact-sales)
- [Observability](#section-anchor-id-0 "Observability")
- [Control](#section-anchor-id-1 "Control")
- [Continuous improvement](#section-anchor-id-2 "Continuous improvement")
- [What's next](#section-anchor-id-3 "What's next")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
Keep reading
[View more](/blog "View more")
[
Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)[
The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)[
AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)[
What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[View more](/blog "View more")
## Introducing Agent Steward: The first AI agent that owns customer issues end to end
Source: https://delight.ai/blog/product/introducing-agent-steward
## Introducing Agent Steward: The first AI agent that owns customer issues end to end
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai](https://delight.ai/author/cheryl-chai)
Product marketing lead
July 7 2026
A steward is someone who takes responsibility for something that matters and sees it through. Not a middleman. Not a coordinator. An owner.
We're excited to introduce **Agent Steward**, the first fully autonomous [AI agent](https://delight.ai/ai-agent) that takes ownership of complex customer issues and resolves them end to end, across systems, teams, and channels.
### Why AI still can't resolve most customer issues
Even the best AI agents today only resolve a fraction of incoming cases fully. The rest get handed off to humans. And it's not because the AI doesn't know the answer. It's because of two walls that current AI simply cannot cross.
Even within the use cases businesses choose to automate, a significant portion of conversations still end up with a human. We see three consistent reasons across the industry.
**The trust gap.** High-stakes decisions are expensive to get wrong. Businesses don't trust AI to approve refunds, cancel bookings, or make exceptions. Every case with a judgment call gets routed to a human.
**The time gap.** Real resolution often takes longer than a single chat session. A customer can't sit in live chat for 3 hours while you coordinate with an airline, a hotel, and finance. The moment it spans beyond the current conversation, today's AI loses the thread.
**The coordination gap.** When your human agent needs a refund approved, they Slack their manager. When they need a flight rebooked, they call the airline. When they need the hotel extended, they email the partner. Today's AI can't do any of that. It's trapped in a single conversation, unable to reach out, follow up, or coordinate across the people and systems needed to close a case.
This is the resolution gap. AI can answer within a session. But it can't make decisions your business trusts, work across time and channels, or take the cross-system actions that actually close a case.
#### Here's what that looks like in practice.
A customer's flight gets cancelled. They need a refund, a rebooking, and the hotel extended if the connection has an overnight layover. Three requests, four systems, two third parties.
Today's AI handles the first message, maybe the second. Then it transfers. The human spends hours re-investigating, emailing the airline, calling the hotel, waiting for approvals, and circling back to the customer. Five minutes of AI. Five hours of human coordination.
### Agent Steward closes the trust, time, and coordination gaps
Steward doesn't just answer the question. It takes ownership of the entire resolution, even when that resolution spans hours, days, and multiple channels. It coordinates across systems, makes decisions within your guardrails, and follows up with the customer wherever they are.
When Steward encounters that same cancelled flight, it says "I'll take care of everything." Then it builds a resolution plan, shows it to the customer, and starts executing.
The customer watches every step in real time. Identity verified. Reservation pulled. Policy checked. Refund processing. Airline contacted. Hotel contacted.
And here's what matters most: when the airline takes 2 hours to confirm the rebooking, the customer doesn't have to sit in a chat waiting. Steward keeps working in the background and reaches back out through whatever channel makes sense: an SMS with the new flight details, an email with the updated itinerary, a push notification when the hotel confirms. The resolution continues across time and across channels, even after the original conversation ends.
This is what we mean by **end-to-end resolution**. Not just answering within a session. Owning the outcome across sessions, across channels, across however long it takes.
### One agent orchestrating across your entire stack
Behind that single conversation, Steward dispatches specialized sub-agents, each focused on a different system or third party, and coordinates them all in parallel.
All running simultaneously. All reporting back to Steward, which holds the full picture and drives the case to resolution. The customer experiences one conversation. Your operations get coordinated, parallel action across every system.
### Full visibility into every action
Every action Steward takes is logged in a detailed activity trail. You can open any resolved case and trace the entire resolution: what was triggered, what calls were made, what was approved, and how long each step took.
### Closing the trust gap with graduated autonomy
We mentioned the trust gap earlier: businesses don't trust AI to make high-stakes decisions. That's a valid concern. The answer isn't to force full autonomy on day one. It's to let AI earn it.
Steward supports four levels of autonomy. You start wherever you're comfortable and move up as confidence grows.
**L0: AI handles simple tasks.** This is where most businesses are today. AI answers FAQs and does basic lookups. Everything else goes to a human.
**L1: AI acts, human approves.** Steward investigates, coordinates, and proposes a resolution. A human reviews and approves every decision before it's executed. You see exactly what the AI would do, and you decide whether to let it.
**L2: AI earns autonomy, use case by use case.** As the AI-human match rate approaches 100%, you unlock autonomous resolution for specific use cases within your guardrails. Refunds under $1,000. Standard rebookings. Routine returns. Each use case earns its way independently.
**L3: AI writes its own playbook.** Today, your business defines every workflow the AI follows. At L3, Steward can handle novel situations it hasn't seen before and propose new resolution playbooks for your team to review. You set the strategy. Steward figures out the execution.
The key is that every case gets compared: what did the AI recommend vs. what did the human decide? You can see the match rate for each use case in real time. When the numbers consistently match, you level up that use case.
### Built for industries where resolution is complex
Agent Steward is designed for any business where customer issues require coordination across multiple systems, teams, or third parties.
[**Travel & airlines**](https://delight.ai/industries/travel-and-hospitality): Cancelled flights that need rebooking, refund, and hotel coordination across multiple systems and partners.
[**Food & delivery**](https://delight.ai/industries/on-demand): Late orders requiring driver outreach, restaurant remake, new courier dispatch, and proactive customer updates.
[**Healthcare**](https://delight.ai/industries/healthcare): Test results requiring multi-day follow-up across labs, providers, and patients.
[**Fintech & banking**](https://delight.ai/industries/financial-services): Fraud investigations spanning multiple internal systems, managed end to end.
[**Retail**](https://delight.ai/industries/retail): Cross-channel returns across online, warehouse, and in-store systems, resolved in one conversation.
### Built on delight.ai's platform
Steward isn't standalone. It runs on the same infrastructure powering [customer conversations](https://delight.ai/customers) at some of the world's largest brands.
[**Agent Memory Platform**](https://delight.ai/agent-memory-platform-amp) gives Steward full context across sessions and channels. It never starts from scratch. It remembers every past interaction, preference, and issue, so each resolution is informed by the complete customer history.
[**Omnipresence**](https://delight.ai/omnipresent) is what closes the time gap. Remember: today's AI loses the thread when resolution spans beyond a single chat session. Omnipresence means Steward can continue the resolution across any channel the customer uses: start in chat, follow up via SMS, confirm by email, call the customer back by [voice](https://delight.ai/voice). The conversation doesn't end when the chat window closes. It continues wherever the customer is, for as long as the resolution takes.
### Give us your hardest use case
Agent Steward is generally available today. We want to see the use case that keeps your team buried in coordination work. The one where a customer needs three things done across four systems and two third parties. The one that takes your best agent hours to close. Bring it to us and watch Steward resolve it live, with your data, in a fraction of the time.
**Your customer's problem becomes ours. Start to finish.**
### [**Request a live walkthrough →**](https://delight.ai/contact-sales)
- [Why AI still can't resolve most customer issues](#section-anchor-id-0 "Why AI still can't resolve most customer issues")
- [Agent Steward closes the trust, time, and coordination gaps](#section-anchor-id-1 "Agent Steward closes the trust, time, and coordination gaps")
- [One agent orchestrating across your entire stack](#section-anchor-id-2 "One agent orchestrating across your entire stack")
- [Full visibility into every action](#section-anchor-id-3 "Full visibility into every action")
- [Closing the trust gap with graduated autonomy](#section-anchor-id-4 "Closing the trust gap with graduated autonomy")
- [Built for industries where resolution is complex](#section-anchor-id-5 "Built for industries where resolution is complex")
- [Built on delight.ai's platform](#section-anchor-id-6 "Built on delight.ai's platform")
- [Give us your hardest use case](#section-anchor-id-7 "Give us your hardest use case")
### Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/zendesk-to-delight-desk-migration)
[Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration](/blog/product/zendesk-to-delight-desk-migration)
[View more](/blog "View more")
## Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements
Source: https://delight.ai/blog/product/voice-v2
- [Blog](/blog)\n- [Product](/blog/product)\n\n## Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements\n\n[](https://delight.ai/author/leighanne-manwiller)\n\n[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)\n\nProduct marketing manager\n\n\n\nJuly 7 2026\n\nPicture this: a customer's order is sitting in a warehouse, delayed by three days. Your support team knows. Your system knows. But the customer doesn't. Not until they check the tracking link, see the bad news, and pick up the phone already frustrated.\n\nEven with an inbound voice AI agent, there was no better option. Your AI could only wait for the call to come in. There was no way to get ahead of the problem, no way to reach the customer first, no way to turn a reactive situation into a proactive one.\n\nWe're excited to share that's no longer the case. With **Outbound Voice**, your AI can place calls. With **Proactive Voice**, it knows when to. Underpinning both are over 2,000 quality improvements that make every conversation cleaner, faster, and more reliable.\n\n### Outbound Voice\n\nUntil now, even an inbound voice AI agent was purely reactive. Your AI was always waiting. The customer had to recognize a problem, decide to reach out, and initiate the conversation — all before your AI said a word.\n\nOutbound Voice flips that model. Your AI can now initiate calls to confirm an order flagged for fraud review, follow up on a ticket that's been open too long, remind a customer that their subscription renews tomorrow, or close the loop on a resolution that required a callback.\n\n> *The best customer service call is one the customer never had to make.*\n\nOutbound calls are powered by the same voice AI that handles inbound. Same natural conversation, same ability to take action in your systems, same end-to-end resolution. The difference is who initiates.\n\nOutbound calls can be triggered via API or automatically based on workflow rules you define, tied to the same events and conditions you're already tracking in your systems. You control the use cases, the call cadence, and when calls should escalate to a live agent.\n\n### Proactive Voice\n\nOutbound Voice gives your AI the ability to call. Proactive Voice gives it the intelligence to know when.\n\nProactive Voice monitors signals across your customer data. Order statuses, open ticket age, payment failures, subscription events. It triggers outbound calls automatically when the right conditions are met, turning a reactive support model into a proactive one.\n\nProactive Voice triggers are built in your workflow editor, the same place you manage inbound call flows. You define the signal, the timing, the message goal, and the escalation path. Every call is logged, summarized, and tied back to the original ticket or order.\n\nFor teams dealing with high volumes of predictable issues, the impact adds up quickly. When customers get a call before they even realize there's a problem, inbound volume for those issue types drops. The support team spends less time explaining bad news and more time handling the complex cases that actually need a human.\n\n[Dr. Cool Services](https://delight.ai/customers/dr-cool-services) is one of the Las Vegas Valley's most trusted HVAC and home services brands. They’re using delight.ai Voice to handle inbound booking — and proactive outreach for maintenance scheduling.\n\n### 2,000+ improvements to the foundation\n\nThe biggest part of Voice 2.0 isn't one headline feature. It's the 2,000+ fixes and improvements that make every call better: cleaner, faster, more reliable, more natural.\n\nThese improvements touch every layer of the voice stack. We've bucketed them into six categories, each addressing a real failure mode that our customers and their customers were running into.\n\nIndividually, each of these improvements is incremental. Together, they represent a meaningful step change in call quality. The average call handled by Voice 2.0 is faster, cleaner, and more reliable.\n\nIf you're not yet using delight.ai Voice, this is the moment to start. The foundation has never been stronger. With Outbound and Proactive Voice, you're no longer limited to meeting customers where they are. You can reach them before they ever pick up the phone.\n\n### [**Experience Voice 2.0 →**](https://delight.ai/voice)\n\n- [Outbound Voice](#section-anchor-id-0 "Outbound Voice")\n- [Proactive Voice](#section-anchor-id-1 "Proactive Voice")\n- [2,000+ improvements to the foundation](#section-anchor-id-2 "2,000+ improvements to the foundation")\n\n## Keep reading\n\n[View more](/blog "View more")\n\n[](/blog/product/introducing-trust-os-v2)\n\n[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)\n\n[](/blog/product/introducing-agent-steward)\n\n[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)\n\n[](/blog/product/low-csat-analysis)\n\n[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)\n\n[](/blog/product/zendesk-to-delight-desk-migration)\n\n[Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration](/blog/product/zendesk-to-delight-desk-migration)\n\n[View more](/blog "View more")\n
## Low CSAT Analysis: Turn bad scores into actionable failure categories
Source: https://delight.ai/blog/product/low-csat-analysis
## Low CSAT Analysis: Turn bad scores into actionable failure categories
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller — Product marketing manager](https://delight.ai/author/leighanne-manwiller)
July 7 2026
A CSAT score of 1 or 2 tells you something went wrong. It doesn't tell you what. Most teams handle it the same way. Someone opens a queue of flagged conversations, reads through them, and tries to find the pattern. It's slow, it's incomplete, and it often just doesn't happen.
Low CSAT Analysis, part of [Trust OS 2.0](https://delight.ai/blog/product/introducing-trust-os-v2)'s Observability layer, handles that automatically. Every time a customer submits a score of 1 or 2, the AI reads the conversation and categorizes it. By the time you open the dashboard, the breakdown is already there.
### Five ways conversations fail
Low CSAT Analysis sorts every flagged conversation into one of five failure types. Each one maps to a real, distinct way that customer service interactions break down.
1. **Inadequate Response:** The agent gave a wrong or incomplete answer. The customer came with a problem and left without a resolution.
2. **Inadequate Compensation:** The resolution was technically correct but not generous enough. The customer expected a larger refund, a credit, or some other gesture of goodwill and didn't get it.
3. **Escalation Refusal:** The customer asked to speak to a human and the agent didn't make it happen. It either declined outright or deflected without giving them a real path forward.
4. **Situation Frustration:** The customer came in already upset. The agent handled the request correctly but didn't acknowledge the frustration, which made things worse.
5. **Others:** Edge cases that don't fit any of the above. Worth reviewing individually, since new failure patterns usually show up here first.
### From score to priority
Categorization is what makes a score useful. "Your CSAT is 3.8" doesn't tell you what to fix. "35% of your low scores are Escalation Refusals" does. You go update the escalation logic in your Actionbook.
> You can't fix "low CSAT." You can fix "35% of your low scores are Escalation Refusals." One is a number. The other tells you exactly where to go.
Each failure type points somewhere specific. A spike in Inadequate Compensation usually means your resolution thresholds are too conservative. A spike in Situation Frustration usually means your agent needs better acknowledgment logic before jumping to a solution. Different category, different fix.
### The first step in the improvement loop
Low CSAT Analysis is also the starting point for [Zero-Touch Improvement (ZTI)](https://delight.ai/blog/product/introducing-zero-touch-improvement), delight.ai's system for autonomously identifying and fixing agent failures. Before ZTI can propose a fix, it needs to know where to look. The failure category breakdown is what gives it that direction.
Without it, someone on your team has to read through conversations to find the pattern. With it, that work is already done.
Low CSAT Analysis is part of [Trust OS](https://delight.ai/trust-os), delight.ai's operational layer for running AI agents at production scale. It's available now.
### [**See release notes →**](https://delight.ai/product-release/low-csat-analysis)
- [Five ways conversations fail](#section-anchor-id-0 "Five ways conversations fail")
- [From score to priority](#section-anchor-id-1 "From score to priority")
- [The first step in the improvement loop](#section-anchor-id-2 "The first step in the improvement loop")
### Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/zendesk-to-delight-desk-migration)
[Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration](/blog/product/zendesk-to-delight-desk-migration)
[View more](/blog "View more")
## Introducing Openbook: Navigate your actionbook like a map
Source: https://delight.ai/blog/product/introducing-openbook
## Introducing Openbook: Navigate your actionbook like a map
[Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
May 7 2026
Actionbooks grow. What starts as a clean set of rules — handle returns here, escalate there, respond to billing questions like this — compounds over time. New intents. New conditionals. Policy updates layered on top of previous policy updates. A returns workflow that was 10 rules in January is 35 by June.
The editing part is solved. [Actionbook Editor](https://delight.ai/blog/product/introducing-actionbook-editor) lets your ops team maintain the logic directly, without engineering. But there's a problem that comes before editing: knowing where to look.
When legal updates the return window from 30 to 45 days, which condition needs to change? When a new product launches and you need to add an intent, where does it fit in the existing structure? When something behaves unexpectedly, which branch is responsible?
You open the source view and start scrolling. At scale, that looks like this:
The fix you need to make is simple. Finding it in 2,847 characters of nested markup is not.
### The map your actionbook never had
Openbook renders your actionbook as a visual flow. Not to trace decisions — to navigate the document. Toggle to Flow view and the entire actionbook opens up as a structure you can actually read: section headers at the top, intent blocks branching below, conditional paths fanning out, every node labeled.
This is the 10,000-foot view. You're not editing yet — you're orienting. You can see that the refund threshold lives in the third branch of Global Actions. You can see there are five intents, and the escalation path connects them all. You can see how the whole thing fits together before you touch any of it.
The Flow view doesn't change what your actionbook does. It makes the structure visible — the same logic, rendered as a map instead of a wall of markup.
### 10,000 feet to 10 feet in one click
Once you know where you're going, getting there is instant. Click any node in the flow and the editor jumps directly to that section — no scrolling, no counting lines, no searching.
The 10,000-foot view is the structure. The 10-foot view is the specific rule — the one condition that needs updating, the threshold that changed, the intent that's missing. Openbook makes the distance between them a single click.
### Built to work with Actionbook Editor
Openbook and Actionbook Editor live in the same workspace by design. You don't find a problem in the flow and then go somewhere else to fix it. You click the node, land in the editor at that exact section, make the change, and save. The loop from *I need to update this* to *done* closes in under a minute.
Most teams underestimate how much the navigation tax slows down maintenance. It's not the editing that's hard — it's the hunting. Every time someone needs to make a change and spends five minutes finding where the rule lives, that's friction that compounds. Teams start avoiding updates. Actionbooks drift. The AI runs on logic that nobody's touched in months because touching it felt like too much work.
Openbook removes that excuse. Openbook is the *See it* pillar of Trust OS 2.0 — and it's the one that makes the other two actually usable.
### The bigger the actionbook, the more you need this
When an actionbook is 10 rules, you hold it in your head. When it's 35 sections with nested conditionals, cross-intent anchor links, and six months of accumulated policy changes — you need a map.
Openbook is that map. The teams that maintain the most sophisticated AI deployments don't just edit their actionbooks — they navigate them. They zoom out to understand the full structure, zoom in to make a precise change, and move between those two views without friction. That discipline is what keeps complex logic from becoming unmaintainable over time.
Your actionbook is already written. Openbook makes it readable.
### [See Openbook in action →](https://delight.ai/contact-sales)
- [The map your actionbook never had](#section-anchor-id-0 "The map your actionbook never had")
- [10,000 feet to 10 feet in one click](#section-anchor-id-1 "10,000 feet to 10 feet in one click")
- [Built to work with Actionbook Editor](#section-anchor-id-2 "Built to work with Actionbook Editor")
- [The bigger the actionbook, the more you need this](#section-anchor-id-3 "The bigger the actionbook, the more you need this")
Keep reading
[View more](/blog "View more")
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## Introducing Actionbook Editor: Edit your AI's rules like a Notion doc
Source: https://delight.ai/blog/product/introducing-actionbook-editor
## Introducing Actionbook Editor: Edit your AI's rules like a Notion doc
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai](https://delight.ai/author/cheryl-chai)
Product marketing lead
May 7 2026
Actionbooks get long. Fast.
What starts as ten clean rules — when to escalate, how to handle a refund, what to say when an order is missing — turns into dozens. New intents. Nested conditionals. Policy exceptions stacked on policy exceptions. Edge cases your ops team discovered at 11pm on a Friday.
And they're never done. A refund threshold changes. A new product launches. Legal updates the cancellation policy. Your AI needs to know — today, not next sprint. But the only person who can touch the actionbook is in engineering, buried in a backlog.
So the AI keeps running the old rules. And every conversation it handles is a conversation running on stale logic.
## Edit your AI's rules like a doc
The problem was never that ops teams didn't understand the logic. They wrote the original playbooks, trained the human agents, and caught the edge cases at 11pm. They knew exactly what needed to change. They just couldn't touch the file.
Actionbook Editor is built for them. It opens inside Workspace Settings, but instead of a raw prompt in a textarea, there's a structured document — sections for behavioral rules, global actions, and conditional blocks, all editable by anyone on your team without a line of code.
The layout is designed around how CX teams actually work — write and edit rules on the left, watch the Preview update in real time, then test with the built-in Tester against your actual AI agent without ever leaving the screen.
The people who should own AI behavior are the ones closest to customers — not the ones managing the engineering backlog. That's what this layout is designed for.
As actionbooks grow, finding the right block to edit becomes its own problem. A workflow that spans dozens of sections, nested conditionals, and branching intents is hard to navigate as a flat document. Toggle from **Source** to **Tree** in the editor toolbar and the right panel becomes a clean hierarchical outline of the entire actionbook — every section, every intent block, every conditional branch, all visible at once. Click any item to jump straight to it. No scrolling.
The arrow below shows how the sync works: clicking **Global Actions** in the Tree panel on the right jumps the editor directly to that section on the left. You're never hunting for a block — you navigate by name, land exactly there.
It's the difference between editing a document and editing a codebase. You always know where you are — and where everything else is.
Under the hood, every actionbook follows the same structure. Not a monolithic prompt. Not inscrutable code. Sections that map directly to how CX teams think about their workflows, each with a clear purpose.
The conditional blocks — the if / else logic — are rendered as visual blocks with their own context: condition on top, outcomes below. Your CX manager can read exactly what the AI will do in each case, and edit those conditions directly in the UI without ever touching the underlying template syntax.
## Governance built in — not bolted on
Actionbook Editor sits inside [Trust OS](https://delight.ai/trust-os) — delight.ai's governance layer for AI agents. Every change is versioned. Every published actionbook carries a version number and a change log. If a change causes unexpected behavior, you can see exactly what changed, when, and who made the call.
The Version history button is right there in the top bar — same screen as the editor. Your ops team doesn't need a separate system to audit AI behavior. They're looking at both the rules and the history at the same time.
A returns actionbook at v3.2 with a 96.4% resolution rate and 45,200 conversations handled isn't just a file. It's a record of decisions made, performance measured, and trust earned over time. Actionbook Editor makes that record legible and maintainable by the people responsible for CX outcomes.
Actionbook Editor is the *Steer it* pillar of Trust OS 2.0 — delight.ai's governance framework for AI agents built around three capabilities: *See it. Steer it. Let it learn.*
## The teams winning with AI aren't just deploying it. They're running it.
Every team that's deployed AI customer service has been told the same thing: get it working, then let it run. Set it up, monitor the metrics, file a ticket when something breaks.
The teams pulling ahead aren't doing that. They're iterating. Weekly. Refining conditional logic, updating response language, adjusting which intents trigger which actions — and watching their resolution rates climb as a result.
When your operations team can directly author and maintain AI behavior, something shifts in how they relate to the AI. It stops being a system they're subject to. It becomes a system they're responsible for. Teams who own their AI's logic tune it more often, catch edge cases faster, and build more sophisticated flows over time — because they're not waiting on another team to make changes happen.
The customers who experience this don't know any of it is happening. They just notice that the AI always seems to know the right answer, handle the right edge cases, and never get the policy wrong.
Your AI already has rules. Your team should be able to edit them.
### [**See Actionbook Editor in action →**](https://delight.ai/contact-sales)
- [Edit your AI's rules like a doc](#section-anchor-id-0 "Edit your AI's rules like a doc")
- [Governance built in — not bolted on](#section-anchor-id-1 "Governance built in — not bolted on")
- [The teams winning with AI aren't just deploying it. They're running it.](#section-anchor-id-2 "The teams winning with AI aren't just deploying it. They're running it.")
## Keep reading
[View more](/blog "View more")
[](https://delight.ai/blog/product/introducing-trust-os-v2) [Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](https://delight.ai/blog/product/introducing-agent-steward) [Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](https://delight.ai/blog/product/voice-v2) [Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[](https://delight.ai/blog/product/low-csat-analysis) [Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## Staging: The missing layer between dev & production
Source: https://delight.ai/blog/product/staging
- [Blog](/blog)
- [Product](/blog/product)
- Staging: The missing layer between dev & production
## Staging: The missing layer between dev & production
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
May 7 2026
Delight.ai has always given teams two environments to work with: **Development** for building and initial testing, and **Production** for live customer traffic. For many teams, that was enough. But as AI agents take on more consequential work — deeper integrations, stricter compliance requirements, higher stakes per conversation — the gap between dev and prod has become a real liability.
There are two ways dev falls short. The first is configuration fidelity: development environments run simplified setups that don’t fully mirror production. A new webhook that breaks escalation routing, or a knowledge base update that changes behavior in unexpected ways, won’t necessarily surface in dev — but will the moment it hits real customers.
The second is isolation. In dev, you’re typically working on several things at once — different flows, different policy updates, different knowledge changes, all at different stages of readiness. When you want to ship just one of those changes, you have no clean way to test it on its own. It was only ever validated alongside everything else in flight. Conversely, a change that keeps failing in dev might be blocked by another unfinished change — and would work perfectly fine in production — but you have no way to know that without going live.
> *Dev environments are built for building. The problem is that “built for building” and “built for validating production-grade behavior in isolation” are two different things.*
[**Staging environments**](https://delight.ai/product-release/staging-environment) add the dedicated layer between development and production that was missing — a full mirror of your production configuration, running completely isolated from customers.
### A copy of production, not a better dev environment
This is the key distinction: staging isn’t another place to build. It’s a copy of production, cut off from real customer traffic.
When you promote from development to staging, you get your full production setup — real integrations, real security policies, real configuration — running in an environment where zero customers are affected. You can test with production-grade complexity without production-grade consequences.
By default, the deployment path is Development → Staging → Production. Every promotion is logged with a timestamp, the author, and a diff of what changed. If something goes wrong, you can roll back to any prior version. Teams that want to keep things simple can hide staging and collapse back to Development → Production — but the guardrails are on by default.
### Why this matters at scale
For smaller agents handling straightforward queries, skipping straight from dev to prod is manageable. But as agents take on more consequential work — resolving billing disputes, routing escalations, pulling from live order systems — the blast radius of a bad deploy grows. A knowledge base update that inadvertently changes how refunds are handled, or a policy change that breaks handoff to a human agent, isn’t a test failure. It’s a customer incident.
Staging is the place where those issues surface before they become incidents. And once you have a stable staging environment, it opens up more sophisticated ways to ship safely. [Gradual rollout](https://delight.ai/blog/product/gradual-rollout-and-a-b-testing) lets you promote a change to a percentage of live traffic first, so you can validate behavior at scale before fully committing. A/B testing takes it further — running two configurations simultaneously against real traffic so you can compare performance, resolution rates, or CSAT before deciding which version wins.
Staging environments are part of [Trust OS](https://delight.ai/trust-os) — delight.ai's operational layer for running AI agents responsibly at production scale. Trust OS brings together the deployment controls, governance, and observability that enterprise teams need to move quickly without losing oversight of what their agents are doing.
### [See release notes →](https://delight.ai/product-release/staging-environment)
- [A copy of production, not a better dev environment](#section-anchor-id-0 "A copy of production, not a better dev environment")
- [Why this matters at scale](#section-anchor-id-1 "Why this matters at scale")
## Keep reading
[View more](/blog "View more")
- [](/blog/product/introducing-trust-os-v2) Introducing Trust OS 2.0
- [](/blog/product/voice-v2) Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements
- [](/blog/product/introducing-agent-steward) Introducing Agent Steward: The first AI agent that owns customer issues end to end
- [](/blog/product/low-csat-analysis) Low CSAT Analysis: Turn bad scores into actionable failure categories
[View more](/blog "View more")
## Thinking Messages: The gap between question and answer
Source: https://delight.ai/blog/product/thinking-messages
- [Blog](/blog)
- [Product](/blog/product)
- Thinking Messages: The gap between question and answer
## Thinking Messages: The gap between question and answer
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
May 7 2026
The moment between a user's question and an agent's answer is usually just a second or two. But in that time the user gets nothing back. No confirmation the agent understood. No sense of what's happening. No signal for how long to wait. The typing indicator fills the space visually, but it doesn't say anything. Whether the agent is writing a plain response or making an API call in the background to look something up, what users see looks identical. Three animated dots, then an answer.
That gap is getting longer. Newer reasoning models work through a problem before they respond rather than generating an answer immediately. That's generally good news for quality. But the silence before a first response arrives gets longer with it, and the dots become an even weaker signal.
Trust OS 2.0's **Thinking Messages** treat this as a design problem, not a latency one.
### Waiting is fine. Not knowing what you're waiting for isn't.
There's a reason a progress bar feels better than a spinner even when both take the same amount of time. Nielsen Norman Group's research on wait states found that users are more satisfied and willing to wait longer when given a specific progress indicator versus a generic animation. The mechanism is the same whether you're downloading a file or waiting on an API call: visible progress turns uncertainty into information, and that shift changes how people experience the time.
AI agents have the same dynamic. When a user asks "What plan am I on?" and sees the dots, they don't know if the agent is pulling their account, misunderstanding the question, or stuck in a loop. By the time the answer comes, they've already tensed up a little. Across every tool call in every conversation, that accumulates into a real tax on the experience that has nothing to do with how good your agent actually is.
> Thinking Messages don't make tool calls faster. They make the wait feel shorter, because users know something specific is being done for them.
### Specific beats generic every time
From where a user sits, the difference is immediate. Instead of three dots, they see something like "Retrieving your current subscription plan..." The wait is the same. What's changed is that they know what it's for.
That specificity is something builders set for each tool. Every tool gets its own message, written to describe what it actually does.
That specificity matters more than it sounds. "Checking your recent orders" is neutral and fast-sounding. "Processing your cancellation. This may take a moment." does something different. It's not filling silence; it's setting expectations for something that actually matters to the person on the other end.
Thinking Messages is part of [Trust OS](https://delight.ai/trust-os), delight.ai's operational layer for running AI agents in production.
### [**Talk to sales →**](https://delight.ai/contact-sales)
- [Waiting is fine. Not knowing what you're waiting for isn't.](#)
- [Specific beats generic every time](#)
### Keep reading
[View more](/blog)
[](/blog/product/introducing-trust-os-v2) Introducing Trust OS 2.0
[](/blog/product/voice-v2) Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements
[](/blog/product/introducing-agent-steward) Introducing Agent Steward: The first AI agent that owns customer issues end to end
[](/blog/product/low-csat-analysis) Low CSAT Analysis: Turn bad scores into actionable failure categories
[View more](/blog)
## Gradual rollout and A/B testing: Stop guessing whether your AI changes worked
Source: https://delight.ai/blog/product/gradual-rollout-and-a-b-testing
- [Blog](/blog)\n- [Product](/blog/product)\n- Gradual rollout and A/B testing: Stop guessing whether your AI changes worked\n\n## Gradual rollout and A/B testing: Stop guessing whether your AI changes worked\n\n[](https://delight.ai/author/leighanne-manwiller) \n[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller) \nProduct marketing manager\n\n\n\nMay 7 2026\n\nWhen you change an Actionbook, update your agent’s knowledge base, or swap in a new model, you expect things to get better. But with AI agents, the connection between a change and its outcome is rarely obvious. A refund flow that reads more naturally might actually confuse users in ways that drive up recontact rates. A shorter greeting might improve response times while quietly lowering satisfaction. By the time the damage shows up in your metrics, you may have already heard about it from customers.\n\nDelight.ai’s Trust OS 2.0 adds two tools to close that gap: **Gradual Rollout** and **A/B Testing**. Both run two versions of your agent side by side against real traffic, but they answer different questions.\n\n### Gradual Rollout asks whether a change is safe to ship\n\nThe core problem gradual rollout solves is exposure. Before it existed, every change you promoted to production was immediately live for every customer. Even with instant rollback, you’d already affected thousands of conversations by the time a problem surfaced.\n\n[**Gradual Rollout**](https://delight.ai/product-release/gradual-rollout) lets you start smaller. When you promote a change from staging, it becomes a *candidate* running in the Idle slot alongside your Live slot. You set the traffic split (1% to the candidate, 99% to live), let real conversations accumulate, and watch the metrics. Resolution rate, CSAT, and containment rate are all tracked separately for each slot. If the numbers look good, you scale up. If something’s wrong, you cut the Idle slot’s traffic to zero instantly. The Live slot never changes; you’re just dialing the candidate in or out.\n\n\n\n> *With gradual rollout, a bad deploy is still a bad deploy. It just affects 1% of your traffic, not 100%. And you see it before you’ve fully committed.*\n\nWhen you’re satisfied with the candidate’s performance, you promote with a single action. Traffic flips to 100% on the new version and the previous version is kept as a backup. Every version in your history is preserved as a candidate, so you can re-run any previous configuration at any time.\n\n### A/B Testing asks whether a change actually improved things\n\nGradual rollout answers the safety question well. What it doesn’t answer is whether the change actually made things better. “No obvious regressions” and “statistically meaningful improvement” are two different things. If you’re trying to optimize rather than just ship safely, you need the latter.\n\nThat’s what A/B Testing adds. The mechanics are familiar: two versions of your agent run in parallel, traffic is split between them, and results accumulate from real conversations. The difference is how you frame and measure the outcome.\n\nBefore the test starts, you articulate a hypothesis. Something like: *if we simplify the refund Actionbook from five steps to three, resolution rate will improve.* You pick a target metric. The system calculates how many conversations you’ll need to reach a statistically meaningful conclusion and estimates how long that will take at your current traffic volume.\n\n\n\nWhile the test runs, traffic is locked, since changing the split mid-experiment would corrupt the results. When you end the test, results are evaluated against your hypothesis. The key output is a **result reliability score**, expressed as a percentage, that tells you whether the difference you’re seeing is real or noise.\n\n- **95% or higher (High):** The result is reliable enough to act on.\n- **80–95% (Medium):** Trending in the right direction, but not yet confirmed.\n- **Below 80% (Low):** Not enough data yet to draw a conclusion.\n\nEvery completed test generates an AI analysis report covering what changed, what improved, which secondary metrics to watch, and what to try next. Secondary metrics that moved in the wrong direction are flagged with their own reliability score, so you can tell whether a potential tradeoff is a real concern or just noise.\n\n### Use gradual rollout to ship safely, A/B testing to prove it worked\n\nGradual rollout is for shipping safely. A/B testing is for proving something worked.\n\n\n\nIf your primary concern is stability and you want to limit blast radius on a meaningful change, gradual rollout is the right tool. No hypothesis required, no traffic lock. You just need to know it’s safe before you fully commit.\n\nIf you’re optimizing and want to know whether a specific change to an Actionbook, knowledge base, or model actually moved a metric you care about, A/B testing gives you the statistical rigor to distinguish signal from noise. It also measures tool call rates, so if your change is supposed to reduce unnecessary coupon issuances or cut escalations, you can verify that directly.\n\nBoth features are part of [Trust OS](https://delight.ai/trust-os), delight.ai’s operational layer for running AI agents at production scale. Trust OS brings together the deployment controls, governance, and observability that enterprise teams need to move quickly without losing oversight of what their agents are doing.\n\n### [**See the Gradual Rollout release note →**](https://delight.ai/product-release/gradual-rollout)\n\n- [Gradual Rollout asks whether a change is safe to ship](#section-anchor-id-0 "Gradual Rollout asks whether a change is safe to ship")\n- [A/B Testing asks whether a change actually improved things](#section-anchor-id-1 "A/B Testing asks whether a change actually improved things")\n- [Use gradual rollout to ship safely, A/B testing to prove it worked](#section-anchor-id-2 "Use gradual rollout to ship safely, A/B testing to prove it worked")\n\n## Keep reading\n\n[View more](/blog "View more")\n\n- [ Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)\n- [ Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)\n- [ Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)\n- [ Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)\n\n[View more](/blog "View more")
## Introducing Zero-Touch Improvement: Your AI gets better while you sleep
Source: https://delight.ai/blog/product/introducing-zero-touch-improvement
- [Blog](/blog)\n - [Product](/blog/product)\n\n## Introducing Zero-Touch Improvement: Your AI gets better while you sleep\n\n[\n\nCheryl Chai\n\nProduct marketing lead](https://delight.ai/author/cheryl-chai)\n\n\n\nMay 7 2026\n\nYour AI passes 50,000 test conversations at 100% accuracy. Every scenario handled. Every edge case covered. Every metric green.\n\nThen a real customer says something your tests never imagined — and your AI falls flat.\n\nThis is the local maxima problem, and it affects every AI team that"s optimized their way to a plateau. Your AI isn"t getting smarter. **It"s getting more efficient at being the same.**\n\nToday, we"re introducing **Zero-Touch Improvement** — an AI improvement system that graduates from human-guided learning to fully autonomous optimization, no human touch required. Part of [Trust OS 2.0](https://delight.ai/blog/product/introducing-trust-os-v2), ZTI is how your AI gets better while you sleep.\n\n## Your AI is optimizing. It"s not improving.\n\nEvery AI team hits this wall. You"ve fine-tuned your model. You"ve iterated through hundreds of prompt variations. Your AI is excellent — within the boundaries you"ve defined.\n\nBut those boundaries *are* the problem. A fully autonomous AI optimizes within its training distribution — it finds edge cases in your test suite and patches them, but it"s not learning anything new. Your AI handles 95% of cases flawlessly. The other 5% — the novel scenarios, the situations that require judgment — those are the ones your customers remember.\n\n> *Optimization makes your AI faster at what it already knows. Improvement teaches it what it doesn"t.*\n\n## Why "just add more autonomy" doesn"t work\n\nThe instinct is obvious: if AI is good, more autonomous AI must be better. Remove the human. Speed things up. Let the machine run.\n\nBut full autonomy without a feedback mechanism is a ceiling, not a destination.\n\n**The test suite trap.** Your AI optimizes against your tests. But your tests are a model of reality, not reality itself. An AI that scores 100% on 50,000 test conversations hasn"t mastered customer service — it"s mastered your test suite.\n\n**The drift problem.** Customer needs change. Products evolve. Policies update. Your AI"s behavior was optimized for last quarter"s reality. Without a mechanism to learn from what"s happening *now*, it slowly drifts out of alignment.\n\n**The unknown unknowns.** The hardest conversations aren"t the ones you"ve prepared for. They"re the ones you couldn"t have predicted — and they can"t be solved with a bigger test suite.\n\n> *The human is what teaches AI what the tests can"t.*\n\n## The autonomy ladder: A graduated path to zero-touch\n\nZero-Touch Improvement isn"t a switch you flip. It"s a path you walk. Four levels, each building trust and capability on the one before it.\n\n\n\n**Level 0: Manual.** The status quo. Your team manages every prompt change, every rule update, every test manually. It works, but it doesn"t scale.\n\n**Level 1: AI proposes, human approves.** Your AI starts proposing improvements — new test cases, prompt adjustments, workflow optimizations. Nothing ships without human review. You"re building trust.\n\n**Level 2: Human steers the loop.** Once your AI-human match rate exceeds 97%, you unlock autonomous improvement within approved boundaries. The AI handles research, testing, and deployment. The human sets direction and intervenes on judgment calls.\n\nThink of it like managing a great team member. You don"t review every email they send. You set the strategy, check in on the important decisions, and trust them with the rest.\n\n**Level 3: Zero touch.** The human steps out of the loop entirely. The AI runs the full improvement cycle — research, hypothesis, testing, deployment — continuously and autonomously. Not because the human doesn"t matter. Because the human already did the hard work: they steered the system to the point where it can steer itself.\n\n## How the improvement loop works\n\nAt Level 2, here"s what the improvement pipeline looks like:\n\n\n\nEach step is a concrete product experience, not an abstraction:\n\n\n\n**Research** — ZTI scans real conversations — not test cases. It finds the 34 out of 847 that failed, clusters them by root cause, and surfaces the pattern: a specific actionbook branch is skipping an eligibility check on high-value refunds.\n\n\n\n**Hypothesize** — ZTI doesn"t say "update the prompt." It proposes a precise change — with an actionbook diff showing exactly what"s being added, removed, and modified. You see the proposed fix in context, not in the abstract.\n\n**Human steer** — Your team reviews the proposal. They might approve it, scope it ("don"t touch the escalation logic in Step 3"), or reject it. Every decision teaches the system not just *what* to improve, but *how your team thinks about improvement*.\n\n\n\n**Test & deploy** — The fix is tested against those same 34 real conversations. Iteration 1: 85%. ZTI refines and retests. Iteration 2: 94%. Refine again. Iteration 3: 100%. The change ships to production.\n\n### At Level 3, the human node disappears.\n\nThis is the dramatic beat. You don"t read "zero touch" — you *see* it. The human node collapses out of the pipeline. The gap closes. Four stages remain.\n\n\n\nThe pipeline runs faster. The improvements compound. No human bottleneck. No sprint cycles. Just continuous, autonomous evolution.\n\n> *It never stops improving.*\n\n## What changes for your team\n\n\n\nThe AI handles the volume. Your team raises the ceiling.\n\n## Built on Trust OS\n\nZero-Touch Improvement runs on the same [Trust OS](https://delight.ai/trust-os) infrastructure that governs everything at delight.ai:\n\n- [**Openbook**](https://delight.ai/blog/product/introducing-openbook) — Every improvement is fully traceable. See what changed, why it changed, and what the measured impact was. No black boxes.\n- [**Actionbook Editor**](https://delight.ai/blog/product/introducing-actionbook-editor) — The rules your AI follows are editable documents, not buried code. When ZTI proposes a change, you see exactly what"s being modified — in plain language.\n- **Staging Environment** — Every improvement is tested before it touches production. Real conversation data, controlled conditions, measured outcomes.\n- **Approval Gates** — Even at Level 3, you set the boundaries. Financial thresholds, compliance requirements, sensitive topics — you define what the AI can and can"t change autonomously.\n\n## Why we didn"t just ship full autonomy\n\nWe could have shipped a fully autonomous improvement system on day one. The models are capable.\n\nWe didn"t. Because AI optimizes within the box. Human steering is how you raise the bar.\n\n\n\nEvery frontier AI model improves this way. Human feedback is the signal that breaks through local maxima. The same applies to your AI agent — the human catches what the metrics can"t: wrong tone, edge cases, judgment calls that need context.\n\nAn AI that"s never been steered by humans doesn"t know what good looks like. It optimizes for metrics — resolution rate, handling time, CSAT scores — but metrics are proxies, not goals. A human understands that sometimes the right answer takes longer. That sometimes the "optimal" response isn"t the one that tests best — it"s the one that builds the relationship.\n\nHuman steering at Level 2 isn"t a limitation. It"s the training phase. Every decision your team makes teaches the AI not just *what* to do, but *why*. And that "why" is what makes Level 3 possible.\n\n## See it. Steer it. Let it learn.\n\nZero-Touch Improvement is available now as part of [Trust OS 2.0](https://delight.ai/trust-os).\n\nStart at Level 1. Let your team build confidence. Graduate to Level 2 when you"re ready. And when the data shows your AI has earned it — let it run.\n\nYour AI gets better while you sleep.\n\n### [**See Zero-Touch Improvement in action →**](https://delight.ai/contact-sales)\n\n- [Your AI is optimizing. It"s not improving.](#section-anchor-id-0 "Your AI is optimizing. It\'s not improving.")\n- [Why "just add more autonomy" doesn"t work](#section-anchor-id-1 "Why \"just add more autonomy\" doesn\'t work")\n- [The autonomy ladder: A graduated path to zero-touch](#section-anchor-id-2 "The autonomy ladder: A graduated path to zero-touch")\n- [How the improvement loop works](#section-anchor-id-3 "How the improvement loop works")\n- [What changes for your team](#section-anchor-id-4 "What changes for your team")\n- [Built on Trust OS](#section-anchor-id-5 "Built on Trust OS")\n- [Why we didn"t just ship full autonomy](#section-anchor-id-6 "Why we didn\'t just ship full autonomy")\n- [See it. Steer it. Let it learn.](#section-anchor-id-7 "See it. Steer it. Let it learn.")\n\nKeep reading\n\n[View more](/blog "View more")\n\n[\n\nIntroducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)[\n\nVoice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)[\n\nIntroducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)[\n\nLow CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)\n\n[View more](/blog "View more")
## Best Decagon alternatives in 2026: 7 competitors compared
Source: https://delight.ai/blog/ai-agent/decagon-alternatives
## Best Decagon alternatives in 2026: 7 competitors compared
[
[LeighAnne Manwiller
Product marketing manager](https://delight.ai/author/leighanne-manwiller)
April 2 2026
### Key takeaways
- **Delight.ai stands out for memory and governance.** Enterprises can add persistent customer context and production controls without replacing their existing support stack.
- **Published pricing narrows the field.** Fin offers the clearest public outcome price, and existing technology commitments can make Zendesk AI or Gorgias easier to evaluate.
- **Specialized requirements shape the shortlist.** Complex automation and global contact center needs may point buyers toward more specialized platforms.
### The shortlist
- [**Delight.ai**](https://delight.ai/agent-memory-platform-amp): Best for enterprises that want customer context to compound across conversations, channels, and time.
- [**Fin (Intercom)**](https://fin.ai/): Best for teams that want transparent, outcome-based pricing and broad helpdesk compatibility.
- [**Sierra AI**](https://sierra.ai/): Best for large enterprises that need agents to complete complex actions across business systems.
- [**Ada**](https://ada.cx/): Best for teams that prioritize an established omnichannel automation platform.
- [**Cognigy**](https://www.cognigy.com/): Best for global contact centers that need multilingual voice automation and deployment flexibility.
- [**Zendesk AI**](https://www.zendesk.com/): Best for current Zendesk customers that want AI inside their service platform.
- [**Gorgias**](https://www.gorgias.com/): Best for Shopify brands that connect support automation with ecommerce actions and revenue.
Teams searching for Decagon alternatives usually have a concrete mismatch to solve. Decagon keeps pricing behind a sales conversation, so buyers cannot model the commercial impact from public information before they engage, and its **quote-based pricing** can make early comparisons harder.
Other teams need **governance, integration, or customer memory** to sit at the center of the platform decision. [Delight.ai](https://delight.ai/ai-customer-service) brings persistent context and production controls to an existing support stack, giving enterprises a direct path from each customer interaction to a more informed next one.
### Why enterprise teams look for Decagon alternatives
Enterprise teams look for Decagon alternatives when its commercial model or operating approach does not match their buying requirements. Three higher-level questions usually shape the decision:
- **Cost clarity:** Can the team understand and forecast its investment before entering a sales-led evaluation?
- **Operational fit:** Will the platform work within the team's existing service environment and governance expectations?
- **Customer continuity:** Can the platform build useful context across a long-running customer relationship?
Delight.ai addresses these requirements as one operating model. The [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) turns conversation history, preferences, sentiment, and business context into an **evolving customer profile**, and [Trust OS](https://delight.ai/trust-os) gives teams the controls to govern how that context is used.
### How we evaluated these Decagon alternatives
We evaluated every Decagon alternative against 3 decision-level criteria:
- **Business fit:** Can the team afford, implement, and manage the platform with its current resources and systems?
- **Customer impact:** Do verified customer outcomes show meaningful value for a comparable service operation?
- **Long-term control:** Can the platform support ongoing customer relationships within the team's governance standards?
We checked current primary sources for each criterion, and the audit records the supporting links. Vendor-defined resolution rates **remain directional** because each company measures a successful outcome differently.
### Platform comparison (summary)
| Platform | Public customer proof | G2 rating | Pricing model | Best fit |
|---|---|---:|---|---|
| Decagon | Duolingo reports 80% support-volume deflection | 4.7/5 (28 reviews) | Contact sales | Enterprises prepared for a sales-led evaluation and high-touch implementation |
| Fin (Intercom) | Intercom reports 76% average resolution across 7,000+ teams | 4.5/5 (3,845 reviews) | $0.99 per chat/email resolution on Intercom | Teams that value transparent outcome pricing and fast adoption |
| Sierra AI | Ramp reports 90% case resolution | 4.4/5 (14 reviews) | Outcome-based; contact sales | Large enterprises with complex action-taking requirements |
| Delight.ai | Hanssem improved resolution from 48% to ~86% over 5 months | 4.6/5 (126 reviews for parent company) | Contact sales; outcome-based | Enterprises with repeat customers, strict governance, and long-running relationships |
| Ada | IPSY reports a 64% increase in automated resolution and 943% ROI | 4.6/5 (172 reviews) | Contact sales | Established enterprises seeking omnichannel automation |
| Cognigy | Skoda reports 25% cost reduction and ROI under 12 months | 4.6/5 (13 reviews) | Contact sales | Multilingual contact centers and regulated deployments |
| Zendesk AI | Unity attributes ~8,000 deflected tickets and $1.3M savings | 4.3/5 (6,824 reviews) | Plans start at $19/agent/month; AI uses resolution allowances | Current Zendesk customers wanting one service platform |
| Gorgias | Orthofeet automated 56% of tickets in under 2 months | 4.6/5 (557 reviews) | Helpdesk plans + AI Agent add-on; ~ $0.90 per AI-resolved conversation | Shopify brands linking support automation with ecommerce actions |
The table narrows the field, but each vendor defines and reports customer outcomes differently. A useful pilot gives every platform **the same customer conversations and business goals**, which creates a shared baseline for comparing results and understanding how each platform could improve your operation.
### Fin (Intercom): The transparent-pricing AI agent
- **Category:** [Fin](https://fin.ai/) is an AI customer service agent for teams using Intercom or another supported helpdesk.
- **What it does:** Fin automates common support work across the channels a team already uses and passes complex conversations to people with context.
- **Customer proof:** Intercom reported in March 2026 that more than 7,000 teams used Fin and that its [average resolution rate had reached 76%](https://www.intercom.com/blog/from-resolutions-to-outcomes-evolving-how-fin-delivers-value/). GoodBuy Gear separately reported a 50% resolution rate in an Intercom customer example.
- **G2 rating:** Fin holds a [4.5/5 rating from 3,845 reviews](https://www.g2.com/products/fin-by-intercom/reviews?page=3).
- **Pricing:** Intercom charges [$0.99 per chat or email resolution](https://www.intercom.com/help/en/articles/8205718-fin-ai-agent-outcomes) when Fin runs on Intercom. Procedure handoffs and sales outcomes can use different billable definitions, and voice or specialized deployments require sales pricing.
- **Delight.ai vs. Fin:** Fin emphasizes efficient support outcomes and broad deployment options, but delight.ai is a stronger fit when the evaluation centers on persistent customer memory, enterprise governance, and context that compounds across a long-running relationship. [Compare delight.ai and Fin](https://delight.ai/fin-vs-delight).
### Sierra AI: The enterprise autonomy platform
- **Category:** [Sierra](https://sierra.ai/) is an enterprise AI agent platform for complex customer workflows.
- **What it does:** Sierra handles customer journeys that require actions across multiple business systems and gives teams a way to improve those agents over time.
- **Customer proof:** [Ramp reports a 90% case resolution rate](https://sierra.ai/uk/customers/ramp), and Airtable reports an 80% AI resolution rate and a workload equivalent to about 95 full-time support agents.
- **G2 rating:** Sierra holds a [4.4/5 rating from 14 reviews](https://www.g2.com/es/products/sierra/reviews).
- **Pricing:** Sierra uses [outcome-based pricing](https://sierra.ai/product) and asks buyers to contact sales. The public product page does not provide a unit price.
- **Delight.ai vs. Sierra:** Sierra is built for ambitious enterprise automation, and delight.ai adds a clear memory-first position by layering evolving customer context and [Trust OS governance](https://delight.ai/trust-os) onto an enterprise's existing systems. [Compare delight.ai and Sierra](https://delight.ai/sierra-vs-delight).
### Delight.ai: The memory-first AI agent platform
- **Category:** [Delight.ai](https://delight.ai/agent-memory-platform-amp) is a memory-first AI customer experience platform for enterprises with repeat customers and long-running service relationships.
- **What it does:** Delight.ai builds every interaction on what the business already knows about the customer and gives teams control over how the AI operates in production.
- **Customer proof:** [Hanssem's resolution rate rose from 48% to about 86% over 5 months](https://delight.ai/customers/hanssem), and transfers to human agents fell by 50%. The same customer story reports more than 90% accuracy on repetitive queries.
- **Review profile:** Delight.ai's parent company, [Sendbird, holds a 4.6/5 rating from 126 reviews on G2](https://www.g2.com/products/sendbird/reviews). Its product evidence includes named enterprise customer results and infrastructure that powers more than 7 billion conversations each month.
- **Pricing:** Contact sales for enterprise pricing. Delight.ai prices around successful AI resolutions and does not charge a per-agent seat fee for Delight Desk.
- **Why delight.ai:** Persistent memory is the organizing foundation, and governance runs through the same system. That combination carries useful context into the next interaction and gives the business visibility and control.
### Ada: Broadest integration coverage with a credibility question
- **Category:** [Ada](https://www.ada.cx/) is an omnichannel AI customer service platform for established enterprises.
- **What it does:** Ada automates service across major customer channels and gives enterprise teams a structured way to manage those experiences.
- **Customer proof:** Ada reports that IPSY achieved a [64% increase in automated resolution rate, a 41% improvement in customer satisfaction, and 943% ROI](https://www.ada.cx/resources/guide/what-is-automated-resolution-in-ai-customer-service/) during its first 4 months.
- **G2 rating:** Ada holds a [4.6/5 rating from 172 reviews](https://www.g2.com/products/ada-support-inc-ada/reviews).
- **Pricing:** Ada asks buyers to contact sales. A pilot should document the billable event, included channels, implementation services, and volume assumptions before comparing total cost.
- **Delight.ai vs. Ada:** Ada provides mature omnichannel automation, and delight.ai offers a sharper fit when persistent relationship memory and a unified governance layer drive the business case. [Compare delight.ai and Ada](https://delight.ai/ada-vs-delight).
### Cognigy: The analyst-recognized platform for global language coverage
- **Category:** [Cognigy](https://www.cognigy.com/) is an enterprise conversational AI platform for global voice and digital contact centers.
- **What it does:** Cognigy supports large, multilingual contact centers that need voice and digital automation within their existing infrastructure.
- **Customer proof:** [Skoda reports a 25% reduction in operating costs and ROI in under 12 months](https://www.cognigy.com/en/case-study/skoda) after deploying Cognigy across a multilingual customer service operation.
- **G2 rating:** Cognigy holds a [4.6/5 rating from 13 reviews](https://www.g2.com/products/cognigy-ai/reviews).
- **Pricing:** Cognigy does not publish list pricing and directs buyers to sales. Deployment scope, channels, hosting, and integration work can materially affect the commercial proposal.
- **Delight.ai vs. Cognigy:** Cognigy has deep contact center and multilingual capabilities, and delight.ai is the more focused choice for enterprises that want every interaction to enrich a persistent customer profile across channels.
### Zendesk AI: The zero-migration path for existing Zendesk shops
- **Category:** [Zendesk AI](https://www.zendesk.com/) is the native AI path for teams already operating on Zendesk's customer service suite.
- **What it does:** Zendesk brings AI into the service environment its customers already use, keeping automation and human support in the same workspace.
- **Customer proof:** [Unity reports almost 8,000 deflected tickets and about $1.3 million in savings](https://www.zendesk.com/customer/unity/) from Zendesk self-service and automation. The case study predates Zendesk's current resolution-tier model.
- **G2 rating:** Zendesk for Customer Service holds a [4.3/5 rating from 6,824 reviews](https://www.g2.com/products/zendesk-for-customer-service/reviews).
- **Pricing:** [Zendesk plans start at $19 per agent per month](https://www.zendesk.com/pricing/) when billed annually. Suite Team starts at $55, Suite Professional at $115, and AI consumption uses resolution allowances tied to outcome tiers.
- **Delight.ai vs. Zendesk AI:** Zendesk offers the lowest-friction path for an existing Zendesk operation, and delight.ai can layer onto Zendesk with persistent customer memory and enterprise AI governance.
### Gorgias: The Shopify-native AI with revenue attribution
- **Category:** [Gorgias](https://www.gorgias.com/) is an ecommerce helpdesk and AI agent built around Shopify operations.
- **What it does:** Gorgias uses Shopify data to automate ecommerce support and help teams resolve service and sales questions in one place.
- **Customer proof:** Gorgias reports that [Orthofeet automated 56% of tickets in under 2 months](https://www.gorgias.com/customers). Pepper reports a 54% support automation rate and a 19.2x return on AI-driven sales interactions.
- **G2 rating:** Gorgias holds a [4.6/5 rating from 557 reviews](https://www.g2.com/products/gorgias/reviews).
- **Pricing:** Gorgias publishes helpdesk plans and charges separately for AI Agent. Its [official product information](https://www.gorgias.com/llm-info) lists $0.90 per AI-resolved conversation on most annual plans and $1.00 on monthly billing.
- **Delight.ai vs. Gorgias:** Gorgias is purpose-built for ecommerce and Shopify, and delight.ai serves a broader enterprise customer experience mandate with persistent memory and governance across complex, repeat relationships.
### How to choose the right Decagon alternative
The right Decagon alternative follows from your primary business constraint:
- **Repeat customers, persistent memory, and governance:** delight.ai is a strong fit.
- **Pricing clarity:** Fin's published per-resolution rate is the easiest to model.
- **Existing technology commitments:** Zendesk AI or Gorgias may be simpler to evaluate if you're already on that stack.
- **Specialized requirements:** Sierra AI for complex, multi-system automation; Cognigy for global, multilingual voice operations.
A defensible decision still needs a pilot built around your own conversations. Give every vendor the same:
- Representative cases
- Required actions
- Policy constraints
- Escalation rules
For enterprises where every conversation should improve the next one, [compare Decagon and Delight.ai](https://delight.ai/decagon-vs-delight) and see how a memory-first architecture changes the evaluation.
### Links
- [Why enterprise teams look for Decagon alternatives](#why-enterprise-teams-look-for-decagon-alternatives)
- [How we evaluated these Decagon alternatives](#how-we-evaluated-these-decagon-alternatives)
- [Fin (Intercom): The transparent-pricing AI agent](#fin-intercom-the-transparent-pricing-ai-agent)
- [Sierra AI: The enterprise autonomy platform](#sierra-ai-the-enterprise-autonomy-platform)
- [Delight.ai: The memory-first AI agent platform](#delightai-the-memory-first-ai-agent-platform)
- [Ada: Broadest integration coverage with a credibility question](#ada-broadest-integration-coverage-with-a-credibility-question)
- [Cognigy: The analyst-recognized platform for global language coverage](#cognigy-the-analyst-recognized-platform-for-global-language-coverage)
- [Zendesk AI: The zero-migration path for existing Zendesk shops](#zendesk-ai-the-zero-migration-path-for-existing-zendesk-shops)
- [Gorgias: The Shopify-native AI with revenue attribution](#gorgias-the-shopify-native-ai-with-revenue-attribution)
- [How to choose the right Decagon alternative](#how-to-choose-the-right-decagon-alternative)
### Frequently asked questions about Decagon alternatives
Common questions about delight.ai for ticket deflection, compliance, and team control.
**What's the best Decagon alternative for my situation?**
The best Decagon alternative depends on the constraint driving your search. Delight.ai leads for persistent customer memory and governance across long-running relationships. Pricing priorities, existing technology commitments, and specialized service requirements can point buyers toward the other platforms in this comparison.
**How does Decagon's pricing compare to its alternatives?**
Decagon's pricing is quote-only, so buyers cannot model its cost from public information before a sales conversation. Fin publishes a $0.99 price for chat or email resolutions on Intercom, and Zendesk and Gorgias publish plan and usage structures. Sierra, Ada, Cognigy, and Delight.ai direct enterprise buyers to sales, so procurement teams should compare the billable event, included services, and modeled cost at current and projected volume.
**Why would a team look for a Decagon alternative in the first place?**
Teams look for a Decagon alternative when its quote-only pricing, sales-led evaluation, platform architecture, or governance model does not match their requirements. Existing ecosystem commitments can also narrow the field, and enterprises with repeat customers may prefer a platform that organizes the experience around persistent relationship context.
**Which alternative is best for voice and phone support?**
Cognigy is the strongest fit for global contact centers that need multilingual voice automation and flexible deployment, and Sierra pairs voice with complex autonomous service actions. Delight.ai suits enterprises that want voice to share persistent customer context with chat, SMS, email, and other channels.
**Which alternative is best suited for regulated or compliance-heavy industries?**
Delight.ai is a strong fit when buyers need verified compliance and active AI governance in the same platform. Its confirmed enterprise compliance portfolio works with [Trust OS](https://delight.ai/trust-os) to give teams oversight and control, and Cognigy remains a strong option for deployments with specific data-residency requirements.
### Keep reading
[View more](/blog "View more")
[ Conversational AI in healthcare: What it does and how to evaluate it](/blog/industry/conversational-ai-healthcare)
[ The Trust Stack: AI Agent Governance and Earned Autonomy](/blog/ai-agent/the-trust-stack-how-to-give-an-ai-agent-more-autonomy-without-losing-control)
[ What is a hotel chatbot? Overview, use cases, and more](/blog/ai-for-cx/hotel-chatbot-ai-concierge)
[ What is an AI shopping assistant? How top options compare](/blog/ai-for-cx/ai-shopping-assistant-comparison)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Retail is at the AI tipping point. The race to lead starts now.
Source: https://delight.ai/blog/industry/retail-ai-trust
## Retail is at the AI tipping point. The race to lead starts now.
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
May 22 2026
71% of U.S. consumers used AI-powered customer service in the past year. Retail ranked second across all five industries in the Delight AI Index, scoring 59 out of 100 and clearing the national average of 51 by eight points. Shoppers are already engaging with AI in retail at scale, and many describe those interactions as helpful and time-saving.
That's the tipping point. Retail is not at the beginning of the AI adoption curve. It's past it. The question now isn't whether shoppers will use AI. It's which brands will build the trust that turns first interactions into lasting loyalty.
The data points directly at the opportunity. Shoppers have already told us where they want AI, what would make them trust it more, and exactly what it would take to delight them. The playbook is written. The brands that execute it first will own the next decade of retail CX.
Retail leads every industry except Tech & Media. A score of 59 also makes clear where the ceiling is and how much room there is to reach it. The gap between 59 and "Confident" on the Index scale is 16 points. Those points are earned on trust, not efficiency. The brands building for trust now will be the ones shoppers choose when the market consolidates around AI-native experiences.
## The playbook is already written
One of the clearest findings in the retail data is that shoppers have already sorted their service interactions into two buckets. Fast, routine, informational tasks belong to AI. Anything that touches their money belongs to a human, or at minimum an AI with a clear approval checkpoint.
Shoppers are being specific, not resistant. Nearly half already prefer AI for order tracking. At 49%, that's mainstream behavior. On refunds, 32% are already willing to try AI, with more expected to follow as AI proves itself reliable and correctable.
The complaint row deserves its own attention. When something has already gone wrong and a shopper wants to register a complaint, 51% want a human. That moment, after the failure and before the resolution, is where brand equity is either rebuilt or lost for good. The handoff design there is as important as the AI deployment that preceded it.
The brands that win won't be the ones that force AI into every interaction. They'll be the ones that deploy it where shoppers already want it and build handoffs that earn trust at the moments that matter most.
## AI is now a brand signal
The Index found that shoppers don't separate AI from the brand deploying it. A great AI interaction is a great brand interaction. 64% of consumers expect AI to outperform a human rep, and when it does, the brand gets the credit. That's a significant shift from even two years ago, when AI in CX was still seen as a cost-cutting measure rather than a quality signal.
The Emotional Resonance pillar, which measures whether AI interactions feel meaningful rather than just functional, scores 46 nationally, trailing Brand Alignment at 57. That 11-point gap is the largest in the Index and the biggest opportunity in retail right now. Shoppers are already aligning AI with your brand. Retailers who invest in making those interactions feel personal and considered will close that gap and pull ahead of competitors still treating AI as back-office automation.
## The bar is lower than you think
When we asked shoppers what would genuinely delight them in an AI-powered service interaction, the answer wasn't a more sophisticated AI. It was a more attentive one.
### *70% say they'd feel more delighted if AI simply remembered their prior interactions so they didn't have to repeat themselves. Memory beats empathy. This is achievable today.*
These are solvable problems. Long-term conversational memory. Transparent data practices. Visible human backup for high-stakes moments. None of this requires a fundamental rethink of AI capability. It requires intentional product decisions. The retailers who make those decisions now will delight shoppers who are ready to be won over and build the kind of trust that compounds over time.
54% of all consumers expect to feel comfortable with fully autonomous AI within the next year. The window to earn that trust before it becomes the default expectation is open right now. It's narrowing.
## Your highest-value shoppers are ready
The generational data in the Index challenges a common assumption in retail. AI-forward service doesn't primarily appeal to younger, more tech-native shoppers. The reality is more nuanced and more actionable than that.
Millennials are the highest-value shoppers in retail right now, both in spending power and in AI readiness. 63% are comfortable with AI handling routine retail CX tasks independently, a full 28 points ahead of Boomers, where that number drops to 35%. The generational ceiling for AI adoption in retail is high. At the floor, the gap is real and requires a different design approach.
- ### The gender gap that doesn't exist here
Retail has the smallest gender AI trust gap of any industry we measured. 56% of male shoppers are comfortable with autonomous AI. Among female shoppers, that number drops to 49%, a 7-point gap. Compare that to Financial Services, where the gap is 20 points, or Healthcare at 17. Women contact customer service 38% more often than men, making them the majority of retail AI interactions. Retail is already closer to gender parity on AI trust than any other sector. The challenge is holding that advantage as AI becomes more capable and stakes feel higher.
The implication is straightforward. Design AI-powered service with guardrails that the most skeptical frequent shopper would find reassuring: clear data policies, visible human backup, the ability to reverse a decision. Build for that shopper and you'll have built something that delights everyone else in the process.
## Three moves that put you ahead
### 1. Start with memory.
70% of shoppers would be delighted by AI that simply remembers their prior interactions. No repeated order numbers. No re-explaining the context. Long-term conversational memory is the single highest-impact, lowest-barrier improvement in retail AI right now. It compounds. Every interaction that builds on the last one is an interaction that builds loyalty.
### 2. Follow the playbook shoppers already wrote.
Deploy AI where shoppers already want it: order status, product questions, tracking updates. For refunds, returns, and price disputes, pair AI with explicit approval checkpoints. The most effective brands aren't switching from AI to human at that point. They're running both in parallel, with AI surfacing the order history and options while a human confirms the outcome. Shoppers reward brands that make the process feel supervised, not automated.
### 3. Make trust visible.
84% of shoppers say clear data privacy and security practices would increase their trust in AI. 65% want human oversight available alongside autonomous agents. The brands that communicate their AI safeguards, not just implement them, will close the Emotional Resonance gap faster than those treating trust as a back-end concern. Show shoppers what AI will do before it does it. Tell them how to reverse it if needed.
Retail is at the tipping point. 59 out of 100 means the market has tried AI, found it useful, and is ready to go further with the brands that earn the next level of trust. The brands building for that now, with memory, safeguards, and a clear model for where AI leads and where humans do, will set the standard for the rest of the decade.
[**Read the full 2026 Delight AI Index →**](https://delight.ai/ai-index)
---
*The 2026 Delight AI Index surveyed 1,000 U.S. consumers in March 2026 across Retail, Travel, Tech & Media, Financial Services, and Healthcare. Scores are measured across five pillars: Trust & Confidence, Resolution Effectiveness, Brand Alignment, Comfort with Autonomy, and Emotional Resonance.*
- [The playbook is already written](#the-playbook-is-already-written)
- [AI is now a brand signal](#ai-is-now-a-brand-signal)
- [The bar is lower than you think](#the-bar-is-lower-than-you-think)
- [Your highest-value shoppers are ready](#your-highest-value-shoppers-are-ready)
- [Three moves that put you ahead](#three-moves-that-put-you-ahead)
Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Tech & Media leads on AI trust. Here's what that position can do for the entire market.
Source: https://delight.ai/blog/industry/tech-and-media-ai-trust
## Tech & Media leads on AI trust. Here's what that position can do for the entire market.
[LeighAnne Manwiller — Product marketing manager](https://delight.ai/author/leighanne-manwiller)
May 22 2026
Tech & Media scored 60 out of 100 in the 2026 Delight AI Index, the highest of all five industries measured and nine points above the national average of 51. The lead is real. Tech & Media users are the most AI-fluent consumers in the dataset. They use AI tools more frequently, report more positive experiences with AI-powered customer service, and are more comfortable with autonomous AI handling routine service interactions than any other industry segment.
That lead has implications beyond the industry. Consumer comfort with AI doesn't stay siloed by vertical. A user who learned to trust AI for subscription management or technical troubleshooting carries that comfort into their next AI interaction, whether it's with their bank, their healthcare provider, or their airline. Tech & Media is where consumers are building the baseline expectations that will define AI trust across every industry. What this industry does with that position shapes the entire market.
The same audience that scores Tech & Media highest also has the highest expectations for what AI should deliver. They know the difference between AI that's well-deployed and AI that's adequate. They've used enough of both to have opinions. The data shows where that advantage holds and where even the most AI-positive audience in the Index draws a clear line.
The one-point margin between Tech & Media and Retail matters less than what the position itself represents. The users who become comfortable with AI in streaming, gaming, and software support are the same users who will be less hesitant when AI appears in their financial services app or healthcare portal. Tech & Media's lead is a head start on a much larger market dynamic. The brands that use it well will be the ones that shape what consumers expect from AI everywhere.
### Where AI wins and where it doesn't
Tech & Media users have sorted their service interactions clearly. Product questions and subscription management are AI territory. Billing disputes are not. The pattern is consistent with every other industry in the Index. The moment money is actively at stake, human preference spikes regardless of how AI-positive the overall audience is.
Product questions (39% AI) and subscription management (37% AI) are the strongest AI preferences of any tasks in the Tech & Media dataset, and both are squarely in AI-preferred territory. These routine, informational, low-stakes touchpoints are where Tech & Media companies have built real AI credibility with their users.
Billing disputes hold at 61% human preference. Even in an industry where users are more comfortable with AI than anywhere else in the Index, the moment an error has financial consequences, the preference shifts decisively. That line is consistent across every industry in the dataset. AI fluency doesn't move it.
### The highest expectations in the dataset
64% of consumers expect AI to outperform a human representative. In Tech & Media, where users interact with AI tools more frequently than any other industry segment, that expectation is calibrated against actual experience. A user who runs AI-powered search, uses AI writing tools daily, and subscribes to AI features on their streaming platform knows what good AI looks like. The bar for AI-powered customer service is set by the AI these users already use, not by the industry average.
In Tech & Media, Trust & Confidence lands at 56%, Comfort with Autonomy at 56%, Resolution Effectiveness at 52%, and Brand Alignment at 51%. All above the national average. Emotional Resonance is the one pillar where every industry, including this one, still has work to do. It scores lowest across the Index. In a vertical where users interact with AI constantly and already know what great looks like, the gap between an interaction that resolves an issue and one that leaves a user feeling genuinely recognized is where the next competitive advantage gets built.
### Familiarity raises the bar, not the tolerance
The most AI-familiar audience in the dataset is also the clearest about what's missing from current AI experiences. When users who use AI every day tell you what would delight them in a customer service context, the answer reveals a gap between what AI currently delivers and what they already know is possible.
**70% of consumers say they'd feel more delighted if AI simply remembered their prior interactions so they didn't have to repeat themselves. For users who already rely on AI tools that build on context, this is a baseline expectation. Current customer service AI largely fails to meet it.**
Users in Tech & Media want more than accuracy. They want AI that knows their history, can be corrected when it's wrong, and has a visible human backstop for the interactions that require it. These are features that AI-fluent users notice when present and notice even more sharply when absent. The brands that build them will hold their lead. The ones that don't will lose it to whoever does.
### Millennials are your most AI-ready segment. That's not who you'd expect.
The common assumption about AI adoption in Tech & Media is that Gen Z leads. The 2026 Delight AI Index data shows the opposite. Millennials are the most comfortable with AI handling routine Tech & Media interactions independently. Not Gen Z. By six percentage points.
Millennials at 66% represent the highest AI comfort level of any generation in any industry in the entire dataset. They're also in peak subscription spend, the demographic driving digital platform revenue. The brands that design AI experiences for the Millennial AI comfort level will build the engagement and retention that matters most to the business.
### The user doing most of your AI interactions
Male Tech & Media users are significantly more optimistic about AI overall: 32% describe their attitude toward AI as very positive, compared to 12% of women. But women contact customer service 38% more often than men in a typical month, making them the majority of actual AI interactions. The segment most enthusiastic about AI is not the segment most likely to be using it. Design for the realistic audience, not the most vocal one.
Boomers in Tech & Media sit at 31% comfort. That's lower than Millennials and Gen Z by a significant margin, but notably higher than Boomers in financial services (15%) or healthcare (19%). That relative openness is an opportunity. Boomers in this vertical are reachable in a way they aren't in higher-stakes industries. Designs that explain what AI is doing and provide clear control mechanisms can move that number.
### Three moves that hold the lead
#### 1. Close the Emotional Resonance gap before a competitor does.
Tech & Media leads on Brand Alignment and Resolution Effectiveness. Emotional Resonance is where the opportunity sits. It scores 46 nationally, the lowest of the five pillars, and the 11-point gap between it and Brand Alignment is the largest spread in the Index. AI that resolves a subscription issue in 30 seconds but leaves the user feeling like a ticket number is leaving the highest-value part of the interaction untouched. Build for acknowledgment and context, not just speed.
#### 2. Use memory as the differentiation layer.
70% of users would be more delighted by AI that simply remembers their prior interactions. In Tech & Media, where users already expect this from other AI tools they use daily, the gap between what customer service AI delivers and what they know is possible is acutely felt. Long-term conversational memory is the single highest-impact improvement available right now. No re-entering account info. No re-explaining the history. Every interaction that builds on context is an interaction that builds loyalty.
#### 3. Build the path from billing frustration to billing trust.
61% of Tech & Media users prefer a human for billing disputes. That's the line today. Moving it requires AI that is visibly accurate, correctable on the spot, and paired with a clear audit trail that users can review after the fact. Millennials, already at 66% comfort for routine tasks, are the users most likely to extend that comfort to billing if the trust architecture is right. The platforms closing that gap fastest aren't switching from AI to human for disputes. They're running both in parallel, with AI surfacing the transaction history and account context while a human confirms the resolution and owns the outcome.
Tech & Media leads with a 60. The opportunity in that position is larger than the score suggests. Every AI interaction that earns genuine trust in this vertical raises the baseline for what consumers expect everywhere else. The brands that build AI experiences worth exporting, the kind that feel personal, correctable, and human-aware, won't just hold the lead. They'll set the standard the rest of the market will spend years trying to meet.
[Read the full 2026 Delight AI Index →](https://delight.ai/ai-index)
---
*The 2026 Delight AI Index surveyed 1,000 U.S. consumers in March 2026 across Retail, Travel, Tech & Media, Financial Services, and Healthcare. Scores are measured across five pillars: Trust & Confidence, Resolution Effectiveness, Brand Alignment, Comfort with Autonomy, and Emotional Resonance.*
- [Where AI wins and where it doesn't](#)
- [The highest expectations in the dataset](#)
- [Familiarity raises the bar, not the tolerance](#)
- [Millennials are your most AI-ready segment. That's not who you'd expect.](#)
- [Three moves that hold the lead](#)
## Keep reading
[View more](/blog)
[ Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[ Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[ Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[ Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog)
## Healthcare has the furthest to go on AI trust. The starting point is clearer than you think.
Source: https://delight.ai/blog/industry/healthcare-ai-trust
- [Blog](/blog)
- [Industry](/blog/industry)
## Healthcare has the furthest to go on AI trust. The starting point is clearer than you think.
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
May 22 2026
Healthcare scored 47 out of 100 in the 2026 Delight AI Index, the lowest of the five industries measured and four points below the national average of 51. That score reflects something real. Healthcare is where the potential consequences of an AI error feel most personal, most irreversible, and most high-stakes. Patients and providers are not wrong to approach AI in their care context with more caution than a shopper checking an order status.
The data points directly at where to begin. There are specific, high-value tasks in healthcare where patients already have meaningful AI appetite, and where getting AI right creates a foundation that raises trust across the entire patient journey.
Healthcare organizations that start at the administrative layer — scheduling, insurance verification, benefits lookup — and earn trust there will be the ones that get to expand AI deeper into the patient experience over time. The ones that skip that foundation will find the trust gap harder, not easier, to close.
Healthcare trails every other industry in the Index. The 13-point gap between Healthcare and Tech & Media reflects how much emotional weight patients bring to AI interactions in their care context. Closing that gap is achievable, and requires a clear-eyed view of where patient AI appetite actually exists and where it emphatically does not.
### Where the window is open — and where it's not
The patient preference data in healthcare is both more granular and more consequential than in any other industry. The slope from most AI-accepted task to least AI-accepted is steep, and the design implications of where you land on that slope are significant.
Healthcare may score lowest in the Index, but nearly a third of patients already prefer AI for scheduling. That's a real deployment window. Scheduling also happens to be one of the interactions patients find most frustrating with human agents today. Hold times, wrong information, no-shows that could have been avoided. AI that handles scheduling accurately and quickly earns credibility even with skeptical patients.
Symptoms and treatment questions are a different matter entirely. 64% want a human. That preference reflects the weight of vulnerability in a medical interaction. When a patient is asking about chest pain or a child's fever, they need to feel heard by someone who can be held accountable. That preference won't be moved by a more capable AI. When the stakes involve someone's health, patients need to know there's a person behind the answer who can be held responsible for it.
### When AI gets it wrong here, the stakes are different
Healthcare accountability is concrete. When AI incorrectly tells a patient their procedure is covered and it isn't, that patient faces an unexpected bill. When AI misbooks an appointment and the patient misses care, the consequences can compound. These are the requirements that patient-facing healthcare AI has to be built around from the start.
64% of consumers expect AI to outperform human representatives. In healthcare, earning that expectation requires AI that is first accurate at the administrative layer before anything else. The trust required to go further on the patient journey has to be accumulated interaction by interaction, starting with the parts patients are already willing to try.
### Compliance is the floor. Communication is the product.
When patients were asked what would increase their trust in healthcare AI, the answers converged on data protection and visible human access. Both are achievable today. Neither is a technology problem.
**84% of patients say clear data privacy and security safeguards would increase their trust in AI. In healthcare, where a BAA governs how AI systems can handle patient data, compliance is the minimum. Communicating it is where trust actually starts.**
When patients understand that their health data is protected, that their AI interaction is logged and reviewable, and that a human is available when they need one, they are measurably more likely to engage with healthcare AI and trust the outcomes it produces. That's the trust architecture that makes every patient journey better.
54% of all consumers expect to feel comfortable with fully autonomous AI within the next year. Healthcare will take longer to get there than most industries. The providers and payers that use that time to build a patient trust track record at the administrative level will be in a very different position when AI expectations normalize.
### The generation most resistant to healthcare AI is the one that uses it most
The generational data in healthcare reveals the central design challenge for the industry. The patients who are most resistant to AI are also the patients with the highest healthcare utilization. Designing around that mismatch is the work.
Millennials at 48% comfort with routine healthcare AI represent a real and growing patient population that is ready for more AI-forward experiences today. Boomers at 19% are the industry's core design problem. They are also the patients most likely to interact with healthcare AI systems on a regular basis — and the ones for whom a bad AI experience has the least room for error.
### The patient AI has to earn
Women make healthcare decisions for themselves and for their families at a disproportionate rate. The gender trust gap in healthcare AI is 17 points, with women significantly more skeptical of fully autonomous AI interactions than men. Across all industries, women contact support 38% more often than men. In healthcare, where those interactions span scheduling, billing, and benefits navigation, that frequency is even higher. An AI-forward healthcare experience that fails to earn women's trust is leaving the majority of healthcare decisions behind.
Build for the most cautious frequent patient, and you build something that works for everyone. In practice, that means clear confirmation screens, explicit human access, transparent data use, and visible audit trails. Together they form the foundation every trustworthy healthcare AI experience needs.
### Three moves that build lasting trust
#### 1. Build the administrative layer before anything clinical-adjacent.
Insurance verification, benefit lookups, copay estimates, and prior authorization status are the administrative tasks patients find most frustrating with human agents today. They are also tasks where AI can perform accurately without approaching clinical territory. An AI that handles administrative questions quickly, accurately, and with clear correction mechanisms earns credibility even with the most skeptical patient segment.
The providers building trust fastest don't switch from AI to human when interactions get complex. They run both in parallel, with AI surfacing information and handling the administrative layer while a human remains visible and accessible throughout. That architecture is what a score of 47 is telling us patients need before they'll go further.
#### 2. Start at the scheduling layer and earn the next layer from there.
Scheduling and appointment management are where patients have the most AI tolerance in healthcare. 32% already prefer AI there. These interactions are also among the most consistently frustrating with human agents today. Getting AI right at the scheduling layer builds real credibility with real patients, including skeptical Boomer patients who will come to scheduling first before anything more sensitive. Every accurate, easy scheduling interaction deposits trust that makes the next healthcare AI interaction easier to accept.
#### 3. Make compliance visible, not implicit.
BAA compliance and HIPAA-compliant data handling set the floor. What raises you above it is communicating them where patients can see and understand them. Surface data protections in interaction confirmations, in the product experience, and anywhere a patient is being asked to share health information. Patients who know their data is protected under HIPAA are measurably more likely to trust the AI using it and to stay engaged when the interaction asks for something sensitive.
Healthcare scored 47. The path from 47 to something meaningfully higher runs through administrative AI done well, data practices communicated clearly, and handoff design that never lets a patient feel abandoned by the technology. The providers and payers that build that foundation now will be the ones patients trust as AI becomes standard across every part of the patient journey.
[**Read the full 2026 Delight AI Index →**](https://delight.ai/ai-index)
---
*The 2026 Delight AI Index surveyed 1,000 U.S. consumers in March 2026 across Retail, Travel, Tech & Media, Financial Services, and Healthcare. Scores are measured across five pillars: Trust & Confidence, Resolution Effectiveness, Brand Alignment, Comfort with Autonomy, and Emotional Resonance.*
- [Where the window is open — and where it's not](#section-anchor-id-0 "Where the window is open — and where it's not")
- [When AI gets it wrong here, the stakes are different](#section-anchor-id-1 "When AI gets it wrong here, the stakes are different")
- [Compliance is the floor. Communication is the product.](#section-anchor-id-2 "Compliance is the floor. Communication is the product.")
- [The generation most resistant to healthcare AI is the one that uses it most](#section-anchor-id-3 "The generation most resistant to healthcare AI is the one that uses it most")
- [Three moves that build lasting trust](#section-anchor-id-4 "Three moves that build lasting trust")
Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## In financial services, AI trust has to be earned transaction by transaction
Source: https://delight.ai/blog/industry/financial-services-ai-trust
## In financial services, AI trust has to be earned transaction by transaction
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller — Product marketing manager](https://delight.ai/author/leighanne-manwiller)
May 22 2026
Financial Services scored 50 out of 100 in the 2026 Delight AI Index, landing right at the national average. In the Index, Financial Services falls between Travel at 55 and Healthcare at 47. More trusted than the industry where clinical stakes are highest. Less trusted than the industries where mistakes feel recoverable.
Sitting at the average is a baseline, not a verdict. In an industry where customer trust is literally the product, a client who loses confidence in your institution moves their accounts. The brands that move from 50 to significantly above it will do so by building AI that earns trust deliberately, interaction by interaction.
The data is clear about what that takes. There's real AI appetite in financial services, concentrated in specific tasks. There's equally real resistance, concentrated in others. The brands that read that map accurately will build AI programs that expand trust over time rather than burning it with a single bad interaction.
The gap between Financial Services and Retail is nine points. Nine points earned on the difference between low-stakes errors (a wrong product recommendation) and high-stakes ones (a disputed charge that affects someone's credit). The path to closing that gap runs through AI that feels controlled, correctable, and transparent.
### Balance checks are the easy part
Consumers have sorted their financial service interactions into a clear hierarchy based on how much is at stake when something goes wrong. At the informational end, AI appetite is real. At the transactional end, it drops sharply.
40% of consumers already prefer AI for account balance inquiries. That's a meaningful AI appetite in a cautious industry, and it represents a real deployment window. The moment money moves, the picture changes fast. 65% want a human for transaction disputes — the highest human preference for any task in the entire financial services dataset, tied only with healthcare's most sensitive interactions.
Consumers are being specific about where they want AI. Balances, transaction history, and coverage summaries are where AI can perform without the weight of high-stakes consequences. That's the foundation. The transactional layer comes later, as reliability is demonstrated and trust accumulates.
### An AI error here isn't an inconvenience
Financial AI errors aren't abstractions. A wrong balance display leads to an overdraft. A failed transfer leads to a missed payment. Incorrect account information leads to locked access. Consumers know this. When something has already gone wrong in a financial account, 65% want a human who has both the authority and the judgment to make it right.
64% of consumers expect AI to outperform human representatives. In financial services, that expectation has real teeth. It means AI needs to be more accurate than the average call center agent, more consistent than the average branch employee, and more reliable than any other channel a consumer uses to manage their money.
### The single biggest trust lever
When consumers were asked what would most increase their trust in AI handling financial service interactions, the answer was consistent and specific.
*64% of women say the ability to correct mistakes or reverse decisions would increase their trust in financial AI most. Among men, that number is 49%. Reversibility is the trust mechanism.*
The design implication is direct. AI in financial services needs to be auditable, correctable, and clearly bounded in what it can do autonomously. SOC 2 and ISO 27001 compliance set the floor. The institutions that go beyond compliance to make those safeguards visible — in the product, in the interaction, in the confirmation screens — will build the trust that converts skeptical consumers into consistent AI users.
54% of all consumers expect to feel comfortable with fully autonomous AI within the next year. Financial services has more ground to cover than most industries to get there. The brands that use that time to demonstrate reversibility and accuracy will be ahead when the expectation matures.
### The widest generational gap in the entire dataset
No industry in the 2026 Delight AI Index shows a wider generational split on AI readiness than Financial Services. The gap between Millennials and Boomers here is 40 points. That number has direct implications for who financial brands are actually designing AI for — and who they may be inadvertently leaving behind.
Millennials at 55% comfortable with AI for routine financial tasks represent a real, deployable audience that is actively underserved by current financial AI experiences. Boomers at 15% represent the industry's most challenging design problem — and its highest-stakes one.
#### The segment that determines your ceiling
At 15% comfort with autonomous financial AI, Boomers represent the lowest readiness of any generation across any industry in the Index. They are also the demographic with the highest financial assets under management. The financial brands that earn Boomer trust in AI won't just retain customers. They'll be protecting the relationships that represent the most value in their portfolios. That requires a different design standard. More explanation, more reversibility, more visible human backup. The features that earn Boomer trust also happen to be the features that build trust with everyone else.
The pattern holds for gender as well. 24% of women say they do not plan to rely on AI for customer service in financial contexts, compared to 14% of men. Designing AI that earns their trust is a core revenue question.
### Three moves that put you ahead
#### 1. Start with the informational layer.
Account balances, transaction history, benefit summaries, and coverage lookups are where consumers already have AI appetite in financial services. Deploy AI here first, measure accuracy rigorously, and build a reliability track record before extending into anything transactional. Every successful informational interaction adds to the trust budget that eventually funds more autonomous AI capabilities.
#### 2. Make reversibility the product, not the footnote.
57% of consumers rank reversibility and correction capability as their top trust driver in AI, placing it above both accuracy and transparency. Build AI with clear correction, review, and reversal workflows baked in, and surface them where consumers can see them before they need them. This is what converts skeptical Boomers and women from AI avoiders to AI users. The institutions closing the trust gap fastest are moving beyond a clean handoff model. Platforms where AI drafts the action and a human reviews and confirms deliver the reversibility consumers need without sacrificing the speed that makes AI worthwhile.
#### 3. Go beyond compliance to communication.
SOC 2, ISO 27001, and ISO 42001 compliance set the security and AI governance floor. 84% of consumers say transparent data practices would increase their trust in AI. Having the certifications gets you to the starting line. Communicating them clearly, at the moments consumers are deciding whether to engage, is where trust actually accumulates. Surface data safeguards in the product, in the interaction flow, and in confirmation screens. Consumers who understand how their financial data is protected are measurably more likely to trust the AI using it.
Financial Services scored 50. The average means the opportunity to lead is wide open. Every point above it is earned through AI that's accurate, correctable, and honest about what it can and can't do. The institutions building that track record now will hold the relationships that matter most when the industry moves.
[**Read the full 2026 Delight AI Index →**](https://delight.ai/ai-index)
---
*The 2026 Delight AI Index surveyed 1,000 U.S. consumers in March 2026 across Retail, Travel, Tech & Media, Financial Services, and Healthcare. Scores are measured across five pillars: Trust & Confidence, Resolution Effectiveness, Brand Alignment, Comfort with Autonomy, and Emotional Resonance.*
- [Balance checks are the easy part](#section-anchor-id-0 "Balance checks are the easy part")
- [An AI error here isn't an inconvenience](#section-anchor-id-1 "An AI error here isn't an inconvenience")
- [The single biggest trust lever](#section-anchor-id-2 "The single biggest trust lever")
- [The widest generational gap in the entire dataset](#section-anchor-id-3 "The widest generational gap in the entire dataset")
- [Three moves that put you ahead](#section-anchor-id-4 "Three moves that put you ahead")
Keep reading
[View more](/blog "View more")
[ Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[ Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[ Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[ Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Travel has the most to gain at the AI inflection point. Here's where to start.
Source: https://delight.ai/blog/industry/travel-ai-trust
## Travel has the most to gain at the AI inflection point. Here's where to start.
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller — Product marketing manager](https://delight.ai/author/leighanne-manwiller)
May 22 2026
71% of U.S. consumers used AI-powered customer service in the past year. Travel scored 55 out of 100 in the 2026 Delight AI Index, four points above the national average and squarely in the middle of the five industries measured. Guests are already using AI for travel — and many describe the routine interactions as fast, frictionless, and accurate.
But that's not the full picture. Travel is also the industry where the gap between AI appetite and AI resistance is widest of any vertical in the Index. Guests have strong, context-specific preferences about when AI should be in the room — and when it absolutely should not.
The data draws a clear line. On one side: the predictable, informational moments that define most travel touchpoints. On the other: the crisis moments that define travel brands forever. The AI strategy that earns loyalty in travel isn't the one that deploys the most AI. It's the one that knows exactly where each belongs.
The five points between Travel and Retail are not a ceiling. They're a gap that closes with better handoff design. The brands that crack that design problem will pull ahead of an industry still deploying AI broadly without thinking carefully about when to hand off to a human.
## The split between routine and crisis
Travel shows one of the sharpest splits in guest preference across tasks in the entire Index. Guests are genuinely AI-forward for routine interactions. They want humans for emergencies. That's not ambivalence — it's context-sensitivity, and it's the clearest preference signal in the data.
39% of guests prefer AI for baggage and flight status. 64% prefer a human for a missed connection emergency. That spread — from the most AI-preferred task to the most human-preferred one — defines the travel AI design problem more than any aggregate score does.
The emergency use case explains why. A missed connection isn't one problem. It's a cascade: a flight rebooking, a hotel, meal credits, a rental car, missed meetings, and a guest who is stressed, exhausted, and needs someone to own the entire situation end-to-end. 64% of guests want a human at that moment. The AI that fails to recognize this and tries to handle it alone is not just failing the interaction. It's damaging the brand.
Travel brands should be aggressive with AI for status checks, confirmations, and basic reservations, and equally deliberate about designing the moment when AI steps aside.
## AI speaks for your brand at every moment
Guests don't separate the AI experience from the brand delivering it. A smooth AI check-in earns brand credit. A mishandled rebooking at 2am earns brand blame. 64% of consumers expect AI to outperform a human rep. In travel, where disruptions are real, immediate, and expensive, failing to clear that bar doesn't just lose the interaction. It loses the guest.
The Emotional Resonance pillar of the Index, which measures whether AI interactions feel meaningful rather than merely transactional, scores 46 nationally compared to Brand Alignment at 57. That 11-point gap is the largest in the Index. Travel brands that invest in making AI interactions feel genuinely responsive — not just fast — will close that gap and build the loyalty that drives repeat bookings.
## The bar is lower than the disruption suggests
When guests were asked what would genuinely delight them in an AI-powered travel interaction, the answer wasn't more sophisticated routing or faster response times. It was memory.
### 70% say they'd feel more delighted if AI simply remembered their prior interactions so they didn't have to repeat themselves. No re-entering loyalty numbers. No re-explaining the situation. Memory is the easiest win in travel AI right now.
These are achievable product decisions, not capability gaps. Long-term conversational memory. Transparent data handling. A clear, visible path to a human when stakes escalate. Guests who experience all three will trust AI more broadly — including in the disruption moments where trust matters most.
54% of all consumers expect to feel comfortable with fully autonomous AI within the next year. In travel, where AI already handles the easy interactions well, the window to extend that trust into higher-stakes moments is open. The brands that earn it now will be the ones guests book by default.
## The guests most ready for AI are your highest-value ones
The generational picture in travel AI mirrors the broader Index pattern — with one important difference. The gap between Millennials and Boomers in travel is wider than in retail, and the implication for loyalty program strategy is significant.
Millennials are at peak travel spend and at peak AI readiness. 57% are comfortable with AI handling routine travel tasks independently. Boomers, at 27%, are less than half that. Both groups are frequent travelers. The AI experience that works for one will frustrate the other without deliberate design choices.
### The guest the AI has to earn
Women are 38% more likely than men to contact customer service in a typical month, making them the majority of travel AI interactions by volume. Overall, 40% of women say they may never feel comfortable with fully autonomous AI. That's not a niche concern — it's the design brief. Build AI guardrails that the most skeptical frequent traveler would find reassuring, and you've built something that delights everyone else in the process.
The clearest design implication from the segment data: don't build one AI experience and hope it fits all guests. Millennials want speed and autonomy. Boomers want visibility and control. The architecture that serves both is the one with real AI capability on the front end and real human access on the back end.
## Three moves that put you ahead
### 1. Build AI trust before disruption tests it.
Start with the touchpoints where guests already have AI appetite: reservation confirmations, check-in status, loyalty balance inquiries, property questions, itinerary details. Whether the guest is booking a hotel, renting a car, or managing a flight, these routine interactions are where AI can perform consistently and predictably. Every successful one deposits trust that can be spent when disruptions hit. Guests who trust AI for the easy stuff are more forgiving and more willing to accept AI assistance when things go wrong.
### 2. Design the handoff as deliberately as the deployment.
64% of guests want a human when a trip goes seriously wrong — a booking that can't be recovered by self-service, a disruption that cascades into multiple problems, a moment where someone needs to own the full situation end-to-end. How the handoff triggers, what it communicates, and what information it carries to the human matters as much as every AI interaction that preceded it.
A seamless handoff is not a sign that AI failed. It's a sign the brand designed the experience correctly. The most effective brands are moving beyond a clean AI-to-human switch entirely. Look for platforms that let AI and humans work in parallel, with AI surfacing context and options in real time while a human owns the relationship and drives the resolution. The guest never feels transferred. Just supported.
### 3. Surface data practices before the journey starts.
Travel brands hold sensitive guest data: itineraries, payment information, loyalty history, passport details. 84% of guests say clear data privacy and security practices would increase their trust in AI. That trust is built before the first AI interaction, not during it. Communicate your data safeguards at account creation, in the app, and in the pre-trip confirmations guests already open and read. Guests who understand how their information is protected are more willing to let AI use it well.
Travel scored 55. The opportunity isn't in the score. It's in the spread between the routine touchpoints guests are happy to hand to AI and the high-stakes disruptions where they want a human who owns the situation completely. The brands that master both ends of that range, with AI that delights in the routine and humans who own the emergency, will be the ones guests return to regardless of price.
### [**Read the full 2026 Delight AI Index →**](https://delight.ai/ai-index)
---
*The 2026 Delight AI Index surveyed 1,000 U.S. consumers in March 2026 across Retail, Travel, Tech & Media, Financial Services, and Healthcare. Scores are measured across five pillars: Trust & Confidence, Resolution Effectiveness, Brand Alignment, Comfort with Autonomy, and Emotional Resonance.*
- [The split between routine and crisis](#section-anchor-id-0 "The split between routine and crisis")
- [AI speaks for your brand at every moment](#section-anchor-id-1 "AI speaks for your brand at every moment")
- [The bar is lower than the disruption suggests](#section-anchor-id-2 "The bar is lower than the disruption suggests")
- [The guests most ready for AI are your highest-value ones](#section-anchor-id-3 "The guests most ready for AI are your highest-value ones")
- [Three moves that put you ahead](#section-anchor-id-4 "Three moves that put you ahead")
Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2) [Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/low-csat-analysis) [Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[](/blog/product/introducing-agent-steward) [Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/voice-v2) [Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## 8 best AI customer service platforms for enterprise support (2026)
Source: https://delight.ai/blog/ai-for-cx/best-ai-customer-service
## 8 best AI customer service platforms for enterprise support (2026)
[
Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
May 28 2026
[Eighty-one percent of consumers](https://www.prnewswire.com/news-releases/contextual-intelligence-becomes-the-new-standard-for-exceptional-customer-experience-in-2026-302617972.html) expect support agents to pick up where the last conversation left off, so they never have to repeat themselves. A growing number of AI customer service platforms claim to do exactly that, but few do it well.
This comparison evaluates what actually separates enterprise-grade tools: the depth of customer memory across sessions, whether the AI takes action in live systems or just answers questions, and if it holds up in regulated environments. We'll look at eight platforms, with their advantages, weak spots, and ideal use cases.
## How do businesses evaluate the best AI customer service software?
Certain capabilities stand out when you [vet an AI agent platform](https://delight.ai/ebooks/key-questions-to-vet-an-ai-agent-platform) through the right questions.
- **Does the AI remember customers?** Customers shouldn't re-explain their situation every time they call. Session-level memory gets you through a single call. Cross-session memory is what makes support feel like it belongs to a brand that pays attention. Most listicles don't evaluate this criterion at all, but platforms like delight.ai, Sierra (via its Agent Data Platform), and Decagon have started building persistent memory into their architectures—each with different approaches to depth and implementation.
- **Can the AI resolve complexity, not just escalate it?** Whether AI passes full context on handoff used to be the baseline question—plenty of tools still fail it. But the more important one now: Does the AI even need to escalate? Routine deflection is largely solved. The next bar is end-to-end ownership of genuinely complex cases: issues that span multiple sessions, systems, or third parties. Platforms that can coordinate across teams and tools to drive an issue to close without involving a human are solving a structurally different problem than those that simply hand off gracefully.
- **Can you trust it in a regulated environment?** For healthcare or fintech teams, performance alone isn't the question. It comes down to whether or not there's an audit trail, who sees the logs, and what happens when the AI encounters a situation it wasn't trained on.
- **Does it get better on its own?** Most platforms require manual retraining to improve—you find the failures, update the logic, redeploy. The emerging standard is AI that learns from live interactions and self-optimizes without engineering cycles. If a platform compounds its own performance over time, the gap between it and a manually tuned system grows with every week of operation. Ask vendors specifically: What triggers a model update, and who has to do the work?
## At a glance: Best AI customer service software comparison
```
Platform Best for Pricing Channels Memory
Zendesk AI Zendesk-native teams $55+/agent/mo + $1.30–$2.00/resolution + add-ons Chat, email, voice, SMS, social, in-app Ticket-scoped
Sierra Large enterprise, voice-heavy Custom enterprise pricing Chat, voice, email, SMS, WhatsApp Persistent
delight.ai Regulated industries, retention-focused Custom enterprise pricing Chat, voice, SMS, email, social, in-app Persistent
Decagon Product-led tech, voice-first Custom enterprise pricing Chat, email, voice, SMS Persistent
Fin by Intercom SaaS / PLG companies $0.99/resolution 14+ channels incl. chat, email, WhatsApp Per-conversation
Salesforce Salesforce-native enterprises $2/conversation or usage-based Chat, SMS, WhatsApp, voice, social CRM-integrated
Agentforce
Freshdesk Freddy AI Mid-market, budget-conscious $49/agent/mo + ~$0.50/session Chat, email, messaging Session-scoped
Gorgias DTC / Shopify ecommerce $10–$900/mo + $0.90–$1.00/resolution Email, chat, SMS, voice, social Session-scoped
```
## What are the best AI customer service platforms in 2026?
### 1. Zendesk AI
[Zendesk AI](https://www.zendesk.com/) adds ticket classification, intelligent routing, agent-assist suggestions, and autonomous resolution natively to a platform most enterprise support teams already run. In addition, Zendesk brings a data advantage that newer entrants cannot match, including over 20,000 AI customers, AI annual recurring revenue (ARR) on track to reach $400–500 million in 2026 (up from $200 million in 2025), and a training dataset of [20 billion customer service interactions](https://technovapartners.com/en/insights/best-ai-customer-service-platforms-2026).
With 1,710-plus marketplace integrations—the widest network in this comparison—teams already on Zendesk add AI with no new data plumbing. Everything reads from the same system of record.
**Disadvantages**: The most useful AI features are in the enterprise plan, with several [capabilities requiring paid add-ons](https://myaskai.com/blog/zendesk-ai-agent-complete-guide-2026). Automated resolutions cost $1.30 to $2.00 per ticket—for a team handling 5,000 AI-resolved tickets a month, that's [an additional $6,500 to $10,000](https://technovapartners.com/en/insights/best-ai-customer-service-platforms-2026) on top of base licensing.
And while Zendesk handles question-answering well, action-taking capability (e.g., processing refunds, updating accounts, etc.) is more limited than what AI-native platforms deliver.
**Best for:** Teams already on Zendesk needing high-volume enterprise support with strong internal documentation.
### 2. Sierra
[Sierra](https://sierra.ai/) arrived in 2023 with a high-profile founding team—Bret Taylor (former Salesforce co-CEO) and Clay Bavor (Google AR/VR)—and approximately [40% of Fortune 50 companies](https://www.eesel.ai/blog/sierra-ai) as customers. Sierra runs multiple AI models simultaneously, with a supervisor layer that checks answers before they reach the customer. The November 2025 launch of its Agent Data Platform added persistent memory across sessions, pulling in CRM records and behavioral history to recognize repeat customers.
**Disadvantages:** Sierra is essentially a managed service—the vendor's team typically builds and configures agents, creating a dependency some buyers find uncomfortable and extending deployment timelines. There are also [documented cases of context loss](https://www.eesel.ai/blog/sierra-ai) in longer, multi-turn conversations, and the platform's safety model remains configuration-dependent, as a [December 2025 guardrail misconfiguration at Gap.com](https://delight.ai/perspective/the-gap-incident-what-trust-infrastructure-would-have-changed) illustrated. The pricing model creates its own friction. Sierra bills on resolved conversations, but resolution is defined by Sierra.
**Best for:** Large enterprises willing to pay for managed implementation and voice-heavy deployments.
### 3. Delight.ai: Best AI customer service platform with persistent memory
[Delight.ai](https://delight.ai/) is an AI customer service platform built on persistent memory, pairing an [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) (AMP) that compounds customer profiles across sessions with [Trust OS](https://delight.ai/trust-os) audit trails for regulated industries. [South Korean furniture retailer Hanssem](https://delight.ai/customers/hanssem) deployed Delight.ai and grew its resolution rate from **48% to about 86% over five months**.
Rather than resetting each session, AMP compounds a profile of each customer, learning what they've asked, what they care about, and where they're likely to have friction next. Delight.ai runs on Sendbird's infrastructure, which handles [over 7 billion conversations a month for more than 300 million users](https://www.prnewswire.com/news-releases/sendbird-launches-delightai-the-first-branded-ai-concierge-built-on-long-term-memory-302618780.html).
It's built around a question most platforms in this category don't ask: what would support look like if the AI actually knew the customer? [Agent Steward](https://delight.ai/blog/product/introducing-agent-steward) extends that idea further, taking end-to-end ownership of complex issues instead of handing them off to a human. It coordinates across systems, teams, and third parties to resolve issues that span multiple sessions and channels.
Where compliance requirements are real, such as healthcare, fintech, or insurance, Trust OS provides per-interaction audit trails and access controls built for regulated-industry governance.
**Disadvantages:** The integration ecosystem is narrower than what Zendesk or Salesforce-native platforms offer.
**Best for:** Mid-market to enterprise teams in regulated industries, such as healthcare, fintech, and insurance, that need persistent memory and audit-ready governance to drive retention and lifetime value, not just deflection rate.
### 4. Decagon
[Decagon](https://decagon.ai/) builds persistent, cross-channel memory into its own architecture, carrying customer context across sessions. It pairs that memory with a voice-forward approach, including Voice 2.0's sub-second latency, customizable tone, and proactive outbound calling, added in spring 2026.
Agent Operating Procedures (AOPs) let teams define resolution logic in natural language rather than code, and the Watchtower and Guardrails layer monitors AI behavior in real time. Customers include Notion, Duolingo, and Rippling, and Decagon supports 15-plus languages with automatic detection. Decagon integrates with Salesforce, Zendesk, and Intercom for handoff workflows.
**Disadvantages**: Deployment typically takes around [6 weeks](https://myaskai.com/blog/decagon-ai-complete-guide-2026) from discovery to launch, and non-technical teams may find setup demanding. Decagon limits the Agent Assist copilot feature[ to Zendesk only](https://myaskai.com/blog/decagon-ai-complete-guide-2026), which constrains teams on other helpdesks. [eesel AI's review](https://www.eesel.ai/blog/decagon-ai-review) flags Decagon's audit logs and user permissions as lacking depth for regulated environments, a notable gap given its enterprise positioning.
**Best for:** Product-led tech companies and mid-to-large enterprises prioritizing persistent memory and voice-first support.
### 5. Fin by Intercom
[Intercom](https://www.intercom.com/) built Fin for conversational resolution: over 40 million AI-resolved conversations, with a [67% average resolution rate](https://myaskai.com/blog/intercom-fin-ai-agent-complete-guide-2026) across customers and a ceiling close to 93% for top performers.
Intercom holds SOC 2 Type II, HIPAA, GDPR, CCPA, ISO 27001, ISO 27018, ISO 27701, and ISO 42001—one of the most broadly certified platforms in this comparison. For SaaS companies and product-led growth (PLG) businesses where support and engagement are closely connected, Fin integrates naturally into Intercom's messenger experience.
**Disadvantages**: The per-resolution pricing model ($0.99 per resolution) is transparent in theory but unpredictable in practice. User complaints about [unexpectedly large bills](https://myaskai.com/blog/intercom-fin-ai-agent-complete-guide-2026) are common enough to be a pattern, and add-ons like Copilot and premium reporting behind higher tiers make total cost of ownership harder to forecast.
Fin also performs best inside the Intercom ecosystem. Organizations deploying it as a standalone agent across a broader infrastructure stack will encounter [architectural friction](https://inkeep.com/blog/inkeep-vs-intercom-fin-ai).
**Best for:** PLG and SMB companies already on Intercom; teams that want fast setup and conversational quality without enterprise-level complexity.
### 6. Salesforce Agentforce
[Agentforce's](https://www.salesforce.com/agentforce/) core advantage mirrors Zendesk's: If your CRM, case management, and service workflows already live in Salesforce, a native AI layer eliminates a class of integration problems. What sets it apart technically is the Atlas Reasoning Engine, which handles multi-step business processes. With $800 million in ARR and over 29,000 deals, the product has reached meaningful enterprise scale.
**Disadvantages:** Outside the Salesforce ecosystem, the logic inverts. Agentforce doesn't natively integrate with third-party helpdesks, and connecting external systems requires custom MuleSoft API work. Pricing is published but complex: Three competing models coexist, and Data Cloud, which Agentforce depends on for its deepest capabilities, [adds $180,000 or more per year](https://www.usefini.com/guides/top-ai-agents-customer-service). Some capabilities remain [locked behind specific license tiers](https://www.usefini.com/guides/top-ai-agents-customer-service), and full enterprise rollouts typically span several months.
**Best for:** Salesforce-native enterprises where service and CRM are already unified on the platform.
### 7. Freshdesk Freddy AI
[Freddy AI](https://www.freshworks.com/freshdesk/) is worth considering if the goal is genuine capability without the enterprise pricing structure. Ticket classification, agent suggestions, and autonomous resolution are all present, with [per-session pricing](https://technovapartners.com/en/insights/best-ai-customer-service-platforms-2026) that starts at roughly $0.50 per session. However, it charges for sessions even when the issue isn’t resolved—unlike platforms such as Fin and Zendesk AI, which bill only on successful resolution.
At a typical 50 to 70% resolution rate, Freddy's effective cost per resolved issue ends up being $0.71 to $1.00. This is comparable to Intercom's $0.99 per resolution, not the dramatic savings the headline pricing might suggest.
**Disadvantages**: The trade-off is depth. [Analytics and reporting are limited](https://monday.com/blog/service/ai-customer-service-software/) compared to what enterprise-grade platforms offer, and customization runs into constraints as workflow complexity increases. Teams underserved by their current stack who need to get AI working quickly will find Freddy a reasonable path. Conversely, teams building toward a sophisticated, compliance-heavy, or cross-channel operation will probably outgrow it within a year or two.
**Best for:** Mid-market teams, IT service management (ITSM) through Freshservice, and organizations that want to move fast without large upfront investment.
### 8. Gorgias
If your support operation is primarily post-purchase ecommerce (e.g., order lookups, returns, subscription changes, etc.), [Gorgias](https://www.gorgias.com/) is one of the more purpose-fit tools in this comparison.
[Over 17,000 merchants](https://www.prnewswire.com/news-releases/gorgias-introduces-its-conversational-ai-platform-for-support-and-sales-to-redefine-the-customer-experience-in-ecommerce-302353308.html) are on the platform, and the Shopify integration in particular goes deep: Agents can view order history, issue refunds, and edit subscriptions without leaving the support thread. The July 2025 release of AI Agent 2.0 extended this into proactive territory, with the AI initiating sales conversations and handling more complex flows at checkout.
**Disadvantages**: The ecommerce focus is both the product's strength and boundary. The compliance layer wasn't designed for regulated industries.
Pricing can scale unpredictably; AI resolutions also count as billable helpdesk tickets, creating a double-billing structure that inflates total cost of ownership beyond what the sticker price suggests. If your organization operates across verticals or needs AI that maintains a long‑term understanding of each customer, the architecture will show its limits quickly.
**Best for:** Direct-to-consumer (DTC) and ecommerce brands, especially on Shopify.
### Honorable mentions
Building enterprise AI support since 2016, **Ada** serves [over 350 enterprise customers](https://myaskai.com/blog/ada-ai-agent-complete-guide-2026), including Verizon, Pinterest, and YETI. Channel coverage spans web, mobile, social, SMS, email, and voice, with integrations across 13-plus helpdesk and contact center systems. Annual pricing ranges from [$30,000 to $300,000-plus](https://www.twig.so/blog/ai-customer-support-best-tools), and deployments typically take [8 to 16 weeks](https://myaskai.com/blog/ada-ai-agent-complete-guide-2026).
**Lorikeet** focuses on regulated industries with strong audit trail capabilities. **Kore.ai** brings multi-agent orchestration with 450-plus Global 2000 customers and strong analyst recognition (Gartner MQ Leader, Forrester Wave Leader). **Forethought**, now part of Zendesk, processes over a billion monthly interactions.
## How to choose the best AI for customer service
The honest version of this decision depends on three factors: your existing stack, your industry, and what you're optimizing for.
**If your stack drives the decision:** Teams on Zendesk, Intercom, or Salesforce should start with their native AI layer; the integration advantage alone justifies the evaluation. For Shopify-first ecommerce, Gorgias is the clear starting point.
**If your industry constrains the options:** Healthcare, fintech, and insurance teams should evaluate compliance infrastructure early. Intercom, Sierra, and delight.ai's Trust OS are the strongest options here.
**If you're a large enterprise with real budget:** Sierra for managed, voice-heavy deployments; Decagon for voice-first with persistent memory; Agentforce if your data already lives in Salesforce—but plan for a several-month implementation, not the weeks the marketing suggests.
**If you're mid-market and need to move fast:** Freshdesk Freddy AI gets you started without enterprise pricing. Watch the session-vs-resolution billing math carefully before committing.
**If persistent memory matters to your CX strategy:** delight.ai, Sierra, and Decagon are the three platforms building it into their architectures, each with a different approach. This is worth a dedicated conversation during evaluation.
Want to see what AI customer service looks like when it's built around knowing the customer? [Explore how delight.ai's Agent Memory Platform handles cross-session context](https://delight.ai/contact-sales).
- [How do businesses evaluate the best AI customer service software?](#section-anchor-id-0 "How do businesses evaluate the best AI customer service software?")
- [At a glance: Best AI customer service software comparison](#section-anchor-id-1 "At a glance: Best AI customer service software comparison")
- [What are the best AI customer service platforms in 2026?](#section-anchor-id-2 "What are the best AI customer service platforms in 2026?")
- [How to choose the best AI for customer service](#section-anchor-id-3 "How to choose the best AI for customer service")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
Keep reading
[View more](/blog "View more")
[
Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)[
Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)[
Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)[
The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[View more](/blog "View more")
## 11 Ada CX alternatives worth evaluating in 2026
Source: https://delight.ai/blog/ai-agent/ada-cx-alternatives
## 11 Ada CX alternatives worth evaluating in 2026
[LeighAnne Manwiller
Product marketing manager](https://delight.ai/author/leighanne-manwiller)
June 5 2026
If you're comparing Ada CX alternatives, you've probably already seen what Ada does well. It's a mature enterprise platform with strong G2 ratings, customers including Pinterest, monday.com, and Zapier, and a newly launched Unified Reasoning Engine that signals ongoing investment in AI capabilities.
But three factors tend to push customer experience (CX) leaders toward alternatives: per-resolution pricing that gets harder to predict as AI success grows, session-scoped context that resets between conversations and channels, and integration constraints that require Zendesk or Salesforce for full feature availability. Which trigger matters most to your team will shape which alternative fits best.
This guide evaluates 11 Ada CX alternatives across 8 criteria—from pricing transparency and deployment speed to compliance certifications and market validation. We applied the same framework to every platform, including our own. If you're evaluating [AI customer service platforms](https://delight.ai/ai-customer-service) more broadly, the criteria below apply beyond this specific comparison.
> **Quick take for enterprise contact centers:** if you need voice and digital channels in one platform, evaluate Sierra AI, Cognigy, and Delight.ai. Cognigy brings Gartner validation, Sierra brings autonomous action-taking, and delight.ai brings persistent customer memory and end-to-end resolution via Agent Steward.
### Why buyers look for Ada CX alternatives in 2026
Three issues come up again and again when CX leaders start exploring alternatives to Ada.
#### 1. Your costs grow every time your AI gets better
Because Ada charges per resolution ($1 to $3.50 each), your bill scales directly with your AI's success. The better it performs, the more you pay. For teams scaling from 10,000 to 50,000 monthly resolutions, that's a cost curve that's tough to plan around—first-year contracts can land anywhere from $30,000 to $250,000+.
#### 2. Every conversation starts from scratch
Ada runs on session-scoped context—each new conversation is a blank slate. A customer who called yesterday about a billing issue and comes back today via chat has to re-explain everything. If you're tracking CSAT and NPS across touchpoints, that memory gap creates a frustrating experience that automation alone can't fix.
#### 3. You'll need a separate helpdesk (and the bill that comes with it)
Ada doesn't handle human agent workflows on its own, so you're adding another platform at $55 to $175+ per agent per month to your total cost of ownership (TCO)—plus the overhead of keeping two systems in sync. And if your team's knowledge lives in Confluence, Notion, Google Drive, or internal wikis, you'll hit a wall—Ada primarily pulls from structured help center articles.
These architectural choices create friction for specific use cases, and that friction is what drives the search for Ada AI alternatives.
### Which Ada CX alternative is right for you?
Before reading all 11 profiles, start with the scenario closest to your situation.
- **Mid-market SaaS teams on Intercom or Zendesk seeking an affordable plugin.** Look at My AskAI ($199/mo + $0.10/ticket), eesel AI ($239–$799/mo), or Fin ($0.99/resolution) first. These platforms deploy in minutes, price transparently, and integrate natively with the helpdesks you already use. They work best when your primary need is ticket deflection without a platform overhaul.
- **Enterprise contact centers needing voice and digital channels in one platform.** Evaluate Sierra AI, Cognigy, and delight.ai. All 3 handle voice alongside chat, email, and messaging—but they come with enterprise price points and implementation timelines to match. Cognigy brings Gartner validation; Sierra brings autonomous action-taking; delight.ai brings persistent customer memory and end-to-end resolution via Agent Steward.
- **High-volume teams needing persistent memory and personalization across channels.** Consider delight.ai, Kustomer, or Decagon. Each approaches customer context differently—delight.ai through conversational memory, Kustomer through CRM-based timelines, Decagon through cross-channel user profiles—but all retain context beyond a single session or ticket.
- **Regulated industries needing compliance-first AI.** Start with Fini AI, Cognigy, delight.ai, or Ada CX itself. Fini holds 3 audited compliance certifications (SOC 2, ISO 27001, GDPR). Cognigy offers on-premise deployment. delight.ai inherits Sendbird's enterprise security infrastructure and holds SOC 2 Type II, HIPAA, GDPR, and ISO 27001. Ada's own compliance portfolio—SOC 2, HIPAA, GDPR, PCI, and AIUC-1 certification—is strong, and may be sufficient if your switching triggers are pricing or integration concerns rather than compliance gaps.
- **Already on Zendesk?** Zendesk AI is the path of least resistance—you eliminate a vendor and keep your workflows intact. The question is whether its AI layer goes deep enough for your automation goals.
- **Not sure you need to switch at all?** Ada remains a strong choice for teams that need pure ticket deflection with no cross-session memory requirement, have stable conversation volumes with pre-defined flows, and are satisfied with Zendesk or Salesforce as their primary helpdesk.
### How we evaluated these Ada CX competitors
We assessed each platform against 8 criteria, organized into 2 tiers based on what we hear from CX leaders evaluating AI platforms.
**Tier 1—table-stakes criteria** (these come up in nearly every evaluation conversation we have):
1. **Pricing transparency and predictability**—Can you model your costs before signing? Is the pricing published? This matters especially for AI customer service agent pricing, where models vary widely across vendors.
2. **Deployment speed and ease of setup**—How quickly can you go from contract to live AI, and what resources does that require?
3. **Helpdesk integration flexibility**—Does the platform work with your existing helpdesk, or does it force you to change systems?
4. **AI accuracy and resolution rate**—What percentage of conversations does the AI resolve autonomously, and how is that measured?
**Tier 2—emerging criteria** (important for specific buyer segments but not yet universal in evaluation conversations):
1. **Omnichannel coverage**—Does the platform support voice, chat, email, SMS, and social from one deployment?
2. **Compliance and security certifications**—Which audited certifications does the platform hold?
3. **Knowledge source flexibility**—Can the AI learn from sources beyond your help center—past tickets, wikis, internal documents?
4. **Market presence and independent validation**—How many independent reviews exist, and what do analyst firms say?
We applied every criterion to every vendor, including delight.ai. In the comparison table below, entries marked with an asterisk (*) are vendor claims that haven't been independently verified. We used the same evidence standard across all platforms.
### Quick-reference comparison table
Vendor | Pricing | Deployment | Integrations | Resolution rate | Rating / market position
--- | --- | --- | --- | --- | ---
Ada CX | $1–$3.50/resolution (custom) | 8–16 weeks | Zendesk, Salesforce; 13+ total | 83% upper bound*; 30–50% typical | 4.6/5
Intercom Fin | $0.99/resolution + platform ($29–132/seat/mo) | Under 1 hour | Intercom native; Zendesk, Salesforce, HubSpot | 50–67% | Strong (G2 #1 AI Agent)
Zendesk AI | $1.50–$2.00/resolution + $55–115+/agent/mo | Varies | Zendesk native; 1,710+ apps | Not public | Established presence
Sierra AI | Enterprise custom (six-figure) | 4–10 weeks | Zendesk, Intercom, Freshdesk, Salesforce | 70%+* | Limited reviews
Decagon | ~ $386K/yr median | Weeks | Zendesk, Intercom, Freshdesk, Salesforce | 70% | Limited reviews
Maven AGI | Custom enterprise | Days–weeks | 100+ integrations | 93% | 4.8/5 (9 reviews)
Cognigy | $300K+/yr | 2–4 months | Amazon Connect, Genesys, 8x8, Avaya | Not public | Gartner Leader 3x
My AskAI | $199/mo + $0.10/ticket | Under 10 min | Zendesk, Intercom, Freshdesk, HubSpot, Gorgias | 72% | Growing presence
Fini AI | $0.69/resolution ($2,999/mo min) | 48 hours* | Zendesk, Intercom | 98%* | Early stage
eesel AI | $239–$799/mo | Under 15 min | Zendesk, Freshdesk, Intercom, Gorgias, Jira SM, HubSpot | 50–80%* | Growing presence
Kustomer | Contact sales | Varies | CRM-native; ecommerce platforms | 40%* | 4.5/5 (513 reviews)
delight.ai | Contact sales | Days* | Zendesk, Salesforce, Freshworks, ServiceNow, Sendbird (native) | 73%* | No reviews yet
* = vendor claim, not independently verified.
### TCO estimates at scale
For platforms with published pricing, here's what monthly costs look like at three volume levels. This comparison uses published rates only—actual costs depend on contract negotiations.
Vendor | 10K convos | 25K convos | 50K convos
--- | ---: | ---: | ---:
Intercom Fin | ~ $9.9K/mo | ~ $24.8K/mo | ~ $49.5K/mo
My AskAI | ~ $1.2K/mo | ~ $2.7K/mo | ~ $5.2K/mo
Fini AI | ~ $6.9K/mo | ~ $17.3K/mo | ~ $34.5K/mo
eesel AI | $239–$799/mo | $239–$799/mo + overages | $239–$799/mo + overages
Kustomer | Contact sales | Contact sales | Contact sales
Zendesk AI | ~ $15K–$20K/mo | ~ $37.5K–$50K/mo | ~ $75K–$100K/mo
For Ada CX, Sierra AI, Decagon, Maven AGI, Kustomer, delight.ai, and Cognigy, you'll need a direct quote—these use custom enterprise contracts, so there's no way to model TCO from public data alone.
Note: These estimates use published per-resolution or per-conversation rates and assume all conversations result in resolutions. Actual costs depend on resolution rates, plan tiers, add-ons, and contract negotiations. Platform subscription fees are included where published.
### Platform profiles
Below are concise summaries of each vendor: what they do well, where they fall short, and who they're best suited for.
#### 1. Fin by Intercom: Transparent pricing with ecosystem gravity
Intercom's Fin AI agent shows up most frequently in Ada evaluation cycles, and for good reason. Its $0.99 per resolution pricing directly addresses the opacity that pushes many buyers away from Ada. You know what you'll pay before you sign.
Fin achieves 50 to 67 percent autonomous resolution, powered by Anthropic's Claude, and deploys in under an hour for teams already on Intercom. The compliance portfolio is substantial: SOC 2 Type II, HIPAA, GDPR, CCPA, ISO 27001, ISO 27018, ISO 27701, and ISO 42001.
Trade-offs: scale economics (at 50,000 monthly resolutions $0.99 adds up), best within Intercom ecosystem, and no persistent memory across conversations.
Best for: SaaS teams already in Intercom who want transparent per-resolution pricing and fast deployment.
#### 2. Zendesk AI: The path of least resistance for existing customers
Zendesk offers 1,710+ marketplace integrations and pretrained models on 18 billion support tickets. Upgrading to Zendesk AI removes a vendor and keeps data/workflows consolidated.
Trade-offs: ticket-scoped context rather than customer-scoped memory, pricing complexity, and weaker autonomous action-taking.
Best for: companies already on Zendesk wanting consolidation rather than adding tools.
#### 3. Sierra AI: Premium enterprise autonomy at premium enterprise cost
Sierra delivers autonomous action-taking—processing refunds, updating CRM records, modifying subscriptions—turning AI into an operational participant for large B2C enterprises.
Trade-offs: six-figure contracts, 4–10 week implementations, limited independent reviews, and data split across systems.
Best for: large B2C enterprises with budgets for autonomous backend integrations.
#### 4. Delight.ai: Persistent memory and end-to-end resolution
delight.ai's Agent Memory Platform builds evolving customer profiles from interactions across chat, SMS, email, WhatsApp, and voice. Agent Steward orchestrates sub-agents for end-to-end resolution, continuing work across sessions and channels. Native Sendbird Desk eliminates separate helpdesk costs. Certifications include SOC 2 Type II, HIPAA, GDPR, and ISO 27001. Voice AI supports 15+ languages with sub-second latency.
Trade-offs: sales-led pricing and narrower integration ecosystem than Zendesk or Salesforce-native platforms.
Best for: high-volume teams in travel, healthcare, fintech, retail needing persistent memory and cross-channel ownership.
#### 5. Decagon: Engineering-grade AI for fintech and SaaS
Decagon compiles natural-language Agent Operating Procedures into executable workflows, avoids LLM lock-in, and reports production resolution rates like 70% (Chime). It offers cross-channel persistent memory and sub-second voice latency.
Trade-offs: high median contract (~$386K/yr), engineering resources required, Agent Assist limited to Zendesk, and sales-led deployment.
Best for: tech-forward fintech and SaaS companies with engineering resources and budget.
#### 6. Maven AGI: Fast-deploying enterprise AI with strong resolution data
Maven reports high vendor-published resolution figures (93% in some case studies), integrates as a layer-on-top with 100+ integrations, and supports voice via OpenAI Realtime API. Compliance includes SOC 2 Type II, ISO 27001, and ISO 42001.
Trade-offs: earlier-stage vendor, custom pricing, and small but growing team.
Best for: mid-market and enterprise teams seeking rapid deployment and strong published resolution performance.
#### 7. Cognigy: Voice-first enterprise automation backed by Gartner
Cognigy is a Gartner Magic Quadrant Leader for Conversational AI and integrates deeply with Amazon Connect, Genesys, 8x8, and Avaya. NICE acquisition extends CXone reach. On-prem deployment available for data residency.
Trade-offs: contracts typically $300K+/yr, 2–4 month implementations, chat is secondary to voice, and voice latency (~500–800 ms) is slightly higher than some competitors.
Best for: large voice-first contact centers with enterprise budgets.
#### 8. My AskAI: The fastest, cheapest path off Ada
Self-serve setup under 10 minutes; pricing starts at $199/mo + $0.10/ticket. Native apps cover Zendesk, Intercom, Freshdesk, HubSpot, and Gorgias. Reports 72% average resolution across 1.1M+ tickets and trains on 10+ knowledge sources (tickets, Notion, Confluence, Google Drive).
Trade-offs: plugin architecture (no voice), no HIPAA/PCI compliance.
Best for: mid-market teams wanting a quick, affordable helpdesk plugin.
#### 9. Fini AI: Compliance-first reasoning for regulated industries
Fini verifies responses against sources and refuses to answer when context is incomplete—valuable for regulated industries. Certifications: SOC 2 Type II, ISO 27001, GDPR. Pricing $0.69/resolution with $2,999 monthly minimum. Vendor claims 98% accuracy across 2M+ queries.
Trade-offs: API-only with no native helpdesk apps (requires development), earlier-stage company, monthly minimum may exclude smaller teams.
Best for: regulated industries on Zendesk/Intercom needing compliance-first AI.
#### 10. eesel AI: Test on real tickets before going live
eesel lets you run your historical tickets through the AI to validate performance before deployment. Connects to 42+ knowledge sources, deploys in under 15 minutes, and serves 2,000+ companies. Pricing $239–$799/mo.
Trade-offs: SOC 2 in progress, plugin architecture limits scope (no voice/advanced backend automation).
Best for: risk-averse teams that want validated performance on historical data.
#### 11. Kustomer: CRM-first unified customer timeline
Kustomer builds a unified customer timeline (CRM-native) rather than ticket-first views. Kustomer AI launched as a standalone product that can layer onto other helpdesks without full migration. Compliance includes SOC 2 Type II, ISO 27001, GDPR, and CCPA; HIPAA as add-on.
Trade-offs: reporting inflexibility and steep learning curve; strongest traction in ecommerce.
Best for: high-volume ecommerce brands and teams on Zendesk interested in Kustomer AI without full migration.
### Next steps for your evaluation
Request demos from 2–3 vendors in your scenario. Where possible, test on real ticket data—My AskAI and eesel both support this. Model your TCO at your actual monthly conversation volume, not a vendor's best-case estimate. And compare what "resolution" means to each platform—deflection rate, resolution rate, and containment rate aren't the same metric, and vendors don't measure them consistently.
Want to see how delight.ai handles it? [Request a demo](https://delight.ai/contact-sales).
- [Why buyers look for Ada CX alternatives in 2026](#section-anchor-id-0 "Why buyers look for Ada CX alternatives in 2026")
- [Which Ada CX alternative is right for you?](#section-anchor-id-1 "Which Ada CX alternative is right for you?")
- [How we evaluated these Ada CX competitors](#section-anchor-id-2 "How we evaluated these Ada CX competitors")
- [Quick-reference comparison table](#section-anchor-id-3 "Quick-reference comparison table")
- [1. Fin by Intercom: Transparent pricing with ecosystem gravity](#section-anchor-id-4 "1. Fin by Intercom: Transparent pricing with ecosystem gravity")
- [2. Zendesk AI: The path of least resistance for existing customers](#section-anchor-id-5 "2. Zendesk AI: The path of least resistance for existing customers")
- [3. Sierra AI: Premium enterprise autonomy at premium enterprise cost](#section-anchor-id-6 "3. Sierra AI: Premium enterprise autonomy at premium enterprise cost")
- [4. Delight.ai: Persistent memory and end-to-end resolution](#section-anchor-id-7 "4. Delight.ai: Persistent memory and end-to-end resolution")
- [5. Decagon: Engineering-grade AI for fintech and SaaS](#section-anchor-id-8 "5. Decagon: Engineering-grade AI for fintech and SaaS")
- [6. Maven AGI: Fast-deploying enterprise AI with strong resolution data](#section-anchor-id-9 "6. Maven AGI: Fast-deploying enterprise AI with strong resolution data")
- [7. Cognigy: Voice-first enterprise automation backed by Gartner](#section-anchor-id-10 "7. Cognigy: Voice-first enterprise automation backed by Gartner")
- [8. My AskAI: The fastest, cheapest path off Ada](#section-anchor-id-11 "8. My AskAI: The fastest, cheapest path off Ada")
- [9. Fini AI: Compliance-first reasoning for regulated industries](#section-anchor-id-12 "9. Fini AI: Compliance-first reasoning for regulated industries")
- [10. eesel AI: Test on real tickets before going live](#section-anchor-id-13 "10. eesel AI: Test on real tickets before going live")
- [11. Kustomer: CRM-first unified customer timeline](#section-anchor-id-14 "11. Kustomer: CRM-first unified customer timeline")
- [Next steps for your evaluation](#section-anchor-id-15 "Next steps for your evaluation")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
#### FAQ: Ada CX alternatives
Q: How much does Ada CX cost?
A: Ada uses per-resolution pricing ranging from $1 to $3.50 per resolution, with first-year contracts typically between $30,000 and over $250,000. The median annual payment across 103 Vendr purchases is $70,230. There is no published pricing page—contracts are sales-driven. Per-resolution model means costs scale with AI success; Ada's pricing also doesn't include the cost of a separate helpdesk for human agent workflows, which adds $55 to $175+ per agent per month depending on the platform.
Q: What is the difference between Ada and Intercom?
A: Ada is a standalone AI automation platform that sits on top of your existing helpdesk. Fin is an AI agent built into Intercom's own messaging and support platform. Ada doesn't publish pricing; Intercom charges $0.99 per resolution. Ada reports up to 83% resolution in optimized deployments (30–50% typical); Intercom reports 50–67%. Ada takes 8–16 weeks for enterprise deployment; Intercom deploys in under an hour for existing customers. Ada requires Zendesk or Salesforce for full features; Intercom works natively in its ecosystem.
Q: Is Ada CX worth it?
A: For certain use cases, yes. Ada has enterprise traction, strong G2 ratings, and a robust compliance portfolio. Ada is best for straightforward ticket deflection across well-defined flows, with stable volumes and Zendesk or Salesforce as the helpdesk. Alternatives are worth evaluating when you need persistent memory across sessions, when per-resolution pricing creates budget unpredictability, when your knowledge lives outside formal help center content, or when you want AI and human agents on one platform without a separate helpdesk.
Q: What are the best AI chatbots for customer support?
A: It depends on team size, budget, technical resources, and use case. Quick summary: Fin and My AskAI for accessible pricing; Sierra AI and Decagon for enterprise action-taking; Cognigy for voice-first contact centers; delight.ai for persistent memory and end-to-end resolution; Kustomer for CRM-based context.
Q: How does Ada compare to Zendesk AI?
A: Ada is a specialist AI automation platform; Zendesk is a comprehensive service ecosystem with an AI layer. Ada offers deeper AI automation but requires Zendesk or Salesforce for full features. Zendesk has the broadest integration marketplace but the AI is constrained by a ticket-based architecture.
Keep reading
[View more](/blog "View more")
[Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)
[Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[View more](/blog "View more")
## AI for customer service: Real-world examples and results
Source: https://delight.ai/blog/ai-for-cx/ai-customer-service
[](https://delight.ai/author/leighanne-manwiller)
LeighAnne Manwiller
[](https://delight.ai/blog/ai-for-cx)
June 5 2026
AI customer service is no longer a bet on the future. The results are already in, and the numbers are hard to ignore. The market is projected to grow from $12.06 billion in 2024 to [ $47.82 billion by 2030](https://www.marketsandmarkets.com/PressReleases/ai-for-customer-service.asp), driven by real pressure. Support teams are being asked to handle more with the same headcount, while customers have less patience for wait times than ever.
And the early adopters are proving what’s possible. Across industries, companies that have redesigned their customer service around AI are seeing results that would have looked unrealistic several years ago. A few noteworthy examples of AI in customer service that we’ll examine:
- Bank of America's Erica has handled [more than 3 billion client interactions](https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/08/a-decade-of-ai-innovation--bofa-s-virtual-assistant-erica-surpas.html), with 98% of users getting what they need without a human agent.
- The Home Depot's [AI voice agents](https://delight.ai/voice) resolve customer calls [four times faster](https://corporate.homedepot.com/news/company/home-depot-delivers-customer-store-phone-support-four-times-faster-using-google-clouds) than traditional phone menus.
- Mixpanel cut first-response investigation time from 10 minutes to near-instant by giving its AI full account context before the first reply, [turning support into a growth channel](https://delight.ai/customers/mixpanel).
- Swiggy [automated 70% of its customer support tickets](https://www.pubnub.com/customers/swiggy/) and cut resolution time from five minutes to under 40 seconds.
But for every deployment that holds up, plenty don't. In 2025, 42% of companies scrapped substantial AI projects—up from 17% the year before—and only [20% of AI initiatives](https://www.getnextphone.com/blog/ai-customer-service-statistics) delivered on expectations.
The demand is there; the execution is where teams diverge. We’ll break down each case study mentioned above to show the mechanics behind their success and how you can replicate them.
To start, let’s define the scope of AI in customer service.
## How is AI used in customer service?
The term AI customer service has become a catch-all to describe a variety of functions. In reality, here's what's under the hood:
- **Generative AI customer support** uses large language models (LLMs) to draft responses, summarize tickets, or power conversational agents capable of handling open-ended queries
- **AI chatbots and virtual assistants** include rule-based or natural language processing (NLP) systems that handle structured interactions like order status, account lookups, and appointment scheduling
- **Agent-assist tools** involve AI that works behind the scenes, surfacing relevant knowledge base articles, drafting suggested replies, and flagging sentiment shifts during live conversations
- **Routing and triage automation** classifies incoming contacts and directs them to the right queue or self-service path before a human ever touches them
In practice, these capabilities come together into a single, seamless workflow. It can handle a conversation, brief a human agent, and flag an escalation at the same time.
## What are real-world examples of AI customer service?
From retail and financial services to healthcare and travel, organizations are leveraging AI tools for customer service in almost every sector.
### Financial services: Bank of America's Erica
Bank of America (BoA) launched Erica in 2018 as the first widely adopted AI-driven virtual financial assistant. Almost a decade later, it has become one of the most concrete proof points in the industry.
In 2024, clients interacted 676 million times with Erica, bringing total interactions since launch to [more than 2.5 billion](https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/02/digital-interactions-by-bofa-clients-surge-to-over-26-billion--u.html). And those aren't passive views. More than 98% of users find the information they need through Erica, significantly decreasing call center volume and allowing financial specialists to focus on more complex client conversations.
The internal deployment is equally telling. Over 90% of BoA employees use [Erica for Employees](https://www.prnewswire.com/news-releases/ai-adoption-by-bofas-global-workforce-improves-productivity-client-service-302423333.html), and the virtual assistant has reduced calls into the IT service desk by more than 50%. The BoA case has a deliberate architecture behind it. Erica relies on deterministic AI rather than generative AI, which can hallucinate.
As Jorge Camargo, BoA’s head of digital platforms, says of [AI customer service for finance](https://delight.ai/industries/financial-services): 'The AI is all about trying to understand your question, but then we're going to give you an answer that we are 100% comfortable with. For spaces like financial services, we can't afford to be 90% right.'
That instinct is backed by data. The [2026 Delight AI Index](https://delight.ai/ai-index) found that 64% of consumers expect AI to outperform human representatives in financial services, meaning the bar isn't 'good enough.' It's better than the best agent on the floor. In an industry where a wrong balance display can cause an overdraft and a failed transfer can mean a missed payment, that standard isn't unreasonable. It's the only one that earns trust.
**The takeaway**: Erica works because BoA made a deliberate decision—prior to launch—about what the AI would and wouldn’t do. Most teams make that call only after something breaks. BoA treated it as an [AI trust](https://delight.ai/blog/industry/financial-services-ai-trust) decision, not a capability limitation. That’s why Erica can deliver a 98% resolution rate while many generative deployments are still wrestling with accuracy.
### B2B: AI customer service becomes a retention system with delight.ai
[AI customer service for B2B](https://delight.ai/industries/b2b) companies involves a different problem profile than consumer support. The bottleneck usually isn't knowledge, but context. Before answering queries, companies need to know who's asking, which includes details like their account tier, permissions, implementation setup, and history. In B2B, that deep dive can take longer than providing the actual answer.
Delight.ai’s [Mixpanel case study](https://delight.ai/customers/mixpanel) shows the depth of that problem. Every ticket demands context before it can be resolved.
'Our support agents were spending the first 10 minutes of every interaction just figuring out who they were talking to,' says Kathleen Matthews, Global Support at Mixpanel. 'Checking account status, verifying permissions, reviewing their setup. The actual problem-solving came second.'
Mixpanel, the analytics platform behind high-growth startups and enterprises like Salesforce and DocuSign, deployed delight.ai with intelligent AI personalization: The AI agent authenticates users and retrieves their account history, tier, access level, and current workflow context before it responds. The investigation that once took 10 minutes happens instantly, before the first reply.
But the more significant shift was what that context enabled beyond deflection. Support became a growth channel. When a free-tier user asks about an advanced feature, the AI explains how it applies to their specific situation and presents relevant upgrade options. When an enterprise customer has paid for a feature they've never configured, the AI proactively walks them through setup.
'We're not just answering faster,' Matthews says. 'We're answering smarter. The AI knows if you're asking because you don't have access yet, or because you're trying to configure something advanced. In the first case, we're converting curiosity into revenue. In the second, we're driving deeper product adoption.'
That reframe—from support as cost center to support as growth function—is the principle behind delight.ai's B2B architecture. Instead of deploying AI reactively, delight.ai uses its [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp), hyper-personalized [For You Conversations](https://delight.ai/for-you-conversations-fyc), and behavioral signals to intervene proactively, before a customer churns or disengages.
Context gets maintained across chat, voice, SMS, and email, so when a customer switches channels or returns weeks later, the AI picks up where it left off. That [omnipresent support](https://delight.ai/omnipresent) proves particularly useful for B2B relationships, where context accumulates over months of interactions.
For B2B teams, the [ROI calculation](https://delight.ai/roi-calculator) is also different. A single enterprise account relationship often represents enough revenue that improving resolution quality—and expanding it—matters as much as reducing resolution cost.
**The takeaway**: What separates Mixpanel's AI deployment from the ones that stall is that the company identified the actual bottleneck—context, not knowledge—before choosing a solution.
The AI agent wasn't layered on top of an existing broken process. Instead, it was purpose-built to solve the specific issue making that process break. For B2B teams, that diagnostic step is often exactly what the failed deployments skip.
### Retail: The Home Depot and the voice AI overhaul
Deployments for [AI customer service in retail](https://delight.ai/industries/retail) face a distinct set of challenges: high contact volume, repetitive inquiry types (e.g., order status, product availability, store information, etc.), and customers who expect fast answers regardless of channel or time of day.
The Home Depot, with more than 2,300 North American stores, identified a specific friction point: Customers calling stores were funneled through complex, multi-level phone menus that frustrated them and slowed resolution. To address this, The Home Depot announced the launch of an AI voice agent system.
The system eliminates phone menus entirely. Customers describe their situation, and the [AI understands the reason for the call within 10 seconds](https://corporate.homedepot.com/news/company/home-depot-delivers-customer-store-phone-support-four-times-faster-using-google-clouds). It checks order status, confirms product availability, initiates service requests, sends items to a digital cart, and completes purchases over the phone—with real-time support, multilingual translation, and a direct path to a human associate available.
A pilot program across 50 stores produced the headline result. Customers reach a solution four times faster than with traditional phone menus. Associates in the pilot reported higher job satisfaction, freed from routine call-handling to focus on more complex issues and in-store customers. The Home Depot plans to expand the system to all US stores in the year ahead.
Jordan Broggi, EVP of customer experience at The Home Depot, explains: 'Using customer service AI voice agents, we’re moving away from "please listen to these options" and toward "how can I help?" AI does a tremendous job at recognizing customer intent and taking direct action to help complete a purchase or even start a service request. And of course, if they need to speak with an associate, we’ll quickly connect them.'
**The takeaway**: The Home Depot case reflects a pattern in retail where the most tractable AI wins come from replacing the most friction-filled, lowest-complexity touchpoint. In this case, menu-driven phone support led to the win rather than attempting to transform the entire customer journey at once.
### On-demand: Swiggy and the scale problem in real-time delivery
[AI customer service for on-demand](https://delight.ai/industries/on-demand) platforms is a volume problem first. Swiggy, India’s leading on-demand delivery platform for food, groceries, and household goods, handles up to 200,000 customer inquiries per day across 700-plus cities, with peak concurrency of 2,000 simultaneous conversations. At that scale, every second of resolution time and each escalation to a human agent has a direct cost.
Before automating its support, Swiggy's phone-based resolution process averaged around five minutes per inquiry. Most of those inquiries were routine: order status, delivery delays, refund requests—high volume, low complexity, and well-suited to automation. Swiggy deployed an AI-powered chatbot that [brought resolution time down to 30–40 seconds](https://www.pubnub.com/customers/swiggy/) and automated 70% of all customer support tickets.
By 2025, Swiggy had moved further, deploying an enterprise-scale agentic AI system that routes all customer queries through the AI agent, with human escalation reserved for genuinely complex cases. It delivers [accuracy above 99%](https://www.databricks.com/blog/redefining-customer-support-swiggys-enterprise-scale-ai-agent-built-databricks) and answers almost instantly—within half a second for 99% of responses. Customer satisfaction rose alongside the speed gains, driven by responses that were faster, more consistent, and more personalized.
**The takeaway**: This case makes a point that applies broadly to on-demand support. The ROI on AI is highest when the inquiry volume is large and the issue types are predictable. Swiggy's support load (e.g., order tracking, refunds, delivery exceptions, etc.) maps directly onto what AI handles well.
Swiggy’s results came from a disciplined deployment. The team started with the highest‑volume, lowest‑complexity tier of support and proved the model there before expanding. The agentic system the company runs today is the result of that phased approach, not the starting point. Teams that try to automate everything at once rarely see numbers like Swiggy’s.
### Healthcare: Constraints are the point
Facilities using [healthcare AI customer service](https://delight.ai/industries/healthcare) tools have to consider accuracy requirements, privacy regulations, and the stakes of bad information. Delight.ai's 2026 AI Index found that [84% of patients](https://delight.ai/blog/industry/healthcare-ai-trust) say clear data privacy and security practices would increase their trust in healthcare AI. This means healthcare organizations tend to deploy AI narrowly, with hard guardrails, and measure success differently.
Healthcare use cases often center on appointment scheduling, reminders, intake questions, medication guidance, and basic service inquiries, not complex clinical decision-making. Strong deployments define clearly what the system can answer, when it must escalate, and how compliance requirements shape the experience.
Check out the [2026 Delight AI Index](https://delight.ai/ai-index) for more insights on consumer preferences.
On the patient-facing side, Kaiser Permanente’s AI-powered patient portal for Southern California’s 4.9 million members lets patients describe their needs in plain language instead of choosing from preset menus. The system references patient data to recommend appropriate care pathways and flag urgent cases.
Across nearly 3 million encounters between October 2024 and March 2025, it [detected high-acuity cases with 97.7% accuracy](https://permanente.org/ai-powered-patient-portal-enhances-access-safety-and-patient-satisfaction/,%20https://www.nature.com/articles/s41746-025-01838-1). The system also raised booking success from 34% to about 53.7%, improved patient satisfaction by roughly nine percentage points, and kept abandonment near 3%.
Many hospitals have deployed AI chatbots to handle routine patient questions (e.g., surgery prep instructions, rescheduling requests, billing inquiries, etc.), freeing clinical staff from administrative interruptions. Apollo Hospitals, for example, has [allocated 3.5% of its digital budget to AI](https://www.reuters.com/business/healthcare-pharmaceuticals/indias-apollo-hospitals-bets-ai-tackle-staff-workload-2025-03-13/) with a focus on automating routine tasks like medical documentation.
**The takeaway**: Healthcare AI deployments that succeed don't try to do too much. The teams that get it right spend as much time deciding what the AI won't handle as what it will. That clarity—built into the implementation before go-live, not patched in afterward—is what keeps the system trustworthy enough to scale.
### Travel and hospitality: Volume, language, and the disruption problem
Among the clearest use cases is [travel and hospitality AI customer service](https://delight.ai/industries/travel-and-hospitality), which involves heavy ticket volume, predictable issue categories (e.g., booking changes, loyalty questions, cancellations, etc.), and a multilingual customer base that's hard to staff for around the clock.
A McKinsey study found that AI-driven service orchestration [reduced check-in times by nearly 20%](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/agents-for-growth-turning-ai-promise-into-impact) and boosted customer satisfaction scores by 15%.
The nuance in travel AI is timing. A chatbot can handle booking support or itinerary changes efficiently. During an actual disruption, such as a weather cancellation or delayed flight, customers can be distressed, and the bar for escalation to a human agent drops substantially.
Travel AI case studies, such as [Norse Atlantic Airway’s partnership with delight.ai](https://delight.ai/customers/norse-atlantic-airways), are worth studying closely. They show that in this industry, 'when' the AI responds matters just as much as 'how' it responds.
Norse Atlantic Airways is a useful counterpoint to how most airlines approach AI customer service. The industry default is defensive: Deploy a bot to contain costs, measure resolution rate, call it done. Norse asked another question, 'What does an AI-ready organization actually look like?'—and that reframe changed everything.
In under two months, Norse sunset its legacy chatbot and launched Freya, its flagship AI agent powered by delight.ai. Within two weeks of going live, containment climbed from 60% to 80% through iterative tuning with delight.ai's forward deployment team. But the more significant shift was organizational.
Norse created a role the airline industry hadn't seen before—an AI Agent Manager, embedded in customer support but working across product, engineering, and operations to own Freya's accuracy, behavior, and continuous improvement.
'Freya became a core part of the team, and we quickly realized that Freya needed a manager,' says Alf Lim, Chief Product Officer at Norse Atlantic. 'We literally co-authored the job description from scratch.'
Freya has since moved beyond containment into commerce. A question about legroom can convert into a seat upgrade; an inquiry about baggage limits becomes a cross-sell. The wall between support and revenue is coming down. For Norse, that's the bottom line: not a cheaper support operation, but a fundamentally different one.
**The takeaway**: Norse’s advantage wasn’t that it simply deployed a better bot. Instead, it designed the organization required to make that bot succeed. Treating Freya as a real team member, with ownership, oversight, and a dedicated AI Agent Manager, turned the system into an operational capability rather than a cost-cutting experiment.
The travel and hospitality teams that win aren’t the ones with the flashiest models; they’re the ones that redesign the work around them. In an industry defined by disruption, the differentiator is the AI execution and organizational design behind it.
## What the best companies using AI for customer service do differently
Zoom out across industries and the story is similar. Some [teams get real results from AI](https://delight.ai/resources/ai-readiness-101-webinar); others go live, see middling numbers, and quietly scale back. The difference usually comes down to a handful of decisions made before the AI ever talks to a customer.
### Start narrow, then earn the right to expand
Every deployment that held up in this piece started with a deliberately limited scope: one problem, one user segment, one interaction type.
Swiggy didn't try to automate its entire support operation on day one. It started with the most predictable, highest-volume tier (e.g., order tracking, refunds, delivery exceptions) and proved the model there before expanding into a fully agentic system. BoA took a similar path, building Erica on a constrained set of deterministic responses before broadening its capabilities over years.
Norse Atlantic didn't launch Freya across every customer interaction simultaneously. The company tuned it, watched the containment rate climb from 60% to 80% in two weeks, then pushed further.
The narrow starting point wasn't a limitation in any of these cases. It was the foundation the results were built on.
### Emphasize escalation quality over escalation rate
Most teams measure how often their AI hands off to a human. Far fewer measure what happens in that handoff, such as if the agent receives enough context to pick up the conversation without making the customer start over.
That’s the fault line where CSAT starts to slip. Consider what Mixpanel's support team was dealing with before delight.ai: agents spending the first 10 minutes of every interaction just establishing whom they were talking to.
A smooth handoff keeps the customer's trust intact. A clumsy one (e.g., 'Can you explain the issue again?') signals that the AI created more friction than it resolved. Designing that transition well is as important as designing the AI interaction itself.
### Track the right metrics, not only automation rate
Automation rate is the vanity metric of [AI customer experience](https://delight.ai/ai-customer-experience-platform). The industry-wide average AI resolution rate sits at 44.8%, meaning more than half of AI-initiated interactions still require a human. That number isn't alarming on its own. What’s meaningful is whether those escalations are handled well or poorly.
Optimizing for automation rate alone is how teams end up with impressive-looking dashboards and a declining CSAT. Swiggy doesn't lead with automation rate; the metric that matters is that resolution time dropped from five minutes to under 40 seconds, and customer satisfaction improved alongside it.
Kaiser Permanente measures if high-acuity cases get flagged correctly and if patients actually complete their bookings. What those teams track instead: resolution quality, repeat contact rate, customer effort score, and—especially in B2B like Mixpanel—if AI-assisted interactions correlate with better retention and revenue downstream.
### Let AI manage volume and humans handle what matters most
Forty-two percent of customers expect empathy and compassion from AI assistants, and consumers favor businesses whose AI is 73% managed by humans.
According to the [2026 Delight AI Index](https://delight.ai/ai-index), 71% of consumers have already used AI customer service, but only 57% report a positive experience. That 14-point gap is where the execution conversation gets real.
The most durable implementations—Mixpanel, The Home Depot, Norse Atlantic Airways—treat AI as handling the routine tier while keeping humans accessible for complexity and emotional weight. Customers don't object to talking to AI; they object to being trapped by it.
### Establish the knowledge base before building the AI
Sixty-one percent of customer service leaders say they have a backlog of knowledge base articles that need updating, and more than a third have no formal process for keeping that content current.
Generative AI brings up what it's given. When the source material is accurate and well-structured, the responses are, too. When it's patchy or outdated, the AI confidently delivers wrong answers.
Mixpanel's deployment worked partly because the context the AI needed (e.g., account history, tier, permissions) was structured and accessible from the start. Teams that deploy AI on top of a messy knowledge base and then blame the AI for underperforming are solving the wrong problem. AI readiness starts with getting the foundations right.
## How to get AI customer service right with delight.ai
AI for customer service works. The evidence across industries is consistent enough that the question is no longer whether or not to deploy it—it's where, how, and with what expectations.
The companies with the strongest outcomes typically share the same fundamentals:
- scoped their initial deployment narrowly
- invested in the knowledge infrastructure the AI depends on
- designed escalation paths as carefully as they designed the AI interactions
- measured outcomes, not just automation rates
Organizations that fell short often used AI for customer service as a substitute for people instead of a redesign of the customer journey. That framing may deliver short‑term cost savings, but it can lead to service‑quality issues soon after.
For teams aiming to get AI right from day one, delight.ai provides the strategy, tooling, and support to turn good intentions into meaningful results. [Request a demo](https://delight.ai/contact-sales) to get started today.
- [How is AI used in customer service?](#section-anchor-id-0 "How is AI used in customer service?")
- [What are real-world examples of AI customer service?](#section-anchor-id-1 "What are real-world examples of AI customer service?")
- [What the best companies using AI for customer service do differently](#section-anchor-id-2 "What the best companies using AI for customer service do differently")
- [How to get AI customer service right with delight.ai](#section-anchor-id-3 "How to get AI customer service right with delight.ai")
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog "View more")
## Human in the loop AI: Why consumer permission is the real bottleneck
Source: https://delight.ai/blog/industry/human-in-the-loop-ai
## Human in the loop AI: Why consumer permission is the real bottleneck
[Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
June 16 2026
The question most enterprise AI teams are asking is: "What can our AI do?" The question most haven't asked is: "Have our customers said yes?" The gap between those two questions is where deployments fail. Not because the technology misfired, but because the AI ran ahead of the permission that authorizes it to act.
We surveyed 1,001 U.S. consumers across five industries for the [2026 Delight AI Index](https://delight.ai/reports/ai-index), and the permission picture is concrete. **47% of consumers want to stay in control and approve AI actions before they execute.** Only 16% are comfortable with AI acting without any approval step. Most enterprise AI is deployed well beyond what most consumers have explicitly said yes to — and the distance between those two positions is where deployments break down.
This is not a technology problem. It is a human-in-the-loop design problem, and it is solvable with the same precision that enterprise teams apply to any infrastructure challenge. The first step is understanding what "human in the loop" actually means in a production AI system, as opposed to its academic definition.
The U.S. AI Delight Score sits at 51: a tipping point. Functional expectations are being partially met, but emotional connection is lagging. The data makes it clear that consumers aren't rejecting AI. They are granting it permission in stages. Understanding where those stages sit, and where most enterprise AI assumes they are, is the starting point for closing the gap.
### The AI permission curve
Consumer trust in AI is not a binary state. It follows a [predictable curve](https://delight.ai/tools/ai-permission-curve), from answering questions to acting autonomously. Each stage carries different comfort levels, different design requirements, and different failure modes. Most enterprise AI is deployed at the top of the curve while most customers sit somewhere in the middle, waiting on infrastructure that was never built.
Acts with Approval is the most important deployment target for 2026. It is where the majority of consumer permission exists for consequential actions, and it is the stage most brands skip entirely, jumping from Recommends to Acts Autonomously without the confirmation infrastructure that earns the right to go further.
When it comes to refunds, billing disputes, health-related questions, and any interaction where money or safety is involved, consumers swing hard toward human involvement. The division of labor is clear: AI wins routine. Humans win risk, emotion, and high-stakes decisions. Getting that boundary wrong doesn't just lose a transaction: it loses the customer's willingness to engage with AI at all.
[**Ready to map where your AI sits on the Permission Curve? See the framework →**](https://delight.ai/tools/ai-permission-curve)
### What human in the loop actually means in production
In academic and policy contexts, "human in the loop" typically refers to a human reviewing AI outputs before they are acted upon. In a production customer service system, that definition is too blunt to be useful. The relevant question is not whether a human reviews the output, but whether the consumer has visibility into what is about to happen and a genuine mechanism to stop or redirect it.
Human-in-the-loop AI in production requires three things that are distinct and all necessary.
- **Visibility:** the consumer can see what the AI intends to do before it happens.
- **A real confirmation step:** "act on my behalf" requires an actual signal from the consumer, not a passive opt-out or buried consent.
- **A genuine undo mechanism:** the ability to reverse the action is visible, accessible, and functional, not decorative.
Most enterprise AI deployments have none of these three things at the action layer. The AI executes, a log entry is written, and the consumer finds out afterward. That architecture produces exactly the trust numbers the [Index](https://delight.ai/reports/ai-index) found: 83% of consumers hold the brand accountable when AI makes a mistake, and 72% rank "wrong decisions" as their top concern about AI handling customer service autonomously.
The implication is operational, not philosophical. Build the rollback mechanism before you build the action. Wire the confirmation screen before you deploy the automation. The brands doing this consistently see higher consumer willingness to grant Stage 4 permission.
### AI oversight is not the same as AI monitoring
These terms are often used interchangeably in product and engineering discussions. They address different problems, and conflating them is one of the most common reasons AI governance fails to move consumer trust.
AI monitoring and observability tell you what went wrong and help you fix it, after the consumer has already had the experience. AI oversight operates before. It is the infrastructure that keeps AI inside the zone of consumer consent at the moment of action, not in the post-mortem. [Trust OS](https://delight.ai/trust-os) is built around this distinction: permission gates, staged rollouts, and approval checkpoints that operate at the action layer, not the log layer.
### Three design decisions that move consumers up the curve
The permission gap is not a data problem. Most brands know their AI is capable of operating at Acts with Approval or Acts Autonomously. What they are missing is the infrastructure that makes consumers willing to grant that permission. The [AI Permission Curve worksheet](https://delight.ai/tools/ai-permission-curve) maps exactly where your deployment sits today.
#### 1. Build undo before you build action.
Reversibility is the number one trust driver in the entire Index, ranking above both accuracy and transparency. If consumers know they can take it back, they let AI go further. The mechanism has to be real and visible: a one-tap undo screen, a clear confirmation state, and a genuine rollback path. Use [Gradual Rollout](https://delight.ai/product-release/gradual-rollout) to validate changes against live traffic before committing, [Dynamic Safeguards](https://delight.ai/product-release/dynamic-safeguards) to contain high-stakes actions with context-aware fallbacks, and [Desk AI Copilot](https://delight.ai/product-release/desk-ai-copilot) to provide a clear escalation path to a human when the AI reaches its limit.
#### 2. Show your reasoning before you act.
Consumers grant more autonomy when they can see the plan. "Based on your last order, I'm recommending X" dramatically outperforms a blank recommendation. "Here's what I'm about to do and why" outperforms silent execution at every stage. [Thinking Messages](https://delight.ai/product-release/thinking-messages) make this concrete: real-time status updates while the AI works, so consumers always know what is happening. [Activity Log 2.0](https://delight.ai/product-release/activity-log-v2) extends the same transparency internally, with full reasoning traces for every agent response. This is the mechanism that moves consumers from Recommends to Acts with Approval. When AI shows its reasoning, consumers feel in control even when they choose not to override.
#### 3. Earn the next stage. Don't declare it.
Consumers don't grant Acts with Approval or Acts Autonomously permission because the capability exists. They grant it because the brand demonstrated competence and transparency at Answers and Recommends over time. [Trust OS](https://delight.ai/trust-os) tracks this evidence: every conversation where AI performed accurately, every action the consumer confirmed. [Human evaluators](https://delight.ai/product-release/humans-as-ai-evaluators) define what good looks like. [Zero-Touch Improvement](https://delight.ai/product-release/zero-touch-improvement) closes the loop autonomously, with human oversight built in. The result is [an agent that earns full autonomy](https://delight.ai/autonomous-agent) one proven decision at a time.
### AI governance that scales with trust
Human-in-the-loop AI is not a permanent state. It is the starting architecture for systems that earn the right to operate autonomously. The governance layer that makes this work tracks evidence, not just rules: every confirmed action, every accurate resolution, every consumer who chose not to override.
The permission picture differs by industry. Where your vertical sits determines where you should start on the curve:
- [**Tech & media**](https://delight.ai/blog/industry/tech-and-media-ai-trust) (60) — leads on AI trust; consumers most comfortable with autonomous action
- [**Retail**](https://delight.ai/blog/industry/retail-ai-trust) (59) — at the inflection point; recommendations accepted, autonomous purchasing needs confirmation
- [**Travel**](https://delight.ai/blog/industry/travel-ai-trust) (55) — moving fastest toward Acts with Approval; most to gain from closing the permission gap
- [**Financial services**](https://delight.ai/blog/industry/financial-services-ai-trust) (50) — trust earned transaction by transaction; any action involving money requires explicit sign-off
- [**Healthcare**](https://delight.ai/blog/industry/healthcare-ai-trust) (47) — furthest to close; consumers want AI to inform, not act without a visible human in the loop
[Trust OS](https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents) is built to govern at any stage, across any vertical.
The U.S. AI Delight Score of 51 reflects a system still catching up to where consumers are willing to go. The [AI Permission Curve](https://delight.ai/tools/ai-permission-curve) is the framework for closing that gap, one confirmed action at a time.
### **Ready to deploy AI your customers have actually said yes to?** [**Talk to sales →**](https://delight.ai/contact-sales)
- [The AI permission curve](#section-anchor-id-0 "The AI permission curve")
- [What human in the loop actually means in production](#section-anchor-id-1 "What human in the loop actually means in production")
- [AI oversight is not the same as AI monitoring](#section-anchor-id-2 "AI oversight is not the same as AI monitoring")
- [Three design decisions that move consumers up the curve](#section-anchor-id-3 "Three design decisions that move consumers up the curve")
- [AI governance that scales with trust](#section-anchor-id-4 "AI governance that scales with trust")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
Keep reading
[View more](/blog "View more")
[Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)
[What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[View more](/blog "View more")
## AI receptionist for home services: Why the trades need more than a phone bot
Source: https://delight.ai/blog/industry/ai-receptionist-home-services
## AI receptionist for home services: Why the trades need more than a phone bot
[
Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
June 16 2026
You already know you need an AI receptionist. The question is what that actually means for a [home service business](https://delight.ai/home-services-and-warranty), because the answer is very different from what it means for a law firm, a dental office, or a hair salon.
For most businesses, an AI front desk is a scheduling tool. It answers the phone, books the appointment, sends a confirmation, and the job is done. For HVAC companies, plumbers, electricians, pest control operators, and home warranty providers, the first call is maybe 20 percent of the work. The other 80 percent — coordinating a technician, managing a parts delay, following up on a warranty claim, keeping the customer informed across multiple days — happens entirely after the phone is answered, and most AI receptionists aren't built for any of it.
This distinction matters more than it used to because the AI receptionist market is growing fast, and most of what's being sold targets the simpler use case. **If you buy a generic AI answering service for your HVAC or home warranty operation, you'll answer more calls and still lose jobs.** The problem was never the unanswered call. It was everything that followed.
This guide covers what home service businesses actually need from an AI receptionist: where the standard tools fall short, what the full capability set looks like, how to evaluate vendors honestly, and what the right solution handles from the first ring to the five-star review.
## Why the trades are the hardest use case for AI receptionists
[Home services](https://delight.ai/home-services-and-warranty) sits at an awkward intersection for AI. The call volume is high enough to justify automation, but the job complexity is high enough that simple automation creates new problems. Three things make this category harder than most.
### Every job requires a technician, not just an answer
When a customer calls a hotel to change a reservation, the AI can resolve the whole interaction in one session. When a customer calls an HVAC company about a broken system in 95-degree heat, the resolution requires scheduling a real person, routing them to the right address, confirming their availability against your board, and managing the customer's expectations around a time window.
The AI receptionist isn't just taking a message. It's triggering a dispatch workflow that involves your field service platform, your tech roster, and your scheduling logic. That's a fundamentally different integration challenge.
### Parts and coordination happen after the call ends
In a large portion of home service jobs, the first visit doesn't close the job. The technician diagnoses the issue and leaves. A part gets ordered. A second visit is required. The warranty adjuster needs to approve the repair. Each of these creates a new communication need that most AI receptionists have no ability to handle, because they're built for inbound calls, not ongoing job management. The result: your CSR team manually chases every open job to keep customers informed, and customers who don't hear anything call back or leave.
### Warranty operations add a third party to every interaction
For home warranty operators, the coordination surface is even wider. A single claim involves the homeowner, a dispatched contractor, a parts supplier, and the warranty administrator. Each party needs status updates on a different timeline and through different channels. The homeowner wants a text. The contractor needs a job update in the field service platform. The adjuster needs an approval request. None of this is a phone call, and none of it happens automatically without an AI agent that can track multi-party state across multiple days.
The economics are clear. Every missed call is a job that goes to a competitor. Every unanswered status question is a customer who calls back and consumes CSR time that could have been avoided with a proactive text. And every closed job that doesn't generate a review or a re-engagement is LTV left on the table.
[**See how HVAC and warranty operators automate the full job lifecycle with delight.ai →** ](https://delight.ai/home-services-and-warranty)
## What home service customers actually expect from AI
The bar for [AI in home services](https://delight.ai/home-services-and-warranty) isn't set by what's technically possible. It's set by what customers have already experienced from the best operators in the space. Once someone has received a proactive text telling them their technician is 20 minutes away, or a same-day message explaining a parts delay and offering a new window, they expect that from everyone. The companies that can't match it lose repeat business.
Across home services customers, the expectations break down like this:
- **24/7 availability is a baseline, not a differentiator.** Customers assume they can book after hours. If they can't, they move on. The HVAC call that comes in at 9pm on a Friday is often worth more than the one that comes in Monday morning, because the customer has fewer options.
- **Speed matters more than perfection at booking.** A confirmed window sent within two minutes of a call beats a "we'll call you back" every time, even if the window is next-day. Customers want certainty.
- **Proactive communication on delays is expected, not appreciated.** Customers don't give you credit for telling them something changed. They penalize you for not telling them. A missed delay notification creates a callback and a CSAT hit; a proactive one prevents both.
- **Follow-up after the job influences the review, not just the service.** The review prompt sent four hours after job completion gets five times the response rate of one sent the next day. The AI that closes the loop also closes the review cycle.
- **Channel preference varies by customer, not by company.** Some customers want a call back. Most prefer a text. A few want email. The AI receptionist that can only do one of these is leaving communication on the table.
The AI that wins in home services is the one that can hold a job from the first call to the five-star review, without a human touching every step in between.
## The five jobs your AI receptionist needs to own in home services
When evaluating AI for an HVAC company, a plumbing operation, or a home warranty provider, the question isn't whether the AI can answer the phone. Every AI receptionist can answer the phone. The question is what happens next. Here are the five capabilities that separate a phone bot from a genuine AI front desk for the trades.
### 1. Inbound call and multi-channel booking
The base requirement is real-time booking across every channel a customer might use: phone, SMS, and web. For home services, this means the AI needs to handle common inbound intents without a script: new service requests (urgent and non-urgent), reschedule requests, emergency dispatches, status check-ins, billing questions, and warranty inquiries.
It also needs to handle the way real customers actually speak, not just structured menu inputs. A customer who says "my AC is making a grinding sound and it's really hot in here" is communicating urgency and a symptom, not a service code.
The bar for voice quality in home services is high because the call volume is high and the stakes per call are high. The AI needs to handle accents, background noise, emotional customers, and interruptions without breaking down. If customers get frustrated during the booking call and hang up, you've spent money on a problem.
### 2. Tech scheduling and real-time dispatch confirmation
Booking a time window is not the same as confirming dispatch. An [AI receptionist for home services](https://delight.ai/home-services-and-warranty) needs to see technician availability in real time from your field service platform, assign the right tech based on skill set and service zone, and confirm the dispatch back to the customer with the tech's name and an accurate arrival window.
This requires a live integration with your field service software. ServiceTitan, Jobber, Housecall Pro, and FieldEdge all have API access, but the quality of the integration varies enormously between vendors. An AI that "integrates with ServiceTitan" via a nightly export is not the same as one that reads live board availability and writes confirmed appointments in real time.
### 3. Mid-job status management and proactive communication
Once a job is in progress, communication tends to break down. The technician is running behind because the previous job took longer. The part needed for the repair isn't on the truck. The customer has a two-hour window and they're watching the clock. Each of these is an inbound call that your CSR team handles manually, or that falls through and becomes a bad review.
The right AI agent monitors job status from the field service platform and proactively messages the customer when something changes. Not when asked: proactively, before the customer calls. The message that says "your technician is running 30 minutes behind and will now arrive between 2:00 and 4:00pm" prevents a callback and a frustrated customer. That same AI should handle the incoming "where is my technician?" question without routing to a human.
### 4. Parts delay and multi-visit coordination
Multi-visit jobs are the norm in HVAC, plumbing, and appliance repair. The technician diagnoses the issue, a part gets ordered, a second visit is scheduled. Each transition creates a communication requirement: the customer needs to know why the first visit didn't resolve the issue, when the part arrives, when the second visit is scheduled, and who to call if something changes.
This is where most AI receptionists disappear entirely. The call was answered. The booking was made. The job is now in the system. What happens between visit one and visit two is entirely manual, and in a high-volume operation, that gap generates a constant stream of status calls, rescheduling requests, and cancellations.
### 5. Post-job review capture and re-engagement
The job isn't done when the technician leaves. The job is done when the customer confirms satisfaction, the invoice is settled, and the relationship is positioned for the next service. Each of these has an automation opportunity.
Review requests sent within four hours of job completion consistently outperform any manual cadence. Re-engagement sequences, whether a seasonal maintenance reminder, a filter replacement alert, or a "how is your system running?" check-in, are where lifetime value compounds. A customer who booked one emergency HVAC repair and receives zero follow-up will call whoever answers the phone next time their system breaks. A customer who receives a maintenance reminder every six months is yours.
### Go deeper
- [Agent Steward: AI that takes full ownership from first message to final confirmation](https://delight.ai/autonomous-agent)
- [Voice AI Agent: Natural conversation, sub-250ms latency, built for high call volume](https://delight.ai/voice)
- [Omnipresent AI for customer service: What it is, why it matters, and how to get started](https://delight.ai/blog/ai-for-cx/omnipresent-ai-for-customer-service)
## Where generic AI receptionists fail for the trades
The AI receptionist category exists because the base problem is real: [home service businesses](https://delight.ai/home-services-and-warranty) miss a significant share of inbound calls, and every missed call is a missed job. But most solutions being marketed as AI receptionists share a common limitation: they treat the call as the unit of work. Once the call ends, the tool's job is done.
The gap between columns two and three isn't a minor feature difference. It's the difference between a tool that captures calls and a system that closes jobs. The companies with the highest repeat-booking rates in home services are almost always the ones whose AI covers the right side of that table.
[**See how Dr. Cool Services handles peak-season HVAC calls with zero hold time →**](https://delight.ai/customers/dr-cool-services)
## How to evaluate AI receptionists for home services: 8 questions
Most AI vendors will tell you they can handle home services. Here are the questions that separate real capability from marketing claims before you sign a contract.
### 1. Is the field service integration live or batch?
An AI that "integrates with ServiceTitan" via nightly data syncs cannot check real-time availability or confirm dispatch in a live booking conversation. Ask specifically: does the AI read live tech availability and write confirmed jobs to the platform in real time? If the answer involves any delay, you'll have double-bookings.
### 2. How does it handle emergency calls at 2am?
After-hours emergencies are the highest-value calls in home services and the ones most likely to go to competitors when not answered immediately. Ask for a demo of how the AI handles an urgent HVAC failure call at 2am, including escalation to an on-call tech.
### 3. Does it send proactive updates or only respond to inbound?
There's a meaningful gap between AI that answers "where is my tech?" and AI that texts the customer before they think to ask. Ask to see a live demo of a parts-delay scenario: what does the AI do when a tech logs a part order in ServiceTitan?
### 4. Can it hold context across multiple days and multiple visits?
Most AI receptionists reset context after every call. For a job that spans visit one, a parts delay, and visit two over three days, you need an AI with persistent memory. Ask: if the customer calls back on day three to check on their part, does the AI know who they are and where their job stands?
### 5. How does it handle an angry customer mid-job?
Parts delays and no-shows create frustrated customers. The AI will encounter them. Ask how escalation to a human works: does the AI brief the CSR on the full job context before the handoff, or does the customer start over?
### 6. Does the post-job follow-up sequence run automatically?
Review requests that require manual triggering don't get sent. Ask whether post-job follow-up fires automatically when a job is marked complete in the field service platform, and what the default timing and channel logic looks like.
### 7. What does oversight and control look like for your CSR team?
The AI is not a replacement for your CSR team. It's a force multiplier. Ask what your CSRs can see in real time: which AI conversations are open, which have been escalated, and where can they intervene if something goes wrong.
### 8. Can it coordinate warranty claims across multiple parties?
If you operate home warranty plans or work with warranty administrators, ask specifically about multi-party claim workflows. The AI needs to coordinate homeowner, contractor, and adjuster communications simultaneously, not sequentially.
### Considerations before you sign anything
- Ask for a live demo using your actual field service platform, not a sandboxed environment
- Run a test call through the AI with an emergency scenario and time how long it takes to confirm dispatch
- Ask for references from home service operators at your call volume specifically, not just any customer
- Check whether the contract covers field service integration setup, or whether that's billed separately
- Confirm whether the post-job automation is included or an add-on module
[**Not sure where your operation stands on AI readiness? Take the 5-minute assessment →**](https://delight.ai/ai-readiness-intro)
## Three ways home service AI fails in practice
The issues that sink AI receptionist deployments in home services are predictable. They fall into three categories, and all three can be screened for before you sign.
### The phone bot that takes a message and stops
This is the most common failure mode. The AI answers the phone correctly, collects the customer's name and problem, and then either routes to a human or sends an email to the CSR queue. The customer heard a professional-sounding AI, waited for a callback, and called a competitor while waiting. The AI increased your call answer rate on paper while your conversion rate dropped. Answering is table stakes. What happens after is the product.
### The scheduling tool that doesn't dispatch
The second failure mode is an AI that books appointments against a static calendar without connecting to real tech availability. The customer gets a confirmed window. The tech is already scheduled in ServiceTitan for the same window. Your CSR has to call the customer back to reschedule. The AI created work instead of reducing it. This happens whenever the integration between the AI and the field service platform is shallow or delayed.
### The follow-up sequence that doesn't close the loop
The third failure is an AI that handles the booking correctly, manages the job correctly, and then goes silent after the tech marks the job complete. No review request. No re-engagement. No maintenance reminder. The customer's experience ends with a paid invoice, not a relationship. That customer will call whoever picks up the phone next time something breaks, which is usually your competitor.
## How delight.ai approaches home services
Delight.ai is built for customer interactions that don't end at the first message. For home service businesses, that means [Agent Steward](https://delight.ai/autonomous-agent) operates across the full job lifecycle: inbound booking, dispatch confirmation, mid-job status, multi-visit coordination, warranty claim management, post-job follow-up, and re-engagement. Each stage is tracked, each transition is communicated proactively, and each closed job feeds automatically into the sequences that drive repeat revenue.
The platform integrates live with ServiceTitan and Jobber and connects across [voice, SMS, and chat](https://delight.ai/omnipresent). When a customer calls after hours, the [AI voice agent](https://delight.ai/voice) confirms dispatch against real tech availability and sends a confirmation within 90 seconds. When a job status changes in ServiceTitan, the AI sends an update to the customer before they think to call. When the job closes, the review request goes out automatically, and the maintenance reminder is queued for 90 days out.
For home warranty operators, the AI tracks all active claims simultaneously: homeowner communications, contractor dispatch confirmations, adjuster approvals, and parts order status. [Agent memory](https://delight.ai/agent-memory-platform-amp) retains the full context of every claim across every party, so when any party goes silent or a deadline is approaching, the AI follows up without waiting for a human to notice.
[Trust OS](https://delight.ai/trust-os) sits underneath all of it. Every AI decision is visible to your team in real time. Escalation paths are configurable. Your CSRs can see every open conversation in [Desk](https://delight.ai/desk), intervene in any of them, and receive a full briefing when they take over from the AI. The AI handles the volume. Your team handles what the AI can't.
- [**Voice + SMS simultaneously:**](https://delight.ai/voice) Confirmation, address, and appointment slot land in a text while the customer is still on the call. No spelling addresses twice. No dropped bookings from call fatigue.
- [**Booked directly to ServiceTitan and Jobber:**](https://delight.ai/integrations) Live availability read and confirmed job written in under 4 minutes. No manual entry. No double-bookings from delayed syncs.
- [**24/7 across voice, SMS, and chat:**](https://delight.ai/omnipresent) After-hours emergencies, peak season overflow, and mid-job status checks all handled without adding headcount.
- [**Persistent job memory across every interaction:**](https://delight.ai/agent-memory-platform-amp) When the customer calls back on day three, the AI knows who they are, where their part is, and when the tech returns.
- **Contractor reschedules and parts delays handled automatically:** New window confirmed, homeowner updated with revised ETA, before they call in to ask.
- **Lapsed customers re-engaged before a competitor gets there:** Maintenance reminders, expiring service agreements, and unsold estimates followed up automatically, not when your CSR remembers.
- [**Trust OS**](https://delight.ai/trust-os) **oversight on every conversation:** Your CSRs see every open case in [Desk](https://delight.ai/desk) in real time, can intervene in one click, and receive a full briefing on handoff.
### **Ready to stop losing jobs between the booking and the bill?** Talk to a home services specialist about your current job lifecycle. [**Book a demo →**](https://delight.ai/contact-sales)
## The bottom line: The AI front desk that actually moves revenue
An AI receptionist for home services has to be more than a voice that answers the phone. The call is the entry point. The job is the unit of work. And the job spans calls, texts, parts orders, dispatch windows, and follow-ups across multiple parties over multiple days.
The businesses winning with AI in the trades are the ones that automate the full lifecycle: they answer more calls, dispatch faster, communicate proactively on every status change, coordinate multi-party warranty claims without manual chasing, and re-engage customers before those customers think to call a competitor.
That's what an [AI front desk for home services](https://delight.ai/home-services-and-warranty) looks like. Not a phone bot. An AI that owns the job from the first ring to the five-star review.
### Keep reading
- [Home Services and Warranty: The AI operations layer for contractors and warranty operators](https://delight.ai/home-services-and-warranty)
- [Introducing Agent Steward: From AI that answers to AI that owns](https://delight.ai/autonomous-agent)
- [Voice AI Agent: Natural-sounding AI built for high-volume home service calls](https://delight.ai/voice)
- [Why the trades are the hardest use case for AI receptionists](#section-anchor-id-0 "Why the trades are the hardest use case for AI receptionists")
- [What home service customers actually expect from AI](#section-anchor-id-1 "What home service customers actually expect from AI")
- [The five jobs your AI receptionist needs to own in home services](#section-anchor-id-2 "The five jobs your AI receptionist needs to own in home services")
- [Where generic AI receptionists fail for the trades](#section-anchor-id-3 "Where generic AI receptionists fail for the trades")
- [How to evaluate AI receptionists for home services: 8 questions](#section-anchor-id-4 "How to evaluate AI receptionists for home services: 8 questions")
- [Three ways home service AI fails in practice](#section-anchor-id-5 "Three ways home service AI fails in practice")
- [How delight.ai approaches home services](#section-anchor-id-6 "How delight.ai approaches home services")
- [The bottom line: The AI front desk that actually moves revenue](#section-anchor-id-7 "The bottom line: The AI front desk that actually moves revenue")
## FAQ: AI receptionist for home services
**What is an AI receptionist for home services?**
An AI receptionist for home services is an AI agent that handles customer communication across the full job lifecycle: answering calls and booking appointments, confirming dispatch, sending proactive status updates, coordinating parts delays and multi-visit jobs, managing warranty claims across multiple parties, and following up after the job closes. Unlike a generic AI answering service, a home services AI receptionist integrates live with field service platforms like ServiceTitan and Jobber to read real tech availability and manage job state in real time.
**Can AI receptionists integrate with ServiceTitan?**
Yes, but the quality of that integration varies significantly between vendors. **Live integration** means the AI reads real-time technician availability from ServiceTitan and writes confirmed appointments directly to the platform during the booking conversation. **Batch integration** means the AI syncs with ServiceTitan periodically, which creates a window for double-bookings and means the AI can't confirm dispatch in real time. Always ask vendors whether their ServiceTitan integration is live bidirectional or scheduled sync before buying.
**What's the difference between an AI receptionist and an AI front desk for home services?**
The terms are often used interchangeably, but in practice they can refer to different scopes. An AI receptionist typically refers to answering inbound calls and booking appointments. An AI front desk more often implies broader operational coverage: handling multiple communication channels (voice, SMS, chat), managing job status across the lifecycle, coordinating dispatch, and running post-job sequences. For home services, you need the broader scope regardless of what the vendor calls it.
**How does AI handle emergency calls for HVAC companies?**
A properly configured AI receptionist handles emergency HVAC calls by: (1) detecting urgency signals in the customer's language, (2) checking real-time on-call tech availability, (3) confirming dispatch against the available tech, and (4) sending a confirmation text with the tech's name and estimated arrival. The AI should also have a defined escalation path for situations where no tech is available, which may include routing to an on-call CSR or offering the next available morning slot with a priority flag. Emergency call handling should be demonstrated in any vendor demo before purchase.
**How does AI handle home warranty claims?**
Home warranty claim coordination requires an AI that can manage multiple simultaneous communication threads: the homeowner needs status updates on their claim, the contractor needs dispatch confirmation and job details, and the warranty administrator needs approval requests and documentation. The AI should track the state of each party independently and follow up proactively when any party goes silent or a deadline approaches. This is substantially more complex than single-party booking and requires an AI with persistent multi-party state management, not a simple phone bot.
**What field service platforms does AI typically integrate with?**
The most common field service platforms in residential home services are **ServiceTitan** (HVAC, plumbing, electrical), **Jobber** (general contractor and trades), **Housecall Pro** (small to mid-size home service companies), and **FieldEdge** (HVAC and plumbing). Any AI receptionist vendor you evaluate should be able to demonstrate a live integration with the platform your technicians already use. If the vendor requires you to switch field service platforms as part of the deployment, treat that as a major red flag.
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## 8 best Fin AI alternatives for CX teams in 2026
Source: https://delight.ai/blog/ai-agent/fin-ai-alternatives
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- 8 best Fin AI alternatives for CX teams in 2026
[
Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
June 16 2026
If you're evaluating Fin AI alternatives, you've probably already seen what Intercom's AI agent does well. Fin resolves 50–67% of support conversations out of the box, deploys in under an hour, and ties cost directly to outcomes at $0.99 per resolution. For teams with straightforward support needs and predictable volume, it's a strong product.
But 3 things tend to push customer experience (CX) teams toward alternatives like the ones on this list.
1. **The invoice surprises.** Per-resolution pricing sounds efficient until volume grows. At 5,000 monthly resolutions, Fin charges roughly $4,950 per month—before Intercom seat costs. CFOs start asking questions around month 3, once enough historical data reveals the pattern. If Intercom Fin AI pricing is what triggered your search, you're not alone.
2. **The complexity ceiling.** Fin handles FAQ-style queries well. Multi-step workflows and context-dependent conversations are a different story—escalation guidance becomes unreliable as conversations get longer and more layered.
3. **The context resets.** Fin can access Intercom customer data objects and past conversation logs, but it doesn't build a persistent understanding of each customer across separate sessions. It can pull up prior conversations, but returning customers often still repeat themselves—and your CX metrics reflect that friction.
If any of those sound familiar, here's how to evaluate what's out there—starting with the criteria that actually matter.
## How to choose an AI customer service platform (what actually matters)
These 6 criteria reflect what buyers actually ask about in sales conversations and vendor demos.
**1. Pricing model and total cost of ownership.** Per-resolution, per-ticket, flat-rate, and per-seat models all behave differently at scale. The question isn't just 'what does it cost today' but 'what does it cost at 1,000, 3,000, and 5,000 monthly conversations?' Fin AI resolution pricing adds up fast—model it before you sign.
**2. Resolution definition and measurement honesty.** What counts as 'resolved' varies dramatically. Fin counts an 'assumed resolution' when a customer leaves without escalating—even if they gave up. Zendesk has auto-billed for resolutions teams disagreed with since January 2026. Before you compare resolution rates, make sure you're comparing the same thing. Also ask: does the AI resolve by acting, or can it take actions—process refunds, edit orders, update subscriptions?
**3. Implementation speed and migration path.** Some tools deploy in minutes; others take months to reach target performance. Know the difference between 'time to go live' and 'time to reach the resolution rate you need.'
**4. Integration depth with your existing stack.** Does it connect to your helpdesk, CRM, and knowledge sources? 'Integration' means different things—pulling data for context isn't the same as executing actions in your backend systems. Test integration depth in a proof-of-concept, not on a vendor's word. Teams evaluating [AI customer service solutions](https://delight.ai/ai-customer-service) should be especially rigorous here.
**5. Channel coverage.** Chat, email, voice, social, and messaging. Most AI agents are chat-first. If phone support matters, check whether voice is native or absent.
**6. Human handoff and escalation quality.** When the AI can't resolve, what transfers to the human agent? The full conversation context? A summary? Nothing? This determines whether handoff feels seamless or forces the customer to start over.
With those criteria in mind, let's compare the top Fin AI alternatives side by side. We picked the eight platforms below to span the range of teams actually evaluating Fin. That means free and pay-per-task tools for SMBs (Tidio, eesel AI, Freshdesk), voice-first and B2B-specific options (Ringly.io, Helply), and mid-market platforms with deeper AI and persistent memory (Zendesk AI, delight.ai).
We close with a look at the enterprise tier (Decagon, Sierra AI, Ada). [Delight.ai](https://delight.ai/ai-customer-service) is one of them; where a figure comes from a vendor's own claims, we say so.
## Side-by-side comparison: 8 Fin AI alternatives at a glance
[](https://sendbird.imgix.net/cms/fin-alts-table.png)
The tools with documented action execution include delight.ai (Actionbooks), Ringly.io (Shopify order processing), and Freshdesk (pre-built ecommerce integrations). eesel AI, Helply, and Tidio/Lyro are primarily knowledge-base responders. Zendesk's Forethought acquisition (March 2026) targets the action-execution gap, but the integration is early.
## The alternatives: A closer look at each platform
### Fin AI (Intercom)—the anchor you're evaluating against
Since every alternative on this list positions against Fin, it's worth reviewing what it does well to set the comparison baseline.
Resolution rates of 50–67% are documented across thousands of deployments. [Fin Express Deploy](https://www.intercom.com/fin) gets teams live in under an hour with as few as 10 Help Center articles, and the $0.99/resolution model aligns cost with value at low conversation volumes.
Fin Apex 1.0 is Intercom's proprietary model, post-trained on customer service data—a serious investment in domain-specific AI. Intercom reports it outperforms GPT-5.4 on resolution benchmarks (73.1% vs 71.1%); the benchmark is Intercom's own design, though the results were reported by VentureBeat rather than independently re-run. Fin supports 10+ native channels including email, phone, SMS, WhatsApp, and social messaging.
The gaps mirror what drives most teams to this article: per-resolution costs that are hard to model before month 3, an assumed resolution definition that can overcount, and diminishing reliability in multi-step conversations. Fin Voice is also sales-gated—not available through self-serve.
**Best for:** Small and mid-size business (SMB)-to-mid-market teams with straightforward support needs, predictable conversation volume, and primarily standalone interactions where returning-customer context is less critical.
### Zendesk AI—the ecosystem play for teams already invested
If you're weighing Fin AI vs Zendesk AI, the core distinction is architectural: where Fin is a standalone AI agent you bolt onto Intercom, Zendesk AI is the full-platform bet. If your team already runs on Zendesk Suite, adding AI within that ecosystem avoids the rip-and-replace conversation entirely.
[Zendesk's AI](https://www.zendesk.com/service/ai/) draws on over 18 billion customer service interactions for training data, and the Forethought acquisition in March 2026—bringing 1 billion monthly interactions—strengthens autonomous resolution capabilities. Native voice AI agents shipped in October 2025. The marketplace connects to major CRM and ecommerce platforms, and enterprise compliance credentials check the boxes most procurement teams require.
Where it gets complicated is cost. Zendesk stacks 3 billing layers: per-seat licensing ($55–$169/agent/month), per-resolution AI charges ($1.30–$1.50 at base, up to $2.00 on overages), and an Advanced AI add-on ($50/agent/month). For a 20-agent team handling 3,000 tickets monthly, that lands at roughly $7,000–$10,000/month all-in. Billing unpredictability is a frequent [G2 complaint](https://www.g2.com/products/zendesk-support-suite/reviews)—conversations tagged as 'resolved' that teams disagree with, plus auto-billing for overages since January 2026.
The AI is also stronger at answering knowledge-base questions than at executing backend actions like refunds or order changes. Forethought targets that action-execution gap, but the integration is early.
**Best for:** Mid-market to enterprise teams already invested in the Zendesk ecosystem who want AI within their existing stack and can absorb the cost complexity.
### Delight.ai—persistent memory across sessions, predictable pricing at scale
The core architectural difference here is memory. Delight.ai's Agent Memory Platform (AMP) maintains persistent conversational context across sessions and channels—a customer contacts you on chat Tuesday and follows up by email Friday, and the AI carries its understanding forward instead of starting from scratch. Enterprise-tier platforms like Decagon and Sierra AI (profiled below) offer their own cross-session context—Ada's reasoning engine is strong, though reviewers report context-loss issues—but all sit at a higher price point.
[Agent Steward](https://delight.ai/blog/product/introducing-agent-steward) moves the [AI agent platform](https://delight.ai/ai-agent) from answering to acting—coordinating across systems and third parties to process refunds, rebook flights, or update subscriptions in a single workflow. A graduated autonomy model lets teams start with human approval on every action and expand autonomous resolution use case by use case.
Pricing uses consumption-based enterprise contracts without per-resolution billing, so there's no billing shock as volume scales. The trade-off is pricing visibility: delight.ai doesn't publish pricing tiers. Unlike Fin, Tidio, or eesel AI, you can't model costs before a sales conversation. That's a genuine advantage for teams that need predictability once committed, but could be a stumbling block for those who want to self-serve a comparison before engaging sales.
Trust OS enforces policies at the response level before the AI's output reaches the customer, and dev/prod environment separation with rollback and audit trails gives leadership visibility into exactly what the AI decided and why. The platform runs on Sendbird's decade-plus production infrastructure, with [Voice AI](https://delight.ai/voice) available to all customers.
The gaps: delight.ai's integration ecosystem is narrower than what Zendesk or Salesforce-native platforms offer out of the box. And the memory architecture means ramp time is real—Hanssem progressed from 48% to 90% resolution over 5 months as AMP compounded context. Teams that need broad connectors today or peak performance in week 1 may find other platforms a better near-term fit.
**Best for:** Mid-market CX teams (1,000+ employees, 10K+ tickets/month) who measure success by customer lifetime value, need persistent context across interactions, and require governance controls.
### Freshdesk / Freddy AI—the budget-friendly starting point
Where most platforms on this list charge per resolution or require a sales call, Freshdesk starts with a free tier that actually works. Pre-built ecommerce integrations with Shopify, Stripe, PayPal, and FedEx ship out of the box, and the broader Freshworks ecosystem (CRM, marketing, and IT) gives growing teams a unified stack without enterprise pricing. For teams looking for Fin AI alternatives that are cheaper to operate, Freshdesk is hard to beat on accessibility.
[Freddy AI Copilot](https://www.freshworks.com/freshdesk/) is genuinely useful for agent productivity—drafting replies, summarizing conversations, and running sentiment analysis. The autonomous Freddy AI Agent handles routine queries across chat, email, WhatsApp, and social channels.
The trade-off is AI depth. Freddy's resolution quality is generally reported as lower than Fin or dedicated AI agent platforms, and session-based pricing for the AI Agent can surprise teams—sessions expire at the end of each billing cycle with no rollover, so costs scale non-linearly as volume increases. For complex enterprise workflows, Freshdesk hits a ceiling faster than purpose-built AI platforms.
**Best for:** SMBs and growing teams under budget constraints, especially those already in the Freshworks ecosystem. If affordability matters more than AI sophistication, Freshdesk is a legitimate choice.
### Ringly.io—AI phone support built for ecommerce
No other tool on this list covers what Ringly does: voice-first AI support for online stores.
The [AI phone agent](https://www.ringly.io/) answers calls around the clock, looks up Shopify order information in real time, and processes returns and exchanges without human intervention. A 70–73% call resolution rate is backed by a 90-day guarantee—if resolution drops below 65%, you get a refund. Native Shopify integration means the AI works from live order data, not stale exports, and setup takes under an hour with no-code configuration.
The limitation is scope. Voice only—no chat, no email, no social messaging. If phone support is one channel among many, you still need a separate solution for everything else, and at $349/month minimum for voice-only, the per-channel cost is relatively high.
**Best for:** Ecommerce and Shopify stores where phone support is a significant channel—particularly when serving older demographics or handling high-value orders. This is a complement to your text-based support tool, not a full Fin replacement.
### eesel AI—the customizable sidecar for your existing helpdesk
Rather than replacing your helpdesk, [eesel AI](https://www.eesel.ai/) layers a cheaper, more customizable AI agent on top of it—which means a fundamentally different buying decision than most tools here.
The standout is prompt-level customization. G2 reviewers cite this as the #1 reason they switched from Fin—eesel lets you control exactly how the AI responds, not just set a persona and hope for the best. You can test AI performance against historical tickets before going live, which reduces deployment risk significantly. It plugs into Intercom, Zendesk, and Freshdesk, so you keep your existing helpdesk and add AI without migration.
Transparent pay-per-task pricing ($0.40 per support ticket, with light tasks free) makes cost modeling straightforward. But the sidecar model means double-platform costs—you still pay for your underlying helpdesk subscription plus eesel on top. There's no voice channel support, and the G2 review sample is small relative to their claimed customer base.
**Best for:** Teams wanting hands-on control over AI behavior without ripping out their existing helpdesk. If you want to test AI support with low commitment and high customization, eesel is the lowest-friction option on this list.
### Helply—support AI that doubles as a revenue signal tool
[Helply](https://www.helply.ai/) targets a gap most AI support platforms ignore: the intelligence buried in support conversations. For business-to-business (B2B) companies in the $1M–$50M annual recurring revenue (ARR) range, that positioning is compelling.
The cost comparison stands out: according to Helply's own data, the platform costs roughly $250 at 500 monthly AI-resolved issues versus Fin's approximately $495 at the same volume. A free helpdesk with unlimited seats removes the per-agent cost layer entirely. Beyond resolution, Helply describes churn detection, upsell identification, and competitor monitoring features on its website—though no independent reviews verify these capabilities yet.
The gaps are straightforward: Helply is a newer brand with less enterprise track record than Zendesk or Intercom, it targets B2B specifically (making it a poor fit for ecommerce), and integration depth along with exact AI resolution capabilities remain unverified by third parties.
**Best for:** B2B SaaS companies wanting support AI that also surfaces revenue signals—churn risk, upsell opportunities, and competitor mentions. The pricing story is attractive if the revenue-engine features deliver as described.
### Tidio / Lyro AI—the fastest path to live
Need an AI support chatbot for ecommerce running today with minimal setup and zero upfront cost? [Tidio](https://www.tidio.com/) gets you there.
The free plan delivers real value—50 conversations and a one-time allotment of 50 Lyro AI interactions at no cost. One-click Shopify install gets stores live in minutes, and Lyro's 67% average resolution rate comes with a money-back guarantee if it falls below 50%, which is unusual confidence from a vendor. The visual chatbot builder lets non-technical teams configure flows without code.
The ceiling shows up when interactions get complex. Lyro deflects routine questions more effectively than it fully resolves issues requiring backend actions—enterprise workflows aren't Tidio's design target. And the Lyro AI add-on ($39+/month on lower tiers) compounds with the base plan as volume grows, narrowing the cost advantage at scale.
**Best for:** Small to mid-size businesses wanting affordable AI chat support without enterprise complexity. If you need something working this week and your support queries are primarily informational, Tidio gets you there faster than anything else on this list.
### Enterprise-tier AI customer service platforms: Decagon, Sierra AI, and Ada
The 8 alternatives above cover the range most mid-market teams evaluating Fin will care about. But if you're researching AI customer service agent alternatives at the enterprise level, three platforms operate at a different price point and procurement scale entirely.
[**Decagon**](https://decagon.ai/) has landed strong enterprise logos—Duolingo reports 80% deflection. Its Agent Operating Procedures (AOPs) let non-technical CX teams configure complex AI behaviors in natural language, and Decagon layers on top of existing helpdesks (Zendesk, Salesforce, Intercom, and Kustomer) via API. Pricing isn't public, but third-party data suggests contracts range from ~$95K to $400K+/year depending on volume and complexity—a significant jump from the mid-market tools above. The platform also lacks social media channel support.
[**Sierra AI**](https://sierra.ai/) ($15.8B valuation, $150M+ ARR) positions its agents as autonomous employees that take real backend actions across systems, with significant enterprise adoption. The Agent Data Platform provides persistent memory across sessions. For mid-market teams, the barrier is primarily financial: first-year costs are estimated at $200K–$350K+ including professional services. G2 reviewers have also flagged integration friction and a data dispersion issue where bot conversations live in Sierra while human conversations remain in the original helpdesk.
[**Ada**](https://www.ada.cx/) (founded 2016) sits closer to mid-market price points, with first-year costs estimated anywhere from $30K to $250K+ depending on volume. The platform integrates with 13+ helpdesks—the broadest connector library in this landscape. The caution: Ada charges $1–$3.50 per resolution, creating the same billing unpredictability that drives teams away from Fin. Its Trustpilot rating (1.8–1.9/5) contrasts sharply with a 4.6/5 G2 score—worth investigating during demos. Implementation timelines of 8–16 weeks are longer than Ada's marketing suggests.
For mid-market teams, the key trade-off with these enterprise platforms is capability depth versus implementation cost and timeline. If your needs and budget go beyond the mid-market alternatives profiled above, Decagon, Sierra AI, and Ada belong on your evaluation shortlist.
## How to choose the right Fin AI alternative for your team
With this many Fin AI alternatives on the market, the right tool depends on your team's profile. Here's a decision framework:
- **SMB with low volume and simple queries**—Tidio (free tier) or stay with Fin if you're already on Intercom
- **Budget-constrained team wanting an AI layer on your existing helpdesk**—eesel AI or Freshdesk
- **B2B SaaS wanting support + revenue signals**—Helply
- **Ecommerce/Shopify needing phone support**—Ringly.io
- **Mid-market team outgrowing Fin** (billing shock, context resets, complexity ceiling)—delight.ai
- **Enterprise team invested in Zendesk ecosystem**—Zendesk AI, but model total cost carefully before committing
- **Enterprise team with $100K+ budget and Fortune 500 scale**—Decagon, Sierra AI, or Ada, depending on integration needs and implementation appetite
### 5 questions to ask in every vendor demo
1. How does pricing scale at my conversation volume? Model it at 1,000, 3,000, and 5,000 monthly interactions.
2. What counts as a 'resolution' in the contract? Get it in writing.
3. How does the AI handle returning customers who have contacted us before?
4. What governance controls exist? Can I see exactly what the AI decided and why?
5. What does migration look like, and what should I expect in the first 30, 60, and 90 days?
On migration: sidecar tools like eesel AI require no migration—they layer on top of your existing helpdesk. Full-platform switches take more effort. See the FAQ below for a practical migration breakdown.
Ready to see how persistent memory changes your support experience? [Explore the delight.ai AI agent](https://delight.ai/ai-agent) or [request a demo](https://delight.ai/contact-sales) to evaluate AMP, Trust OS, and predictable enterprise pricing against your team's specific requirements.
- [How to choose an AI customer service platform (what actually matters)](#section-anchor-id-0)
- [Side-by-side comparison: 8 Fin AI alternatives at a glance](#section-anchor-id-1)
- [The alternatives: A closer look at each platform](#section-anchor-id-2)
- [How to choose the right Fin AI alternative for your team](#section-anchor-id-3)
## FAQ
### How much does Fin AI cost per resolution?
Fin AI charges $0.99 per resolution. At 2,000 monthly conversations with a 50% resolution rate, expect roughly $990 per month in Fin charges alone—before Intercom seat costs ($29–$139/agent/month). At 5,000 resolutions, that climbs to approximately $4,950/month for Fin usage only. The critical detail: Fin's assumed resolution definition counts conversations where the customer left without escalating. If a customer gives up and calls your phone line instead, Fin may still count that as resolved. Ask Intercom how assumed resolutions are calculated in your contract before signing.
### Is there a cheaper alternative to Intercom Fin?
Several alternatives cost less per interaction. At 500 monthly AI-resolved issues, Helply runs roughly $250 (at $0.50/resolution) versus Fin's approximately $495 (at $0.99/resolution)—a comparison Helply highlights in its own materials. eesel AI charges $0.40 per ticket with light tasks free. Tidio offers a functional free tier. Delight.ai uses predictable enterprise contracts that eliminate the variable-cost risk of per-resolution billing, but you need a sales conversation to model the actual cost—there are no published pricing tiers to compare directly.
### How do you switch from Intercom to another platform?
It depends on the type of tool. Sidecar tools like eesel AI require no migration—they plug into Intercom and add an AI layer on top, so you can test without disrupting your existing setup. Full-platform switches (Zendesk, Freshdesk, delight.ai) take more effort: exporting conversation history and customer data, rebuilding workflows and automation rules, retraining your team on the new interface, and updating integrations. Plan for 2–4 weeks of basic setup and 2–3 months before the AI reaches optimized performance. Run both platforms in parallel for at least 30 days, and designate 1 person as the migration owner to keep the timeline from drifting.
### What is the best alternative to Intercom Fin AI?
There's no single best alternative—it depends on what triggered your search. The decision framework above maps team profiles to tools, but the short version: billing shock points toward flat-rate or consumption-based models (Helply, eesel AI, delight.ai). Complexity ceilings and context frustration point toward platforms with deeper AI and persistent memory (delight.ai's [AI customer service solution](https://delight.ai/ai-customer-service), or enterprise-tier options like Sierra AI or Decagon). If you're already on Zendesk, Zendesk AI keeps your stack consolidated. Start with the 5 demo questions above and evaluate from there.
## Keep reading
[View more](/blog)
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2) [
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis) [
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward) [
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[View more](/blog)
## AI IVR: Why a smarter phone tree still isn't the answer
Source: https://delight.ai/blog/ai-for-cx/ai-ivr
[
Ethan Hong
Product manager](https://delight.ai/author/ethan-hong)
June 18 2026
You already know your IVR needs to change. The question is what "change" actually means for a contact center in 2026, because most of the answers being marketed right now are solving the wrong problem.
AI IVR — AI Interactive Voice Response — takes the same routing infrastructure that's been running contact centers for four decades and adds a language model to it. Customers can say "update my payment method" instead of pressing 3. The AI detects intent, routes appropriately, and sometimes resolves the issue without a human. That's a real improvement over the touch-tone menu. It's not the same thing as knowing the customer.
The companies winning on voice metrics in 2026 aren't running a smarter phone tree. They've moved past the routing model entirely. Their AI knows who's calling before the call connects. It calls customers first, before a problem turns into a complaint. When the call ends, the conversation doesn't — it continues on SMS, chat, or email with full context intact. That's a different product from AI IVR. And the distinction matters before you sign a contract.
This post covers where AI IVR falls short, what the better model looks like, and what to demand from any vendor before you commit.
*Two fundamentally different approaches to voice in the contact center*
## What AI IVR actually does (and what it doesn't)
AI IVR is an upgrade to routing logic. The customer speaks naturally, the AI interprets intent, and the call goes where it should. Modern systems offer intent detection that handles natural language, caller authentication via voice biometrics or account lookup, queue deflection that resolves simple requests without a human (account balance, order status, payment due dates), and sentiment detection that flags high-priority calls. Each of these reduces handle time and lowers cost-to-serve. If your current system is a legacy IVR from 2015, AI IVR is a meaningful upgrade.
The gap is in what AI IVR assumes: **the call is the unit of work.** The customer called. The system processed the call. The call ended. What the customer did on your website yesterday, what they told your chatbot last week, what their account history reveals about the problem they're about to describe, what the right action would be before they think to call at all — none of that is part of the IVR mental model. It's routing infrastructure. It was never designed to know anyone.
## What your customers experience on every call
Your customer has been with you for two years. They call in. The AI asks them to verify their account. It asks for their billing zip. It routes to billing. The billing agent asks why they're calling.
Nothing failed. Everything worked exactly as designed. That's the problem.
The AI should know who they are before the first word. It should know they already tried to self-serve. It should have the relevant account context ready before they finish the first sentence. AI IVR optimizes the routing. It doesn't close that gap.
The gap between what customers expect and what AI IVR delivers is exactly what the memory layer closes. When the AI knows the customer before the call starts — their open issues, their recent activity, their account context, their preferred channel — the conversation shifts from processing to serving.
[Explore how memory drives personalized voice](https://delight.ai/agent-memory-platform-amp)
## What the companies winning on voice are doing differently
The contact centers with the best voice resolution rates and lowest inbound volumes in 2026 aren't running AI IVR. They've rethought what voice is for. Here's what that looks like in practice.
### The AI knows the caller before the call connects
When a customer calls, the AI already knows their name, their recent activity, their open issues, and the context of their last interaction — whether that was a phone call, a chat, or an email. The greeting isn't "say or press 1 for account services." It's: "Hi Marcus, I can see you opened a case yesterday about your order. Are you calling for an update on that?"
That personalization isn't cosmetic. It reduces average handle time because the AI doesn't spend the first 90 seconds gathering context that already exists. It reduces escalation rates because customers who feel known are less likely to demand a supervisor. It changes the emotional temperature of the call from friction to resolution. The technology behind this is [agent memory](https://delight.ai/agent-memory-platform-amp): a live layer that aggregates customer context from CRM, ticketing, prior conversations, and account data, and makes it available before the call connects.
*Delight.ai's Agent Memory Platform surfaces full customer context before the call connects*
### The AI calls the customer before the customer calls you
The IVR is inherently reactive. The best AI voice deployments in 2026 are proactive: the AI initiates the outbound call when a trigger condition is met. A payment fails. The AI calls the customer to offer resolution options before they notice the failed charge and call in frustrated. A delivery is delayed. The AI calls to explain the new timeline before the customer opens a ticket. A renewal is approaching. The AI calls to confirm, answer questions, or handle the renewal entirely, before the customer churns from inaction.
Proactive outbound changes the economics of contact center volume. Research consistently shows that 30–40% of inbound call volume in subscription and service businesses is predictable and preventable: payment failures, shipment delays, renewal windows. Inbound calls are expensive because customers who call in are already at the point where the issue is urgent enough to demand attention. Proactive outbound gets ahead of that moment entirely. The AI reaches out first, on your terms, with full context, before the customer's frustration has had time to compound.
> #### This isn't robocall territory.
> The AI outbound experience is personalized and conversational — the difference between a mass-dialed prerecorded message and an AI that calls a customer specifically because their renewal is in three days and they haven't used the feature they're paying for.
*Proactive outbound resolves the issue before the customer ever picks up the phone*
### Voice and SMS run simultaneously on the same call
One of voice's persistent limitations is information density. Customers need to confirm an address, capture a case number, or remember an appointment window. Over voice, that's slow and error-prone. People mishear digits. They ask for repetition. They write things down during the call and lose the note.
Running voice and SMS simultaneously solves this at the channel level. While the customer is on the call, the AI sends a text with the confirmation details, the appointment window, the relevant link, the case number. The customer confirms on voice. The specifics arrive in text in real time. Nothing gets lost between the call and the follow-through. Voice handles the conversation. SMS handles the information transfer. Together they close the loop that voice alone consistently fails to close.
[See dual-channel voice + SMS in delight.ai](https://delight.ai/voice)
*Voice handles the conversation. SMS handles the details. Both run on the same call.*
### Channel sequencing closes the gaps voice leaves open
Some conversations start on voice and need to continue somewhere else. A customer calls to report an issue. The AI opens a ticket, confirms it on the call, and the customer needs status updates over the following three days. Those updates don't need to be calls — they should be texts, or in-app messages, or emails, based on what the customer prefers.
[Channel sequencing](https://delight.ai/omnipresent) threads this together automatically: the AI moves the conversation from voice to the next channel without dropping context. When the SMS arrives, the customer doesn't re-explain their issue. When the live agent eventually takes the call, they don't need a briefing. The conversation picks up exactly where it left off, regardless of how many channels it crossed.
The contact center benefit is significant. Not every follow-up interaction needs to be a call. Calls require real-time availability on both ends and are the most expensive channel per interaction. If the initial voice contact resolves the acute issue, the subsequent communication belongs on a channel that's cheaper and more convenient. Channel sequencing makes that automatic.
*A single conversation sequenced across voice, SMS, and chat — full context at every step*
### The AI that knows every prior interaction, not just this call
In a siloed multi-channel deployment, the voice AI doesn't know what the chat AI said. The email ticket isn't visible when the call comes in. Every channel restarts from zero. Omnichannel memory means the customer's full history is available regardless of which channel they're using now. When the customer calls, the AI knows they chatted last week, knows what resolved and what didn't, and picks up the thread instead of starting over.
This is where the AI IVR model breaks down structurally. IVR was designed to handle calls. Omnichannel memory requires a platform that spans channels, stores interaction history, and makes that history available across every surface the customer might reach. You can't add a memory layer to a call-routing system. You have to build with memory as a foundation.
[Read: Connection starts with omnipresence](https://delight.ai/blog/product/connection-starts-with-omnipresence)
### Go deeper
- [Voice AI Agent: Natural conversation, sub-250ms latency, built for high call volume](https://delight.ai/voice)
- [Agent Memory Platform: The memory layer that makes every call personalized from the first word](https://delight.ai/agent-memory-platform-amp)
- [Omnipresent AI: Build once, deploy everywhere, sequence across channels automatically](https://delight.ai/omnipresent)
## What AI IVR vs. a full AI voice platform actually delivers
## Five questions to ask any AI IVR vendor before you sign
Most vendors will show you an impressive demo environment. Here are the questions that separate real capability from a well-rehearsed walkthrough.
### 1. Does the AI know who is calling before the call connects?
Ask whether the AI surfaces caller history, recent channel activity, and account context before the greeting starts. If the demo shows the AI asking the customer to verify their identity mid-call before doing anything useful, that's the old model. The AI should know the caller on ring, not after a verification sequence.
### 2. Does the voice AI support proactive outbound?
Ask for a live demo of the AI initiating an outbound call based on a business trigger: a failed payment, a shipment exception, a renewal reminder. If the vendor's outbound story is a campaign-based prerecorded blast, that's a robocall with a language model in the marketing copy. The capability you want is trigger-based, personalized, and conversational.
### 3. Does it run SMS alongside a live voice call?
Ask specifically: when the AI confirms an appointment over voice, does a text with the confirmation details arrive on the customer's phone during the same conversation? If the answer is "we offer SMS as a separate channel," the simultaneous capability isn't there. Those are two different products with two different value propositions.
### 4. Does channel sequencing maintain full context across the handoff?
Ask the vendor to demonstrate a conversation that starts on voice and continues on SMS or chat. Does the customer need to re-explain when they switch channels? Does the receiving channel know the full history of what was said on the call? If context resets at the channel boundary, it's multi-channel — not omnichannel.
### 5. Where does customer memory come from, and how current is it?
Ask what data sources feed the AI's knowledge of the customer, how often that data refreshes, and whether it pulls from CRM, ticketing, and prior conversation history simultaneously. A memory layer that's a nightly CRM export is not the same as real-time context aggregation. The difference shows up in every call where the customer's situation changed in the last 12 hours.
[Not sure where your contact center stands? Take the AI readiness assessment](https://delight.ai/ai-readiness-intro)
## Three ways AI voice deployments fail in practice
The failure modes in AI voice are predictable. All three can be screened for before you commit.
### The AI that routes but doesn't resolve
The most common failure: the AI answers calls correctly, detects intent, and routes to the appropriate queue. The customer waits on hold and explains the issue again to a human agent. The AI improved routing. It didn't improve resolution. Call volume stays flat, handle time stays flat, and the contact center has a new monthly AI spend with no measurable outcome. Resolution happens when the AI handles the interaction end-to-end, not when it hands off more accurately.
### The reactive AI that creates inbound instead of deflecting it
The second failure mode is an AI built entirely for inbound, deployed in an environment where most call volume is predictable and preventable. The payment that fails generates an inbound call. The delayed shipment generates an inbound call. The approaching renewal generates an inbound call. None of these calls had to happen. An AI that proactively reaches out before the trigger becomes a customer complaint eliminates the call entirely. An AI that only responds to inbound just handles it more efficiently.
### The channel silo with an AI label
The third failure: the contact center deploys AI voice, AI chat, and AI email as separate point solutions. Each has its own memory, its own context, its own conversation history. The customer who called yesterday and is chatting today gets treated as a new interaction. This is multi-channel with AI, not omnichannel AI. The difference is the memory layer that spans all of them.
## How delight.ai approaches voice
[Delight.ai's Voice AI agent](https://delight.ai/voice) is built on the premise that voice is one channel in a continuous customer relationship, not a routing layer for isolated calls. When a customer calls, [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) surfaces their full context before the call connects: open issues, recent channel activity, account history, interaction preferences. The AI doesn't ask the customer to re-identify. It picks up the thread from wherever the relationship left off.
Outbound calling is native to the platform. The AI initiates calls based on configurable triggers — payment failures, renewal windows, shipment exceptions, proactive service check-ins — with the same conversational quality as inbound. Every outbound call is a personalized conversation, not a broadcast. Voice and SMS run simultaneously. [Channel sequencing](https://delight.ai/omnipresent) means the conversation follows the customer across every channel they use next.
[Trust OS](https://delight.ai/trust-os) sits underneath every voice interaction: every call logged, observable, and auditable. And delight.ai integrates with your existing telephony stack and CRM without a rip-and-replace — so the upgrade is additive, not a rebuild.
- **Personalized inbound from the first ring:** The AI knows the caller's history, open cases, and account context before the greeting.
- **Proactive outbound calls:** Trigger-based outbound that's conversational and personalized, not prerecorded.
- **Voice + SMS simultaneously:** Confirmation details, appointment windows, and case numbers delivered in text while the customer is still on the call.
- **Channel sequencing across voice, SMS, chat, and email:** Full context at every channel transition. No restarting.
- **Trust OS observability on every call:** Every conversation logged and available for review. Escalation in one click from Desk.
### Ready to see what AI voice looks like when it knows your customer?
Talk to an expert about your current call volume and what proactive outreach could deflect. [Book a demo](https://delight.ai/contact-sales)
## The bottom line
AI IVR is better than the phone tree it replaces. It routes more accurately, deflects more simple intents without a human, and lowers cost-to-serve. For a contact center running on legacy infrastructure, it's a meaningful step forward.
But the contact centers winning on CX metrics in 2026 have moved past the routing model entirely. Their AI knows the caller before the call connects. It reaches out before the customer has a reason to call. It runs voice and SMS simultaneously so nothing gets lost between the conversation and the follow-through. It sequences across channels without dropping context. And it builds a continuous record of every interaction that makes each subsequent one faster to resolve.
That's what an AI voice platform looks like. Not a smarter phone tree. An AI that knows the caller, starts the conversation before they pick up the phone, and holds the relationship across every channel they use.
### Keep reading
- [Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](https://delight.ai/blog/product/voice-v2)
- [Connection starts with omnipresence: How delight.ai sequences conversations across every channel](https://delight.ai/blog/product/connection-starts-with-omnipresence)
- [Omnipresent AI for customer service: What it is, why it matters, and how to get started](https://delight.ai/blog/ai-for-cx/omnipresent-ai-for-customer-service)
- [Introducing Agent Steward: The first AI agent that owns customer issues end to end](https://delight.ai/blog/product/introducing-agent-steward)
- [What AI IVR actually does (and what it doesn't)](#section-anchor-id-0)
- [What your customers experience on every call](#section-anchor-id-1)
- [What the companies winning on voice are doing differently](#section-anchor-id-2)
- [What AI IVR vs. a full AI voice platform actually delivers](#section-anchor-id-3)
- [Five questions to ask any AI IVR vendor before you sign](#section-anchor-id-4)
- [Three ways AI voice deployments fail in practice](#section-anchor-id-5)
- [How delight.ai approaches voice](#section-anchor-id-6)
- [The bottom line](#section-anchor-id-7)
## FAQ: AI IVR
### What is AI IVR?
AI IVR (AI Interactive Voice Response) is a voice automation system that uses natural language processing to handle inbound phone calls, replacing numeric menu navigation with conversational intent detection. Customers speak naturally and the AI interprets what they want. Modern AI IVR systems can authenticate callers, resolve simple inquiries without a human agent, and route complex calls accurately. The core limitation of most AI IVR systems is that they treat each call as an isolated event — without access to the customer's history across channels or the ability to reach out proactively before the customer calls.
### How is AI IVR different from an AI voice agent?
AI IVR is routing infrastructure with language capabilities added. It handles the call, detects intent, and routes or resolves. An AI voice agent with memory and omnichannel integration goes further: it knows the customer's history before the call begins, personalizes the interaction based on that context, can initiate proactive outbound calls, runs SMS alongside the voice conversation simultaneously, and sequences context across channels after the call ends. The difference is not in voice quality or intent detection accuracy — it's in whether the AI knows the caller or only knows the call.
### Can AI voice agents make outbound calls?
Most AI IVR systems are inbound-only — they wait for the customer to call. AI voice platforms with proactive outbound capability can initiate calls based on configurable trigger conditions: failed payments, shipment exceptions, renewal windows, follow-ups after a service interaction. The critical distinction from robocalls is that proactive AI outbound is a personalized, conversational interaction based on the customer's specific account context, not a prerecorded message sent to a contact list. Proactive outbound is one of the highest-ROI use cases in contact center AI because it deflects inbound calls before they happen.
### What is channel sequencing in contact center AI?
Channel sequencing is the ability of an AI platform to move a conversation from one channel to another while maintaining full context. A customer who starts an interaction on voice can receive follow-up on SMS, continue on chat, or reach a live agent — with the complete conversation history visible at every step. Without channel sequencing, multi-channel contact centers operate in silos: each channel restarts the customer context, requiring customers to re-identify and re-explain. Channel sequencing is a platform-level capability, not something that can be added to a point-solution AI IVR.
### What does running voice and SMS simultaneously mean?
Running voice and SMS simultaneously means the AI sends relevant information in text while the customer is still on the phone. When the AI confirms an appointment, the details arrive in SMS before the call ends. When the AI opens a case, the case number lands in text so the customer doesn't need to write it down. This combination closes the information-density gap that voice alone consistently struggles with, because voice is good for conversation but poor for transferring specific details that customers need to retain after the call ends.
### What is the ROI of replacing AI IVR with a full AI voice platform?
The ROI case runs through three levers. First, proactive outbound deflects predictable inbound call volume: each call that doesn't happen eliminates handle time, agent capacity, and customer frustration. Second, personalized inbound via memory reduces average handle time because the AI doesn't spend the first 90 seconds gathering context that already exists in your systems. Third, channel sequencing reduces repeat contacts: customers who receive follow-up in the right channel after a voice interaction are less likely to call back to confirm. The combined effect compounds across volume.
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## What is omnichannel retail? Definition & strategy guide
Source: https://delight.ai/blog/industry/omnichannel-retail
- [Blog](/blog)
- [Industry](/blog/industry)
- What is omnichannel retail? Definition & strategy guide
## What is omnichannel retail? Definition & strategy guide
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller — Product marketing manager](https://delight.ai/author/leighanne-manwiller)
June 22 2026
Omnichannel retail should be seamless, but the shopper’s experience tells a different story. Imagine a customer browsing jackets on their phone during their lunch break, then adding one to their cart, but doesn't buy.
That evening, they open their laptop and the cart is gone. They find the jacket again, but now they have a sizing question, so they open the chat widget. The agent asks for their email, but has no idea about the cart, the jacket, or anything else. Frustrated, the shopper calls the store the next morning; the associate can't see any of it either.
The customer buys the jacket from a competitor instead.
Every retailer has a version of that story, and most have spent real money trying to fix it with more channels and integrations, plus chat and SMS and app notifications. But the experience still falls apart. The channels are all there, but they simply don’t share any memory.
That’s the real omnichannel retail problem. In this guide, we’ll explore what it actually means, how it differs from multichannel retail, what great execution looks like from brands that get it right, how to build a strategy that works, and why so many initiatives lose momentum before they ever come together.
### What is omnichannel retail?
Omnichannel retail is a customer experience strategy that connects every channel (e.g., physical stores, ecommerce, mobile app, social, phone, email, etc.) into a single, continuous shopper experience.
The channels don't merely coexist; they share the same customer data, purchase history, and context. A shopper can start a return on your website, ask a question in your app's chat, and walk into a store to finish the process—without ever having to explain who they are or what they're doing.
The key word is continuous. Omnichannel retailing doesn't mean being everywhere at once. It’s about making sure wherever a shopper engages, the experience picks up where it left off.
This is increasingly what retailers themselves say they're working toward. [More than 80% of retail and ecommerce marketers](https://www.moengage.com/blog/omnichannel-marketing-statistics/) plan to increase their investment in marketing technology within the next year, with seamless omnichannel execution ranking as their single biggest challenge.
In short, traditional omnichannel focuses on connecting systems. [Modern omnichannel](https://delight.ai/omnipresent) extends that connection into every customer interaction through [memory-aware AI and agents](https://delight.ai/agent-memory-platform-amp).
### Omnichannel vs. multichannel: What's the real difference?
The confusion between multichannel and omnichannel is understandable, because on paper they look similar. Both involve selling and engaging across more than one channel. The difference is if those channels share information with each other, and it's a bigger gap than most retailers initially think.
Multichannel retail means you have a website, a physical store, an app, an email program, and maybe an SMS channel. Each one works. But they work independently. A return started online creates a record in your ecommerce system. The in-store associate has no idea it exists, and the support agent in chat sees a different system.
So the shopper who calls on Friday about a return she started online on Tuesday gets: "I'm sorry, I don't see anything in your account. Do you have the order number?"
That's multichannel. The channels are present. The experience isn't connected.
Omnichannel retail means those systems talk to each other. The in-store associate can pull up Tuesday's return request, and the chat agent sees that the shopper called yesterday. As a result, the AI handling WISMO queries knows the order is already in dispute. The shopper never has to start over.
The gap between these two states is where most retailers live right now. Despite years of omnichannel investment and infrastructure consolidation, [only 5% of retailers](https://www.ringly.io/blog/omnichannel-retail-statistics-2026) have achieved what researchers categorize as true [omnipresent mastery](https://delight.ai/omnipresent). That's a data and context gap, and it shows up in every survey about customer frustration.
### What are real-world omnichannel retail examples?
Abstract definitions are useful; concrete examples are indispensable. Take a look at four brands that have designed noteworthy omnichannel retail experiences and what specifically makes them work.
#### Sephora turns loyalty data into a sales tool
Sephora wired its Beauty Insider program across app, web, and physical stores so that in-store associates can see a shopper's online purchase history and past product preferences during a visit. That's a sales capability, and the numbers bear it out.
[More than 80% of Sephora’s revenue](https://www.social.plus/blog/over-80-of-sephoras-sales-come-from-loyal-customers---why-emotional-loyalty-drives-growth) comes from its [46 million loyalty members](https://www.forbes.com/sites/shelleykohan/2026/02/09/why-30-of-sephoras-46-million-loyalty-members-now-play-games-to-shop/). In addition, Sephora's revenue has [grown more than tenfold](https://www.glossy.co/beauty/inside-sephoras-2024-record-earnings-sephoria-wins-and-global-growth/) since LVMH acquired the company in 1998. This is a trajectory LVMH CEO Bernard Arnault directly attributed to Sephora's retail model on the company's 2024 earnings call.
**Bottom line**: The loyalty program isn't the product; the data it generates is. Sephora turned purchase history into a sales tool that travels with the shopper into every channel, including the store floor.
#### The Home Depot is a model for store‑led supply chains
The Home Depot has turned its physical store network into the backbone of its digital experience. In Q1 of fiscal year 2025, stores—not distribution centers—directly fulfilled more than [45% of online orders](https://www.nasdaq.com/articles/home-depots-focus-omnichannel-real-impact-or-buzzword). This means a customer who orders online and picks up in an hour is being served by the same inventory the in-store associate can see in real time.
That integration has made digital a growth engine without cannibalizing stores. [Online sales grew 9% year-over-year](https://www.pymnts.com/earnings/2025/home-depot-strengthens-pro-market-with-digital-and-in-store-investments/) in Q4 fiscal year 2024, with physical locations serving as the fulfillment layer behind most of it.
**Bottom line**: Physical stores aren't a liability to route around; they're a fulfillment asset. The Home Depot made digital faster and cheaper by running it *through* its stores, not parallel to them.
#### Target uses BOPIS as a unified commerce proof point
Target made BOPIS (buy online, pick up in store) into a competitive advantage, but the deeper capability is what supports it. A unified account and purchase history that makes in-app store maps, curbside pickup, and in-store returns for online orders feel like one system, not four.
For shoppers, it's frictionless; for Target, it drives incremental in-store spend. Since 2019, Target's investment in BOPIS has turned fulfillment into a major growth engine, with [curbside and pickup revenue growing 400%](https://www.bigcommerce.com/articles/ecommerce/bopis/).
**Bottom line**: BOPIS only works as a competitive advantage if the underlying systems are unified. Target's edge isn't the pickup option; it's that one account, one cart, and one purchase history powers every channel behind it.
#### Starbucks keeps customers engaged with omnichannel marketing
Starbucks remains one of the [clearest examples of loyalty and channel unification](https://www.ngrow.ai/blog/the-power-of-omnichannel-marketing-how-starbucks-keeps-customers-engaged) done right. Load your card on your phone, earn a reward in-store, get a personalized offer in the app based on what you've ordered at three different locations.
The system knows you, not just your account number. That continuity is reflected in how members engage. Rewards members loaded [$3.5 billion onto Starbucks gift cards](https://www.sec.gov/Archives/edgar/data/829224/000082922425000013/sbux-12292024xexhibit991.htm) in a single quarter. This is money committed to the brand before the customers have even ordered.
**Bottom line**: Starbucks has customers funding their next visit before they've finished their current one. That's what happens when customers trust the system enough to pay in advance, and that trust is built by a loyalty experience that's genuinely the same everywhere they engage.
### How to build an omnichannel retail strategy
There's a version of omnichannel strategy that's fundamentally a channel expansion plan. This involves adding SMS, in-app chat, social commerce, then calling it omnichannel. That version doesn't work. A successful retail omnichannel strategy starts with data and ends with continuity.
#### Unify your customer data
Every channel should pull from the same source of truth: purchase history, support history, preferences, channel behavior. Without this, you don't have an omnichannel strategy. Instead, you have a multichannel stack with a shared brand logo. This is harder than it sounds, especially for retailers with legacy OMS platforms and separate support tooling, but it's the prerequisite for everything else.
#### Map the actual shopper journey, not your org chart
Shoppers don't think in channels; they think in tasks. "I need to return this." "Will this fit?" "Where's my order?" Somewhere in your operation, there's a handoff point where context breaks. That's always where the experience falls apart. Map the full journey from browse through post-purchase and find every one of those handoffs. They're the places to invest.
#### Build cross-channel continuity, not just cross-channel presence
Being on SMS and live chat is not omnichannel. That's multichannel. The upgrade is when channels share context, allowing shoppers to seamlessly continue conversations across touchpoints—whether they start in your app and resume via SMS days later or vice versa. That's the capability to aim toward.
#### Make it proactive, not just reactive
Waiting for shoppers to contact you is the minimum. Real omnichannel includes knowing when to reach out before the shopper has to: a shipping delay they don't know about yet, a low-stock alert on an item they've viewed four times, a reminder about a cart they left on a different device. These signals already exist in your data. The question is whether you're acting on them.
Retailers that design these capabilities consistently outperform on retention. The real-world omnichannel retail examples above aren't anomalies; they're the predictable result of giving every channel access to the same underlying context.
These case studies all rely on a common foundation: shared context. Inventory context in The Home Depot's case, customer context for Sephora, purchase and fulfillment context for Target, and loyalty context for Starbucks. The gap shows up in lifetime value, repeat purchase rate, and support cost. The experience is the explanation.
### Why most omnichannel strategies stall: The memory problem
In many organizations, the infrastructure is no longer the primary bottleneck. The channels are connected, the data is flowing, the platform vendor gave you a unified dashboard. And the experience remains broken.
Shoppers still get asked for their order number right after logging in. They call back about the same return three times because nobody recorded what happened last time. The chatbot says, "I see you have a recent order," then asks what it is. These aren't system failures; they're memory failures.
The data that would make the experience feel continuous exists somewhere in your stack. The AI or agent on the other end of the channel just doesn't have it at the moment it matters, which is why the interaction still starts over.
This is the part of omnichannel that most strategy guides skip, and the part shoppers notice most. According to the 2026 Delight AI Index, [roughly 70% of retail shoppers](https://delight.ai/ai-index) say they would be genuinely delighted if an AI agent remembered their prior interactions. Not impressed. Not satisfied. Delighted—a word retailers work hard for in any context.
The downstream effect is measurable. [Only 49% of retail shoppers](https://delight.ai/blog/industry/retail-ai-trust) currently prefer AI for checking on order status, meaning the majority of WISMO queries, the single highest-volume request category in retail support, still route to humans. Not because shoppers don't want AI help. Because they don't trust it to remember who they are.
Retailers that solve for this by giving their AI a persistent memory of each shopper's history, preferences, and past interactions occupy a genuinely different tier of omnichannel experience. It's the backbone of delight.ai's AI agent platform: cross-channel memory that makes each interaction feel like a continuation, not a restart.
The platform's retail score in the AI Index was 59 out of 100—second highest across all industries—which reflects how close retail shoppers already are to expecting this from the brands they buy from.
Omnichannel strategy has a Phase 1 and a Phase 2. The former is connecting channels, and the latter involves giving the intelligence layer a memory. Most retailers are stuck at the end of Phase 1 wondering why it doesn't feel right.
### How to evaluate omnichannel retail solutions
When reviewing omnichannel retail platform options—whether you're consolidating, upgrading, or adding an AI layer—the checklist that matters isn't "which channels does it support." It's whether or not the platform can answer yes to these five questions:
1. **Does it maintain shopper context across channels, or does each session start fresh?** This is the memory question. If a shopper who chatted yesterday calls today, does the platform know? If not, you're buying more multichannel infrastructure, not an omnichannel capability.
2. **Can it initiate conversations proactively, not just respond to them?** Reactive AI handles inbound. Proactive AI prevents the inbound from happening by flagging a delay before the shopper asks, recovering an abandoned cart before it's gone cold, or surfacing a relevant offer at the right moment.
3. **Does it integrate with your existing stack without requiring a rip-and-replace?** Most retailers have years of investment in their OMS, CRM, and support platforms. The right omnichannel layer works with that infrastructure, not against it. Salesforce, Zendesk, your existing data warehouse—the platform should plug in, not displace.
4. **Can it handle peak season volume?** Black Friday is not the time to discover your platform's throughput limits. The scale question is genuinely important for retail and often underweighted in evaluations that happen in Q1.
5. **Does it give you analytics at the channel and use case level, not just aggregate CSAT?** You can't improve what you can't see. Knowing that chat CSAT is 4.1 doesn't tell you that WISMO in SMS is degrading at scale. Channel-level and use-case-level analytics are how you find the next problem before shoppers find it for you.
Research suggests AI in retail can drive meaningful efficiency gains, with some companies reporting [up to 45% productivity improvement](https://indatalabs.com/blog/ai-cost-reduction) and roughly 10% revenue or bottom-line uplift, depending on the use case.
Delight.ai's [AI agent for retail](https://delight.ai/industries/retail) offers persistent cross-channel memory, proactive engagement, and an architecture that integrates with existing retail tech stacks rather than replacing them. Retail use cases, such as [delight.ai](http://delight.ai)’s partnership with [Lotte Homeshopping](https://delight.ai/customers/lotte-homeshopping) and [Hanssem](https://delight.ai/customers/hanssem), span the full purchase cycle, from cart recovery through post-purchase support.
### How delight.ai solves the omnichannel retail gap
Omnichannel retail is no longer a differentiator. It's the expectation. Shoppers assume that the brand knows who they are, regardless of which channel they're using.
The retailers that are winning aren't the ones with the most channels. They're the ones who've figured out the memory layer and given their AI and their agents the context they need to make every interaction feel like a continuation of the last one.
Channel connectivity is Phase 1. It's necessary, but it's not sufficient. Phase 2 is the intelligence layer that remembers.
If you're evaluating where your omnichannel strategy is actually breaking down—and what it would take to close the gap—[see how delight.ai empowers retail brands to build AI that remembers](https://delight.ai/contact-sales).
- [What is omnichannel retail?](#section-anchor-id-0 "What is omnichannel retail?")
- [Omnichannel vs. multichannel: What's the real difference?](#section-anchor-id-1 "Omnichannel vs. multichannel: What's the real difference?")
- [What are real-world omnichannel retail examples?](#section-anchor-id-2 "What are real-world omnichannel retail examples?")
- [How to build an omnichannel retail strategy](#section-anchor-id-3 "How to build an omnichannel retail strategy")
- [Why most omnichannel strategies stall: The memory problem](#section-anchor-id-4 "Why most omnichannel strategies stall: The memory problem")
- [How to evaluate omnichannel retail solutions](#section-anchor-id-5 "How to evaluate omnichannel retail solutions")
- [How delight.ai solves the omnichannel retail gap](#section-anchor-id-6 "How delight.ai solves the omnichannel retail gap")
### Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-trust-os-v2)
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[](/blog/product/voice-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[](/blog/product/introducing-agent-steward)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[](/blog/product/low-csat-analysis)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## What are check calls in trucking — and why AI is replacing them
Source: https://delight.ai/blog/industry/ai-check-calls
## What are check calls in trucking — and why AI is replacing them
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
June 23 2026
Every freight broker knows the rhythm. A load goes out. A few hours pass. Someone picks up the phone, dials the carrier, asks where the truck is. The carrier calls the driver. The driver calls back. The broker updates the TMS. Repeat. All day. Every day.
That workflow has a name: the check call. And for an industry that moves hundreds of billions in freight annually, it remains stubbornly, almost defiantly, analog.
That is starting to change. AI voice agents are entering freight operations not as a novelty, but as a genuine operational upgrade. Here is what check calls are, why they persist, and what it looks like when automation actually works.
## What is a check call in trucking?
A check call is a status update request, made by a freight broker to a carrier or driver, to confirm the location and progress of a load in transit. It covers the basics: Where is the truck? What is the ETA? Has pickup happened? Are there any exceptions?
The term comes from the practice of physically calling, as in picking up a phone and dialing. Even as the industry has adopted tracking platforms, ELDs, and visibility tools, the phone call has persisted as the standard confirmation mechanism. Voice is still how freight gets its ground truth.
Check calls happen at every major stage of a shipment: pickup confirmation, in-transit status updates, delivery ETA, exception notification, and proof of delivery. For a busy brokerage managing hundreds of active loads, that adds up fast.
## Why freight still runs on voice calls
If you are wondering why a multi-trillion-dollar industry still relies on phone calls, the answer is trust — or more precisely, the lack of it in automated data feeds.
TMS systems, load boards, and visibility platforms provide data. But that data is only as current as the last manual update, the last app ping, or the last ELD sync. When a broker needs to know if a driver is actually at the pickup dock, or if an exception is real or a data lag, they call. The call confirms what the system says, or surfaces what the system missed.
This is not a technology failure. It is a trust gap that technology alone has not yet closed. Carriers vary in their adoption of tracking tools. Drivers do not always update apps. And brokers, whose reputation lives and dies on accurate shipment visibility, cannot afford to find out at delivery that something went wrong three states ago.
> ### Voice calls remain the dominant channel in freight precisely because they bypass the gaps that automated feeds leave open.
[AI voice agents](https://delight.ai/voice) are now being built to close this gap at scale, giving brokers the ground-truth confirmation they need without the manual overhead of placing every call.
[**See how delight.ai closes the freight visibility gap as a logistics solution →** ](https://delight.ai/logistics-and-supply-chain)
## The cost of reactive check calls
The standard check call model is reactive: a broker waits, then calls. This creates a structural problem that compounds across a full book of business.
When brokers are the ones initiating, they are dependent on carrier availability. Drivers are driving. Dispatchers are managing multiple assets. The callback loop can take minutes. Multiply that by dozens of active loads and the math becomes clear: a meaningful share of every operations person's day is spent chasing updates that should surface automatically.
The burden runs in both directions. Carriers field a steady stream of inbound calls from brokers across every load on their board. For smaller carriers and owner-operators, this is interruption-as-workflow. It pulls attention from the road, from dispatch decisions, from the job itself.
The model is simply wrong for the scale of modern freight operations. The phone call was designed for one-to-one communication. Freight brokerage is one-to-many, and the reactive model does not scale.
## The shift: from reactive to proactive
Here is where AI changes the equation — and it is worth being precise about how.
The dominant model in AI freight automation is not to make the check call faster. It is to invert the model entirely: instead of the broker calling the carrier to ask for an update, the AI agent sends the update automatically when something happens.
Geofencing makes this possible. When a driver enters or exits a defined zone — a pickup facility, a delivery address, a major waypoint — the AI agent detects the event and sends a status update to the broker, the shipper, or both. The check call never has to happen because the information is already on its way.
This is not a marginal improvement. It is a different operating model. The broker goes from waiting and chasing to monitoring and intervening only when something needs a human decision.
The same inversion applies to inbound call volume. AI voice agents can now resolve many routine inbound calls from carriers — including shipment status requests, ETA confirmations, and exception reports — without a human transfer. When the agent has access to TMS data, it can answer the question, log the update, and close the loop autonomously. This is the same pattern delight.ai applies across field service operations, where AI coordinates between multiple parties in real time without waiting for a human to route each call.
[**Discover how delight.ai automates logistics operations end-to-end →**](https://delight.ai/logistics-and-supply-chain)
## Voice and text, simultaneously
The reactive-to-proactive shift is one part of the model. The other part is channel — and this is where most freight automation leaves something on the table.
Replacing the phone call with a faster phone call is not the same as running voice and text at the same time. When a geofence event fires, the AI does not choose between channels. It sends a proactive text to the broker and shipper while simultaneously staying live on voice for drivers or carriers to report exceptions. Both channels run in parallel, triggered by the same event, resolved without waiting on each other.
*Both run simultaneously. Text handles the routine. Voice handles the exception. Neither waits on the other.*
This is how operations actually scale. A dispatcher running 80 active loads cannot place 80 status calls simultaneously. The AI pushes proactive texts in parallel while staying live on voice for any load that needs a human conversation. The routine confirmation never blocks the exception path.
*Steward coordinating across voice and text in parallel — proactive updates outbound, exceptions resolved without routing delays.*
## What AI check call automation actually handles
Being honest about capabilities matters here, because vendor claims in this space have gotten ahead of the technology.
### What AI handles well today
- **Outbound proactive status updates** triggered by geofence events — no broker action required
- **Inbound carrier calls** for standard shipment status and ETA — resolved without human transfer
- **Exception intake:** accepting and logging delay or issue reports from drivers and carriers
- **Escalation routing:** surfacing calls that need a human decision without making the caller wait on hold
### What requires integration depth to work
- **Accurate status updates depend on TMS access.** An agent without load data cannot answer load questions. Integration quality determines capability quality.
- **Exception resolution** beyond intake — negotiating solutions, rebooking capacity — still requires human judgment in most cases.
The honest pitch is not that AI eliminates check calls entirely. It is that AI handles the high-volume, low-complexity calls that consume most of the time, so the team can focus on the exceptions that actually require judgment.
This is the same principle behind AI agent design across every high-volume customer operation: automate the predictable, elevate the complex.
## What to look for in a solution
If you are evaluating AI check call automation for your brokerage, the questions that matter most are not about AI model capability. They are about integration and deployment.
1. **TMS integration:** Does the platform connect to your TMS natively? Without load data, the agent cannot answer load questions. Ask specifically which TMS platforms are supported and how the data sync works.
2. **Outbound proactive capability:** Can the platform send automated updates triggered by geofence events, not just respond to inbound calls? The proactive model is where the operational leverage lives.
3. **Carrier coverage:** How does the platform handle carriers who are not using tracking apps or ELDs? The solution should have a voice fallback that works even when data feeds are absent.
4. **Escalation handling:** What happens when the AI cannot resolve a call? A good system escalates cleanly to a human with full context, not a cold transfer.
5. **Scale readiness:** Mid-market brokerages growing toward higher load volumes need a platform built for concurrency. [This is a core consideration across logistics and any operation where AI is handling customer-facing calls at volume.](https://delight.ai/logistics-and-supply-chain)
**Is your brokerage ready for AI voice automation?** Take the delight.ai readiness assessment — 5 minutes, no sales call required. [**Take the assessment →**](https://delight.ai/ai-readiness-intro)
## How delight.ai approaches freight voice automation
Delight.ai is a [voice AI platform](https://delight.ai/voice) built for operations that run on high call volumes, high stakes, and tight SLAs. The logistics use case sits naturally inside that scope: freight brokerages that field hundreds of carrier and shipper calls daily, across a mix of status checks, exception reports, and booking activity.
The Delight approach prioritizes TMS integration depth and proactive outreach over call deflection alone. The goal is not just to handle inbound volume. It is to get ahead of it, by surfacing updates before anyone has to ask.
The same architecture that powers [field service coordination across multi-party job sites](https://delight.ai/field-services) applies here: an AI agent that holds context across the full lifecycle of a shipment, escalates with full context when a human needs to step in, and integrates with the systems your team already runs.
*Agent Steward holds full context across a shipment's lifecycle — every status update, exception, and channel interaction in one place.*
The trust layer matters too. Shippers and brokers need confidence that AI is acting within defined guardrails — not freelancing on load decisions. [Trust OS](https://delight.ai/trust-os) is delight.ai's operating system for AI governance: it sets the approval thresholds, escalation rules, and audit trails that let teams deploy AI at scale without losing control of the exception cases that require human judgment.
For mid-market brokerages managing growth, the operational math is clear: headcount cannot scale as fast as load volume. AI voice automation is how you grow the book without growing the ops team at the same rate.
## The bottom line
Check calls are not going away because carriers are lazy or because technology has failed. They persist because they solve a real trust problem in freight visibility. What AI changes is not whether that problem exists. It is who, or what, has to solve it on every load, every day.
The brokerages moving fastest right now are not the ones with the most dispatchers. They are the ones that have figured out which calls need a human and which ones do not. AI is how you draw that line.
If you are running a freight brokerage and your team is still dialing carriers for routine status updates, that is time that could be running proactively, automatically, and at a scale no headcount can match.
### Ready to move from reactive to proactive? See how delight.ai handles freight voice automation at scale. [**See the logistics solution →**](https://delight.ai/logistics-and-supply-chain)
### Related reading
- [How delight.ai handles logistics voice automation end-to-end](https://delight.ai/logistics-and-supply-chain)
- [AI for field service operations: Coordinating parts, techs, and customers](https://delight.ai/field-services)
- [AI agents that read the room: How proactive outreach changes CX](https://delight.ai/perspective/ai-agents-read-the-room)
- [How retail brands are building trust with AI customer experience](https://delight.ai/blog/industry/retail-ai-trust)
- [Introducing Agent Steward: The first AI agent that owns customer issues end-to-end](https://delight.ai/blog/product/introducing-agent-steward)
- [What is a check call in trucking?](#section-anchor-id-0)
- [Why freight still runs on voice calls](#section-anchor-id-1)
- [The cost of reactive check calls](#section-anchor-id-2)
- [The shift: from reactive to proactive](#section-anchor-id-3)
- [Voice and text, simultaneously](#section-anchor-id-4)
- [What AI check call automation actually handles](#section-anchor-id-5)
- [What to look for in a solution](#section-anchor-id-6)
- [How delight.ai approaches freight voice automation](#section-anchor-id-7)
- [The bottom line](#section-anchor-id-8)
## Frequently asked questions about check calls
### What is a check call in trucking?
A check call is a phone call made by a freight broker to a carrier or driver to confirm the current status of a load in transit — including location, ETA, pickup confirmation, and any exceptions. Despite the availability of ELDs and visibility platforms, voice calls remain the standard because they provide ground-truth confirmation that automated data feeds alone cannot always guarantee.
### How does AI automate check calls in freight?
AI voice agents automate check calls in two ways. First, they invert the model from reactive to proactive: instead of a broker calling to ask for an update, the AI sends a status update automatically when a geofence event fires (truck arrives at pickup, crosses a waypoint, reaches delivery). Second, they handle inbound calls from carriers — answering status questions, logging exceptions, and routing escalations to a human when needed. Both capabilities depend on TMS integration to access live load data.
### What is check call automation for freight brokers?
**Check call automation** is the use of software, typically AI voice agents, to handle the routine status update calls that freight brokers place to carriers throughout a shipment's lifecycle. Automated systems can send proactive updates at key milestones, respond to inbound carrier calls with accurate load data, and escalate exceptions to a human dispatcher. [See how delight.ai approaches this for mid-market freight brokerages.](https://delight.ai/logistics-and-supply-chain)
### How often do freight brokers do check calls?
Check call frequency varies by broker and shipper requirement. Standard practice involves calls at pickup confirmation, in-transit status (often once or twice daily), delivery ETA, and final delivery confirmation. High-stakes loads — time-sensitive freight, temperature-controlled shipments — may require more frequent updates. Some shippers contractually specify update intervals in their carrier agreements.
### What is track and trace in freight, and how does it relate to check calls?
Track and trace refers to the full process of monitoring a shipment's progress from pickup to delivery. Check calls are the voice-based component of track and trace — the manual confirmation layer that brokers use when automated systems are not sufficient. AI agents that automate check calls are typically part of a broader track-and-trace automation stack that combines geofencing, TMS integration, and voice or text outreach.
### Can AI handle freight exception management?
AI can handle the intake and initial triage of freight exceptions — receiving reports of delays, breakdowns, or delivery issues and logging them with full context. Resolution of complex exceptions (rebooking capacity, negotiating with shippers, managing claims) typically still requires human judgment. The value of AI is ensuring that when a human does step in, they have full context immediately rather than starting from scratch. [Delight.ai uses the same escalation architecture across field service operations](https://delight.ai/field-services) with similar multi-party coordination requirements.
## Keep reading
[View more](/blog "View more")
[
Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2) [
Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2) [
Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward) [
Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## The 2026 Global AI Adoption Index
Source: https://delight.ai/blog/industry/2026-global-ai-adoption-index
## The 2026 Global AI Adoption Index
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller) — Product marketing manager
June 10 2026
The narrative around artificial intelligence has moved decisively from theoretical potential to practical application. The following resource maps the shift from AI innovation to AI integration across the global workforce. As enterprises and nations alike scale their digital infrastructure, global AI adoption is accelerating at varying speeds depending on regional priorities and structural investments.
### Key takeaways
- The United Arab Emirates has officially crossed the 70% adoption threshold among its working-age population, leading the globe in regular AI use.
- The global average for working-age AI adoption has reached 17.8%, up 1.5% in just the first quarter of 2026.
- A "diffusion gap" is widening between the Global North (27.5% adoption) and the Global South (15.4%), though Asian markets are driving the fastest growth.
- India is outpacing global enterprise adoption averages, with 40% of its enterprises reporting full-scale AI integration.
- The United States currently ranks 21st globally, an unexpected low, prioritizing deep enterprise integration over widespread public-sector rollout.
### The new global leaderboard: Strategy over scale
The pace at which countries integrate artificial intelligence is largely determined by their strategic drivers. The United Arab Emirates currently leads the world, boasting a 70.1% adoption rate among working-age adults as of Q1 2026, up 10.7% since the first half of 2025. This growth is largely fueled by a cohesive national AI strategy. Close behind is Singapore with a 63.4% adoption rate, driven primarily by robust digital infrastructure investments.
European nations like Norway (48.6%) and Ireland (48.4%) follow, relying heavily on public sector integration and proximity to major tech hubs, respectively. In contrast, the United States has a 31.3% adoption rate, ranking 21st globally. This position highlights a U.S. focus on complex enterprise integration over ubiquitous consumer use. On a broader scale, 17.8% of the world's working-age population now uses AI regularly, driven largely by GenAI ubiquity.
### The widening diffusion gap
While global averages show steady upward movement, the disparity between regions is becoming more pronounced. In Q1 2026, the Global North reached a 27.5% adoption rate, up from 24.7% in the second half of 2025. Meanwhile, the Global South has an adoption rate of 15.4%, a smaller proportional increase from 14.1% during the same period.
However, regional momentum tells a different story. Asia is currently functioning as the primary growth engine for the technology, accounting for 12 of the 15 fastest-growing AI markets worldwide. This surge suggests that emerging economies with heavy tech investments are rapidly closing the execution gap.
### Scaling execution: India's enterprise advantage
The transition from pilot programs to full-scale deployment represents the hardest hurdle in corporate AI strategy. India has emerged as a clear leader in this area, demonstrating what a "jagged frontier" looks like when developer scale meets enterprise execution.
India currently boasts over 27 million developers on GitHub, representing the world's largest developer community. This massive talent pool directly translates into business outcomes. Today, 40% of Indian enterprises report "full AI use" at scale, compared to a global average of just 28%. To support this aggressive expansion, India's data center capacity is projected to surge from 1.6 gigawatts in 2025 to 5.0 gigawatts by 2030.
### Methodology
The data presented in this index reflect the Q1 2026 landscape of global artificial intelligence adoption. The primary benchmark for the 70% threshold is based on the Microsoft Q1 2026 Diffusion Report, which measures active users (minimum of 90 minutes per month) among the working-age population (ages 15–64). Additional data points regarding enterprise usage and regional infrastructure growth were synthesized from Visual Capitalist, Deloitte, and GitHub Constellation.
### About delight.ai
Delight.ai provides an enterprise-grade, branded AI concierge that helps businesses deliver deeply personalized, human-like customer experiences at scale. By combining persistent memory, omnichannel continuity, and enterprise trust, delight.ai ensures that every customer feels seen and remembered. As the global shift from AI innovation to AI integration accelerates, delight.ai equips mid-market and enterprise brands with the tools necessary to turn customer engagement into a powerful, revenue-driving advantage.
### Sources
-
-
-
-
-
-
- [Key takeaways](#section-anchor-id-0 "Key takeaways")
- [The new global leaderboard: Strategy over scale](#section-anchor-id-1 "The new global leaderboard: Strategy over scale")
- [The widening diffusion gap](#section-anchor-id-2 "The widening diffusion gap")
- [Scaling execution: India's enterprise advantage](#section-anchor-id-3 "Scaling execution: India's enterprise advantage")
- [Methodology](#section-anchor-id-4 "Methodology")
- [About delight.ai](#section-anchor-id-5 "About delight.ai")
- [Sources](#section-anchor-id-6 "Sources")
### Keep reading
[View more](/blog "View more")
[Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog "View more")
## Best Sierra AI alternatives for enterprise customer experience
Source: https://delight.ai/blog/ai-agent/sierra-ai-alternatives
## Best Sierra AI alternatives for enterprise customer experience
[](https://delight.ai/author/leighanne-manwiller) LeighAnne Manwiller
Product marketing manager
June 25 2026
Sierra is a strong autonomous-resolution platform. It positions itself as an Agent OS, providing autonomous, action-taking agents that sit above your existing CX infrastructure, running across every major channel and orchestrating more than 15 models under the hood.
Its agents take real actions across backend systems (e.g., refunds, order updates, scheduling) at scale, with a roster the company describes as roughly 40% of the Fortune 50 and strong G2 ease-of-use scores. [Chime lifted its resolution rate from 50% to over 70%](https://sierra.ai/customers/chime) after deploying Sierra, then added Decagon on top for voice.
That pattern—deploying Sierra and still reaching for a second platform to fill the gaps—is exactly the kind of multi-vendor complexity that sends enterprise CX teams looking for alternatives.
So why shortlist alternatives? Four frictions tend to surface during evaluation:
1. outcome-based pricing that's hard to forecast
2. changes that route through engineering or a services team
3. bot conversations that live apart from the rest of your helpdesk
4. platform gaps that force you to string together multiple vendors
The right alternative is transparent on cost and timeline, lets your customer experience (CX) team operate it without a developer, and keeps customer history unified across bot and human, chat and voice.
This guide compares the alternatives buyers actually shortlist alongside Sierra—starting with Decagon, the one teams most often run head-to-head—crediting each platform's strengths, naming its gaps, and telling you which buyer it fits.
### Why teams look for Sierra AI alternatives
Sierra delivers when the budget and engineering depth are there. The friction shows up at the edges. Here are the four main reasons teams evaluate competitors.
#### 1. Outcome-based pricing that's hard to forecast
Pricing is outcome-based—you pay per successful resolution—with no public rates and a sales-led motion. Budget conversations start in the six figures. But the forecasting challenge goes deeper than unknown volume: You first have to agree with Sierra on what counts as a resolution, and that definition shapes every invoice that follows.
Once you're live, you'll need to audit your resolutions on an ongoing basis to verify you're being charged correctly. A common thread in any Sierra AI review is limited pricing transparency, and that auditing burden is the part most buyers don't anticipate until they're already in the contract.
#### 2. Changes that route through engineering or services
Implementation runs four to 10 weeks and can stretch to months. Integration runs through Sierra's Agent SDK (custom API work) and Integration Library (a no-code connector path for standard integrations, launched late 2025). G2 reviewers consistently score Sierra lower on integrations than on ease of use or support.
One caveat: When people call Sierra "rip-and-replace," that applies to the agent layer, not your helpdesk; Sierra is explicitly a layer-on-top via APIs. The real complaint is that changes route through engineering or services, not that you tear out your stack.
#### 3. Bot conversations that live apart from your helpdesk
Then there's data dispersion: Bot conversations live inside Sierra while human conversations stay in your original helpdesk, so there's no single unified view of the customer.
#### 4. Platform gaps that require a second vendor
Sierra's strengths are autonomous resolution and action-taking depth. But when a channel isn't mature enough—voice being the most common example—enterprise teams don't abandon Sierra; they add another tool.
Chime is a documented case: Sierra for core resolution; Decagon for voice. Running two autonomous-agent platforms in parallel means two vendor relationships, two pricing models, two integration layers, and two sets of guardrails to maintain. For most CX orgs, that's operational overhead they'd rather not carry.
Those four frictions are where evaluations start, but they aren't the whole test. The right alternative also has to hold up on the criteria CX teams weigh once Sierra is on the table.
### How to evaluate a Sierra alternative (what actually matters)
Based on our conversations with CX leaders evaluating an AI customer service platform, these are the criteria that matter the most.
- **Pricing model and cost predictability.** The No. 1 reason teams leave Sierra. Can you forecast annual spend, or does a usage-based model make budgeting a guessing game?
- **Sierra-vs-Decagon fit.** Decagon is the alternative teams most often run head-to-head with Sierra—covered in its own section below.
- **Implementation time and time-to-value.** How long from contract to production, and who's in the room—your CX team or a vendor's engineers?
- **Integration depth and stack fit.** Native connectors to Salesforce, Zendesk and the rest, versus custom API work—where buyer frustration runs hottest.
- **Voice maturity and latency.** If voice is a real channel for you, latency under concurrency is a hard requirement, not a nice-to-have.
- **CX-team operability vs. engineering dependency.** Who owns the agent after launch?
- **Enterprise governance, security and compliance.** Audit trails, role-based access control (RBAC), and certifications.
- **Persistent customer memory and context unification.** Whether bot and human history stay connected so a customer never starts over.
- **Scale and production-proven deployments.** Proof the platform holds up at volume.
- **Platform completeness—can one vendor cover your full footprint?** If the answer is no, factor in the cost and complexity of the second platform you'll need.
One note upfront: On pricing transparency, the enterprise field—Sierra, Decagon, Cognigy, Kore.ai, Parloa, and delight.ai alike—is quote-only. The vendors that win on published pricing are developer- and voice-focused tools like Retell AI (per minute) and Intercom Fin (per resolution).
### Sierra AI alternatives at a glance
Intercom Fin (helpdesk-native, with published per-resolution pricing) and Ada (digital-first CX automation) were not included in this analysis because they serve a different category of buyer than the autonomous-agent platforms above—the digital-first, helpdesk-native buyer rather than the enterprise autonomous-agent one. Both publish or semi-publish pricing.
### Decagon—the most-co-evaluated alternative
In our conversations with buyers, [Decagon](https://decagon.ai) is the alternative they co-evaluate with Sierra most often, and the one most frequently named as a direct competitor. It builds autonomous, LLM-native agents across chat, email, and voice, with confirmed cross-channel persistent memory so context carries across sessions.
**Where it's strong**
- **Agent Operating Procedures (AOPs).** Plain-English workflows your CX team writes and updates, executed with deterministic guardrails. Watchtower analytics and Simulations shorten feedback loops.
- **Reviewer sentiment.** Relatively easy implementation, responsive support, and deterministic control.
- **Autonomy track record.** Case studies show significant deflection gains at scale.
**Where it gives ground**
- **Integration.** Connects to helpdesks via API rather than being plug-and-play; no Freshdesk connector and no built-in ticketing.
- **Forecasting.** Usage-based, quote-only billing that's hard to predict.
- **Voice.** Newer but maturing quickly; vendor-stated sub-second latency (p95 <400ms) and references like Chime. Many customers still run Decagon alongside another platform.
**Best fit:** fast-moving tech, consumer and fintech support orgs running primarily chat and email volume.
### delight.ai—native-integrated, CX-team-operable, unified platform
delight.ai (a Sendbird product) covers the full footprint in a single platform: native integrations, unified memory, omnipresent support, and CX-team operability without a developer in the loop.
The value proposition is eliminating the operational overhead of stitching vendors together: two pricing models to manage, two integration layers to maintain, two sets of guardrails to update when something changes.
**Where it's strong**
- **Native, CX-owned integration.** Zendesk, Salesforce, Freshworks, ServiceNow, and Desk AI Copilot connect without custom API work; no-code Actionbooks let CX teams make changes.
- **Unified memory.** The Agent Memory Platform (AMP) keeps bot and human history unified across channels and sessions.
- **Omnichannel voice without a separate vendor.** Voice AI runs on the same memory architecture as chat and email.
- **Concrete results.** Average 73% deflection rate and documented customer improvements.
**Where it gives ground**
- **Ramp time.** Memory compounds over months; peak performance can take longer than other platforms.
- **Pricing visibility.** Consumption-based and quote-only.
- **Connector breadth.** Native connectors are strong but catalog may be narrower for some stacks.
**Best fit:** >1,000-employee CX orgs with high-volume support that want native-integrated, CX-operable, governed, unified-memory CX and are comfortable with quote-based pricing.
### Cognigy (NiCE Cognigy)—the enterprise CCaaS incumbent
[Cognigy](https://www.cognigy.com) is a contact-center incumbent with strong analyst and customer credentials, named a Gartner Magic Quadrant Leader multiple times.
**Where it's strong**
- **Analyst and customer credentials.** Customers like Mercedes-Benz and strong G2/Gartner Peer Insights ratings.
- **NiCE ecosystem.** Deep CXone integration via NiCE acquisition.
**Where it gives buyers pause**
- **Cost.** Enterprise-tier minimums and modular billing can drive TCO up.
- **Voice latency.** Higher than voice-first players; latency details not widely published.
- **Implementation.** Typically two to four months and often requires developer support.
**Best fit:** large regulated enterprises in or near the NiCE ecosystem.
### Kore.ai—broad enterprise automation
[Kore.ai](http://kore.ai) spans customer experience, employee experience, and operational process automation under one governance layer.
**Where it's strong**
- **Analyst recognition.** Forrester Wave and Gartner recognition.
- **Deployment breadth.** Cloud, hybrid, on-prem, and broad integrations.
- **Proven at scope.** Documented internal automation wins.
**Where it gives ground**
- **Operability.** Steep learning curve; console/UI can be complex.
- **Scale management.** Migration and versioning are complicated; resource-heavy.
- **Pricing.** Quote-only.
**Best fit:** large enterprises with technical staff to run a wide, complex platform.
### Parloa—production-grade voice for regulated enterprises
[Parloa](https://www.parloa.com/) focuses on production-grade voice with global language support and regulated-buyer compliance.
**Where it's strong**
- **Production-grade voice.** Full voice lifecycle and 130+ languages.
- **Regulatory coverage.** DORA and enterprise certifications; customers like Swiss Life.
**Where it gives ground**
- **Pricing.** Opaque, quote-only; enterprise-tier entry.
- **Timeline.** Simple deployments can be weeks; complex rollouts need technical resources.
- **Review footprint.** Limited public reviews as of mid-2026.
**Best fit:** regulated enterprises, particularly in Europe, needing production-grade voice at global scale.
### Retell AI—the transparent-pricing benchmark (and a different buyer tier)
[Retell AI](https://retellai.com) is notable for pricing transparency, publishing per-minute rates and targeting developer teams building voice agents.
**Where it's strong**
- **Pricing transparency.** Published per-minute pricing, no six-figure minimum, vendor-stated sub-second latency.
- **Developer experience.** Drag-and-drop building and webhooks.
- **Governance.** SSO, RBAC, audit logs, encryption, PII redaction, SOC 2, HIPAA, GDPR.
**Where it gives ground**
- **Platform scope.** Voice tooling for developers, not a full omnichannel CX platform.
- **Residency and resourcing.** US-only data residency and production needs developer resources.
- **Cost at volume.** Per-minute costs rise when adding LLMs and telephony.
**Best fit:** developer teams building voice agents or POCs that want transparent per-minute pricing and low latency.
### Sierra vs. Decagon: Control vs. convenience
This is the head-to-head most buyers actually run.
- **Pricing.** Both are quote-only and usage-based; Sierra bills per successful resolution, Decagon combines a platform fee with per-conversation or per-resolution charges.
- **Control and operability.** Decagon's Agent Operating Procedures let CX teams update agent logic in plain English; Sierra often routes changes through SDKs or services.
- **Integration.** Both integrate via API; neither is a marketplace plug-and-play overlay and both often require a separate helpdesk.
- **Voice.** Both are newer to voice; Decagon reports sub-second latency (p95 <400ms) in vendor materials.
- **Autonomy track record.** Both have proven autonomous-resolution deployments.
- **Platform completeness.** Neither covers the full CX footprint alone; many customers run two platforms in parallel.
### How to choose the right Sierra alternative
A quick map by situation:
- Developer team building voice agents or POCs → Retell AI.
- Voice-first regulated enterprise → Parloa (or PolyAI).
- Already in the NiCE/CCaaS ecosystem → Cognigy.
- Broad CX + EX + ops automation and an AI/IT team → Kore.ai.
- Closest autonomous chat/email comparator with plain-English control → Decagon.
- Helpdesk-native, pay-per-resolution simplicity → Intercom Fin.
- Native-integrated, CX-team-operable, unified-memory enterprise CX → delight.ai.
If you're still early in scoping the category, see the overview of enterprise AI customer service platforms for more depth: https://delight.ai/blog/ai-for-cx/best-ai-customer-service
### See it on your own stack
If the frictions that sent you looking—opaque cost forecasting, changes that wait on engineering, and a customer history split between bot and helpdesk—are the ones you're solving for, the fastest way to judge fit is to see it against your own integrations and ticket volume.
Request a delight.ai demo: https://delight.ai/contact-sales and ask how AMP unifies bot-and-human history and how your CX team would own Actionbooks without a developer in the loop.
- [Why teams look for Sierra AI alternatives](#section-anchor-id-0)
- [How to evaluate a Sierra alternative (what actually matters)](#section-anchor-id-1)
- [Sierra AI alternatives at a glance](#section-anchor-id-2)
- [Decagon—the most-co-evaluated alternative](#section-anchor-id-3)
- [Delight.ai—native-integrated, CX-team-operable, unified platform](#section-anchor-id-4)
- [Cognigy (NiCE Cognigy)—the enterprise CCaaS incumbent](#section-anchor-id-5)
- [Kore.ai—broad enterprise automation](#section-anchor-id-6)
- [Parloa—production-grade voice for regulated enterprises](#section-anchor-id-7)
- [Retell AI—the transparent-pricing benchmark (and a different buyer tier)](#section-anchor-id-8)
- [Sierra vs. Decagon: Control vs. convenience](#section-anchor-id-9)
- [How to choose the right Sierra alternative](#section-anchor-id-10)
- [See it on your own stack](#section-anchor-id-11)
### Frequently asked questions
**Sierra vs. Decagon—which is better?**
Neither is universally better; it's control vs. convenience. Decagon offers structured, plain-English control through AOPs; Sierra offers lower-overhead, faster deployment. Both are quote-only and require engineering upfront.
**What does Sierra AI cost? What is outcome-based pricing for AI agents?**
Sierra uses outcome-based pricing: you pay per successful resolution with no public rates. Budget conversations typically start in the six figures; you must agree a resolution definition that shapes invoices.
**How long does it take to deploy a Sierra AI alternative?**
It varies: Sierra typically runs four to 10 weeks and can stretch to months; Decagon roughly six weeks; Cognigy two to four months; Parloa one to three weeks for simple deployments. Delight.ai positions implementation as a transparent, CX-operable timeline—ask vendors for a staged plan tied to your integrations and volume.
### Keep reading
[View more](/blog)
[](/blog/product/introducing-trust-os-v2) Introducing Trust OS 2.0
[](/blog/product/voice-v2) Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements
[](/blog/product/introducing-agent-steward) Introducing Agent Steward: The first AI agent that owns customer issues end to end
[](/blog/product/low-csat-analysis) Low CSAT Analysis: Turn bad scores into actionable failure categories
[View more](/blog)
## Accessibility statement for Delight AI Agent SDK
Source: https://delight.ai/blog/product/delight-ai-agent-sdk-accessibility-statement
## Accessibility statement for Delight AI Agent SDK
[](https://delight.ai/author/sk-kim) SK Kim
Head of engineering, client platform
July 14 2026
### Commitment and scope
At Sendbird, we believe that accessible technology creates better experiences for everyone. We are committed to building products that are inclusive and usable by all individuals, including those who rely on assistive technologies.
This statement covers Delight AI Agent SDK on the following platforms: iOS native, Android native, React web, and React Native. The accessibility assessment was conducted in July 2026.
### Conformance status
Delight AI Agent SDK is **partially conformant** with **WCAG 2.2 Level AA**. Partially conformant indicates that some parts of the content do not fully meet the accessibility standard.
All Level A criteria are fully supported. Most Level AA criteria are fully supported. However, six Level AA items are only partially supported. For details, refer to the Known limitations and alternatives section below.
The SDK is also designed to align with EN 301 549 v3.2.1, the EU harmonized accessibility standard.
Sendbird conducted the evaluation in July 2026, based on iOS SDK 1.3.0, Android SDK 1.3.0, React web SDK 1.24.0, and React Native SDK 1.15.0.
The Voluntary Product Accessibility Template (VPAT) is a standardized form that documents a product's conformance with accessibility standards. Sendbird uses the VPAT to produce an Accessibility Conformance Report (ACR) that describes the accessibility of the SDK's features. For criterion-level conformance details, refer to the Accessibility Conformance Report: https://get.sendbird.com/rs/047-LOL-803/images/VPAT-WCAG22-AA-Sendbird_v1.1.pdf?version=0
### Known limitations and alternatives
The following WCAG 2.2 Level AA criteria are partially supported. For each item, we describe what is limited, who may be affected, and the current status or planned remediation.
### Compatibility
Sendbird tested the SDK with the following assistive technologies.
All tested combinations are fully compatible.
### Customer integration responsibility
Delight AI Agent SDK is a component embedded in the customer's host application. Accessibility of the host application, including page structure, surrounding navigation, and content outside the SDK surface, is the customer's responsibility.
### Feedback and contact
We welcome feedback on the accessibility of Delight AI Agent SDK. If you encounter accessibility barriers or have suggestions for improvement, contact us at https://sendbird.com/contact-sales
### Statement status and methodology
This is version 1.1 of the accessibility statement, based on a self-assessment conducted in July 2026.
- Methodology: Manual screen reader testing across four platforms — iOS, Android, React web, and React Native — along with code audit and design review, conducted internally by Sendbird's Engineering team. The assessment followed the VPAT 2.5 International edition.
- Last updated: July 2026.
### Disclaimer
This accessibility statement and any ACR posted on Sendbird's website, or otherwise publicly available, shall not be construed to be included in any contract, either explicitly or implicitly, in whole or in part, absent Sendbird's express acknowledgment through language in the contract itself that such report or its substantive equivalent is included.
- [Commitment and scope](#section-anchor-id-0)
- [Conformance status](#section-anchor-id-1)
- [Known limitations and alternatives](#section-anchor-id-2)
- [Compatibility](#section-anchor-id-3)
- [Customer integration responsibility](#section-anchor-id-4)
- [Feedback and contact](#section-anchor-id-5)
- [Statement status and methodology](#section-anchor-id-6)
- [Disclaimer](#section-anchor-id-7)
### Keep reading
- [View more](/blog)
- [ Why don't people trust AI customer service?](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
- [ Does AI reflect on your brand reputation?](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
- [ Generational differences in AI trust](/blog/insights/generational-differences-ai-trust)
- [ Do millennials trust AI more than Gen Z?](/blog/insights/do-millennials-trust-ai-more-than-gen-z)
- [View more](/blog)
## Design once, scale everywhere: Scaling brand production with AI
Source: https://delight.ai/blog/ai-workflow/design-once-scale-everywhere
## Design once, scale everywhere: Scaling brand production with AI
[](https://delight.ai/author/eloy-lee) Eloy Lee — AI-native brand designer
June 29 2026
6 min
### Every growing brand faces the same tension
As products grow, the number of webpages, landing pages, campaign assets, and social media content grows with them.
At first, this is simply part of design work. A new campaign needs a landing page. A new customer story needs social banners. A new product message needs web modules. Each request makes sense on its own.
But over time, the work starts to reveal a deeper problem.
The challenge is not creating a single page or a single banner. The challenge is producing them repeatedly while maintaining speed, quality, and brand consistency.
At Sendbird, we started asking ourselves a different question:
That question became the starting point for building an AI-powered brand production system.
### The bottleneck wasn’t creativity. It was production.
Creating a webpage followed a familiar process.
Marketing submitted a request. Design created the page in Figma. The team reviewed and revised it. Development implemented it. QA followed. Then, finally, the page was published.
Each step was necessary. But together, they created a workflow that was slow, repetitive, and difficult to scale.
A single webpage could take more than five days to complete. Not because the design itself was impossible, but because the production process depended on too many handoffs.
We realized we didn’t need more one-off design work. We needed a better way to produce branded experiences.
### Before automation, we needed structure
AI cannot automate inconsistency.
Before using AI to generate anything, we first needed to understand what was already repeatable.
We collected and analyzed the webpages we had created so far. Instead of treating every page as a unique design, we looked for recurring patterns:
- Hero sections
- Feature modules
- Customer logos
- Forms
- CTAs
- Landing page blocks
- And so on
Then we broke those patterns into reusable modules and components.
From there, we defined the rules behind them: typography, color, spacing, grid, layout hierarchy, and responsive behavior.
Automation didn’t begin with AI. It began with turning design into structure.
### From design rules to executable systems
A design system inside Figma is useful, but it is still mostly a reference. We wanted the system to become executable.
Using AI coding tools such as Claude Code, Cursor, and Codex, we began translating design rules into reusable code-based modules and components.
This allowed the system to move beyond static documentation. GitHub became part of the workflow, helping us manage versions, maintain consistency, and evolve the system over time.
The design system was no longer just something designers referenced. It became a foundation that could actually produce websites and brand assets.
### One foundation. Multiple outputs.
The result was the Delight Web Design System.
It brought together the assets and rules we need to produce consistent brand experiences:
- Customer logos
- Brand key visuals
- Typography and color guidelines
- 1,700+ icons
- Form components
- Landing page modules
- A social banner builder
- And more
Instead of managing each asset separately, we created one foundation that could support multiple outputs.
A website page and a social media banner may look like different deliverables. But from a system perspective, they can share the same brand logic.
That was the key shift.
We stopped thinking about design assets one by one, and started thinking about how the brand could be produced as a system.
### Scaling social banner production
Social media banners were one of the clearest examples of this shift.
Previously, creating multiple banner variations could take a full day. A designer had to create the layout, apply the right brand assets, adjust visual hierarchy, export each size, and make revisions.
With the banner builder, a full set of six LinkedIn banners can now be generated in around 30 seconds.
More importantly, the output still follows the same brand rules.
It is about removing repetitive production work, so designers can focus on systems, quality, and direction.
### Redesigning the website workflow
The same idea applies to website creation.
Instead of starting every page from a blank Figma file, marketers can begin with reusable page modules built on top of the design system.
Designers no longer need to manually recreate every similar layout. Instead, they can review, refine, and improve the system itself.
This changes the role of each team.
Marketing can move faster. Design can focus on quality and governance. Development receives more consistent structures. The overall production cycle becomes shorter.
What previously took more than five days can now be completed in about one day.
### What we learned
This project started as an attempt to improve production speed.
But the bigger lesson was about the role of design systems.
A design system is not only a library of visual rules. It can become a production layer for the entire brand.
AI made this possible, but AI was not the starting point.
The starting point was deciding what should stay consistent, what should become reusable, and where human judgment still matters.
For us, the future of design is not about creating more assets faster.
It is about building systems that allow good design to scale.
That is what **Design Once, Scale Everywhere** means.
- [Every growing brand faces the same tension.](#section-anchor-id-0)
- [The bottleneck wasn’t creativity. It was production.](#section-anchor-id-1)
- [Before automation, we needed structure.](#section-anchor-id-2)
- [From design rules to executable systems.](#section-anchor-id-3)
- [One foundation. Multiple outputs.](#section-anchor-id-4)
- [Scaling social banner production.](#section-anchor-id-5)
- [Redesigning the website workflow.](#section-anchor-id-6)
- [What we learned.](#section-anchor-id-7)
### Keep reading
[View more](/blog)
[ Introducing Trust OS 2.0](/blog/product/introducing-trust-os-v2)
[ Voice 2.0: Outbound calling, proactive outreach, and 2,000+ quality improvements](/blog/product/voice-v2)
[ Introducing Agent Steward: The first AI agent that owns customer issues end to end](/blog/product/introducing-agent-steward)
[ Low CSAT Analysis: Turn bad scores into actionable failure categories](/blog/product/low-csat-analysis)
[View more](/blog)
## Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration
Source: https://delight.ai/blog/product/zendesk-to-delight-desk-migration
## Your whole Zendesk, in one click: Introducing Zendesk to Delight Desk migration
[Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
July 7 2026
Here's the uncomfortable truth about Zendesk: most teams don't stay because they love it. They stay because leaving is brutal.
You've got years of ticket history, a knowledge base built article by article, and a support team whose muscle memory is wired to one specific interface. The seat fees add up, but moving all of that feels like the bigger cost, so the switch you keep meaning to make stays on next year's list.
Today, we're introducing [Delight Desk migration](https://delight.ai/zendesk-migration-desk): connect your Zendesk, keep your tickets, macros, and knowledge base, and leave the bill behind. [Delight Desk](https://delight.ai/desk) is free. Pair it with the [Delight AI agent](https://delight.ai/ai-agent) and you pay for the AI agent, never per seat. Same interface your team already knows, and your AI arrives already trained on your own support history.
### We're killing the seat economy
Every legacy helpdesk runs on the same idea: you pay per person. A lot of old software works this way, and there's a reason for it that isn't especially flattering to the vendors.
Traditional software had no way to see whether it actually helped you, so it counted the next best thing: how many people logged in. A seat is just a proxy. It's a vendor quietly admitting, "I can't measure the value I gave you, so I'll charge for how many of your employees touched my product." For twenty years that was the entire model, and everyone went along with it because there was no better way to price software.
AI breaks that model completely. The better your AI agent gets, the fewer people you need logged into the helpdesk, and the less a seat-based vendor earns. Their business depends on you hiring more agents, not fewer. So the exact thing you're working toward, resolving more while staffing less, is the thing that costs them money. Your success is their loss, and no amount of marketing changes that math.
[Delight Desk](https://delight.ai/desk) is built the other way around. There's no seat fee, so growing your team costs you nothing extra, and you pay per conversation your AI handles, not per person you employ. We don't make a cent more when you add headcount, so we're never quietly rooting against the efficiency you're trying to build.
#### Read more
- [Why the seat is the wrong unit for AI-era software](https://delight.ai/perspective/seat-is-wrong-unit-for-ai-era-software)
- [Zendesk to Desk migration](https://delight.ai/zendesk-migration-desk)
**Zendesk and Delight, side by side**
Where the two platforms differ on AI, memory, and what you actually pay for, and where Zendesk is still strong.
[**See the comparison →**](https://delight.ai/compare/zendesk-vs-delight)
### Everything comes with you
When you migrate to [Delight Desk](https://delight.ai/desk), your operation moves intact. Not a fresh start, not a rebuild. Your history, your content, and your customers come across.
Before anything moves, you get a pre-migration report showing exactly what's coming over, so you know precisely what to expect before you approve it.
[**Not sure what your migration looks like? Talk to an expert →**](https://delight.ai/contact-sales)
### Your AI agent arrives already trained
Most migrations hand you an empty AI and a manual to configure it. Ours reads your entire Zendesk ticket history, clusters it by intent, and drafts the playbooks your AI needs to start resolving conversations from day one. We call them Actionbooks.
The Actionbooks are built only from tickets that went well, the ones with high CSAT that were never reopened or escalated, and every draft is cross-checked against your current policies before your team approves it.
Connect your Zendesk and within an hour you'll see the Actionbooks that would have resolved most of last year's tickets, built from your own conversations instead of generic templates.
**Watch the migration run start to finish**
A short demo of the one-click migration, and how your ticket history turns into Actionbooks.
[**Watch the demo →**](https://delight.ai/zendesk-demo)
### The pricing model, flipped
Zendesk charges per agent, per month, and its AI copilot is billed as yet another add-on on every seat. You pay for the size of your team, not the work that gets done.
Zendesk makes money when your agents show up for work. We make money when your AI does the work instead. For the first time, your vendor's incentives line up with yours.
**Put a number on your Zendesk bill**
Enter your seat count and monthly ticket volume to compare per-seat billing with paying per conversation.
[**Estimate your savings →**](https://delight.ai/tools/zendesk-pricing-calculator)
### Move on your timeline, not ours
You don't have to cancel Zendesk to get started. The migration runs alongside your existing setup, and you control the cutover.
### See your Zendesk in Desk
The best way to understand this is to watch it happen to your own workspace. [Connect your Zendesk](https://delight.ai/zendesk-migration-wizard), preview your tickets and knowledge base inside Desk, and see the Actionbooks we'd build from your history, all before you move a thing.
Keep your tickets, your workflows, and your team's muscle memory. Leave the bill.
### [**Connect your Zendesk →**](https://delight.ai/contact-sales)
- [We're killing the seat economy](#section-anchor-id-0)
- [Everything comes with you](#section-anchor-id-1)
- [Your AI agent arrives already trained](#section-anchor-id-2)
- [The pricing model, flipped](#section-anchor-id-3)
- [Move on your timeline, not ours](#section-anchor-id-4)
- [See your Zendesk in Desk](#section-anchor-id-5)
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## Common questions about migrating from Zendesk
**Does my full ticket history come over, including internal notes and attachments?**
Yes. Subject, body, and the entire conversation thread, including public replies and internal notes, migrate along with status, priority, requester, assignee, group, timestamps, and attachments.
**What happens to my Zendesk help center URLs?**
Every old Zendesk Guide URL gets a 301 redirect to its new home in Desk, so Google never sees a broken link and your search rankings stay put.
**Do macros and business rules migrate too?**
Yes, your macros come across along with your tickets, knowledge base, and users, so your team isn't rebuilding workflows from scratch on day one.
**Can I keep using Zendesk while I migrate?**
Yes. Connect and preview without canceling anything. Most teams run Desk alongside Zendesk and switch over at their renewal date, on their own schedule.
**Is Delight Desk really free?**
[Desk](https://delight.ai/desk) itself has no seat fee. Pair it with the [Delight AI agent](https://delight.ai/ai-agent) and you pay per conversation the AI handles, never per seat, so growing your team doesn't grow your bill.
**How is this different from a data-only migration tool?**
Most Zendesk migration tools move your data and stop there. [Delight Desk](https://delight.ai/desk) moves your data and reads it: your resolved tickets become draft Actionbooks, so your AI agent starts trained on your own resolution patterns instead of a blank slate.
## Keep reading
[View more](/blog "View more")
[
Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[
The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[
AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)
[
What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[View more](/blog "View more")
## Why are companies leaving Zendesk?
Source: https://delight.ai/blog/industry/why-are-companies-leaving-zendesk
## Why are companies leaving Zendesk?
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec — Content engineer](https://delight.ai/author/caroline-adamec)
July 15 2026
## Key takeaways
- Zendesk's core ticketing still earns strong reviews, but **its AI is priced and built as a stack of separate add-ons**, and that's the real switching trigger.
- **Zendesk's Suite plans run $55 to $169 per agent per month** billed annually, and the Copilot add-on for agent assist adds another $50 per agent per month on top.
- Zendesk's own AI agents perform best once a mature knowledge base and ticket history already exist, **so new and switching teams often pay full price while that history is still building.**
- **Delight.ai's Desk has a $0 seat fee,** starts pre-trained on a team's own resolved tickets, and bills only for successful resolutions.
Companies aren't leaving Zendesk because the ticketing is broken. Reviewers consistently praise its unified inbox and its automation tools.
They're leaving at the exact point where AI enters the price sheet, because Zendesk's AI wasn't built as one system. It was assembled.
## What's actually pushing CX teams to reconsider Zendesk?
The complaints sound familiar to any CX leader who has renewed a Zendesk contract in the last year. The bill keeps growing, and the tools meant to save time add a new console to check.
### Rising per-seat costs once AI add-ons stack
Zendesk's Suite plans are priced per agent, per month, before AI ever enters the conversation. Once a team adds Copilot for agent assist, that's another $50 per agent per month on top of the base plan. Automated resolutions beyond a plan's included allotment cost extra per resolution, and quality assurance and workforce management are billed as separate add-ons too.
For a 10-person team, that stack can add thousands of dollars a month before anyone measures a single resolved conversation. The sales pitch is one AI platform. The invoice is closer to five.
Zendesk pricing calculator
What would Zendesk actually cost you?
Enter your team size and monthly ticket volume to see your Zendesk bill with the add-ons stacked in.
[Run the numbers](https://delight.ai/tools/zendesk-pricing-calculator)
### A fragmented admin experience across separate consoles
The pricing problem has a twin: an admin experience spread across different consoles for ticketing, AI configuration, and analytics. Setting up automation in one place, training the AI in another, and pulling reports from a third adds friction every time something needs to change.
For a support team already stretched thin, that friction shows up as slower fixes and a bigger training lift for every new hire.
## Why does Zendesk's AI feel bolted on instead of built in?
The fragmentation isn't an accident of design. It traces back to how the AI got there in the first place.
### A patchwork of acquisitions, not one system
Zendesk's AI and quality layers came together through a series of separate acquisitions that were bought, not built from scratch:
- **Quality assurance**, now sold as an add-on, was originally Klaus, a company Zendesk acquired in 2024.
- **Copilot's agent-assist capabilities** draw on technology from that same year's Ultimate acquisition.
Each piece kept its own settings, its own training data, and its own price tag. That's a reasonable way to grow a product line fast. It's a harder way to make AI feel like one coherent system instead of several tools wearing the same logo.
### Full price during the ramp-up period
There's a second cost that rarely comes up in a sales call. Zendesk's own guidance is clear that its AI agents perform best once a clean, mature knowledge base and an established history of resolved tickets already exist behind them.
A new team, or a team mid-migration, often turns AI on before that history exists. They pay the full subscription and add-on price while the AI is still building up the data it needs to be useful. That's not a bug. It's a structural fact of how the AI was designed to learn.
## Zendesk's tool fragmentation problem
Cost complaints and console fatigue are two symptoms of the same root problem: too many separate tools stitched together under one brand.
Forrester's own 2026 customer service predictions make the same point at the industry level. Analysts expect service quality to dip in 2026 as companies wrestle with AI deployment complexity, and they're telling organizations to simplify their tech stacks and consolidate vendor relationships rather than add another point solution ([Forrester, "Predictions 2026: AI Gets Real For Customer Service," Nov 2025](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/)).
At the same time, the pressure to have AI in place at all hasn't eased, and 91% of customer service leaders say they feel it directly from executive leadership heading into 2026 ([Gartner, Feb 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026)). CX leaders are stuck between a mandate to move fast and a platform that makes moving fast expensive.
Zendesk migration demo
See what a Zendesk migration actually looks like
A two-minute demo: move your ticket history and knowledge base in one click, then watch resolved tickets turn into Actionbooks your AI agent can run.
[Watch the demo](https://delight.ai/zendesk-demo)
## What should CX leaders actually look for in a Zendesk alternative?
Before comparing feature lists, it helps to ask two questions that cut through most of the noise, especially for [B2B support teams](https://delight.ai/industries/technology) juggling technical, high-context cases.
### Whether the AI starts trained or starts blank
Ask any vendor what their AI already knows on day one. An AI that needs months of fresh ticket history before it's useful is asking your team to relearn lessons your support history already taught it. An AI that starts from your own resolved tickets skips that wait entirely.
### Resolution-based pricing vs seat-based pricing
Per-seat pricing charges a company for headcount, not outcomes. Ask instead what a vendor charges per resolved conversation, and what containment or resolution rate they can actually show, not just deflection. A 2026 McKinsey survey on AI governance found that security and risk concerns are the top barrier organizations cite when scaling AI further, which makes a governed, auditable rollout worth asking about directly ([McKinsey, "State of AI trust in 2026," Mar 2026](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era)).
## Delight.ai is ready on day one, not months in
This is the exact gap delight.ai was built to close for teams evaluating a move off Zendesk.
### Resolved tickets become contextual Actionbooks
[Delight Desk](https://delight.ai/desk), the AI-native helpdesk behind delight.ai, imports a company's full ticket history during migration. It then turns those past resolutions into step-by-step resolution flows, always reviewed by a person before anything ships. That reviewed, ready-to-use flow is what delight.ai calls an **Actionbook**.
Because the flows come from a team's own past cases, the AI starts already familiar with the work instead of learning it cold. Underneath that, a shared memory carries a customer's context across chat, voice, and every other channel, so a conversation that starts in one place and continues in another never forces the customer to repeat themselves. Delight.ai calls this its [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp).
### Migrate your full history
A Zendesk migration to Delight Desk carries over the parts of a support setup that took years to build:
- **Full ticket history**, so past conversations stay searchable.
- **Knowledge base articles**, migrated with redirects so existing help center links keep working.
- **Resolved-ticket patterns**, turned into the Actionbooks described above instead of discarded.
Pricing follows the same logic as the product. **Delight Desk carries a $0 seat fee**, and teams pay only for successful AI resolutions, so the invoice grows with outcomes instead of headcount.
| Feature | Zendesk Suite + AI add-ons | Delight Desk |
|---|---:|---:|
|Seat fee|$55–$169 per seat, per month|$0 per seat, per month|
|Agent-assist AI|$50 per seat, per month (Copilot add-on)|Included|
|AI training starting point|Builds up over time|Pre-trained on your own resolved tickets|
|Billing basis|Per seat, plus per resolution, plus add-ons|Per successful AI resolution only|
## In closing
Zendesk's ticketing depth and its marketplace of more than 1,800 integrations reflect years of genuine product investment, and teams heavily invested in that ecosystem should weigh that honestly. The question worth asking is whether that ecosystem is solving a ticketing problem or an AI problem, because those two answers point to different platforms.
Once a support team stops paying for AI that starts blank, the rest of a Zendesk migration gets a lot easier to evaluate. See what a resolved-ticket-trained Actionbook would look like for your own queue in [delight.ai's Zendesk migration guide](https://delight.ai/zendesk-migration-desk).
- [Key takeaways](#section-anchor-id-0 "Key takeaways")
- [What's actually pushing CX teams to reconsider Zendesk?](#section-anchor-id-1 "What's actually pushing CX teams to reconsider Zendesk?")
- [Why does Zendesk's AI feel bolted on instead of built in?](#section-anchor-id-2 "Why does Zendesk's AI feel bolted on instead of built in?")
- [Zendesk's tool fragmentation problem](#section-anchor-id-3 "Zendesk's tool fragmentation problem")
- [What should CX leaders actually look for in a Zendesk alternative?](#section-anchor-id-4 "What should CX leaders actually look for in a Zendesk alternative?")
- [Delight.ai is ready on day one, not months in](#section-anchor-id-5 "Delight.ai is ready on day one, not months in")
- [In closing](#section-anchor-id-6 "In closing")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Frequently Asked Questions (FAQs)
Find answers to common questions about why companies are leaving Zendesk
### Why are companies leaving Zendesk?
Most teams aren't leaving because of Zendesk's ticketing, which still gets strong reviews. They're leaving because Zendesk's AI is priced as a stack of separate add-ons on top of per-seat fees, and because that AI needs an established ticket history to perform well, so cost and slow AI ramp-up show up at the same time.
### How much does Zendesk's AI actually cost once you add it up?
Beyond the base Suite plan (roughly $55 to $169 per agent per month billed annually), Copilot adds $50 per agent per month, and automated resolutions beyond a plan's allotment bill separately per resolution. Quality assurance and workforce management are additional add-ons. A 10-agent team can see its actual monthly cost run well past the advertised per-seat price once AI features are turned on.
### Why does Zendesk's AI feel disconnected from the rest of the platform?
Zendesk's AI and quality tools came from separate acquisitions, including Klaus for quality assurance and Ultimate for Copilot's agent-assist capabilities, rather than being built as one system from the start. Each piece keeps its own settings and its own bill, which is why admins often describe managing Zendesk AI as juggling several tools instead of using one.
### What happens to our ticket history and knowledge base if we switch?
A well-built migration path should import your full ticket history and knowledge base articles, with redirects so existing links keep working. Delight Desk's Zendesk migration does exactly this, then turns your past resolved tickets into Actionbooks so the AI starts already familiar with your own cases instead of learning from a blank slate.
### Does delight.ai replace our support team the way an AI chatbot would?
No, delight.ai resolves routine, repetitive requests and hands off anything ambiguous or high-stakes to a person, with full context attached. The goal is freeing your team for the cases that need human judgment, not replacing the team itself.
## Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-secure-caller-keypad-input)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[](/blog/product/introducing-ivr-navigation)
[Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[](/blog/product/introducing-universal-telephony)
[Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[](/blog/product/delight-ai-agent-guardrails-in-action)
[Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## Zendesk alternatives: Why pricing model beats sticker price
Source: https://delight.ai/blog/industry/zendesk-alternatives
## Zendesk alternatives: Why pricing model beats sticker price
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec](https://delight.ai/author/caroline-adamec)
Content engineer
July 15 2026
**Key takeaways**
- The real cost difference between **Zendesk alternatives** isn't the seat price. It's whether the AI fee caps out or keeps climbing as your AI resolves more tickets.
- Legacy helpdesks (Freshworks, Intercom, Salesforce, Kustomer, Help Scout, Gorgias) **all stack an AI fee on top of a seat or plan fee**.
- [Delight.ai's guided Zendesk migration path](https://delight.ai/zendesk-migration-desk) imports your ticket history and uses it to pre-train your AI from day one, so switching doesn't mean starting from a blank slate.
Zendesk still anchors a lot of support desks, but its AI keeps changing shape underneath the people paying for it. The 2024 Ultimate acquisition and the Forethought deal that closed in March 2026 both reshaped what AI means inside Zendesk.
As of May 2026, Zendesk bundles AI agents into every Suite and Support plan, but bills the resolutions themselves at a separate, usage-based rate. CX leaders are tired of paying for AI features that get repriced every year.
Ticket volume is also growing faster than headcount at most companies. That pressure pushes teams toward tools built to resolve issues, not just route them faster.
Nobody wants a rip-and-replace project that stalls for a year, either. Some teams look for a guided Zendesk migration path that imports ticket history instead of starting from a blank slate.
The alternatives worth your time in 2026 split into two groups: legacy helpdesks that are bolting AI add-ons onto seat plans they've sold for a decade, and a newer set of vendors that built pricing and product around AI resolution.
## What should you look for in a Zendesk alternative?
Most comparisons rank Zendesk alternatives by deflection rate or seat price. Those numbers are useful, but they skip the real cost driver.
What matters is what happens to the bill once AI resolves more tickets, not fewer. For a CX leader, the sharper question is whether resolution volume can grow without your per-ticket cost growing right along with it.
A seat plan can look cheaper than a pay-per-resolution plan. That changes fast once your AI starts closing most of your tickets and a separate AI fee kicks in on top.
Integration depth matters too. Ripping out a helpdesk your team has customized for years brings real switching costs, and setup time often runs past the vendor's quoted timeline.
Before you shortlist anything, check three things against your own numbers, not the vendor's pitch.
- Does the AI fee cap out at any point, or does it keep growing linearly with resolution volume?
- How much of your current ticket history, macros, and routing rules actually carries over?
- What does a real customer's rollout timeline look like, not the fastest one in the vendor's marketing?
## Zendesk alternatives: AI pricing vs. human team pricing
| Vendor | AI pricing model | Setup effort | Human team pricing model |
|---|---|---:|---|
| Zendesk (incumbent) | AI agents bundled into every plan, billed per resolution | Already in place | $55–$169 per seat/month (Suite plans), billed annually |
| Freshworks (Freddy AI) | ~$29/agent/month add-on on top of the seat plan | Light, if already on Freshdesk | $19–$89 per seat/month (Freshdesk plans), billed annually |
| Intercom (Fin) | ~$0.99 per resolved conversation on top of the seat plan | Light, if already on Intercom | $29–$132 per seat/month, billed annually |
| Salesforce (Agentforce) | ~$2/conversation on top of Salesforce seat and platform fees | Heavy, often 6+ months | $25–$500 per seat/month (Service Cloud editions), billed annually |
| Kustomer | Seat fee, plus a per-agent AI fee, plus a per-conversation fee | Moderate | $89 or $139 per seat/month, 8-seat minimum |
| Help Scout | ~$0.75 per resolution on top of the base plan | Light | $25–$75 per seat/month, billed annually |
| Gorgias | Ticket fee plus a separate AI fee, both can hit the same ticket | Light, ecommerce-specific | $50–$750+/month by ticket-volume tier (not per seat) |
| Sierra | Pure outcome-based pricing, no seat fee; setup reportedly $50K–$200K | Heavy, engineering-led | No seat fee |
| delight.ai (Desk) | Pure pay-per-resolution, $0 seat fee | Guided Zendesk migration | $0 per seat, forever |
## Freshworks (Freddy AI Copilot on Freshdesk)
Freshworks built its AI, called Freddy AI, on top of Freshdesk's existing ticketing tool. Freddy AI Copilot is priced at roughly $29 per agent, per month, billed annually, stacked on top of the regular plan fee.
Freshworks reports up to 80% ticket resolution in some cases, plus a 97% omnichannel first-contact rate. Both figures come from its own customer benchmark report and are tied to specific use cases, not a universal guarantee.
### Best for
Freshdesk works well if your team already knows the tool and wants AI without switching platforms. Most existing ticket fields and routing rules carry over, so setup stays light.
### Watch out for
The tradeoff is common across legacy helpdesks. Your AI cost line grows apart from your seat cost line, and neither one caps the other.
## Intercom Fin AI Agent
Intercom's Fin AI Agent is priced at roughly $0.99 per resolved conversation, on top of Intercom's seat plans. Fin handles routine deflection well and plugs tightly into Intercom's existing messaging tools, so teams already using Intercom see a smooth rollout.
One thing worth tracking before you commit: Salesforce agreed in June 2026 to acquire Fin for roughly $3.6 billion. The deal is still pending regulatory clearance, so pricing and roadmap could shift once it closes.
### Best for
Fin fits smaller teams with simple, high-volume deflection needs, like answering the same question over and over before it reaches a human.
### Watch out for
It's a weaker fit for complex, multi-step problems that need real backend action, since Fin is built to answer, not to execute full workflows. The seat-plus-resolution setup also means your bill has two moving parts to track as volume shifts.
## Salesforce Agentforce
Agentforce is Salesforce's AI layer, built for companies already running Service Cloud. One of its pricing models runs roughly $2 per conversation, on top of existing Salesforce seat and platform fees. Salesforce also offers a per-user license option, starting near $125 a month.
Real deployments often stretch past a year, far past the vendor-quoted timelines of a few weeks.
### Best for
The platform's strength is depth. It can pull from a company's full Salesforce data, which helps large companies with complex account histories and many connected tools.
### Watch out for
That same depth is the catch. Agentforce assumes you're already committed to Salesforce and have the setup team to match. Teams without an existing Salesforce footprint will find the ramp longer and the cost harder to separate from the rest of their Salesforce bill.
## Kustomer
Kustomer organizes conversations around one customer timeline instead of separate tickets. Its two plans run $89 or $139 per seat, per month, with an 8-seat minimum. AI costs extra: the customer-facing AI agent runs from $0.60 per conversation, and the agent-copilot add-on runs $40 per user, per month.
Together, those fees can push a 10-agent team's real bill well past the seat price alone.
Kustomer has also changed hands more than most vendors on this list. Meta acquired the company in 2020 and spun it off as an independent business in 2023. Kustomer raised a new venture funding round in August 2025 and remains independently owned today. That history doesn't rule Kustomer out, but factor ownership changes into long-term contracts.
### Best for
Teams that want a unified customer timeline instead of a ticket-by-ticket view, and that have already budgeted for a multi-part AI fee.
### Watch out for
Run your own agent count and conversation volume through all three fee lines first. Then compare that total to a simpler pricing model elsewhere on this list.
## Help Scout AI Answers
Help Scout is known for being easy to use, especially for small and mid-sized support teams that don't want a heavy platform. Its AI Answers feature costs $0.75 per resolved ticket, on top of the base plan.
### Best for
Small and mid-sized teams that want a genuinely simple shared inbox and are resolving a modest, predictable ticket volume with AI.
### Watch out for
That per-resolution fee sounds small on its own, but it stacks with plan cost as more tickets get resolved by AI. A team resolving thousands of tickets can see the add-on rival the base plan.
## Gorgias
Gorgias is built for ecommerce, with deep links into Shopify, BigCommerce, and similar platforms. Its pricing includes a ticket fee and a separate AI fee, and both can apply to the same fully AI-resolved ticket.
That's a real double-billing risk, worth checking against your own ticket mix. The AI fee itself runs about $1.00 per resolution on monthly contracts, or $0.90 on annual contracts.
### Best for
Ecommerce tasks like order status and shipping questions, where Gorgias keeps context tied to the order and customer record.
### Watch out for
Outside ecommerce, Gorgias doesn't stretch far. Run your own ticket volume through its fee structure before you trust the sticker price.
## Sierra
Sierra prices things differently. It charges purely on outcomes, with no seat fee at all because they don’t have a helpdesk solution.
Setup fees reportedly run $50,000 to $200,000, billed once, not charged again the way a seat fee would be. Sierra doesn't publish a public pricing page, so treat that setup-fee range as a reported estimate, not a vendor-stated number.
### Best for
Engineering-heavy teams that want tight control over how the AI agent is built and shipped, closer to a software development process than a helpdesk setup.
### Watch out for
Teams without a dedicated engineering team will find Sierra's setup steeper than a self-serve helpdesk. Its enterprise-only, custom pricing also rules out smaller teams from the start.
## Where delight.ai's Desk fits in
[Delight Desk](https://delight.ai/desk) runs ticket management and routing on one platform, backed by built-in knowledge tools. Pricing is pure pay-per-resolution, with no seat fee.
It also works as a [Zendesk migration path](https://delight.ai/zendesk-migration-desk). It imports your existing Zendesk data in one pass, including tickets and macros, so you can evaluate it without a rebuild first.
Here's what makes the migration different from a blank-slate setup.
- Past resolutions get turned into Actionbooks, so the first drafts a human reviews after go-live start are informed by your own support history.
- Every Actionbook draft goes through human review before anything reaches a customer.
Compared with Sierra, the real difference isn't pricing. Both use outcome-based billing. Sierra is built for one CX job; delight.ai is built for all of them, and that comparison holds up honestly.
Sierra's engineering-heavy build tools suit teams with dedicated developers. Delight.ai's Actionbooks, by contrast, are built and edited by ops teams, with no engineering ticket required.
None of this makes AI customer service simple to price or simple to migrate into. It does make the real comparison clearer. What matters is whether a vendor's AI fee caps out or keeps climbing with your ticket volume, and how much of your current setup survives the move.
Still running Zendesk? Delight Desk's free migration path is built to show you what a guided migration looks like, not just tell you.
## See Delight.ai in action
[View demo](https://delight.ai/demo-request)
## Frequently Asked Questions (FAQs)
Explore answers to commonly asked questions regarding Zendesk alternatives.
### What's the biggest hidden cost in Zendesk alternatives with AI features?
Most vendors add AI pricing on top of seat fees, so the bill grows twice, once for seats and once for AI resolutions. Ask each vendor for a real cost model at twice your current ticket volume, not just today's price.
### Do all Zendesk alternatives charge extra for AI resolutions?
No, delight.ai charges purely on results, with no seat fee at all. The other vendors on this list stack a separate AI fee on top of a seat or plan fee, except for Sierra, which doesn't offer a helpdesk solution.
### How long does switching off Zendesk usually take?
It depends on the vendor and how much history you need to bring with you. A migration path built to import existing tickets and macros can go live in weeks rather than months.
### Is delight.ai a fit if I'm not ready to fully leave Zendesk?
Delight Desk's migration path imports your existing Zendesk data in one pass, including tickets and macros, so you can evaluate it without a rebuild first.
## Keep reading
[View more](/blog)
Introducing Secure Caller Keypad Input, with built-in PII data masking
Beyond conversational IVR: Introducing IVR navigation
Introducing Universal Telephony: Contact center integration
Meet Harper: Delight.ai's AI agent guardrails in action
## Zendesk migration: Keep your data, skip the rebuild
Source: https://delight.ai/blog/industry/zendesk-migration
## Zendesk migration: Keep your data, skip the rebuild\n\n[](https://delight.ai/author/caroline-adamec)\n\nCaroline Adamec\n\nContent engineer\n\n\n\nJuly 17 2026\n\n### Key takeaways\n\n- **The exit costs more than the switch.** Most Zendesk migrations stall not on price, but on the work of rebuilding triggers, macros, and SLAs by hand.\n- **Seat-based and resolution-based pricing are different bets.** A 20-seat team on Zendesk Suite Professional, plus its AI Copilot add-on, runs close to $40,000 a year in fees alone.\n- **delight.ai's AI doesn't launch blank.** [Delight.ai](http://delight.ai) actionbooks are synthesized directly from your own resolved ticket history, so the AI already knows your playbooks before day one.\n\nCX leaders don't usually doubt where they're headed when they consider leaving Zendesk. What holds them back is more practical: the 2 weeks of rebuilding triggers, a pile of automations nobody remembers writing, and a knowledge base that has to move without breaking a single support link. Solve that, and swapping platforms stops looking like a quarter of lost productivity and starts looking like a resolution with a clear end date.\n\n### What happens when you migrate off Zendesk?\n\nA Zendesk migration moves 3 things: ticket history, the knowledge base, and the automation logic that runs both. The first two are largely mechanical. Tickets export with their comments, statuses, and attachments intact, and knowledge base articles move with their categories and structure preserved.\n\nThe third is where most projects actually live. Triggers, macros, SLA policies, and routing rules are specific to Zendesk's own configuration model, so they don't carry over automatically to any destination platform. That gap, not the ticket count, is what actually decides how long a migration takes.\n\nWhat moves with reasonable fidelity:\n\n- **Tickets:** subject, body, comments, status, priority, and timestamps.\n- **Knowledge base articles:** categories, sections, and article structure.\n- **Users and organizations:** contact records, groups, and org hierarchy.\n\nZendesk's own export documentation confirms the same split: exports cover tickets, users, and organizations, but the business rules that route and resolve those tickets stay behind. Any team evaluating a switch needs to plan around that rebuild gap from the outset, not discover it mid-project. [Delight Desk's Zendesk migration path](https://delight.ai/zendesk-migration-desk) is built specifically to close this rebuild gap.\n\n### What does it cost to leave, and what does it cost to stay?\n\nRun the math on a 20-seat support team. Zendesk Suite Professional lists at $115 per seat per month, and its AI Copilot add-on, added after Zendesk's acquisition of Forethought, runs another $50 per seat per month on top of that. 20 seats on that plan cost roughly $40,000 a year in fees alone, before a single AI resolution is counted.\n\nZendesk Suite Professional vs Delight Desk (summary):\n\n- Zendesk: Seat fee $115/seat/month; AI add-on +$50/seat/month; costs grow with seats.\n- Delight Desk: Seat fee $0; AI priced per successful resolution; costs grow with resolved tickets.\n\n[Delight Desk](https://delight.ai/desk) flips that model. The seat fee is $0, and [the AI is priced per successful resolution](https://delight.ai/builder) instead of per seat. The two companies are betting on different things: Zendesk's revenue grows as teams scale, and Delight's grows only when the AI actually resolves a ticket.\n\n### Why do most Zendesk migrations stall before they start?\n\n[Gartner found](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) that 91% of customer service leaders felt under pressure to implement AI in 2026. Most of that pressure lands on whatever platform a team already runs, not a platform it's about to choose. That's the quiet reason so many Zendesk evaluations start and then go nowhere.\n\nThe team isn't weighing Zendesk against a competitor's feature list. It's weighing the AI project it needs against the years of triggers, macros, and SLA rules already built around the ticketing system it has.\n\n[Forrester's 2026 customer service predictions](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) point at the same problem from another angle. Forrester expects 2026 to bring "gritty, foundational work" rather than dazzling transformation, warning that service quality will dip short-term as teams wrestle with deployment complexity and change management, the same kind of complexity a migration adds on top of. When a project already means rebuilding an entire operation by hand, it's easy for a team stretched thin on AI deployment to let a contract quietly auto-renew instead of taking on more change at once.\n\nRebuilding routing logic, SLA policies, and years of macros is slow, error-prone work that nobody wants to own. That work is why so many migration decisions stall at the evaluation stage.\n\n### What doesn't survive a Zendesk migration?\n\nWhat doesn't move automatically in almost any Zendesk migration:\n\n- **Triggers and automations:** the rules that route, tag, and escalate tickets.\n- **SLA policies:** response and resolution targets tied to priority and plan.\n- **Macros and canned responses:** reusable replies tied to specific workflows.\n- **Connected integrations:** every tool wired into the old ticketing system.\n\n#### Triggers, macros, and routing rules\n\nZendesk's automation logic is specific to Zendesk. None of it exports in a form another platform can read directly, so a receiving team recreates every trigger, every macro, and every routing rule by hand, one at a time, inside whatever workflow builder the new platform provides.\n\n#### SLA policies and escalation logic\n\nSLA models don't map cleanly between platforms either. A team can't import an SLA policy the way it imports a ticket. It has to reconstruct the policy's logic from scratch, then test it against real cases before trusting it in production.\n\n#### Every connected integration\n\nEvery tool wired into the old ticketing system, including the CRM, the ecommerce platform, and Slack, needs to be reconnected and tested in the new one. None of that work is technically hard. All of it is time, and time is what keeps teams on a platform they've already outgrown.\n\n### What if the AI didn't need you to rebuild anything?\n\nMost migrations hand a support team a blank AI. It answers from whatever knowledge base articles it's given and nothing else, so a team spends the first few months teaching it, in real time, the lessons the old platform's tickets already contained.\n\n#### How to skip the ramp-up\n\nThere's a way to skip that ramp-up. Instead of starting blank, an AI can read a team's own resolved ticket conversations from the old platform directly: which requests came up most often, which resolutions actually worked, and which tickets closed clean without a reopen or an escalation. It groups those conversations by intent, refunds, address changes, troubleshooting steps, and drafts a playbook for each one.\n\n#### Delight.ai actionbooks\n\n[delight.ai](https://delight.ai) calls these drafts Actionbooks: a documented sequence of what to say, what to ask, and when to call a system or hand a case to a person, built from what a support team's own history proved effective. None of that publishes on its own. Every draft goes to a person for review before it goes live, and each one is checked against current company policy first, so an outdated or off-policy resolution pattern doesn't quietly become the AI's default answer.\n\nTicket histories show what a support team actually did, not what it should have done. Learning only from tickets with a good outcome, and checking the result against policy, is what keeps this shortcut honest rather than turning a blank AI into a mistrained one. Coupang Eats already ran a migration like this at scale: more than 14 million users moved off Zendesk in June 2026, with the new AI carrying forward the resolution patterns from its old ticket history instead of starting over.\n\n[McKinsey's 2026 AI trust research](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found that security and risk concerns, not technical limits, are now the top barrier organizations cite when scaling agentic AI. It also found that organizations with clear ownership over how an AI's actions get reviewed reach higher trust maturity than those without it. A reviewed-before-publish step for every AI-drafted playbook is what that ownership looks like in practice, the same discipline a dedicated governance layer, like [Trust OS](https://delight.ai/trust-os), exists to make auditable.\n\n### Questions worth asking before you commit to a migration\n\nBefore signing off on a destination platform, get specific answers to these:\n\n- **What actually migrates, and what doesn't?** Get a list, not a reassurance.\n- **Does the AI start from your data, or from a template?** The difference decides your first quarter.\n- **What happens to every public help center link?** A broken redirect is a broken customer trust signal.\n- **What does compliance actually require you to verify?** A certification name isn't the same as its scope.\n\n#### What actually migrates, and what doesn't?\n\nAsk for a specific list, not a general reassurance. Tickets, knowledge base articles, and user records should have clear answers. Triggers, macros, and SLA policies almost never do, on any platform, so the honest answer describes what happens to those instead of a promise that they simply appear on the other side.\n\n#### Does the AI start from your own data, or from a template?\n\nA generic AI, trained on nobody's specific history, starts every migration from zero. One trained on a team's own resolved tickets starts already knowing the requests that come up most and the answers that work, which changes what the first quarter after go-live actually looks like.\n\n#### What happens to every public help center link?\n\nA support team's knowledge base is indexed by search engines and bookmarked by customers. Migrating it without mapping a redirect from every old article URL to its new equivalent breaks both. That redirect map is not optional detail; it's the difference between a quiet migration and a support inbox full of broken-link complaints.\n\n#### What does compliance require you to verify?\n\nCompliance-led buyers can't stop at a vendor's marketing page. Ask for the actual certifications, SOC 2 Type II, ISO 27001, GDPR, and a HIPAA report, and confirm which type of report it is.\n\nA Type I HIPAA report attests that controls are designed correctly at a single point in time. A Type II report attests they held up over a period. Those are different claims, and a compliance review that treats them as interchangeable finds out the difference at the worst possible time.\n\n### In closing\n\nNone of this makes the decision to leave any easier, and none of it makes a rebuild optional if the destination platform needs one. What changes is which parts of that rebuild a team has to do by hand, and which parts an AI can do first, from the team's own history, before a single seat goes live on the new platform. See how [Delight Desk's guided path off Zendesk](https://delight.ai/zendesk-migration-desk) maps out, end to end.\n\n- [What happens when you migrate off Zendesk?](#section-anchor-id-0 "What happens when you migrate off Zendesk?")\n- [What does it cost to leave, and what does it cost to stay?](#section-anchor-id-1 "What does it cost to leave, and what does it cost to stay?")\n- [Why do most Zendesk migrations stall before they start?](#section-anchor-id-2 "Why do most Zendesk migrations stall before they start?")\n- [What doesn't survive a Zendesk migration?](#section-anchor-id-3 "What doesn't survive a Zendesk migration?")\n- [What if the AI didn't need you to rebuild anything?](#section-anchor-id-4 "What if the AI didn't need you to rebuild anything?")\n- [Questions worth asking before you commit to a migration](#section-anchor-id-5 "Questions worth asking before you commit to a migration")\n- [In closing](#section-anchor-id-6 "In closing")\n\n#### See Delight.ai in action.\n\n[View demo](https://delight.ai/demo-request)\n\n### Zendesk migration FAQs\n\nCommon questions about moving off Zendesk, the cost, the timeline, and the AI of it all.\n\n**How much does it cost to migrate away from Zendesk?**\nIt depends on the destination, but the ongoing cost difference is often larger than the migration cost itself. A 20-seat team on Zendesk Suite Professional, plus its AI Copilot add-on, runs roughly $40,000 a year in fees alone. Platforms priced per AI resolution instead of per seat, like Delight Desk, remove the seat cost entirely and charge only for tickets the AI actually resolves.\n\n**What data actually moves in a Zendesk migration, and what doesn't?**\nTicket history, knowledge base articles, and user and organization records move with their structure intact, including comments, statuses, and attachments. Triggers, macros, SLA policies, and routing rules are specific to Zendesk's configuration and don't transfer to any other platform automatically. Plan to rebuild that logic, or replace the need to rebuild it, rather than expecting it to migrate.\n\n**Will our AI actually work on day one, or does it start blank?**\nA generic AI starts blank and learns a team's patterns over the following months. An AI that reads the last 6 to 12 months of resolved tickets, and drafts playbooks from the requests and resolutions that actually worked, starts already knowing what the team knows. Every draft still goes to a person for review before going live.\n\n**Does delight.ai replace our support team?**\nNo. The AI resolves routine, well-understood requests end to end and hands off anything ambiguous, high-stakes, or emotionally sensitive to a person, with full context attached. The goal is freeing a support team to spend its time on cases that need human judgment, not shrinking the size of the team.\n\n**How long does a Zendesk migration take?**\nTimeline depends mainly on how much automation logic has to be rebuilt, not on ticket volume alone. A small account with a lean rule set can move in days. An account with years of triggers, SLAs, and integrations typically takes weeks, and most of that time goes into rebuilding logic rather than moving the underlying data.\n\n### Keep reading\n\n[View more](/blog "View more")\n\n[](/blog/product/introducing-secure-caller-keypad-input) Introducing Secure Caller Keypad Input, with built-in PII data masking\n\n[](/blog/product/introducing-ivr-navigation) Beyond conversational IVR: Introducing IVR navigation\n\n[](/blog/product/introducing-universal-telephony) Introducing Universal Telephony: Contact center integration\n\n[](/blog/product/delight-ai-agent-guardrails-in-action) Meet Harper: Delight.ai's AI agent guardrails in action\n\n[View more](/blog "View more")
## Can I use Zendesk for free?
Source: https://delight.ai/blog/industry/can-i-use-zendesk-for-free
## Can I use Zendesk for free?
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec](https://delight.ai/author/caroline-adamec)
Content engineer
July 17 2026
### Key takeaways
- **Zendesk has no permanent free plan**. It offers a 14-day free trial, then every plan charges a fee per seat.
- **Paid plans start at $19 per seat per month**. Full omnichannel Suite plans with AI start at $55 per seat per month.
- The Zendesk AI Copilot add-on costs another $50 per seat per month, on top of whatever plan you pick.
- **delight.ai flips that math with no seat fee, ever**, and no separate AI add-on to unlock.
Zendesk does not have a free plan. It offers a 14-day free trial, and once that ends, every plan charges a fee for each seat on your team ([Zendesk, 2026](https://www.zendesk.com/pricing/)). If you are a small business owner or a CX leader comparing helpdesk tools before you commit budget, that per-seat fee is the number that actually decides your cost, not the word free.
That's also true of most helpdesk tools you'll compare Zendesk against, including newer options built around a different model entirely, like [delight.ai's Desk](https://delight.ai/desk), which charges nothing per seat. More on that below. First, here's what Zendesk itself actually offers.
### Does Zendesk have a free plan?
Zendesk does not offer a permanently free plan for its Support or Suite products. What it offers instead is a 14-day free trial, which defaults to the Suite Professional tier unless you choose otherwise.
During the trial, you can add up to 5 team members, and no credit card is required to start. You can also test collision detection, which flags when two people reply to the same ticket, and private notes, which let your team leave context customers never see. Once the 14 days end, though, your tickets and chat history become read-only until you pick a paid plan, and exporting your own data manually is limited and slow.
### What's included in Zendesk's 14-day free trial?
The trial is built to show you the full Suite Professional experience, not a stripped-down demo. That's useful if you want to test actual features before you decide anything.
Here's what's typically included:
- **Omnichannel support** across email, chat, voice, and social messaging.
- **Up to 5 team members**, so you can test how a small support team actually works together.
- **Collision detection and private notes**, which stop two team members from replying to the same ticket at once.
- **Basic automation and reporting**, so you can see how tickets move before you commit to a plan.
None of this carries over automatically once the trial ends. You'll need an active paid plan to keep using any of it.
### How much does Zendesk cost after the trial ends?
Zendesk splits its plans into two tracks: Support plans for ticketing only, and Suite plans that add messaging, a help center, and built-in AI agents. Both tracks charge per seat, per month, billed annually.
#### Support plans
| Plan | Price (billed annually) | What you get |
| --- | ---: | --- |
| Support Team | $19/seat/month | Email and social ticketing, business rules, basic reporting |
| Support Professional | $55/seat/month | CSAT surveys, SLAs, multilingual support |
| Support Enterprise | $115/seat/month | Skills-based routing, custom roles, a sandbox environment |
#### Suite plans
| Plan | Price (billed annually) | What you get |
| --- | ---: | --- |
| Suite Team | $55/seat/month | Ticketing, messaging, 1 help center, basic AI agents |
| Suite Professional | $115/seat/month | Advanced reporting, HIPAA compliance, skills-based routing |
| Suite Enterprise | Custom (talk to sales) | Custom roles, sandbox, advanced AI capabilities, Copilot included |
Zendesk no longer publishes a fixed price for Suite Enterprise. It's quote-based now, and Copilot comes bundled in at that tier instead of billed as a separate add-on.
A 5-seat team on Suite Team pays $275 a month before anything else gets added. That anything else is bigger than most people expect.
### What does Zendesk's AI actually cost on top of that?
The per-seat price on the plan page isn't the full bill. Two things typically add to it.
#### The AI Copilot add-on
Zendesk's more advanced AI features, the ones that draft replies and triage tickets automatically, come from its Forethought acquisition and ship as a separate add-on. That add-on runs $50 per seat per month, stacked on top of whatever plan you're already paying for.
For that same 5-seat team on Suite Team, adding the Copilot brings the monthly bill to $525, almost double the sticker price on the pricing page.
#### Per-resolution overages
Each plan also includes only a limited number of automated AI resolutions per seat, per month. Once you use them up, additional resolutions are billed on top of your seat fees, not included in them.
For a team whose ticket volume grows in busy months, that overage charge is hard to predict from the pricing page alone.
### Why per-seat pricing gets expensive as your team grows
Per-seat pricing looks simple at first. The math works fine at 3 seats and starts working against you at 15.
Every new hire is a new full-price seat, whether that person is fielding 200 tickets a month or 20. Add the AI Copilot, and every seat also carries its own $50 AI tax, whether or not that specific team member uses AI features heavily. Gartner found that 91% of customer service leaders feel pressure to implement AI in 2026 ([Gartner, 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026)). Most growing teams will eventually add that cost, not avoid it.
For that same 5-seat team, here's how quickly a single plan stacks up:
- **Suite Team seats:** 5 seats x $55/month = $275/month
- **AI Copilot add-on:** 5 seats x $50/month = $250/month
- **Total before any per-resolution overage:** $525/month
None of this makes Zendesk a bad product. Its ticketing depth and app marketplace hold up, and for many teams the platform earns its price. It just means the number on the pricing page is rarely the number on the invoice.
### Are there free Zendesk alternatives worth considering?
Some smaller helpdesk tools and AI add-ons advertise free tiers, but most cap ticket volume, seats, or AI usage at a level that a growing support team outgrows within months. A free tier that forces an upgrade the moment you're busiest isn't solving the problem you started with.
If the actual reason you're asking about a free plan is that you don't want to overpay while your team is still small, the more useful question isn't which helpdesk is free. It's which one doesn't charge per seat at all.
### A pricing model that skips the per-seat question entirely
Delight Desk, built by delight.ai, is an AI-native helpdesk where your team and an [AI agent](https://delight.ai/ai-agent) work from the same ticket queue instead of two bolted-together systems. Instead of charging per seat, [Delight Desk](https://delight.ai/desk) has a **$0 seat fee**. You pay only when the AI actually resolves a conversation, never a flat fee per seat sitting in the queue.
That structure means adding a fifth or fifteenth seat to your team doesn't automatically add another $55 to $115-plus line item to your bill. It also removes the guesswork of stacking a separate AI add-on on top of a seat fee, since resolution is what you're already paying for.
Switching helpdesks is usually the part that stalls a decision like this. Delight Desk's [one-click Zendesk migration](https://delight.ai/zendesk-migration-desk) imports your existing ticket history, then turns your own past resolutions into reviewed, step-by-step playbooks the AI agent uses to handle new requests, what delight.ai calls [Actionbooks](https://delight.ai/product-release/openbook). They're always drafts a person reviews before anything ships, never something that goes live untouched.
The [Delight AI Index](https://delight.ai/reports/ai-index) found that 71% of consumers already use AI customer service, but only 57% like it. That gap isn't only about whether AI shows up in support. It's about whether the pricing and setup behind it actually fit how a small team works.
### In closing
Zendesk was never going to be free, and now you know exactly what it costs instead: a per-seat fee starting at $19 a month, climbing to custom Enterprise pricing, plus a $50 AI add-on most teams eventually need. What you do with that information is decide whether you'd rather keep paying per seat or pay only for the resolutions your AI agent actually delivers. [See how Delight Desk's per-resolution pricing compares to your current Zendesk bill](https://delight.ai/desk).
- [Does Zendesk have a free plan?](#section-anchor-id-0 "Does Zendesk have a free plan?")
- [What's included in Zendesk's 14-day free trial?](#section-anchor-id-1 "What's included in Zendesk's 14-day free trial?")
- [How much does Zendesk cost after the trial ends?](#section-anchor-id-2 "How much does Zendesk cost after the trial ends?")
- [What does Zendesk's AI actually cost on top of that?](#section-anchor-id-3 "What does Zendesk's AI actually cost on top of that?")
- [Why per-seat pricing gets expensive as your team grows](#section-anchor-id-4 "Why per-seat pricing gets expensive as your team grows")
- [Are there free Zendesk alternatives worth considering?](#section-anchor-id-5 "Are there free Zendesk alternatives worth considering?")
- [A pricing model that skips the per-seat question entirely](#section-anchor-id-6 "A pricing model that skips the per-seat question entirely")
- [In closing](#section-anchor-id-7 "In closing")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
### Frequently asked questions
Explore related questions to Zendesk pricing and the free, 14-day trial.
**Can I use Zendesk for free?** No. Zendesk offers a 14-day free trial only, which defaults to the Suite Professional tier. After the trial, you need an active paid plan starting at $19 per seat per month for Support Team, or $55 per seat per month for Suite Team, to keep using your account.
**How much does Zendesk cost per seat?** Support plans run $19 to $115 per seat per month. Suite plans, which add messaging channels and built-in AI agents, run $55 to $115 per seat per month for the published tiers; Suite Enterprise is quote-based. Most teams below Enterprise also pay a separate $50 per seat per month for the AI Copilot add-on.
**What happens to my data after the Zendesk free trial ends?** Your tickets and chat history become read-only until you activate a paid plan. You can still export your data manually, but the export tool is limited and can take significant time for larger ticket volumes.
**Does Zendesk charge extra for AI features?** Yes. Basic AI agents are included in Suite plans, but the more advanced AI Copilot, which drafts replies and triages tickets, is a $50 per seat per month add-on. Each plan also caps included AI resolutions, with extra resolutions billed separately.
**Is there a helpdesk that doesn't charge per seat?** Yes. Delight Desk has a $0 seat fee and instead charges only for successful AI resolutions. It also offers a one-click Zendesk migration that imports your ticket history and turns past resolutions into reviewed Actionbooks for the AI agent to use.
### Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-secure-caller-keypad-input)
[](/blog/product/introducing-ivr-navigation)
[](/blog/product/introducing-universal-telephony)
[](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## Zendesk pricing and plans: What CX leaders pay in 2026
Source: https://delight.ai/blog/industry/zendesk-pricing-and-plans
## Zendesk pricing and plans: What CX leaders pay in 2026
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec — Content engineer](https://delight.ai/author/caroline-adamec)
July 17 2026
**Key takeaways**
- **Zendesk's published seat prices, $19 to $115** or more per agent per month, are the smallest number in the real bill. Copilot, Automated Resolutions, and workforce add-ons decide what you actually pay.
- Since May 18, 2026, Zendesk bills only for 'Verified Resolutions', which are confirmed AI-resolved cases, **so costs scale with AI success.**
- Move beyond exploring which Zendesk tier to buy, instead **think about how any AI customer service vendor charges for the AI itself**: per seat, per resolution, or a structurally different model.
- **delight.ai already runs on that structurally different model: no seat fee, ever**. You pay only when the AI resolves something.
**Zendesk's list price runs from $19 to $115 or more per agent per month**, depending on the plan. That's the smallest number in your actual bill, not the biggest one.
Copilot, Automated Resolutions, and a handful of workforce add-ons sit on top of the seat price. One of them scales with how well the AI performs rather than with headcount. For a CX leader building a 2026 budget, the plan tier matters less than a different question. How does Zendesk, or any AI customer service vendor, actually charge for the AI: per seat, per resolution, or something else?
## What do Zendesk's Support and Suite plans actually cost in 2026?
Zendesk sells two product lines: Support and Suite plans.
### Support plans
Support is ticketing only: email and social-message handling, routing, and basic automation, starting at $19 per agent per month on an annual contract. It has no messaging, live chat, voice, or [AI agents](https://delight.ai/ai-agent).
### Suite plans
Suite is the omnichannel line most growing teams end up buying. It bundles ticketing with messaging, live chat, a help center, voice, and AI agents into one per-agent price.
| Plan | Price (annual, per agent/month) | What it adds |
|---|---:|---|
| Support Team | $19 | Email and social ticketing only |
| Suite Team | $55 | Messaging, live chat, one help center, AI agents with a small resolution allowance |
| Suite Professional | $115 | Skills-based routing, IVR, App Builder, multiple help centers |
| Suite Enterprise + Copilot | Contact sales | Custom roles, sandbox, approval workflows, the full Copilot bundle |
**Monthly billing runs roughly 15 to 25 percent higher than the annual rates above**, since Zendesk's published prices assume a year of commitment.
## How does Zendesk price its AI, and why did the model change in 2026?
Zendesk's AI has two separate cost layers, and they behave differently.
### Copilot
**Copilot is a $50-per-agent add-on** that drafts replies, suggests next steps, and triages tickets for a human agent. It sits on top of any Suite plan below Enterprise, where it's bundled in.
### Automated Resolutions
The second layer is usage-based. Zendesk calls it an Automated Resolution: a case its AI agent handles without a human, counted per conversation rather than per agent.
On May 18, 2026, Zendesk restructured how that usage layer bills. The change split what used to be one bucket into three tiers, and only one of them costs money.
| Tier | What happened | Billed? |
|---|---|---|
| Assisted Escalation | The AI helped, then a human finished the case | No |
| Contained Resolution | The AI replied and the customer went quiet, but resolution wasn't confirmed | No |
| Verified Resolution | The AI resolved the case and a check confirmed it | Yes, drawn from the monthly allowance |
Each plan includes a small free allowance of Verified Resolutions, from 5 per agent per month on Suite Team up to 15 on Enterprise. Zendesk doesn't publish the overage rate. Independent pricing teardowns published in 2026 converge on roughly $1.20 to $2.00 per resolution above that allowance, depending on committed volume, though the confirmed number is whatever appears in a specific contract.
The mechanism creates a real budgeting problem. A support team that gets meaningfully better at automating cases doesn't see its AI bill flatten. **It sees the bill grow, because success is the billing event.**
## What hidden costs should CX leaders budget for beyond the sticker price?
Copilot and Automated Resolutions are the two biggest swing factors, but they aren't the only add-ons stacked on a Suite seat.
- **Quality Assurance** — roughly $35 per agent per month, for automated scoring of agent and AI conversations.
- **Workforce Management** — roughly $25 per agent per month, for scheduling and forecasting, sold bundled with QA as the Workforce Engagement Bundle at $50 per agent per month.
- **Contact Center** — $83 per agent per month, for built-in voice, IVR, and call routing.
Beyond the per-agent add-ons, two billing mechanics change the total further. Monthly billing costs more than annual, and multi-year contracts commonly carry single-digit annual escalation clauses.
A real Suite Enterprise rollout, spanning multiple brands or regions, typically needs paid implementation on top of any of the above.
None of this is hidden in the sense of being undisclosed. It's spread across several different pages, which is a different problem: budgeting for Zendesk means adding up five or six separate line items instead of reading one price. Forrester's [2026 customer service predictions](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) describe this kind of unglamorous, add-on-by-add-on accounting as the real work behind any AI rollout, not the flashy part vendors put on a homepage.
## What does a fully loaded Zendesk seat actually cost?
Take Suite Professional, the tier most mid-market teams land on, and add the two things a serious team almost always turns on.
| Line item | Per agent/month |
|---|---:|
| Suite Professional (base seat) | $115 |
| Copilot | $50 |
| Workforce Engagement Bundle (QA + WFM) | $50 |
| Fully loaded seat, before AI overage | $215 |
That's roughly 87 percent above the base plan, and it still excludes the per-resolution AI meter and voice. A 20-agent team on this stack pays about $4,300 a month, or roughly $51,600 a year, before a single Automated Resolution bills separately.
Scale that to 100 agents on Suite Enterprise, and the same three layers, seats, Copilot-equivalent AI assistance, and workforce add-ons, roll into a quote-only number Zendesk negotiates contract by contract. The seat count in that quote is a known integer. The AI meter is not, and it's the line item most likely to move a forecast after the contract is signed.
## Is there a pricing model that doesn't stack a seat fee on top of an AI meter?
### What to ask before you sign
Before signing with any AI customer service vendor, a CX leader can ask one question that cuts through most of the math above. Is the AI billed per seat, per resolution, per outcome, or some stack of all three?
- **Seat plus a usage meter** is Zendesk's model, a per-agent base plus Copilot plus Automated Resolutions stacked on top, scaling with headcount and with AI success at the same time.
- **A single usage-based fee** removes the seat multiplier entirely, so cost tracks the AI's actual work instead of a seat charge sitting underneath it.
Deloitte's [2026 technology predictions](https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2026/saas-ai-agents.html) put this shift in industry-wide terms. Seat-based SaaS licensing is giving way to hybrid models built on usage- and outcome-based pricing. Gartner projects that by 2030, at least 40 percent of enterprise SaaS spend will move to one of those two models.
### Where Delight Desk's model differs
delight.ai sells an AI-native helpdesk built on that second model, called [Delight Desk](https://delight.ai/desk). The seat fee is $0, and Delight Desk bills per successful AI resolution instead, so adding headcount doesn't add a second, separate line item to the invoice.
For teams evaluating a move off Zendesk specifically, that shift also changes what migration looks like. [Delight Desk's one-click Zendesk migration](https://delight.ai/zendesk-migration-desk) carries over ticket history and help-center content with redirects in place, then turns the account's own resolved tickets into the step-by-step playbooks the AI works from. Those playbooks are drafts a person reviews before anything ships, not something that goes live untouched.
None of this makes delight.ai automatically cheaper than Zendesk for every team. A low-volume desk with modest AI ambitions might still pay less on a seat-based plan than on a pure resolution fee. What changes is the shape of the bill: no seat charge stacked under the AI, and no separate add-on required just to get [AI customer service](https://delight.ai/ai-customer-service) that works with the ticketing setup a team already runs.
Gartner has found that [91 percent of customer service leaders](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) feel pressure to implement AI in 2026. That's exactly why the billing question is worth asking before a contract is signed, not after the first invoice carries an AI line item.
## In closing
Zendesk's plan tiers answer a narrower question than the one most CX leaders are actually trying to solve. The tier sets the floor. Copilot, Automated Resolutions, and the workforce add-ons set the real number, and one of them is built to grow as the AI gets better at its job.
Before renewing or evaluating an alternative, get the AI billing model in writing: per seat, per resolution, or a flat fee tied to outcomes. [See how Delight Desk's pricing and Zendesk migration path work](https://delight.ai/zendesk-migration-desk).
- [What do Zendesk's Support and Suite plans actually cost in 2026?](#what-do-zendesk-s-support-and-suite-plans-actually-cost-in-2026)
- [How does Zendesk price its AI, and why did the model change in 2026?](#how-does-zendesk-price-its-ai-and-why-did-the-model-change-in-2026)
- [What hidden costs should CX leaders budget for beyond the sticker price?](#what-hidden-costs-should-cx-leaders-budget-for-beyond-the-sticker-price)
- [What does a fully loaded Zendesk seat actually cost?](#what-does-a-fully-loaded-zendesk-seat-actually-cost)
- [Is there a pricing model that doesn't stack a seat fee on top of an AI meter?](#is-there-a-pricing-model-that-doesnt-stack-a-seat-fee-on-top-of-an-ai-meter)
- [In closing](#in-closing)
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Zendesk pricing FAQs
Explore frequently asked questions about Zendesk pricing and plan models
**How much does Zendesk cost per agent in 2026?**
Zendesk's published annual pricing runs from $19 per agent per month for Support Team, ticketing only, to $115 for Suite Professional, with Suite Enterprise + Copilot sold through sales. Add Copilot, Quality Assurance, Workforce Management, or Automated Resolution overage, and a fully loaded Suite Professional seat commonly runs well above $200 per agent per month.
**What's the difference between Zendesk Support and Suite plans?**
Support plans cover email and social ticketing only, starting at $19 per agent per month. Suite plans add messaging, live chat, a help center, voice, and AI agents, starting at $55. Most teams that want anything beyond email end up on a Suite plan.
**How does Zendesk's AI and Copilot pricing work?**
Copilot is a separate $50-per-agent-per-month add-on for AI-assisted replies and triage, bundled only at the Enterprise tier. Automated Resolutions are billed separately, per case, and since May 2026 only a Verified Resolution, one the AI resolved and a check confirmed, counts against a plan's monthly allowance.
**What hidden costs should I budget for beyond the base plan?**
Beyond Copilot, plan for Quality Assurance at roughly $35 per agent per month, Workforce Management at roughly $25, and Contact Center for voice at roughly $83, plus a 15 to 25 percent premium for monthly over annual billing and typical single-digit annual escalation clauses.
**Is there a Zendesk alternative that doesn't charge per seat plus per AI resolution?**
Delight Desk charges $0 per seat and bills per successful AI resolution instead, so headcount growth doesn't add a second per-agent AI charge. It also offers a one-click migration path for teams moving off Zendesk, carrying over ticket history and turning past resolutions into AI playbooks a person reviews before they go live.
## Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-secure-caller-keypad-input)
[](/blog/product/introducing-ivr-navigation)
[](/blog/product/introducing-universal-telephony)
[](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## How to reduce truck rolls with Delight.ai
Source: https://delight.ai/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service
- [Blog](/blog)
- [AI for CX](/blog/ai-for-cx)
- How to reduce truck rolls with Delight.ai
[
Ethan Hong
Product manager](https://delight.ai/author/ethan-hong)
July 24 2026
**Key takeaways:**
- Most repeat truck rolls in home services come from **a dependency nobody confirmed**, like a back-ordered part, a pending warranty authorization, or a homeowner window that slipped.
- Remote diagnostics help on the first visit, but **coordination between visits** is what keeps a second trip from turning into a third.
- **First-time fix rate and 30-day repeat visit rate** show whether the coordination fix is working.
The most reliable way to reduce truck rolls in home services is to stop [booking the return visit](https://delight.ai/ai-booking-agent) before everything it depends on has been confirmed. That means the replacement part is on the shelf, the manufacturer or warranty company has approved the repair, and the homeowner has agreed to an appointment window.
Second truck rolls often result in stalled jobs or communication with customers. In a [2025 FIELDBOSS survey](https://www.fieldboss.com/blog/hvacs-real-problem-isnt-price-its-poor-communication/) of 1,000 homeowners who had hired an HVAC pro, **roughly 38% named a communication or procedural problem**, like scheduling difficulty, as their top complaint.
Warranty work stretches the wait for homeowners even further. In [This Old House's 2026 survey](https://www.thisoldhouse.com/home-finances/reviews/home-warranty-survey) of 2,000 warranty holders, 29% said a claim took two to three weeks to resolve. For [home services and warranty](https://delight.ai/industries/home-services-and-warranty) operators, every extra trip inside that window costs a technician slot and tests the homeowner's patience.
## What is a truck roll?
A truck roll is any dispatch that sends a technician and their vehicle to a customer's home. In home services, it's the van that shows up for an HVAC repair, a water heater swap, or a warranty claim.
A repeat truck roll, also called a second truck roll or return visit, happens when the first visit doesn't finish the job and someone has to go back. Each one costs fuel and labor, and it pushes another appointment down the schedule.
## Which truck rolls can you avoid?
The avoidable truck rolls are the return visits that happen after the problem is already diagnosed. Some first trips can't be skipped, since a technician can't order a fix for a unit nobody has diagnosed yet.
A second trip booked before the part arrives, before the warranty company approves the repair, or before the homeowner confirms a window is a trip that was never going to finish the job.
Those trips add up. [Aquant's 2025 Field Service Benchmark Report](https://industrialmachinerydigest.com/software/software-general/aquants-2025-field-service-benchmark-report-reveals-ai-enabling-39-faster-machinery-repairs-and-more) found that **one in seven onsite visits is an unnecessary truck roll**, and a failed first visit adds two more visits on average, while stretching resolution by 14 days.
## Why do repeat truck rolls happen in home services?
A repeat visit almost always traces to something outside the technician's control that wasn't ready when the first visit ended. The table below shows how [Aberdeen Group's 2013 survey](https://www.asentria.com/wordpress/wp-content/uploads/2019/09/aberdeen-group-report-repairing-field-efficiency.pdf) of service organizations broke down the reasons for a secondary visit, with a home services example of each.
| Reason for the return visit | Share of secondary visits | What it looks like in a home |
|---|---:|---|
| Missing or wrong part | 51% | The tech diagnoses a failed control board, but the distributor doesn't have one in stock. |
| Technician skill or experience gap | 25% | A newer tech hits an unfamiliar system and a senior tech has to come back. |
| Not enough time scheduled | 13% | A one-hour slot turns into a three-hour repair, and the next job can't wait. |
Parts alone cause more repeat visits than the other two reasons combined. Home warranty work adds a fourth dependency that the survey doesn't break out, which is third-party authorization.
When a homeowner's warranty covers the repair, the contractor usually needs the warranty company's approval before ordering the part. If nobody tracks who is ordering the part and who is supplying it, the job can sit for days while each party assumes someone else has it.
Among the home services and home warranty companies Delight.ai spoke with from March to October 2026, **nearly 7 in 10 raised dispatcher or CSR bandwidth** as a bottleneck. Nearly a quarter named warranty or manufacturer authorization delays.
## What does a repeat truck roll cost?
Aberdeen puts the operating cost of a single truck roll at **$200 to $300** in labor, fuel, and vehicle time. The table below shows what that range adds up to once avoidable return visits become routine.
| Avoidable return visits per month | Monthly cost at $200 to $300 per visit | Yearly cost |
|---:|---|---|
| 10 | $2,000 to $3,000 | $24,000 to $36,000 |
| 20 | $4,000 to $6,000 | $48,000 to $72,000 |
| 50 | $10,000 to $15,000 | $120,000 to $180,000 |
| 100 | $20,000 to $30,000 | $240,000 to $360,000 |
Those figures cover only the trip itself. Each wasted visit also burns technician hours that are hard to replace, and the [Bureau of Labor Statistics](https://www.bls.gov/ooh/installation-maintenance-and-repair/heating-air-conditioning-and-refrigeration-mechanics-and-installers.htm) projects about 40,600 openings a year for HVAC mechanics and installers through 2035.
Some of the cost never shows up on the dispatch board. When a homeowner doesn't hear back about a part or a window, they call, and each of those calls pulls a CSR away from booking new work.
## Why don't remote diagnostics and better triage stop every return visit?
The top results for how to reduce truck rolls mostly come from remote video support vendors, and they focus on the first visit. Live video guidance, pre-dispatch triage, equipment history, and technician training all help a tech walk in prepared and fix more on the first try.
Those tools mostly address skill gaps and some of the parts problem. But they can't confirm that a back-ordered part has shipped, that a warranty company signed off, or that the homeowner will be home on Thursday.
Once the diagnosis is done, the remaining work is coordination between parties who don't share a system. So for most home services teams, repeat truck rolls are a coordination problem more than a diagnostic one.
## What should be confirmed before a return visit gets booked?
Confirming four parties before the return visit goes on the board is the most direct way to reduce truck rolls after a diagnosis. The table below shows what each one needs to confirm and what happens when the visit gets booked first.
| Party | What needs to be confirmed | What happens if the truck rolls first |
|---|---|---|
| Parts supplier or distributor | The exact part is in stock or shipped, with a delivery date before the visit | The tech arrives, the part doesn't, and the job slips again. |
| Manufacturer or warranty company | The repair and the part are authorized under the homeowner's coverage | Work stops on site while someone waits on hold for approval. |
| Warranty administrator or adjuster, on open claims | The claim status and any cost cap are settled | The contractor finishes work that later gets disputed. |
| Homeowner | A window they can keep, plus access to the unit | Nobody answers the door, and the slot is lost. |
None of these checks are hard on their own. The trouble is that a CSR or dispatcher usually works them one at a time between inbound calls, and a single unanswered voicemail can stall the whole job.
Calling more often doesn't solve it either. Contractors fit warranty jobs around their own retail work, so a flood of follow-up calls from a person or an AI agent can cost the relationship.
## How does Delight.ai reduce truck rolls?
Delight.ai reduces truck rolls by keeping a stalled job open until the replacement part, the warranty approval, and the homeowner's availability are all confirmed, and only then booking the return visit. That catches second truck rolls before they ever reach the dispatch board.
The work runs through [Agent Steward](https://delight.ai/autonomous-agent), Delight.ai's long-running coordination agent, built to own a multi-step job from the moment it stalls until it closes. Instead of one CSR working the distributor, the warranty company, and the homeowner in sequence, Agent Steward works all of them in parallel.
### Every party gets the same update at the same time
Agent Steward takes action on a stalled job instead of waiting for someone to pick it up. Those actions include placing outbound calls and sending SMS at the same time. Every party with a stake in the return visit hears from the Delight.ai agent at the same time, whether that's the homeowner, the warranty company, or the parts supplier.
Nobody has to wait for a chain of callbacks to reach them, and the homeowner gets one consistent answer about when the technician is coming back. If a call goes unanswered, follow-up continues by text or email, and a supplier that opens at 6 a.m. gets the call at 6 a.m.
### Contact rules set by your ops team
Your ops team defines who gets contacted and in what order in [Actionbooks](https://delight.ai/product-release/action-books-management), the editable instructions that guide how Delight.ai agents behave. Clear rules and guidelines keep contractors from being over-called. Escalation triggers follow your SLA thresholds, so a disputed claim or a frustrated homeowner goes to a person with the full history attached.
### The return visit gets booked last
The job goes back on the schedule only when the part, the authorization, and the homeowner's window are all confirmed. At that point, Delight.ai's [AI booking agent](https://delight.ai/ai-booking-agent) locks in the appointment with the homeowner. Outcomes write back to the [field service management system](https://delight.ai/integrations) already in place. Named connections include ServiceTitan and Housecall Pro, and other systems can connect through APIs.
### A timestamped record of every follow-up
Each follow-up is logged with exact timestamps in [Trust OS](https://delight.ai/trust-os), Delight.ai's governance layer for AI agents. When a homeowner or warranty company questions why a job took a week, the record shows who was contacted and when.
The dispatcher and CSR keep the conversations that need a person, and the hold music and homeowner phone tag come off their plate.
## How do you measure whether you're reducing truck rolls?
Four numbers show whether efforts to reduce truck rolls are working, and each one comes from job records most teams already keep.
- **First-time fix rate**, the share of jobs closed on the first visit, which ranged from 53% for bottom performers to 86% for top performers in Aquant's 2025 benchmark.
- **30-day repeat visit rate**, the share of homes that needed another dispatch for the same issue within 30 days.
- **Parts-not-available rate**, the share of return visits caused by a part that wasn't on hand.
- **Days to close**, measured from first dispatch to resolution, which a failed first visit stretches by about 14 days.
Days to close is the number homeowners feel most. A job that closes sooner also generates fewer "where's my technician?" calls, which gives CSR time back to new bookings.
## What changes when every return visit is confirmed first?
Once parts, approvals, and homeowner windows are confirmed before booking, the return visits that still happen are the ones that were always needed. That shift is what lets a home services team reduce truck rolls without adding a dispatcher. Technicians stop rolling to jobs that aren't ready, and dispatchers spend the day on judgment calls instead of callbacks.
[Explore how Delight.ai coordinates home services jobs](https://delight.ai/industries/home-services-and-warranty) from the first call to the closed claim. And for teams weighing how much control to hand an agent, the guide to [AI dispatch software](https://delight.ai/blog/ai-for-cx/does-your-dispatcher-lose-control-with-ai-dispatch-software) covers the approval checkpoints.
- [What is a truck roll?](#section-anchor-id-0 "What is a truck roll?")
- [Which truck rolls can you avoid?](#section-anchor-id-1 "Which truck rolls can you avoid?")
- [Why do repeat truck rolls happen in home services?](#section-anchor-id-2 "Why do repeat truck rolls happen in home services?")
- [What does a repeat truck roll cost?](#section-anchor-id-3 "What does a repeat truck roll cost?")
- [Why don't remote diagnostics and better triage stop every return visit?](#section-anchor-id-4 "Why don't remote diagnostics and better triage stop every return visit?")
- [What should be confirmed before a return visit gets booked?](#section-anchor-id-5 "What should be confirmed before a return visit gets booked?")
- [How does Delight.ai reduce truck rolls?](#section-anchor-id-6 "How does Delight.ai reduce truck rolls?")
- [How do you measure whether you're reducing truck rolls?](#section-anchor-id-7 "How do you measure whether you're reducing truck rolls?")
- [What changes when every return visit is confirmed first?](#section-anchor-id-8 "What changes when every return visit is confirmed first?")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## Frequently Asked Questions
Common questions on preventing repeat dispatches in field service.
### What is a truck roll?
A truck roll is any dispatch that sends a technician and a vehicle to a customer's location. In home services, that usually means a visit to a homeowner for a repair or warranty job. A repeat truck roll is a return visit to the same home for the same issue, most often because a part, a warranty authorization, or the homeowner's window wasn't confirmed before the first visit ended.
### How much does a repeat truck roll cost?
Aberdeen Group puts the operating cost of a single truck roll at $200 to $300 in labor, fuel, and vehicle time. A failed first visit also tends to multiply. Aquant's 2025 benchmark found it adds two more visits on average and stretches resolution by 14 days, before counting the status calls homeowners make while they wait.
### Why do repeat truck rolls happen if my techs are good at their jobs?
Most repeat visits trace to something outside the technician's control. Aberdeen found about 51% of secondary visits come from a missing or wrong part, 25% from a skill or experience gap, and 13% from a visit scheduled too short. Home warranty jobs add another dependency, since the warranty company often has to authorize the repair before the part gets ordered.
### How do you measure whether you're reducing truck rolls?
Four numbers cover it. First-time fix rate shows the share of jobs closed on the first visit, and a 30-day repeat visit rate shows how many homes needed another trip for the same issue. Parts-not-available rate isolates the biggest single cause, and days to close shows how long homeowners wait. Tracked together, they show whether coordination changes are cutting avoidable trips.
### Can AI coordinate parts, warranty approvals, and homeowners before a return visit?
Yes, with contact rules set by the ops team. A coordination agent like Delight.ai's Agent Steward contacts the parts supplier, the warranty company, and the homeowner in parallel by voice and SMS, then books the return visit only after each one confirms. Every call and confirmation is logged with a timestamp, which helps when a homeowner or warranty company disputes a claim.
### Does an AI coordination agent replace the dispatcher?
No. The dispatcher still owns the judgment calls, like a pricing exception or an upset homeowner. What changes is who spends hours on hold with a distributor or waiting on a warranty callback. An agent like Agent Steward handles that follow-up volume and escalates to a person whenever a job needs judgment.
Keep reading
[View more](/blog "View more")
[
Best AI voice agents in 2026, tested firsthand](/blog/ai-agent/best-ai-voice-agents)
[
Getting started with voice AI for home services](/blog/industry/voice-ai-appointment-booking-home-services)
[
How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[
Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[View more](/blog "View more")
## Does your dispatcher lose control with AI dispatch software?
Source: https://delight.ai/blog/ai-for-cx/does-your-dispatcher-lose-control-with-ai-dispatch-software
## Does your dispatcher lose control with AI dispatch software?
[
Ethan Hong
Product manager](https://delight.ai/author/ethan-hong)
July 24 2026
**Key takeaways**
- AI dispatch software can coordinate a stalled field service job **without removing your team's sign-off authority**.
- **A field service AI agent can be set up to require human approval** before it contacts a client or an OEM.
- Getting automated dispatch software up and running is closer to **defining a few escalation rules than replacing your dispatch board.**
Field service owners may be skeptical of automation because dispatch is the one job where a bad call shows up immediately: a missed SLA, an angry client, a truck rolling to a site that isn't ready.
Before you hand that coordination to software, the fair question isn't "does it work?" It's "who's actually driving decisions?"
## What does AI dispatch software actually control?
AI dispatch software assigns and coordinates field service work using live operational data instead of a static calendar. A dispatcher normally checks technician skills, parts status, and appointment windows one case at a time. The system evaluates those factors together and recommends, or in some cases automates, the next move.
In [field service](https://delight.ai/field-services), that "next move" is rarely just picking a technician. A stalled job usually means a missing part, a pending diagnostic from an equipment manufacturer, or an unconfirmed appointment window. Closing it means coordinating a parts depot, the original equipment manufacturer's (OEM) support line, the field tech, and the client facility, often at the same time.
### What counts as dispatch?
Traditional dispatch software displays who's available. AI dispatch software adds a recommendation, or an action, on top of that data.
This distinction that matters for control is whether the system can act before a person reviews it, and under what conditions it can take action.
## How much control do you lose when AI coordinates a stalled job?
You lose less control than most owners assume when AI coordinates a stalled job, and for a reason that shows up in the data. [Gartner's April 2026 survey](https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-survey-finds-eighty-five-percent-of-service-and-support-leaders-are-expanding-human-agent-responsibilities-despite-expectations-of-mass-ai-layoffs) of 321 service and support leaders found that 85% of service and support leaders are expanding, not shrinking, human agent responsibilities as AI absorbs routine volume.
Only 31% have implemented or planned AI-related layoffs. The pattern is redesign, not replacement.
That pattern holds in field service coordination specifically. A stalled job creates a lot of repetitive outreach, like calling the depot for an ETA, following up with the OEM, checking whether the client's facility window still holds. That's the manual work that an AI dispatch agent absorbs.
What doesn't move is the judgment call for a disputed invoice, an out-of-policy exception, or a client escalation. These decisions still need a person, because they carry consequences the automation system isn't positioned to own.
| What the AI typically handles | What still routes to a person |
|---|---|
| Calling the parts depot for ETA confirmation | A disputed bill or invoice |
| Chasing an OEM diagnostic that's gone quiet | An out-of-policy exception |
| Confirming a client facility's appointment window | A client escalation that needs a relationship, not an update |
| Logging every attempt, every channel, for the case record | Deciding whether to override a recommendation |
## Can a field service AI agent be required to get approval before contacting a client or OEM?
Yes, a field service AI agent can be required to get approval before taking action, which is where the control question gets answered. [Agent Steward](https://delight.ai/autonomous-agent), delight.ai's agent for coordinating multi-party field service cases, runs on what's called graduated autonomy.
At Level 1, the agent investigates a stalled case and gathers what it finds from the depot, the OEM, and the job record. It packages a recommendation, but it won’t act on its own. A person reviews it, then approves it before anything goes out to a client or an OEM.
The agent only handles a proven case type on its own after its recommendations have matched what a person would have decided, case type by case type. Autonomy is earned through a proven track record, not switched on by default.
### Setting the approval threshold
Your operations team decides where the line sits. A well-built system runs on a governance layer, sometimes called [Trust OS](https://delight.ai/trust-os), that defines what the agent can say, who it can contact, and when it must hand decisions off to a person.
That layer enforces hard approval gates around financial limits and policy boundaries, no matter how much autonomy the agent has earned elsewhere. Wanting every OEM contact reviewed before it happens is simply a configuration decision.
### What the AI automation system logs for every action
A well-built system keeps a full case record, including:
- **Every call, email, and text** the agent sends, timestamped and tied to the case.
- **Call recordings and the reasoning** behind each recommendation.
- **Every party's response**, including a depot or OEM that goes quiet.
- **The full case thread**, exportable for an SLA dispute or compliance review.
That record exists so a stalled-job dispute doesn't come down to someone's memory of what happened. It also means an owner can trace exactly why the agent suggested what it did, rather than trusting a result with no visible reasoning behind it.
## What does a stall actually cost?
The expensive part of a stalled job is everything that happens after.
A tech can't close on the first visit because a part hasn't arrived. A call to the OEM goes unanswered for an hour. A client facility starts calling because nobody warned them the window slipped.
That sequence, one party at a time, is why a second truck roll is one of the most expensive outcomes in field service. A dispatcher working the phone can only call one party at a time and wait for so long, before moving on to the next.
The parts depot, the OEM, the tech, and the client facility all sit on separate timelines. A human coordinating them sequentially is working against the clock from the first call.
## Is AI dispatch coordination hard to set up for a field service team?
AI dispatch coordination is less difficult for a field service team to set up than the phrase "AI dispatch software" makes it sound. The heavier lift isn't the AI, but making sure your job data (technician skills, parts status, SLA definitions) is clean enough for any system, human or automated, to make a good call on top of it.
Most field service teams already run a field service management (FSM) platform like ServiceMax, Salesforce Field Service, or IFS. An AI coordination agent sits on top of that system, working the case the FSM already flagged as stalled, then contacting the parties involved and writing the outcome back.
Your FSM shows you the stalled job. The coordination layer works it.
### What does set-up for an AI dispatch system look like?
In practice, setup is a configuration exercise, not a system migration:
- **Escalation rules**, defining which case types need approval and which don't.
- **Contact order**, setting who gets called first for a given part, OEM, or client type.
- **SLA thresholds**, defining what counts as a risk to a specific contract or window.
- **System connections**, linking to the FSM and communication channels you already run.
## Three questions worth asking any AI dispatch vendor
Before you take a vendor's control claims at face value, these three questions separate a real answer from a marketing one. They also track closely with what [NIST's AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) recommends any organization document an automated system by noting who oversees it, what gets measured, and what happens when it's overridden.
### Can I get visibility into every action the AI system takes?
Ask for the actual record, not a dashboard metric. A system built for accountability logs each contact attempt, each channel, and the reasoning behind a recommendation, and lets you export it for a dispute or a compliance review.
### Who decides what the AI dispatch system is allowed to do without me?
Confirm that permissions are set by your operations team, not fixed by the vendor. If a vendor can't tell you exactly how to tighten or loosen the approval threshold for a specific case type, that's a real gap.
### What happens when it doesn't have enough information to act?
A well-built agent flags what it can't resolve and hands it to a person with full context attached, rather than guessing or stalling silently. Ask what that handoff actually looks like on a bad day.
Automating the coordination around a stalled job doesn't require handing over the sign-off that makes it your operation. The parts depot gets called, the OEM gets chased, and the client gets updated, and every one of those actions happens on the terms your team set, with a record to prove it. If you're evaluating what that looks like for your team, [see how Agent Steward coordinates field service cases](https://delight.ai/field-services).
- [What does AI dispatch software actually control?](#section-anchor-id-0 "What does AI dispatch software actually control?")
- [How much control do you lose when AI coordinates a stalled job?](#section-anchor-id-1 "How much control do you lose when AI coordinates a stalled job?")
- [Can a field service AI agent be required to get approval before contacting a client or OEM?](#section-anchor-id-2 "Can a field service AI agent be required to get approval before contacting a client or OEM?")
- [What does a stall actually cost?](#section-anchor-id-3 "What does a stall actually cost?")
- [Is AI dispatch coordination hard to set up for a field service team?](#section-anchor-id-4 "Is AI dispatch coordination hard to set up for a field service team?")
- [Three questions worth asking any AI dispatch vendor](#section-anchor-id-5 "Three questions worth asking any AI dispatch vendor")
## Frequently Asked Questions
What dispatchers actually keep, and what changes, with AI in the loop.
**Does AI dispatch software replace my dispatcher?**
No, AI dispatch software is built to absorb the repetitive coordination work behind a stalled job, not to replace your dispatcher's job. That means calling a parts depot for an ETA, chasing an OEM diagnostic, and confirming a client's appointment window. Your team still owns exceptions, disputed cases, and anything outside policy. The AI handles the volume, your team handles the judgment.
**Can a field service AI agent be required to get approval before contacting a client or OEM?**
Yes, a field service AI agent can be required to get approval before contacting a client. Systems built on graduated autonomy start at a level where a human reviews and approves every recommendation before it goes out to a client or an OEM. Hard approval gates around financial limits and policy boundaries stay in place regardless of autonomy level. The agent only handles a proven case type on its own after its recommendations have consistently matched what a person would have decided.
**What happens if the AI dispatch software gets something wrong?**
A wrong recommendation by the AI dispatch software gets caught at the approval step, before it reaches a client or OEM, because nothing ships without sign-off in the early stages of graduated autonomy. The full record, including the reasoning behind the recommendation, every party contacted, and every response, stays traceable afterward instead of disappearing into a black box. That record is also what an owner points to if an SLA dispute comes up later.
**How long before a team trusts AI dispatch coordination enough to loosen the approval gate?**
There's no fixed timeline, and that approval gates are by design rather than a gap. The approval requirement lifts case type by case type, only once the agent's recommendations have matched what a person would have decided closely and consistently enough to earn trust. A straightforward case type, like confirming a routine parts ETA, typically earns that trust faster than a judgment-heavy one, like an out-of-policy exception.
**Does AI dispatch coordination replace my field service management system?**
No, AI dispatch coordination does not replace your field service management system. It sits on top of the field service management (FSM) platform you already run, such as ServiceMax, Salesforce Field Service, or IFS, rather than replacing it. Your FSM still shows you the stalled job and holds your job history. The AI dispatch layer works the case from there, contacting the parts depot, OEM, and client, and writing the outcome back into the system you already use.
## Keep reading
[View more](/blog "View more")
[
How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[
Why do home service businesses lose jobs to their competitors?](/blog/ai-for-cx/speed-to-lead-why-home-service-businesses-lose-jobs)
[
How to automate invoice matching for claims](/blog/ai-for-cx/how-to-automate-invoice-matching-for-claims)
[
Automation for home warranty businesses](/blog/ai-for-cx/customer-service-automation-home-warranty)
[View more](/blog "View more")
## Why do home service businesses lose jobs to their competitors?
Source: https://delight.ai/blog/ai-for-cx/speed-to-lead-why-home-service-businesses-lose-jobs
## Why do home service businesses lose jobs to their competitors?
[
Caroline Adamec
Content Engineer](https://delight.ai/author/caroline-adamec)
July 24 2026
## Key takeaways
- Most **lost jobs go to whoever calls the customer back first**, not the best contractor.
- A [2011 Harvard Business Review study](https://hbr.org/2011/03/the-short-life-of-online-sales-leads) found that contacting a lead within an hour **made a business about 7 times more likely to qualify it** than waiting just one hour longer.
- **Delight.ai's AI agents answer every incoming call and text immediately**, so no lead ever waits for a callback.
Home service businesses rarely lose jobs because a competitor did better work or charged less. They lose jobs because a competitor picked up the phone first. The name of that gap is speed to lead, or the time between a customer reaching out and your first real response.
Treat response time as a non-negotiable lead generator, not something you handle in your free time between jobs. The research behind it is specific enough to act on.
## What is speed to lead?
Speed to lead is not a claim you make about your business. It's a number you measure, the minutes between when a customer contacts you and when you actually respond.
Speed to lead applies no matter where the lead comes from. A call from your website, a form fill, a text from a past customer, or a job sent over by [Google Local Services Ads](https://support.google.com/localservices/answer/6224841) all start the same clock. The response that stops that clock has to be real. That means a call answered, a text replied to, or an appointment booked. A voicemail left for the customer to return doesn't count.
## Why am I losing home service leads to competitors?
The most common reason is simple: the call never got answered. [72% of consumers say that a missed call makes them question a business’ professionalism](https://ainora.lt/blog/missed-call-statistics-small-business-2026).
Most callers who don't get through the first time won't call back. [InsideSales](https://www.insidesales.com/response-time-matters/) found that 85% of callers who reach a missed call or voicemail simply move on to the next business on their list, rather than trying again.
Homeowners rarely wait around for one contractor to get back to them, especially for an urgent problem. A clogged drain or a dead air conditioner usually means three or four calls to different companies within a few minutes, and the job goes to whoever answers first.
## Why paid leads make a slow response even more expensive
If you run Google Local Services Ads, Angi, or HomeAdvisor, slow response costs you twice. You already paid for that lead. Losing it to a slower response doesn't just cost you the job, it wastes the ad spend that generated it in the first place.
These platforms often send the same request to several contractors at once, so the lead you paid for is also sitting in a competitor's inbox at the same moment:
- **Google Local Services Ads** charge per lead, and the same request routes to multiple verified providers at once.
- **Angi and HomeAdvisor** work the same way, often sending one homeowner's request to three or more contractors.
- **Your own website or referrals** are the exception. Those leads usually reach you alone, which is one reason a slow response from there is easier to fix.
A missed call on an organic lead is a missed opportunity. A missed call on a paid lead is a missed opportunity you already paid to create.
## How fast should a contractor respond to a new lead?
The clearest answer comes from two studies that have held up for nearly two decades.
### The 2007 MIT study
A [study by MIT researcher James Oldroyd](https://www.leadresponsemanagement.org/lrm_study/) tracked more than 15,000 leads and 100,000 call attempts. Contacting a lead within 5 minutes made a business **21 times more likely** to qualify that lead than waiting 30 minutes. It also made the business **100 times more likely to reach the person at all**.
### The 2011 Harvard Business Review study
A [Harvard Business Review study](https://hbr.org/2011/03/the-short-life-of-online-sales-leads) extended that finding across 2,241 US companies and more than 100,000 leads. Contacting a lead within an hour made a business about **7 times more likely to qualify it** than waiting just one hour longer. The same study found the median company took 42 hours to respond to a new lead, which is exactly why being fast works so well against the competition.
You can't manage a response time you've never measured. Most home service businesses have never actually timed how long it takes them to call a new lead back. The table below gives a reasonable starting point by job type.
| Type of request | Reasonable response window |
|---|---|
| Emergency (no heat, burst pipe, lockout) | Minutes, not hours |
| Urgent but not an emergency (leak, appliance issue) | Within the hour, same day |
| Planned work (quote, estimate, inspection) | Same day |
## How can a contractor respond to every lead instantly, without adding staff?
Most contractors can't sit by the phone between jobs, and hiring a full-time dispatcher to cover nights and weekends rarely works out for a small crew. This is the part delight automation solves well:
- **Voice and text at the same time.** [Voice AI](https://delight.ai/voice) answers the call and sends a confirmation text at the same moment, so no call goes to voicemail, even after hours or during a busy season.
- **Live scheduling, not a static calendar.** [Delight.ai's integration with ServiceTitan and Jobber](https://delight.ai/integrations) reads real technician availability, so what gets booked over the phone actually matches the schedule.
- **No new headcount.** Coverage runs 24/7 without hiring a dispatcher or a receptionist.
[Dr. Cool Services](https://delight.ai/customers/dr-cool-services), an HVAC contractor, uses delight.ai's Voice AI to answer every call instantly through its busiest season in English and Spanish, without adding a dedicated receptionist.
Answering fast gets you in the door. It's the first part of the job, but not all of it. A parts delay with no update or a second visit nobody confirmed can lose a customer just as fast as a slow first response.
## Speed to lead is only step one
The contractor who wins the job is usually just the one who answered first. Once your team is answering every call and text within minutes, day or night, the job itself, the estimate, the schedule, the follow-up, still has to be done well. Speed to lead gets you in the door. What happens after that is its own discipline, one that [delight.ai's home services and warranty page](https://delight.ai/home-services-and-warranty) covers in full, from dispatch to the final follow-up.
- [What is speed to lead?](#section-anchor-id-0 "What is speed to lead?")
- [Why am I losing home service leads to competitors?](#section-anchor-id-1 "Why am I losing home service leads to competitors?")
- [Why paid leads make a slow response even more expensive](#section-anchor-id-2 "Why paid leads make a slow response even more expensive")
- [How fast should a contractor respond to a new lead?](#section-anchor-id-3 "How fast should a contractor respond to a new lead?")
- [How can a contractor respond to every lead instantly, without adding staff?](#section-anchor-id-4 "How can a contractor respond to every lead instantly, without adding staff?")
- [Speed to lead is only step one](#section-anchor-id-5 "Speed to lead is only step one")
### Frequently asked questions
#### Why am I losing home service leads to competitors?
Home service businesses lose leads to competitors mainly because of response time, not price or quality. Nearly half of calls to small businesses go unanswered when the owner or a technician is the one picking up, and most of those callers won't try again. Homeowners often contact several contractors for the same job, and the job usually goes to whoever calls back first.
#### How fast should a contractor respond to a new lead?
A contractor should respond to new leads within minutes for emergencies like no heat or a burst pipe. For urgent but non-emergency requests, respond within the hour. For planned work like quotes or estimates, same-day is reasonable. Research from Harvard Business Review found that contacting a lead within an hour made a business about 7 times more likely to qualify it than waiting one hour longer.
#### What counts as a "lead" for speed to lead purposes?
A lead, for speed to lead purposes, is any first contact from a potential customer: a phone call, a form fill on your website, a text message, or a request sent through a paid platform like Google Local Services Ads or Angi. The clock starts the moment they reach out, not when you notice the missed call.
#### Does speed to lead matter the same way for emergency calls and quote requests?
Speed to lead doesn't matter the same way for emergency calls and quote requests. Emergency requests, like a failed furnace or a lockout, need a response in minutes because the customer is often calling several contractors right away. Quote requests and planned work have a wider window, but same-day response is still far ahead of the average.
#### What's a good speed to lead benchmark for a small home service business?
A good speed to lead benchmark for a small home service business is minutes for emergencies, an hour for urgent requests, and same-day for planned work. The bar is lower than it sounds: research shows the median business takes 42 hours to respond to a new lead, so even a same-day response puts most home service businesses ahead of the competition.
## Keep reading
[View more](/blog "View more")
[
How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[
Does your dispatcher lose control with AI dispatch software?](/blog/ai-for-cx/does-your-dispatcher-lose-control-with-ai-dispatch-software)
[
How to automate invoice matching for claims](/blog/ai-for-cx/how-to-automate-invoice-matching-for-claims)
[
Automation for home warranty businesses](/blog/ai-for-cx/customer-service-automation-home-warranty)
[View more](/blog "View more")
## How to automate invoice matching for claims
Source: https://delight.ai/blog/ai-for-cx/how-to-automate-invoice-matching-for-claims
## How to automate invoice matching for claims
[ ](https://delight.ai/author/cheryl-chai) [Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
July 24 2026
**Key takeaways**
- Most invoice-to-claim mismatches happen because **the approved claim and the contractor's invoice live in different systems**.
- The fix for manual claims work is exception-based automation. **Routine matching happens on its own**, and anything with a dollar threshold or a discrepancy still goes to a person.
- Automating invoice matching with [delight’s Trust OS](https://delight.ai/trust-os) means **the invoice arrives already checked against the claim** it belongs to.
Reconciliation rarely gets the attention that customer-facing AI does, but [Forrester's own 2026 predictions](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) call this kind of back-office work exactly where AI delivers the most real value this year.
Reconciliation is just a coordination problem. A claim gets approved in one system, a contractor completes the job and bills in another, and somewhere in between, a person has to manually confirm the two actually match.
That manual step is where hours disappear every week, and it is exactly the kind of task [delight.ai's home services and warranty](https://delight.ai/home-services-and-warranty) work was built to remove, without asking you to give up the human oversight that keeps a bad invoice from slipping through.
## Why home warranty owners still reconcile invoices and claims by hand
Nobody chooses to manually cross-check every contractor invoice against every approved claim. It happens because the claim's approved scope sits in one system, the contractor's job notes sit in another, and the invoice itself lands in a third, usually as an email attachment or a portal upload. Someone has to be the connective tissue between all three.
AP automation vendors estimate that [manual invoice matching costs $12 to $30 per invoice](https://www.medius.com/blog/reduce-manual-invoice-processing-without-headcount/) once you account for the staff time involved, and that cost compounds fast across dozens of open claims a month. The instinct to protect against a bad payout by checking everything by hand is reasonable. The problem is that manual checking scales linearly with claim volume, and claim volume does not wait for you to hire.
Enterprise AI adoption is also shifting in a direction that supports this. [Deloitte's State of AI in the Enterprise research](https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html) points to back-office operations, not just customer-facing chat, as the next wave of return on AI investment. Learning how to automate invoice matching for claims is a direct, practical version of that shift.
## What actually causes the mismatch between an invoice and an approved claim
The most common mismatch is just scope creep; a contractor submits a final invoice higher than what was approved, often because they found more work once the job was underway. Without a shared record of exactly what was approved and why, resolving that gap means digging back through emails, call notes, and the original claim file.
| Common mismatch | Root cause |
|---|---|
| Invoice total exceeds the approved claim amount | Additional work found on-site was never logged against the original approval |
| Line items on the invoice don't match the job scope | The contractor's notes and the claim record live in separate systems |
| Coverage is disputed after the invoice arrives | The original coverage decision wasn't documented in a way anyone can quickly reference |
Every one of these traces back to the same root cause. There’s no single, persistent record connecting the approval, the job, and the invoice. That is just a coordination gap, which is why more AP software rarely fixes it on its own.
## How do you reduce manual claims work without giving up oversight?
To reduce manual claims work without giving up oversight, automate the intake and verification steps. A system can confirm coverage, pull the original approved scope, and check an incoming invoice against it automatically. **A person should still approve anything that involves a dollar threshold**, a scope change, or a genuine dispute.
### Set your own thresholds for what needs a person
Not every claim carries the same risk, so treating them identically wastes the time of whoever is reviewing them. Delight.ai's [Trust OS](https://delight.ai/trust-os) governance layer lets you set graduated autonomy by task type, so a routine, in-scope invoice clears automatically while anything above your threshold or outside the approved scope routes straight to a person.
### Keep one audit trail instead of five inboxes
The manual work most owners describe is hunting across email, a claims portal, and a field service system to find the approval in the first place. A single, exportable record of every action and every decision is what makes a disputed invoice easy to resolve instead of a multi-day investigation.
## How do you stop manually matching invoices to claims?
To stop manually matching invoices to claims, you’ll have to stop treating the invoice as a document that arrives cold and needs to be tracked backwards. Keep the approved claim, the contractor's job details, and the invoice in the same continuous record from the start, so a mismatch surfaces on its own instead of requiring someone go looking for it.
### Keep the claim and the invoice in the same thread
A home warranty claim already touches a homeowner, a contractor, and often a parts supplier. Delight.ai's [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) carries that full context, including the approved scope and dollar amount, across every interaction on the case, so when the contractor's invoice comes in, it is checked against the same record the claim was approved in.
### Let exceptions surface themselves
The system flags only the invoices that need attention. That is the same logic as the claims-approval guardrails already built into Actionbooks, delight.ai's drafts-only response logic. Routine cases clear on their own, and anything genuinely in question reaches a person with the full history already attached.
### It still goes to your accounting system for payment
Delight.ai's role is just keeping the claim, the job, and the invoice connected before the invoice ever reaches your books, so what lands in your accounting system for payment has already been checked against what was actually approved.
## A quick way to tell if you're ready to cut the manual matching
- Can you currently pull up an approved claim, the contractor's job notes, and the resulting invoice in one place, without opening three separate systems? If the answer is no, that gap is what to close first.
- Confirm your team has already agreed on thresholds, which dollar amounts or scope changes require a person's sign-off, before automating anything.
- Reconciliation work shrinks fastest when routine matches are handled automatically and every real exception still lands on a person's desk with full context attached.
Cutting the manual cross-referencing between systems is what makes claims reconciliation faster without making it riskier. [See how delight.ai supports home services and warranty operations](https://delight.ai/home-services-and-warranty) to see how the shared case record, the audit trail, and the exception-only review work together.
- [Why home warranty owners still reconcile invoices and claims by hand](#section-anchor-id-0 "Why home warranty owners still reconcile invoices and claims by hand")
- [What actually causes the mismatch between an invoice and an approved claim](#section-anchor-id-1 "What actually causes the mismatch between an invoice and an approved claim")
- [How do you reduce manual claims work without giving up oversight?](#section-anchor-id-2 "How do you reduce manual claims work without giving up oversight?")
- [How do you stop manually matching invoices to claims?](#section-anchor-id-3 "How do you stop manually matching invoices to claims?")
- [A quick way to tell if you're ready to cut the manual matching](#section-anchor-id-4 "A quick way to tell if you're ready to cut the manual matching")
## Frequently asked questions
Common questions about automating invoice matching for claims
**Does automating invoice matching mean I lose visibility into what got approved?**
No, automating invoice matching should mean you gain visibility into what claims get approved. A well-built system keeps an immutable, exportable record of every approval and every action taken on a claim, so you can trace exactly what was approved and why, rather than losing that history in an old email thread.
**What happens when a contractor's invoice comes in higher than the approved claim?**
When a contractor's invoice comes in higher than the approved claim, it should route to a person automatically, with the original approved scope and the new invoice shown side by side. The system's job is just surfacing the discrepancy clearly.
**Do I need to replace my accounting software to automate claims approvals?**
No, you don't need to replace your accounting software to automate claims approvals. The reconciliation problem this solves sits upstream of your accounting system, connecting the approved claim to the contractor's invoice before it reaches your books. Your accounting software still handles the actual payment.
**How is automated invoice matching different from general AP automation software?**
Automated invoice matching is different from general AP automation software, which matches invoices against purchase orders and receiving records inside a finance system. This works earlier in the process, keeping the original claim approval, the contractor's job details, and multiple parties connected in one record, so the invoice arrives at your finance system already checked against what was actually approved.
**Will this slow down payments to contractors?**
No, this won't slow down payments to contractors when scoped correctly. It actually speeds up the invoices that already match what was approved, since they no longer wait in a manual review queue. The only invoices that take extra time are the ones that genuinely need a person's judgment, which is exactly the group that should take longer.
## Keep reading
[View more](/blog "View more")
[ ](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service) How to prevent second truck rolls in field service
[ ](/blog/ai-for-cx/does-your-dispatcher-lose-control-with-ai-dispatch-software) Does your dispatcher lose control with AI dispatch software?
[ ](/blog/ai-for-cx/speed-to-lead-why-home-service-businesses-lose-jobs) Why do home service businesses lose jobs to their competitors?
[ ](/blog/ai-for-cx/customer-service-automation-home-warranty) Automation for home warranty businesses
[View more](/blog "View more")
## Automation for home warranty businesses
Source: https://delight.ai/blog/ai-for-cx/customer-service-automation-home-warranty
## Automation for home warranty businesses
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec
Content Engineer](https://delight.ai/author/caroline-adamec)
July 24 2026
**Key takeaways**
- A safe rollout is a narrow one. **Automate one call type or one claim step first**, confirm it holds up, then expand.
- **Claims approval is safe to hand off in stages**. The AI should draft and recommend; a person should approve anything involving money, coverage disputes, or an exception to policy.
- **Automating customer service does not require cutting staff**. It changes what your team spends its day doing, not how many of them you need.
- Delight.ai's [Trust OS governance layer](https://delight.ai/trust-os) lets you **set graduated autonomy, from fully human-reviewed to fully automated**, by task type.
Automating customer service is no longer a question of whether to do it, **but how**. [91% of customer service leaders say they are under pressure to implement AI in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026), according to Gartner, and for home warranty owners, getting it wrong is expensive. A wrong claims call, an angry contractor, or a homeowner who cancels coverage after one bad experience can all hurt your business.
The good news is that rollout risk, claims risk, and staffing risk are three separate problems. Each has a specific solution via automating, and none of them require betting the business on day one. Delight.ai's [home services and warranty](https://delight.ai/home-services-and-warranty) work exists because of a coordination gap, which is how most home warranty operations actually break down.
## Why do home warranty owners hesitate to automate customer service?
Home warranty owners hesitate because the stakes are concentrated. A home warranty claim touches a homeowner, a contractor, a parts supplier, and sometimes an adjuster, so a single mistake shows up fast, as a bad review or a lost renewal. That caution has real backing. A [McKinsey survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found that 80% of organizations deploying agentic AI in 2025 had already run into risky agent behavior, from improper data exposure to actions taken outside their intended limits.
Consumers feel a version of this same tension, [71% now use AI customer service in some form, but only 57% say they actually like the experience](https://delight.ai/ai-index).
That gap exists because most customer service automation for home warranty businesses tries to automate everything at once, with no way to check the work as it goes.
Owners who have been burned by a system that answered wrong, but confidently, are right to be hesitant. They are describing a real failure that a poorly executed rollout can produce. **The businesses that avoid these mistakes treat rollout, claims handoff, and staffing as three separate decisions**, each with its own test for readiness.
## How do you roll out a new customer service system without disrupting operations?
The best way to roll out a new customer service system without disrupting operations is to start narrow. Pick one call type, such as after-hours scheduling or status updates to an open claim, and automate only that. Everything else in your operation keeps running exactly as it does today while you test this one segment.
### Test the rollout on real calls, not a demo
A demo tells you the system can work in a controlled environment. **A pilot on real calls, for one queue, over 2 to 3 weeks, tells you whether it works in real time** for your customers, your contractors, and your call patterns. Delight.ai's [A/B testing and gradual rollout](https://delight.ai/product-release/a-b-testing) capability supports exactly this. It splits live traffic between the old process and the new one and reports on a statistical reliability score, so you can see whether the new system is actually holding up before it touches every call.
### Train your AI customer service automation on resolved cases
A system trained on generic customer service scripts does not know your parts suppliers, your contractor network, or how your warranty terms actually get interpreted in practice. **Look for a rollout built from your own resolved case history rather than a stock script**, so the AI agent starts already familiar with how your team actually handles a stuck claim or a delayed part.
### Keep a visible override for your team
Every phased rollout needs an easy way for a CSR to step in mid-conversation without the customer noticing a handoff, the kind of control that [Trust OS](https://delight.ai/trust-os) is built to give you. **That human override is not a workaround. It's what lets you widen the pilot with confidence**, because your team can catch and correct anything the system misses before it becomes a pattern.
## Is it safe to hand off claims approval to AI?
It's safe to hand off the parts of claims approval that are pure information gathering: confirming coverage, checking a service history, or scheduling the contractor visit. It's not yet safe to hand off decisions involving money, coverage disputes, or exceptions to policy. The dividing line is decision versus research or preparation, and a well-built system should let you set that boundary explicitly, not leave you guessing.
| Safe to hand off | Keep with a person |
|---|---|
| Confirming coverage and policy terms | Approving a claim above a set dollar threshold |
| Pulling service and repair history | Any coverage dispute or denial |
| Scheduling the contractor visit | An exception to standard policy terms |
### Prioritize a system that drafts over one that decides
Delight.ai's Actionbooks never auto-deploy. They are always drafts, reviewed by a person before anything goes live, and authored directly by your own operations team. A related capability, [Zero-Touch Improvement](https://delight.ai/product-release/zero-touch-improvement) researches real production failures and proposes fixes to those drafts, but it carries built-in approvals for anything financial or compliance-related, so **any change to how claims gets handled still gets approved by a person first**.
### Ask what the audit trail actually captures
When does the system hand off to a person, and who decides what it is allowed to do on its own? A safe answer requires an immutable, exportable record of every action the system took and every decision a person made, so a disputed claim can be traced step by step rather than argued from memory.
### Match the autonomy level to the stakes of the decision
Not every claims-adjacent task carries the same risk, so the system should not treat them identically. Delight.ai's [Trust OS](https://delight.ai/trust-os) governance layer lets you set graduated autonomy, from fully human-reviewed to fully automated, by task type, so a coverage confirmation and a disputed claim payout are never handled with the same level of oversight.
## Does automating customer service mean cutting staff?
No, automating customer service does not mean cutting staff. It may change what your team's day looks like, but not the size of the team. Automation absorbs the repetitive, low-judgment parts of the job, such as verifying an address or checking whether a part shipped, so people spend more time on complex calls that actually need a human being.
[Dr. Cool Services](https://delight.ai/customers/dr-cool-services), an HVAC company using delight.ai's voice AI to handle its own call volume, put it plainly: "Our best people shouldn't spend their days reading addresses back and forth. They should be solving problems and calming frustrated customers," said Mark Farley, the company's president.
That is not a smaller job for the team. It is a more useful one.
Experienced CX leaders describe it the same way; they would rather automate a smaller share of interactions at near-perfect satisfaction than a larger share at a noticeably lower one. Cutting corners to hit an automation number is not the goal. Freeing skilled people to handle the calls that need them is.
### What changes for your CSRs
- **Fewer repetitive lookups.** The system pulls account, coverage, and service history automatically instead of a person doing it by hand on every call.
- **More exception handling.** A CSR's day shifts toward the disputes, escalations, and judgment calls that genuinely need a person.
- **Faster context on handoff.** When a call does reach a person, it arrives with the customer's full history attached, not a blank screen.
## A home warranty owner's readiness checklist before expanding automation
Before expanding past an automation pilot, confirm three things:
1. Has the pilot run on real volume for at least 2 to 3 weeks, not a demo?
2. Does every claims-adjacent decision above your comfort threshold still route to a person?
3. Can your team see and correct what the system did, in real time, without waiting for a customer complaint to find out?
If all three are true, widening the rollout becomes a scaling decision. If any of them are not, that is the specific gap to close first, not a reason to abandon automation altogether.
**Getting the rollout, the claims handoff, and the staffing shift right** individually is what keeps a home warranty operation from ever having to choose between moving fast and staying in control, rather than picking one at the expense of the other. [Explore how delight.ai supports home services and warranty operations](https://delight.ai/home-services-and-warranty) to see how the phased rollout, the claims guardrails, and the team-first framing work together in practice.
- [Why do home warranty owners hesitate to automate customer service?](#why-do-home-warranty-owners-hesitate-to-automate-customer-service)
- [How do you roll out a new customer service system without disrupting operations?](#how-do-you-roll-out-a-new-customer-service-system-without-disrupting-operations)
- [Is it safe to hand off claims approval to AI?](#is-it-safe-to-hand-off-claims-approval-to-ai)
- [Does automating customer service mean cutting staff?](#does-automating-customer-service-mean-cutting-staff)
- [A home warranty owner's readiness checklist before expanding automation](#a-home-warranty-owners-readiness-checklist-before-expanding-automation)
## Frequently asked questions
Common questions about automating customer service for home warranty businesses.
**Will automating customer service slow down claims processing?**
No, automating customer service should not slow down claims processing. When scoped correctly, it speeds up the parts that do not need judgment, like confirming coverage or pulling service history, while routing anything with a dollar threshold or a dispute to a person. The slowdown risk only shows up when a system is asked to make judgment calls it was never built to make.
**What happens if the system gets a claim wrong?**
If the system gets a claim wrong, a person catches the error before it reaches the customer, because the system never makes the final call on a claim's outcome by itself. Its role is to draft a recommendation and gather the supporting information; a person reviews and approves it before anything ships.
**How long does a phased rollout actually take?**
A phased rollout typically takes 2 to 3 weeks for the initial pilot on a single call type or queue, enough time to judge whether the system is working on real volume. Expanding beyond that pilot is a separate decision made once the data supports it, not a fixed timeline set in advance. For delight.ai customers, a full phased rollout, from initial pilot to broader expansion, typically lands somewhere between 2 and 8 weeks, depending on how many call types or claim workflows are involved.
**Do I need to tell customers they are talking to an AI system?**
Yes, in a growing number of states you may need to explicitly state that customers are talking to an AI system. [Disclosure requirements expanded significantly through 2026.](https://www.orrick.com/en/Insights/2026/04/2026-State-Chatbot-Laws-Key-Provisions-and-Regulatory-Trends), and several now require disclosing to the customer that they are speaking with an AI agent rather than a person. Check your state's specific rules before launch, since they vary and are still changing.
**Does a phased rollout, staged claims approval, and a smaller automation footprint add up to fewer jobs?**
No. A narrow rollout, staged claims approval, and a deliberate automation footprint all assume a person stays in the loop on anything that needs judgment. The goal is a team doing more useful work, not a smaller one.
## Keep reading
[View more](/blog)
[ How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[ Does your dispatcher lose control with AI dispatch software?](/blog/ai-for-cx/does-your-dispatcher-lose-control-with-ai-dispatch-software)
[ Why do home service businesses lose jobs to their competitors?](/blog/ai-for-cx/speed-to-lead-why-home-service-businesses-lose-jobs)
[ How to automate invoice matching for claims](/blog/ai-for-cx/how-to-automate-invoice-matching-for-claims)
[View more](/blog)
## Why don't people trust AI customer service?
Source: https://delight.ai/blog/ai-for-cx/why-dont-people-trust-ai-customer-service
## Why don't people trust AI customer service?
[
Cheryl Chai
Product marketing lead](https://delight.ai/author/cheryl-chai)
July 31 2026
### Key takeaways
- **AI customer service already has plenty of users.** 71% of consumers have used it in the past year, and more than half call the experience positive.
- **Trust tracks how much control the AI has.** [Delight's 2026 AI Index](https://delight.ai/ai-index) finds people trust AI more when they can approve, override, or undo it, not just when it answers correctly.
- **The real divide is routine inquiries versus high-stakes conflict.** People hand AI the order-status check and keep a human for the billing dispute.
Most people have already talked to an AI agent about a late order, a billing question, or a canceled flight. Delight.ai's 2026 AI Index found 71% of U.S. consumers used [AI customer service](https://delight.ai/ai-customer-service) in the past year, and 57% called the experience genuinely helpful.
Resistance to AI in support is not about whether the technology works, but what happens once an AI agent gets enough authority to make a mistake that costs the customer something. Nobody asked how much of that authority the customer wanted to hand over in the first place.
### What's behind the AI customer service trust gap?
The AI customer service trust gap isn't a satisfaction problem. Most people who've tried it already rate the experience well. **57% of the 71% who used AI-powered support in the past year called it a positive experience**, and 48% said it made their issue easier to resolve.
If AI customer service were failing at its job, those numbers would look a lot worse. What's actually happening is closer to a scope problem.
[Delight.ai's 2026 AI Index](https://delight.ai/ai-index) found that 64% of consumers expect AI to outperform a human agent, and 53% want 24/7 support across every channel they use.
The pressure to deploy AI is becoming more persistent too. [Gartner found](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) that 91% of customer service leaders feel executive pressure to implement AI in 2026. The gap opens where those two forces meet: how much authority people are comfortable handing an AI agent before it has earned that authority for itself.
### Is AI customer service less accurate than a human?
Judging the numbers people report about their own experience, AI customer service is not less accurate than a human. But accuracy alone doesn't explain the hesitation, and treating it as the whole story is a common misread of AI adoption data.
McKinsey's 2026 research on [AI trust](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found that as organizations move from generative AI into agentic AI, risk shifts. It's no longer just a system saying the wrong thing. It's a system doing the wrong thing; taking an action, changing an account, or issuing a refund without a person confirming it first.
If a chatbot gives a customer the wrong answer, that’s an annoyance. An AI agent that reschedules the wrong flight or refunds the wrong amount is a different kind of problem, and consumers already know it.
[Delight's own 2026 AI Index](https://delight.ai/ai-index) found the same pattern from the consumer side. 72% of consumers name mistakes or incorrect decisions as their top concern about autonomous AI agents, ahead of data privacy (62%) and losing control of the interaction (56%).
People are worried about what happens after the AI acts on a misunderstanding or performs the wrong action.
### What would make people trust AI customer service more?
Delight.ai's 2026 AI Index asked exactly this question, and the answers cluster around themes of control and governance. The top-ranked factors for building trust emphasize user agency, giving people the ability to monitor, pause, or reverse AI actions, rather than merely relying on the system to perform correctly.
- **Data privacy and security safeguards.** 84% say this would increase their trust, the single highest factor in the Index.
- **Human oversight on autonomous agents.** 65% want a person able to review what the AI decided.
- **The power to stop or override at any time.** 59% want a real off switch, not just a feedback form after the fact.
- **Approval before AI actions happen.** 57% want to confirm a change before it takes effect, not after.
- **The ability to correct mistakes and reverse decisions.** 47% want undo to be a real option, not a support ticket.
All of these factors surface a common desire to have AI be governed like any employee, with a record of what it did, a way to check it, and a way to pull it back. That's the job of a governance layer like [Trust OS](https://delight.ai/trust-os).
Trust OS gives teams an audit trail, an emergency off switch, and version control over what an AI agent can do. Customers don't have to just take the AI's word for it, and neither does the business that's accountable when something goes wrong.
### Which customer service tasks do people trust AI with?
People don't treat AI as one decision. They split it task by task, and the split tracks risk more than it tracks the channel or the brand.
| Task | Prefer human | Prefer AI |
|---|---:|---:|
| Check order status | 34% | 49% |
| Check baggage or flight status | 36% | 39% |
| Check account balance | 36% | 40% |
| Dispute a price | 54% | 30% |
| Handle a missed connection | 58% | 20% |
| Dispute a transaction | 65% | 22% |
| Discuss symptoms and treatment | 70% | 20% |
This trend persists across industries. Delight.ai's AI Index scored five verticals on a 100-point trust scale, revealing that trust levels directly correlate with the potential cost of an error. Tech and media (60) and retail (59) rank the highest for consumer-facing trust, as mistakes in these fields are generally viewed as minor inconveniences.
**Financial services (50) and healthcare (47) score lowest**, where a wrong answer touches someone's money or their body. Healthcare was ranked the least trusted category even for its most AI-friendly task. 43% of survey respondents still want a human to schedule an appointment.
This means that people are doing a fairly rational risk calculation, and brands that ignore this may end up automating the wrong moments.
### Who's most skeptical of AI customer service?
Millennials, not Gen Z, are the most comfortable with AI handling customer service on its own. 63% of Millennials are fine with AI managing routine tasks independently.
**45% of Millennials use AI tools daily**, ahead of Gen Z (33%), Gen X (33%), and Boomers (13%).
The bigger gap runs along gender, not generation. Women contact customer service 38% more often than men in a typical month, yet trust AI with their experience less. Only 24% of women use AI tools daily versus 43% of men, and 40% say they may never feel comfortable with a fully autonomous AI agent.
Higher contact volume paired with lower trust means women are effectively setting the ceiling on how far AI customer service can go.
### How can brands close the AI customer service trust gap?
The Index points to a specific playbook, not a general call to "build more trust."
#### Make memory the baseline
70% of consumers said they'd feel more delighted if AI simply remembered their past interactions so they didn't have to repeat themselves. That's a low bar with a high payoff.
[Mixpanel's support team](https://delight.ai/customers/mixpanel) described the problem this solves in practice: "Our support agents were spending the first ten minutes of every interaction just figuring out who they were talking to. The actual problem-solving came second." A memory layer like [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) carries a customer's context across every channel, so an agent isn't starting from zero on message one.
#### Build in approval, override, and undo
The five trust levers from the Index (privacy safeguards, oversight, override, approval, and correction) all describe the same need. A customer wants proof that a person can step in before or after an AI agent acts.
That's a governance design decision and it has to be visible to the customer, not just documented internally.
#### Match the channel to the stakes
Routine, low-risk tasks are where AI already wins on its own. High-stakes moments, money, health, and exceptions, still need a human in the loop, at least for now. [Forrester's 2026 predictions](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) warn that overautomating complex or emotional inquiries will frustrate customers and erode satisfaction, which is the same line the Index draws from the consumer side.
54% of consumers expect to feel comfortable with fully autonomous AI within a year, so this line will keep moving. That's the idea behind graduated autonomy, letting an agent like [Agent Steward](https://delight.ai/autonomous-agent) own more of a case over time while still escalating anything that needs a judgment call.
Getting the handoff right today outweighs pushing autonomy further than customers are currently willing to grant it.
### In closing
People don't distrust AI customer service because it can't do the job. Delight.ai's 2026 AI Index shows the opposite: most people who've used AI customer service it helpful, and most expect it to get better. What they're withholding is authority, not confidence.
That authority gets earned through visible control, not a smarter model. Brands that let customers see, check, and reverse what an AI agent does will close this gap faster than brands waiting for the model to get good enough. See the full breakdown, industry by industry, in [delight.ai's 2026 AI Index](https://delight.ai/ai-index).
- [What's behind the AI customer service trust gap?](#section-anchor-id-0 "What's behind the AI customer service trust gap?")
- [Is AI customer service less accurate than a human?](#section-anchor-id-1 "Is AI customer service less accurate than a human?")
- [What would make people trust AI customer service more?](#section-anchor-id-2 "What would make people trust AI customer service more?")
- [Which customer service tasks do people trust AI with?](#section-anchor-id-3 "Which customer service tasks do people trust AI with?")
- [Who's most skeptical of AI customer service?](#section-anchor-id-4 "Who's most skeptical of AI customer service?")
- [How can brands close the AI customer service trust gap?](#section-anchor-id-5 "How can brands close the AI customer service trust gap?")
- [In closing](#section-anchor-id-6 "In closing")
### Frequently asked questions
What builds trust in AI customer service, and where humans still win.
**Why don't people trust AI customer service?**
People don't trust AI customer service primarily because they lack control over the agent's actions, rather than because the technology fails to work. 71% of consumers used AI customer service in the past year and 57% called it a positive experience. What's missing is control. People want the ability to approve, oversee, or reverse what an AI agent does, not just a system that answers correctly most of the time.
**Does AI customer service work?**
Yes, AI customer service works for most consumers, with 57% reporting a positive experience in the past year. Delight.ai's 2026 AI Index found 57% of people who used AI customer service in the past year had a positive experience. 48% said it made their issue easier to resolve.
The hesitation people report is less about whether AI works and more about what happens when it's wrong.
**What increases trust in AI customer service?**
Trust in AI customer service is primarily increased by providing users with greater control and accountability, such as data privacy safeguards, human oversight, and the ability to override or approve actions, rather than by simply improving the system's intelligence. The top factors, from delight.ai's 2026 AI Index, are all about control rather than intelligence. In order, data privacy and security safeguards (84%), human oversight on autonomous agents (65%), and override power (59%).
Rounding out the list: approval before an action happens (57%), and the ability to correct a mistake after the fact (47%). Every factor is about accountability, not raw intelligence.
**Will AI replace human customer service agents?**
AI is unlikely to fully replace human customer service agents because consumers prefer AI only for routine tasks, while maintaining a strong preference for humans during high-stakes moments like transaction disputes or health discussions. People want AI for routine tasks like checking an order or an account balance, where it's already the preferred option by a clear margin.
They still prefer a human for high-stakes moments like disputing a transaction or discussing a health issue, sometimes by more than two to one. The split is by task, not by channel or brand, and it holds across every industry delight.ai's 2026 AI Index measured.
**Which customer service tasks should stay with a human?**
Customer service tasks involving money, health, or policy exceptions should stay with a human because consumers overwhelmingly prefer human intervention for these high-stakes scenarios rather than relying on AI. Delight.ai's 2026 AI Index found 65% of consumers prefer a human to dispute a transaction, and 70% prefer a human for symptoms and treatment, its two most human-favored tasks.
Only about a fifth would rather use AI for those same moments. The pattern holds across industries; financial services and healthcare score lowest on the Index precisely because a mistake there costs more than a mistake on an order-status check.
### Keep reading
[View more](/blog "View more")
[
Does AI reflect on your brand reputation?](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
[
Generational differences in AI trust](/blog/insights/generational-differences-ai-trust)
[
Do millennials trust AI more than Gen Z?](/blog/insights/do-millennials-trust-ai-more-than-gen-z)
[
How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[View more](/blog "View more")
## Does AI reflect on your brand reputation?
Source: https://delight.ai/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation
- [Blog](/blog)
- [AI for CX](/blog/ai-for-cx)
- Does AI reflect on your brand reputation?
## Does AI reflect on your brand reputation?
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec
Content Engineer](https://delight.ai/author/caroline-adamec)
July 31 2026
**Key takeaways:**
- To customers, **the AI and the brand behind it are the same thing**.
- Delight.ai's 2026 AI Index found brand alignment is already the highest-scoring part of AI-driven customer experience, **at 57 out of 100**, while emotional connection trails 11 points behind at 46.
- **The real brand risk is using AI without the memory, oversight, and control** customers say they need before they'll trust it.
Does AI reflect on your brand? To the customer on the other end of the chat window, the answer is already yes. They don't experience "the AI" and "the company" as two separate things.
If an AI agent solves the problem in two minutes, that's a point for the brand. If it loops the customer through the same three questions and still gets the order wrong, that's a point against the brand, not against the technology. As more companies route everyday support through an [AI customer experience platform](https://delight.ai/ai-customer-experience-platform), that judgment call is happening more and more often.
Delight.ai surveyed 1,000 US consumers between March 17 and 23, 2026, for the [2026 Delight AI Index](https://delight.ai/ai-index), asking how people actually feel about AI-powered customer service. The clearest finding is that, to consumers, AI and the brand behind it are the same.
When the AI does something wrong, consumers blame the brand. When it performs well, they credit the brand, often reading it as a sign the company is modern, competent, and respects their time.
## Why do customers blame the brand when the AI gets it wrong?
Consumers don't experience an AI agent as a separate vendor bolted onto your company, they experience it as the company itself.
In the same survey, 53% of consumers said they want 24/7 support across every channel, 48% expect instant assistance, and 64% expect AI to actually outperform a human agent, not just match one.
The pressure to deploy isn't only coming from customers. [Gartner found](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) that 91% of customer service leaders feel pressure from their own leadership to implement AI in 2026, often faster than their AI is actually ready for.
Those expectations don't leave much room for a mediocre bot. [Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways) saw this firsthand after replacing a legacy chatbot with an AI agent named Freya: **containment climbed from 60% to 80% in two weeks**.
> "We are moving past the idea of 'support bots.' The future is one unified agent for sales and support—a single digital concierge that knows you, helps you, and travels with you," said Alf Lim, Chief Product Officer at Norse Atlantic Airways, in 2026.
That bar keeps getting harder to clear. [Forrester predicts](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) that a third of brands will damage customer trust in 2026 by pushing AI self-service into complex or emotional requests it isn't ready for. When that happens, the damage lands on the brand, not on AI as a category.
## How well does AI customer service reflect your brand today?
Across the five areas delight.ai's 2026 AI Index measures, AI-powered customer service scores 51 out of 100 overall. That's a middling grade, and the pattern underneath it is worth a closer look.
| Delight AI Index pillar | Score (out of 100) |
|---|---:|
| Brand alignment | 57 |
| Comfort with autonomy | 52 |
| Resolution effectiveness | 51 |
| Trust and confidence | 49 |
| Emotional resonance | 46 |
Brand alignment scores highest of the five, at 57. Emotional resonance scores lowest, at 46, an 11-point gap and the widest between any two pillars in the index.
In plain terms, customers already connect an AI agent's performance to what they think of the company behind it. They don't yet feel much of an emotional connection to the interaction itself.
That gap is an opportunity. Delight.ai's 2026 AI Index found that **70% of consumers** would feel more delighted if AI simply remembered their prior interactions.
Brands need AI that remembers.
## Where AI earns trust, and where it quietly loses it
Trust isn't abstract to consumers. Delight.ai's 2026 AI Index asked what would specifically make people trust an AI agent more, and the answers were concrete.
| What increases trust in an AI agent | Consumers who agree |
|---|---:|
| Data privacy and security safeguards | 84% |
| Human oversight of autonomous agents | 65% |
| Power to stop or override the AI at any time | 59% |
| Approval required before the AI acts | 57% |
| Ability to correct mistakes and reverse decisions | 47% |
Consumers want the AI guardrails that make handing over responsibility feel safe.
[McKinsey found something similar](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) at the organizational level. Nearly two-thirds of companies cite security and risk, not regulation, as the biggest barrier to scaling agentic AI.
The flip side shows up in what worries people about handing an AI agent real responsibility:
- **Mistakes or incorrect decisions** worry 72% of consumers.
- **Data privacy and security** worry 62% of consumers.
- **Losing control of the interaction** worries 56% of consumers.
Every one of those concerns is answerable with the trust drivers above. The businesses solving for it now are the ones setting the standard their competitors get compared against.
## Not every AI interaction carries the same brand risk
Consumers want AI where the stakes are low, and a person where the stakes are high.
- **Checking an order status:** 49% prefer AI, versus 34% who want a human.
- **Disputing a transaction:** 65% want a human, versus 22% who prefer AI.
- **Getting help with symptoms or treatment:** 70% want a human, versus 20% who prefer AI.
The pattern is consistent. People want AI to handle routine requests and a person to handle anything with real consequences. A brand that ignores this split invites exactly the kind of misstep that erodes trust fastest.
## How the risk of AI customer service maps to industry
That same split shows up when delight.ai's 2026 AI Index scores AI-powered customer service industry by industry.
| Industry | Delight AI Index score |
|---|---:|
| Tech and media | 60 |
| Retail | 59 |
| Travel | 55 |
| Financial services | 50 |
| Healthcare | 47 |
Healthcare scores lowest, and the caution runs deep even there. Even scheduling an appointment, the most AI-friendly task in healthcare, still has 43% of patients who'd rather talk to a person.
The lower an industry's score, the higher the cost of a misstep. A bad checkout agent is an inconvenience. A bad healthcare agent is a headline.
## Your customers aren't one audience
Comfort with AI in customer service isn't spread evenly across the people a brand serves, and treating it as if it were is its own brand risk.
- **Millennials are the real inflection point**, with 63% comfortable letting AI handle routine service tasks on their own, well ahead of Gen Z.
- **Women contact customer service far more often but trust AI less**: 38% more monthly contacts than men, yet 40% say they may never feel comfortable with a fully autonomous AI agent.
Build for the customer who's actually calling, not the persona in a slide deck.
## What protects your brand?
None of this means AI is too risky to use. Instead, it means the brand risk sits in the details most vendors treat as an afterthought: memory, oversight, and control.
### Give the AI a memory
One layer carries a customer's context across every channel they use, so someone who starts in chat and calls back twenty minutes later never has to re-explain themselves. Delight.ai calls this the [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp), or AMP. It's the direct answer to the 70% of consumers who said memory, not a friendlier personality, is what would delight them most.
### Keep a human in the loop
The same survey found human oversight, override power, and approval gates are what make consumers trust an autonomous agent in the first place. A governance layer that logs what the AI did and why, with a clear way for a person to step in, is what delight.ai calls [Trust OS](https://delight.ai/trust-os).
Comfort with full autonomy is still uneven. 54% of consumers expect to feel comfortable with fully autonomous AI within a year, but 32% don't anticipate ever feeling that way. Promising more independence than customers actually want is its own kind of overclaim.
### Treat compliance as a trust feature, not paperwork
84% of consumers said data privacy and security safeguards would most increase their trust in an AI agent. The [security and compliance credentials](https://delight.ai/security) a vendor holds are the trust signal customers already said they want.
Companies that get this right hear about it directly. "We conducted tests and POCs with several chatbot providers and were quite disappointed. Delight.ai's accuracy and flexibility, paired with Claude, solved the problems others couldn't," said Oh Ju-young of [Lotte Homeshopping's AI Promotion Team](https://delight.ai/customers/lotte-homeshopping), in 2025.
[Mixpanel's](https://delight.ai/customers/mixpanel) Kathleen Matthews described a similar shift in her own support operation: "Our support agents were spending the first ten minutes of every interaction just figuring out who they were talking to. The actual problem-solving came second." Closing that gap is what turns a routine support interaction back into a moment that reflects well on the brand instead of one that quietly damages it.
## In closing
Every AI interaction is a small test of whether your brand does what it says it does. Customers already give the AI credit or blame as if it were you. Explore delight.ai's [AI agent](https://delight.ai/ai-agent) to see what a branded, memory-first concierge looks like when it's built to pass that test.
- [Why do customers blame the brand when the AI gets it wrong?](#why-do-customers-blame-the-brand-when-the-ai-gets-it-wrong)
- [How well does AI customer service reflect your brand today?](#how-well-does-ai-customer-service-reflect-your-brand-today)
- [Where AI earns trust, and where it quietly loses it](#where-ai-earns-trust-and-where-it-quietly-loses-it)
- [Not every AI interaction carries the same brand risk](#not-every-ai-interaction-carries-the-same-brand-risk)
- [How the risk of AI customer service maps to industry](#how-the-risk-of-ai-customer-service-maps-to-industry)
- [Your customers aren't one audience](#your-customers-arent-one-audience)
- [What protects your brand?](#what-protects-your-brand)
- [In closing](#in-closing)
## Frequently asked questions
Explore how AI customer service affects brand trust and reputation
**Does AI customer service really affect how people see my brand?**
Yes, delight.ai's 2026 AI Index found that consumers treat an AI agent's performance as a direct reflection of the company behind it. When the AI fails, they blame the brand. When it succeeds, they credit the brand, often reading it as a sign the company is modern, competent, and respects their time.
**What is the delight.ai 2026 AI Index?**
The 2026 Delight AI Index is delight.ai's proprietary survey of 1,000 US consumers, fielded March 17 to 23, 2026, through the Sago research panel. It measures five pillars of AI-driven customer experience: brand alignment, comfort with autonomy, resolution effectiveness, trust and confidence, and emotional resonance. The overall score across all five pillars is 51 out of 100.
**Do customers blame the AI or the company when something goes wrong?**
Customers blame the company when something goes wrong. Delight.ai's research found consumers don't separate an AI agent from the brand operating it. A mistake reads as evidence the company doesn't have its act together.
**Which customer service tasks are safest to hand to AI?**
Routine, low-stakes tasks are the safest to hand to AI. 49% of consumers prefer AI over a human for checking an order status, compared with 34% who want a person. That preference reverses sharply for high-stakes tasks: 65% want a human for disputing a transaction, and 70% want a human for questions about symptoms or treatment.
**What's the fastest way to reduce AI-related brand risk?**
The fastest way to reduce AI-related brand risk is to focus on the trust drivers consumers already named. 84% say data privacy and security safeguards would increase their trust, 65% want human oversight of autonomous agents, and 59% want the power to stop or override the AI at any time. Build those in before adding more autonomy, not after.
## Keep reading
[View more](/blog "View more")
[
[Why don't people trust AI customer service?](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
[
[Generational differences in AI trust](/blog/insights/generational-differences-ai-trust)
[
[Do millennials trust AI more than Gen Z?](/blog/insights/do-millennials-trust-ai-more-than-gen-z)
[
[How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[View more](/blog "View more")
## Generational differences in AI trust
Source: https://delight.ai/blog/insights/generational-differences-ai-trust
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
July 31 2026
### Key takeaways
- **The most AI-comfortable generation isn't the one most people assume.** Delight.ai's 2026 AI Index found Millennials, not Gen Z, lead every generational AI trust metric.
- **Usage and trust aren't the same thing.** Gen Z uses plenty of AI, but that doesn't automatically translate into comfort with handing an AI agent a customer service problem.
- **Gen X and Boomers aren't just cautious.** A meaningful share of both groups say they may never feel comfortable with a fully autonomous AI agent.
Most brands build their [AI customer service](https://delight.ai/ai-customer-service) strategy around one assumption: that younger means more comfortable with AI. The generational differences in AI trust that [delight's 2026 AI Index](https://delight.ai/ai-index) actually found don't support that assumption.
Millennials, not Gen Z, are the most enthusiastic and comfortable generation when it comes to AI in customer service. That single reversal changes who a brand should actually be designing for first.
### Which generation trusts AI the most?
Millennials lead every generational AI trust metric delight.ai's 2026 AI Index measured, ahead of Gen Z, Gen X, and Boomers alike. 63% of Millennials are comfortable with AI handling routine customer service tasks on its own, a level no other generation reaches.
| Metric | Millennial | Gen Z | Gen X | Boomer |
|---|---:|---:|---:|---:|
| Use AI tools at least once a day | 45% | 33% | 33% | 13% |
| Hold a very positive attitude toward AI | 35% | 20% | 17% | 8% |
| Comfortable with AI handling routine tasks independently | 63% | — | — | — |
The gap between Millennials and Gen Z isn't small. Millennials use AI tools daily at nearly the same rate as Gen Z and Gen X combined.
They're also almost twice as likely to describe their attitude toward AI as very positive, at 35% versus Gen Z's 20%.
### Why isn't Gen Z the most comfortable with AI in customer service?
Gen Z isn’t the most comfortable with AI in customer service because using AI constantly and trusting it with a real problem are two different things. Gen Z grew up with AI tools built into everyday apps, which explains the high familiarity.
But familiarity with a tool isn't the same as confidence in giving it authority over an account or a refund. [Gartner found](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) that 91% of customer service leaders feel executive pressure to implement AI in 2026.
[Stanford HAI's 2026 AI Index](https://hai.stanford.edu/ai-index/2026-ai-index-report/public-opinion) describes a related pattern at the population level. Public opinion on AI is more divided than raw adoption numbers suggest, and **comfort varies far more by context than by how often someone uses the technology**.
Gen Z's daily AI usage (33%) sits well behind Millennials (45%). Gen Z's very-positive attitude toward AI (20%) trails Millennials by 15 points, too.
High exposure to AI in general doesn't carry over into customer service the way most brands assume.
### What's behind Gen X and Boomers' resistance to AI?
Gen X and Boomers aren't rejecting AI outright. They're rejecting the idea of it working alone. 36% of Gen X and 48% of Boomers say they may never feel comfortable with a fully independent AI agent running customer service on its own.
That's a different concern than accuracy or speed. It's a concern about what happens with no person available to step in. [McKinsey's 2026 research on AI trust](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) points to the same shift industry-wide, as AI moves from answering questions to taking actions on its own.
Delight.ai's 2026 AI Index found this same pattern across the general population, not just Gen X and Boomers. People consistently rank human oversight and the ability to override an action above raw AI performance.
For Gen X and Boomers specifically, the fix is proving a person is still reachable when they need more support with a complex concern.
### Does this mean brands should design AI customer service for Gen Z first?
No. Brands should not design AI for Gen Z first. Millennials are the actual inflection point, the generation whose expectations are most likely to set the baseline everyone else eventually measures against.
Designing around Gen Z's high usage numbers misreads what the Delight AI Index actually found. Gen Z uses AI constantly for everyday tasks, but Millennials are the ones handing it real customer service problems.
That distinction is exactly why Millennials are the harder, more valuable generation to design for.
### How should brands design AI customer service across generations?
Generational differences in AI trust call for one flexible design, not four separate ones built for each age group.
#### Make memory do the heavy lifting
Repeating account details to a system that should already know them is a friction point every generation notices, skeptical or not. A memory layer like [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) carries a customer's context across channels, so an agent isn't starting over on every new conversation.
#### Give skeptical generations a visible off-ramp
Gen X and Boomers aren't asking for less AI. They're asking for a person they can still reach. A governance layer like [Trust OS](https://delight.ai/trust-os) gives a business the audit trail and override controls to make that handoff real, not just a promise buried in a terms-of-service page.
#### Younger generations aren't necessarily more autonomous
Gen Z's comfort with everyday AI tools doesn't mean Gen Z wants a fully autonomous agent handling customer service either. [Agent Steward](https://delight.ai/autonomous-agent)'s graduated approach lets an AI agent earn more responsibility over time instead of assuming any one generation is ready for full autonomy from day one.
Generational differences in AI trust are really differences in what each generation needs to see before it hands over control.
Millennials lead because they've already seen AI handle something real and get it right. Gen X and Boomers may follow once they see the same thing. Explore the full generational and gender breakdown in [delight.ai's 2026 AI Index](https://delight.ai/ai-index).
- [Which generation trusts AI the most?](#section-anchor-id-0 "Which generation trusts AI the most?")
- [Why isn't Gen Z the most comfortable with AI in customer service?](#section-anchor-id-1 "Why isn't Gen Z the most comfortable with AI in customer service?")
- [What's behind Gen X and Boomers' resistance to AI?](#section-anchor-id-2 "What's behind Gen X and Boomers' resistance to AI?")
- [Does this mean brands should design AI customer service for Gen Z first?](#section-anchor-id-3 "Does this mean brands should design AI customer service for Gen Z first?")
- [How should brands design AI customer service across generations?](#section-anchor-id-4 "How should brands design AI customer service across generations?")
### Frequently asked questions
Generational differences in AI trust, answered.
#### Which generation trusts AI customer service the most?
Millennials trust AI customer service the most. Delight.ai's 2026 AI Index found 63% of Millennials are comfortable with AI handling routine customer service tasks independently. 45% of Millennials also use AI tools daily, both the highest rates of any generation measured.
No other generation comes close on either metric, including Gen Z.
#### Why don't Gen X and Boomers trust AI as much as Millennials?
It's less about distrust of AI itself and more about autonomy. 36% of Gen X and 48% of Boomers say they may never feel comfortable with a fully independent AI agent handling customer service on its own. Both groups want a person that's reachable, not necessarily a smarter AI answering on a person's behalf.
#### Is Gen Z actually comfortable with AI customer service?
Less than most brands assume. Gen Z uses AI tools daily at a lower rate than Millennials (33% versus 45%) and reports a very positive attitude toward AI less often too (20% versus 35%). High general AI use doesn't automatically translate into trust in a customer service context, where a mistake actually costs something.
#### Do men and women show the same generational pattern in AI trust?
Men and women do not necessarily show the same generational patterns with AI trust. Delight.ai's 2026 AI Index found gender differences that cut across generations, not just within them. Women contact customer service more often than men but use AI tools daily far less, 24% versus 43%.
40% of women say they may never feel fully comfortable with autonomous AI, regardless of age group.
#### Should brands design AI customer service differently for each generation?
Brands should not design four separate models for each generation, but their emphasis should shift by generation. Millennials respond well to AI handling more of a case on its own, since they've already seen it work. Gen X and Boomers respond better when a visible option to reach a person is built into the same experience from the start.
### Keep reading
[View more](/blog "View more")
[ Why don't people trust AI customer service?](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
[ Does AI reflect on your brand reputation?](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
[ Do millennials trust AI more than Gen Z?](/blog/insights/do-millennials-trust-ai-more-than-gen-z)
[ How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[View more](/blog "View more")
## Do millennials trust AI more than Gen Z?
Source: https://delight.ai/blog/insights/do-millennials-trust-ai-more-than-gen-z
[LeighAnne Manwiller
Product marketing manager](https://delight.ai/author/leighanne-manwiller)
July 31 2026
**Key takeaways**
- **Millennials trust and use AI in customer service more than any other generation**, including Gen Z, according to delight.ai's 2026 AI Index.
- **45% of millennials use AI tools daily**, compared with 33% of Gen Z, 33% of Gen X, and 13% of Boomers.
- **The primary divide in AI trust is generational**, separating Millennials and Gen Z from the more skeptical Gen X and Baby Boomers.
Gen Z grew up with a phone in one hand and an AI chatbot one tap away. That should make Gen Z the generation most ready to let AI handle a customer service problem.
The data says otherwise. According to [delight.ai's 2026 AI Index](https://delight.ai/ai-index), a survey of 1,000 US consumers fielded March 17 to 23, 2026, millennials trust AI more than Gen Z once the question moves from casual AI use to actual customer service. Millennials use AI tools more often, hold a more positive view of AI, and feel more comfortable letting AI resolve a request on its own than any other generation surveyed.
That finding flips a common assumption about who drives AI adoption. It also points to who is setting the bar the rest of the market has to clear.
## How do consumers feel about AI customer service in 2026?
Here's the overall picture before the generational breakdown. 71% of US consumers engaged with [AI customer service](https://delight.ai/ai-customer-service) in the past 12 months, and 57% came away with a positive experience. Less than half, 48%, said AI made it easier to resolve their issue, and just 33% felt more valued as a customer as a result.
[Delight's 2026 AI Index](https://delight.ai/ai-index) puts the overall AI customer service experience at 51 out of 100. People have adopted AI faster than they've learned to trust it, and that gap between adoption and trust shows up differently by generation.
## Which generation trusts AI customer service the most?
Millennials come out on top across every measure delight.ai's 2026 AI Index tracked. 45% of millennials use AI tools daily, well ahead of Gen Z and Gen X, who are tied at 33%, and far ahead of Boomers at 13%. Millennials also hold the most favorable attitude toward AI of any generation, and the highest comfort level with AI handling a routine customer service task on its own.
Generation | Daily AI tool usage | Very positive attitude toward AI
---|---:|---:
Millennials | 45% | 35%
Gen Z | 33% | 20%
Gen X | 33% | 17%
Boomers | 13% | 8%
63% of millennials say they're comfortable with AI handling routine customer service tasks independently, the highest comfort level of any generation surveyed. For a business deciding how much autonomy to give an AI agent, that's the number worth watching — not who uses AI the most, but who trusts it enough to let it finish the job without a human standing by.
## Why millennials lead in AI trust
The obvious assumption is that a generation raised on smartphones would trust AI the most. The data suggests something closer to the opposite: familiarity with a problem, not familiarity with a device, predicts trust.
Millennials remember what customer service looked like before AI. Hold music, transferred calls, and repeating an account number to yet another agent are recent memories. Against that baseline, an AI agent that resolves a request in one pass reads as an upgrade.
Gen Z's relationship with AI runs the other way. Many grew up watching AI chatbots fail in public and hearing about AI as something to question by default. [Gallup's research on Gen Z](https://news.gallup.com/poll/708224/gen-adoption-steady-skepticism-climbs.aspx) found their AI use has stayed steady over the past year, while their excitement and hopefulness about it have declined.
Gen Z isn't uniformly behind, though. A [2025 Future Commerce survey](https://www.emarketer.com/content/ai-winning-trust-shopping-guide-especially-among-gen-z-buyers) found **23% of Gen Z and 27% of millennials trust AI product recommendations more than human ones**, with both generations using AI shopping tools at similar rates. The gap opens specifically around autonomy in customer service, not around AI use in general.
## Where Gen X and Baby Boomers stand
The bigger trust gap in AI customer service isn't between millennials and Gen Z. It's between both of them and the two older generations. Gen X and Boomers use AI daily at far lower rates, 33% and 13%, and hold far less positive attitudes toward it, 17% and 8% very positive.
36% of Gen X and 48% of Boomers say they may never feel comfortable with a fully independent AI agent running customer service on its own. Almost half of Boomers, in other words, aren't waiting for better AI. They've already decided their answer is no. [Source: [delight.ai's 2026 AI Index](https://delight.ai/ai-index)]
## What tasks can AI complete more efficiently than humans?
The generational gap looks different once tasks are split by stakes. Across delight.ai's 2026 AI Index, routine requests favor AI. High-stakes requests still favor a human, regardless of generation.
- **Checking an order status:** 49% prefer AI, 34% prefer a human.
- **Disputing a transaction:** 65% prefer a human, 22% prefer AI.
- **Asking about symptoms or treatment:** 70% prefer a human, 20% prefer AI.
The pattern holds across every generation surveyed, millennials and Gen Z included. People don't want an AI to decide who's right in a billing dispute or offer medical guidance. They want AI to move fast on tasks that don't carry much risk if something small goes wrong.
Delight.ai's 2026 AI Index breaks the overall experience into five pillars, scored out of 100:
- **Brand Alignment:** 57
- **Comfort with Autonomy:** 52
- **Resolution Effectiveness:** 51
- **Trust & Confidence:** 49
- **Emotional Resonance:** 46
Comfort with Autonomy sits second from the top, just above the overall trust score. That's the pillar the generational differences run through most directly; how much they're willing to let it do without asking permission first.
## What builds AI trust across every generation?
Every generation shares the same worries about letting AI act on its own:
- **Mistakes or incorrect decisions:** 72% cite this as a top concern.
- **Data privacy and security:** 62% cite this as a top concern.
- **Losing control over what AI does:** 56% cite this as a top concern.
Those concerns point directly to what would influence people's opinions on AI. delight.ai's 2026 AI Index asked what would increase trust in an autonomous AI agent, and the answers line up closely with the concerns above.
- **Data privacy and security safeguards** would increase trust for 84% of consumers.
- **Human oversight of autonomous agents** would increase trust for 65%.
- **The power to stop or override the AI at any time** would increase trust for 59%.
- **Approving AI actions before they happen** would increase trust for 57%.
- **The ability to correct mistakes and reverse decisions** would increase trust for 47%.
Those five levers, in order, describe a specific kind of infrastructure. A person can see what the AI is about to do, approve it in advance, and override or undo it if something goes wrong.
Delight.ai calls that governance and observability layer [Trust OS](https://delight.ai/trust-os), because trust in an autonomous agent gets built through those controls, not through the AI simply getting smarter.
## What does this mean for businesses building AI customer experiences?
[Gartner found 91% of customer service leaders](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) feel pressure to implement AI in 2026. Whichever generation your customers belong to, there’s real pressure to deploy AI now. What's still a choice is how much autonomy to give it.
Millennials are the current north star for what a good AI customer service experience feels like. If your AI experience doesn't clear the bar that millennials already expect, it will struggle with every generation coming up behind them, especially the ones who are already skeptical.
One lever shows up across every generation, not just millennials. 70% of consumers say they'd feel more delighted if AI simply remembered their prior interactions instead of starting over every time.
That's the gap a branded [AI concierge](https://delight.ai/ai-agent) is meant to close for your customers. It carries context across chat, voice, and every other channel someone uses, instead of treating each conversation as the first one.
Delight.ai is built on Sendbird's infrastructure for exactly that kind of continuity. It combines long-term conversational memory, omnichannel presence, and the same Trust OS controls over autonomy that consumers say they want.
## In closing
Yes, millennials trust AI more than Gen Z by a wide margin, at least once the AI is handling an actual customer service problem instead of drafting an email. The open question is whether Gen X, Boomers, and even Gen Z eventually follow the millennials’ path, or whether comfort with full AI autonomy stays generational for good.
For the full breakdown of all five trust pillars, including how comfort with autonomy splits by task and channel, see the complete [2026 Delight AI Index](https://delight.ai/ai-index).
- [How do consumers feel about AI customer service in 2026?](#section-anchor-id-0 "How do consumers feel about AI customer service in 2026?")
- [Which generation trusts AI customer service the most?](#section-anchor-id-1 "Which generation trusts AI customer service the most?")
- [Why millennials lead in AI trust](#section-anchor-id-2 "Why millennials lead in AI trust")
- [Where Gen X and Baby Boomers stand](#section-anchor-id-3 "Where Gen X and Baby Boomers stand")
- [What tasks can AI complete more efficiently than humans?](#section-anchor-id-4 "What tasks can AI complete more efficiently than humans?")
- [What builds AI trust across every generation?](#section-anchor-id-5 "What builds AI trust across every generation?")
- [What does this mean for businesses building AI customer experiences?](#section-anchor-id-6 "What does this mean for businesses building AI customer experiences?")
- [In closing](#section-anchor-id-7 "In closing")
## Frequently asked questions
Generational AI trust, answered with 2026 AI Index data.
**Do millennials trust AI more than Gen Z?**
Yes. delight.ai's 2026 AI Index found millennials use AI tools daily at a higher rate than Gen Z (45% versus 33%), hold a more positive attitude toward AI (35% versus 20% "very positive"), and are more comfortable letting AI handle a routine customer service task independently (63%). Gen Z uses AI often but shows more caution about full AI autonomy than millennials do.
**Which generation uses AI customer service the most?**
Millennials are the generation that uses AI customer service the most. 45% of millennials use AI tools daily, compared with 33% of Gen Z, 33% of Gen X, and 13% of Boomers, according to delight.ai's 2026 AI Index. Millennials also report the most positive attitude toward AI of any generation surveyed.
**Will Gen X and Boomers ever trust fully autonomous AI?**
Many Gen X and Boomers say they will never fully trust autonomous AI. 36% of Gen X and 48% of Boomers told delight.ai's 2026 AI Index they may never feel comfortable with a fully independent AI agent running customer service on its own, a far higher holdout rate than either millennials or Gen Z report.
**What increases trust in autonomous AI agents?**
Data privacy and security safeguards top the list of factors that increase trust in AI, cited by 84% of consumers in delight.ai's 2026 AI Index. Human oversight (65%), the power to stop or override the AI at any time (59%), approving actions before they happen (57%), and the ability to reverse a mistake (47%) round out the top five.
**Do people prefer AI or humans for customer service?**
Whether people prefer AI or humans for customer service depends on the task. delight.ai's 2026 AI Index found people prefer AI for low-stakes requests like checking an order status, 49% AI versus 34% human, and prefer a human for high-stakes moments like disputing a transaction, 65% human versus 22% AI, or discussing symptoms and treatment, 70% human versus 20% AI.
## Keep reading
[View more](/blog "View more")
[
Why don't people trust AI customer service?](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
[
Does AI reflect on your brand reputation?](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
[
Generational differences in AI trust](/blog/insights/generational-differences-ai-trust)
[
How to prevent second truck rolls in field service](/blog/ai-for-cx/how-to-prevent-second-truck-rolls-in-field-service)
[View more](/blog "View more")
## Should AI Handle Customer Complaints?
Source: https://delight.ai/blog/ai-for-cx/should-ai-handle-customer-complaints
## Should AI Handle Customer Complaints?
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai — Product marketing lead](https://delight.ai/author/cheryl-chai)
August 5 2026
**Key takeaways:**
- **AI can resolve routine, low-ambiguity complaints**, like updating a customer on their order status, faster than a human agent. **It may struggle with complex or higher-stakes tasks**, such as rebooking a missed flight.
- The real question when implementing AI customer service is **which tasks the AI should have autonomy over**, and which tasks require human input.
- Trust grows when the handoff between AI and a human is **visible and governed**, which is exactly what [delight.ai](http://delight.ai)'s [Trust OS](https://delight.ai/trust-os) is built to do.
Most support teams already send some of their customer complaints through AI, whether they planned it that way or not. A missed-delivery message that lands in a chatbot, a billing question routed through an automated flow, a "where is my order" text answered by a script.
The real question for a [CX leader](https://delight.ai/ai-customer-service) in 2026 is how much of the complaint-handling process AI should own, and where a human needs to take over.
### What does it mean to let AI handle a complaint?
"Handling" a complaint covers a wide range of work, including reading what the customer is upset about, deciding what to do, taking action to resolve it, and following up. AI can do all four steps for a simple case, like reissuing a shipping label. For a complicated one, like a service failure that touches three different departments, AI might only handle the first step, then hand the rest to a person.
That range is exactly why the answer to "should AI handle complaints" is a firm yes, with a condition. AI should handle routine customer complaints, but a human should handle higher-stakes, more emotionally involved tasks.
### Can AI handle customer complaints effectively?
Yes, AI can handle the customer complaints that don't require judgment effectively. [Gartner found that 91% of customer service leaders](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) feel pressure to implement AI in 2026, and the leaders who've deployed it well on the right complaints are seeing resolution rates climb rather than stall, the way [Hanssem's resolution rate roughly doubled over five months](https://delight.ai/customers/hanssem) once it narrowed AI to the complaints it was actually built to resolve.
Effectiveness drops fast once a complaint turns emotional or ambiguous. A customer who's angry about a missing package, disappointed after a service failure, or unsure what outcome they even want needs a different kind of response than what AI can provide. That gap is real enough that [Forrester predicts a third of companies will actively harm the customer experience](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) in 2026 by deploying AI self-service before it's ready for the complaints it's given.
### What kinds of complaints is AI good at handling?
The complaints AI resolves well share three traits:
- **High volume** — the same request repeats across hundreds or thousands of customers.
- **Low ambiguity** — there's one clearly correct action, not a judgment call.
- **Known resolution path** — the fix already exists in policy or a workflow.
Complaints outside that shape need a person.
#### Routine and transactional
Order status, appointment changes, account access, and refund requests that follow a clear policy. These are the complaints where a customer just wants the fix, not a conversation.
#### Proactive and preventive
Some of the best complaint handling never becomes a complaint. AI features can flag a payment failure or a delayed shipment and message the customer before they think to reach out, which is the difference between resolving an issue and preventing one.
#### Repetitive
A customer asking the same question a hundred other customers asked this week still deserves a fast, accurate answer. Volume is what makes a complaint a strong candidate for automation.
### Should AI or human agents handle customer complaints?
Neither AI nor a human should handle every customer complaint on their own. The complaints that fit AI's strengths should go to AI. The ones that need judgment, empathy, or a decision no policy has covered yet should go to a person. **Graduated autonomy** is what makes this workable. It's a system where AI earns more responsibility as its track record becomes stronger, always inside limits a human sets and can revoke.
The AI coordination required to resolve complaints that span multiple steps or systems is called [Agent Steward](https://delight.ai/autonomous-agent) within the [delight.ai](http://delight.ai) product. Agent Steward owns a case end to end across every channel and party involved, while a human still sets the limits it can act within. Autonomy grows because AI keeps earning it, not because a company wants to cut headcount. The point is freeing up a support team's time for the complaints that actually need a person's judgment.
#### Complaint routing examples
| Complaint type | Best handled by |
|---|---:|
| Order status, account access, appointment changes | AI, end to end |
| Multi-step case across departments or vendors | AI coordinates every party in parallel, a human resolves judgment calls |
| Angry, emotional, or ambiguous request | Human, with full context AI already gathered |
| Policy exception or financial dispute | Human decision, AI-prepared |
### When should AI hand off a complaint to a human?
The handoff moment matters more than the AI's raw capability, because a bad handoff undoes any goodwill the AI built up. Three signals should trigger a handoff from AI to a human every time:
- **Emotional intensity** — frustration or distress that needs empathy.
- **Policy exceptions** — no existing rule covers the situation.
- **Financial or compliance risk** — anything with real dispute or legal exposure.
#### Emotional intensity
When a customer's language signals real frustration or distress, the response needs empathy that a script can't be programmed to invoke. AI can flag the signal, but a person should deliver the response.
#### Policy exceptions and ambiguity
Not every complaint fits an existing rule. When the right answer isn't written down anywhere, that signals a judgment call, a decision for a person to make, not something for AI to generate.
#### Financial or compliance risk
Refund amounts above a set threshold, disputes that touch a regulated process, or anything with legal exposure needs a human sign-off.
[McKinsey's 2026 research](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) on agentic AI found that organizations now have to govern what an AI agent *does*, not just what it says, since an agent that takes action can misuse a tool or act outside its guardrails in ways a chat-only system never could.
That's exactly the kind of boundary a governance layer needs to enforce, one that defines what an AI agent can commit to on its own, flags low-confidence responses before they reach a customer, and lets a team pause automated responses instantly if something looks wrong.
At [delight.ai](http://delight.ai), that layer is called [Trust OS](https://delight.ai/trust-os), the system that keeps a human in control of what the AI is allowed to do and reviews its decisions after the fact.
#### Signals mapped to routing
| Signal | Example complaint | Routed to |
|---|---|---|
| Routine status request | "Where is my order? It was supposed to arrive yesterday." | AI |
| Simple refund within policy | "I'd like to cancel my subscription and get a refund for this month, per your 30-day policy." | AI |
| Emotional intensity | "As a long-standing customer, I'm frustrated that my order has been delayed and you haven't done anything to fix it." | Human |
| Policy exception | "My return window closed two days ago, but the product arrived damaged. Can you make an exception?" | Human |
| Financial or compliance risk | "I was charged twice for the same order and need a $340 refund." | Human, AI-prepared |
### Does using AI for complaints hurt customer trust?
Using AI for complaints doesn't automatically hurt customer trust, but the risk is real if the AI's reasoning stays invisible. [Delight.ai](http://Delight.ai)'s own research found that [71% of consumers already use AI customer service, and only 57% like it](https://delight.ai/ai-index). That gap between adoption and satisfaction is the actual problem to solve moving forward, and it isn't solved by faster responses alone.
It's solved by making the AI's decisions visible and reviewable, which allows for constant iteration to ensure support tools are meeting customer needs. This visibility is what closes the gap between adoption and satisfaction.
Norse Atlantic Airways replaced a legacy chatbot with an AI agent and grew containment from [60% to 80% in two weeks](https://delight.ai/customers/norse-atlantic-airways), a result the airline's own product chief credited to treating the agent like a team member with a clear goal and a manager watching its work, not a black box left to run unsupervised.
[Hanssem](https://delight.ai/customers/hanssem) saw a similar pattern. Resolution rates roughly doubled over five months, and its IT director pointed to that same visibility as the reason the team trusted the system enough to keep expanding it. A capable AI plus a reviewable trail of what it decided and why is what turns adoption into trust instead of just tolerance.
We've watched companies treat "should AI handle complaints" as a switch to flip, all or nothing. It rarely works that way. The complaints worth automating are the ones with a clear answer and no emotional weight. The rest still need a person, and the job of a good system is routing each one correctly and proving, after the fact, that it did. [See how delight.ai's customer service tools help close the gap between adoption and satisfaction](https://delight.ai/ai-customer-service)
- [What does it mean to let AI handle a complaint?](#what-does-it-mean-to-let-ai-handle-a-complaint)
- [Can AI handle customer complaints effectively?](#can-ai-handle-customer-complaints-effectively)
- [What kinds of complaints is AI good at handling?](#what-kinds-of-complaints-is-ai-good-at-handling)
- [Should AI or human agents handle customer complaints?](#should-ai-or-human-agents-handle-customer-complaints)
- [When should AI hand off a complaint to a human?](#when-should-ai-hand-off-a-complaint-to-a-human)
- [Does using AI for complaints hurt customer trust?](#does-using-ai-for-complaints-hurt-customer-trust)
### Frequently asked questions
**Can AI handle customer complaints effectively, or does it still need a human?**
AI handles routine, low-ambiguity complaints effectively, including order status, account access, and appointment changes. It needs a human for anything emotional, ambiguous, or high-stakes, where judgment matters more than speed.
**Should AI or human agents handle customer complaints?**
Both AI and human agents should handle customer complaints, split by what each complaint actually requires. Routine complaints with a known correct action go to AI. Complaints needing empathy, judgment, or a decision outside existing policy go to a person, ideally with the context AI already gathered.
**When should AI hand off a complaint to a human agent?**
AI should hand off a complaint to a human when there's emotional intensity, it falls outside existing policy, or carries financial or compliance risk. A governance layer, like Trust OS, should flag these moments automatically rather than relying on the AI to recognize its own limits.
**Does using AI for complaints hurt customer trust?**
Using AI for complaints won't inherently hurt customer trust, but a hidden decision-making process increases risk. Consumers already use AI customer service more than they say they like it, and closing that gap depends on making the AI's reasoning and limits visible, not just making it fast.
### Keep reading
[View more](/blog "View more")
- [ Introducing Delight Agent MCP: Ask your AI agent about your AI agent](/blog/product/introducing-delight-agent-mcp)
- [ Why don't people trust AI customer service?](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
- [ Does AI reflect on your brand reputation?](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
- [ Generational differences in AI trust](/blog/insights/generational-differences-ai-trust)
[View more](/blog "View more")
## Introducing Delight Agent MCP: Ask your AI agent about your AI agent
Source: https://delight.ai/blog/product/introducing-delight-agent-mcp
- [Blog](/blog)\n- [Product](/blog/product)\n\n## Introducing Delight Agent MCP: Ask your AI agent about your AI agent\n\n[](https://delight.ai/author/leighanne-manwiller)
LeighAnne Manwiller
Product marketing manager\n\n\n\nAugust 5 2026\n\nYour AI agent handles thousands of customer conversations a day, and understanding it has always meant leaving the tools you already work in, pulling up a dashboard, exporting a report, and piecing the answer together by hand. That process is slow, and it only ever answers the question you thought to ask before you started digging. The Delight Agent MCP changes that. Ask a question about your agent in any MCP-compatible AI client, including Claude, ChatGPT, Cursor, or Codex, and it answers from your agent's live data directly, no export required and no second tool to open.\n\n### Picking up where the dashboard leaves off\n\nSay your handoff rate jumps three points overnight. The dashboard shows you the number, but not the reason behind it. Finding that out today means pulling the conversations that got escalated, reading through a sample by hand, cross-referencing which knowledge articles were live that week, and guessing at a pattern. That's an afternoon of work to answer one question, and the moment a different question comes up, that process starts over from zero.\n\n\n\nThis isn't a data problem. Delight.ai's backend already tracks almost everything you'd need, which knowledge sources actually got used, which tools fired and which sat idle, exactly where the agent's confidence dropped mid-conversation. None of that reasoning has ever lived anywhere you could easily question it, because dashboards are built to show you what happened in aggregate, not to let you interrogate why.\n\nThe Delight Agent MCP puts those signals directly in the AI tool you already use to think through a problem like this one, so the investigation happens in the same place the question occurred to you. And once you know the check order status tool is the culprit, fixing it doesn't mean switching tools either. The same conversation that found the failure can update the tool directly, so diagnosis and fix happen without ever leaving the chat.\n\n### Ask it like you'd ask a colleague\n\nThere's no dashboard to open and no report to export. You ask a question the same way you'd ask a colleague, in plain language, in whatever AI tool you already use, and it answers from your agent's live data. "What was our AI resolution rate this week, and which inquiry category drove the most handoffs?" Ask it once, the way you'd ask a teammate, and get an answer built from your actual conversations instead of a chart you have to interpret yourself.\n\n\n\nAnd it doesn't stop at the first number. Ask why handoff rate went up last week, and it traces the answer back through the actual conversations behind it, notices that a cluster of them touched a topic your knowledge base barely covers, and tells you that instead of just handing you an updated chart. Narrow the date range, pull the transcripts behind a specific spike, compare this week against last, all in the same conversation, without switching tools or waiting for someone else to run the report.\n\nYou already know how to ask the right question. Now your AI assistant has the data to actually answer it.\n\n### How early customers are already using it\n\nSince launch, a growing list of teams, including Mixpanel, have turned to the Delight Agent MCP, not for new data, but for a new way to work with the data that's already there. Here are a few examples of what that looks like.\n\n- *"What excites us about this feature is that piecing together dashboards and transcripts eats up an afternoon most weeks, so being able to just ask, in the tool we're already working in, and get an answer, would be a huge unlock for us."*\n\n — **Mixpanel**\n\n### The knowledge blind spot\n\nYour dashboard shows usage counts for every knowledge source, but a low usage count doesn't automatically mean a source is safe to cut. It might be the one article your highest-value accounts rely on.\n\nAsk your AI assistant which sources are safe to prune, and drawing on the MCP, it can weigh usage against your business's own data in the same AI session, like which customers hit each source and what they're worth, so a rarely used article tied to your biggest accounts doesn't get flagged the same way as one nobody important ever reads. If a source is genuinely safe to cut, or a gap needs filling, it can make that change right there instead of leaving you a list to act on later.\n\n### The dead tool problem\n\nA usage graph, or even a scheduled alert on call volume, can tell you a tool went quiet. It can't tell you a tool that's still firing constantly isn't actually helping, because a simple volume count never checks whether those conversations are resolved.\n\nAsk your AI assistant which idle tools are worth fixing first, and through the MCP, it can cross-reference call volume against resolution outcomes in the same answer, so a tool that fires often but still ends in a handoff gets flagged the same way as one that never fires at all. Once you've found the one worth fixing, it can make the fix directly, rather than just handing you a name to go look up in the dashboard.\n\n### The silent misunderstanding\n\nSomewhere in your conversation history, your agent misunderstood a customer, and nobody caught it because CSAT still looked fine on average. Ask your AI assistant about any conversation, and pulling from the MCP, it can walk you through exactly where the agent lost the thread, in plain language, instead of you reading through a transcript line by line trying to spot it yourself.\n\n### The stalled fix\n\nYou already know what's wrong, maybe the wrong actionbook is firing, a tool's headers need fixing, an article needs rewriting. But knowing the fix and actually shipping it have always been two different things, one happens right here, the other has always meant leaving this conversation to open the dashboard.\n\nAsk your AI assistant to update the actionbook, fix the tool, or add the missing source, and it makes the change directly, in the same conversation where you found the problem. Agent-level changes, like instructions, safeguards, or CSAT settings, apply to your dev environment only, so your AI assistant can experiment freely without ever touching what customers see in production.\n\nThe delight.ai dashboard shows you what happened. The Delight Agent MCP lets you ask why, and fix it, in the same AI tool you're already working in. Turn it on in your dashboard settings, and start with whatever question has been sitting in the back of your mind.\n\n- [Picking up where the dashboard leaves off](#section-anchor-id-0 "Picking up where the dashboard leaves off")\n- [Ask it like you'd ask a colleague](#section-anchor-id-1 "Ask it like you'd ask a colleague")\n- [How early customers are already using it](#section-anchor-id-2 "How early customers are already using it")\n\n### Keep reading\n\n[View more](/blog "View more")\n\n[](/blog/ai-for-cx/should-ai-handle-customer-complaints)
Should AI Handle Customer Complaints?\n\n[](/blog/ai-for-cx/why-dont-people-trust-ai-customer-service)
Why don't people trust AI customer service?\n\n[](/blog/ai-for-cx/does-ai-reflect-on-your-brand-reputation)
Does AI reflect on your brand reputation?\n\n[](/blog/insights/generational-differences-ai-trust)
Generational differences in AI trust\n\n[View more](/blog "View more")\n
## Retell vs. delight.ai: Which fits your CX team?
Source: https://delight.ai/blog/ai-for-cx/retell-vs-delight-ai
## Retell vs. delight.ai: Which fits your CX team?
[
Shailesh Nalwadi
Head of product management](https://delight.ai/author/shailesh-nalwadi)
August 6 2026
**Key takeaways:**
- **Retell AI is developer-first infrastructure for building voice AI phone agents.** [Delight.ai](http://delight.ai) is a full AI customer experience platform where voice is one channel among several.
- Retell's advertised $0.07 per minute covers voice infrastructure only. **Real cost stacks to $0.13–$0.31 per minute** once text-to-speech, the LLM, and telephony are added.
- Retell gives you a single voice channel you build yourself. [Delight.ai](http://delight.ai) gives you a complete CX platform, voice included, with memory, governance, and proof across every channel a customer uses.
[Retell](https://www.retellai.com/) vs. [delight.ai](http://delight.ai) comes up in almost every AI phone agent evaluation, and for good reason. Both handle voice conversations, but they aren't built to do the same job.
Retell AI helps a team build a voice agent from scratch. [Delight.ai](http://Delight.ai) is the platform that voice agent needs to plug into once a customer's next message shows up on chat, SMS, or email instead of a call. Behind that distinction sits a real stake.
**71% of consumers already use AI for support, and 70% want it to remember them across every conversation**, according to the [Delight AI Index](https://delight.ai/ai-index), [delight.ai](http://delight.ai)'s proprietary study of 1,001 U.S. consumers. Memory is exactly where a voice-only tool like Retell runs out of road. For a tech or B2B SaaS team, the more useful question is what happens the moment the conversation leaves the phone call, not which voice sounds more natural.
## What is Retell AI?
[Retell AI](https://www.retellai.com) is developer-facing infrastructure for building real-time voice AI phone agents. A team picks a large language model, a voice engine, and a telephony provider, and Retell orchestrates the pieces into a working phone agent.
[Independent reviews](https://getvoip.com/blog/ai-voice-agents/) consistently point to three things Retell does well:
- **Sub-second latency** — measured around 500 to 620 milliseconds, fast enough that a call feels like a real conversation instead of a request queued behind a delay.
- **Structured conversation flows** — pre-built call scripts that follow a fixed, step-by-step path, good for predictable, repeatable calls like appointment confirmations.
- **Flexible prompt-based agents** — single- or multi-prompt configurations for interactions that need more range than a fixed script.
What Retell doesn't do is manage anything beyond the call itself. It has no built-in chat, SMS, or email channel, and no shared memory that carries a conversation from one channel to the next. It's a voice engine, not a customer experience system. Independent reviews describe it the same way. It's strong at the one thing it was built for, and quiet on everything around it.
## What is delight.ai?
[Delight.ai](http://Delight.ai) is a full AI customer experience platform. Voice is one channel inside it, alongside chat, SMS, email, and WhatsApp, all carried by the same underlying memory.
That memory layer is called [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) (AMP), the intelligence layer that combines conversation history with business context so an agent already knows who it's talking to and what happened last time, regardless of which channel the customer used to reach out. For [Mixpanel](https://delight.ai/customers/mixpanel), a B2B SaaS company, that authentication and context lookup used to take a support agent **about 10 minutes per interaction** before AMP eliminated the manual step.
[Delight.ai](http://Delight.ai) also carries its own track record: **over 7 billion conversations a month** across Sendbird's infrastructure, more than 4,000 apps built on it, and a company with backing from ICONIQ, SoftBank, Tiger Global, and Y Combinator. None of that replaces a voice engine built specifically for phone calls. It does mean a customer who starts on chat and calls in twenty minutes later doesn't have to explain themselves twice.
Retell builds the call. [Delight.ai](http://delight.ai) remembers everything around it, inside a broader [AI customer experience platform](https://delight.ai/ai-customer-experience-platform).
## At a glance
| Feature | Retell AI | delight.ai |
|---|---|---|
| Core scope | Voice only | Voice, chat, SMS, email, WhatsApp |
| Setup | Developer-required (prompts, telephony, webhooks) | Built for CX/ops teams; no engineering ticket for routine changes |
| Pricing model | $0.07/min advertised; $0.13–$0.31/min real once TTS, LLM, and telephony are added | $0 seat fee; billed per successful AI resolution |
| Cross-channel memory | None | Agent Memory Platform carries context across every channel |
| Governance & compliance | Not documented | Trust OS, plus GDPR, CCPA, ISO 27001:2022, SOC 2 Type II |
## Is Retell AI good for customer service, or just a voice tool?
Retell AI resolves the call in front of it well. It's a purpose-built voice pipeline, and multiple independent reviews rate it highly for exactly that. Sub-second response times, natural backchanneling, and the ability to interrupt and recover mid-sentence make it feel like a real phone conversation.
The limitation shows up the moment a case doesn't end when the call does. Retell has no shared memory across channels, so a customer who calls to report an issue and then follows up by email or chat effectively starts a new case, because nothing carries the first conversation's context forward.
For simple, self-contained calls, that's not a problem. Booking an appointment, confirming an order, or answering a routine question is genuinely done when the call ends. But **a lot of customer service isn't that simple**. A billing dispute, a multi-step troubleshooting case, or anything touching more than one team tends to span more than one channel, and that's exactly where a voice-only tool runs out of road.
## How does Retell AI pricing compare to a full CX platform?
[Retell AI advertises $0.07 per minute](https://www.cloudtalk.io/retell-ai-pricing/), and that number is accurate as far as it goes. It's what Retell charges for its own voice-infrastructure layer.
It isn't the whole bill. A working voice agent also needs text-to-speech, a large language model, and a telephony provider, and those three components get billed separately on top of Retell's own rate. Once a team configures all four pieces, [independent cost breakdowns](https://www.dialora.ai/blog/retell-ai-pricing) put the real per-minute rate at **$0.13 to $0.31**, depending on which LLM and voice provider get chosen. New accounts get $10 in free credits, covering roughly 67 to 90 minutes of testing before any of that becomes a real bill.
[Delight.ai](http://delight.ai) prices differently. [Delight Desk](https://delight.ai/desk), the AI-native helpdesk where the AI agent and human agents work side by side, has a **$0 seat fee**. The business pays per successful AI resolution, not per agent seat and not per minute of voice usage.
Neither model is objectively cheaper in every scenario. A single narrow voice use case with predictable call volume can come out ahead on Retell's stacked per-minute rate. A team resolving cases across multiple channels, where a per-seat or per-minute model multiplies fast, tends to find a per-resolution model easier to forecast.
## Do you need developers to run Retell AI?
### What Retell requires
Retell calls itself low-code, but most of what makes an agent actually useful still needs an engineer:
- **Writing and iterating on prompts** as call patterns change.
- **Wiring up telephony and webhook logic** for each new use case.
- **Handling API changes** that come with platform updates.
[Reviewers](https://www.cekura.ai/blogs/retell-ai-competitors) consistently flag the lack of a full visual builder as the gap between what Retell advertises and what a non-technical team can configure alone. Support runs through a community Discord channel, with multiple reviews describing response times as slow.
### What delight.ai requires
[Delight.ai](http://Delight.ai)'s [agent-building tools](https://delight.ai/builder) are made for the people who own the CX operation, not just the engineers who ship it. Actionbooks, the AI agent's version of a standard operating procedure, get authored and edited directly by operations teams, with no engineering ticket required for routine changes. They're always drafts a person reviews before anything ships, never live without approval.
For a CX leader who needs to fix a policy on a Friday afternoon, that difference decides whether the fix ships the same day or waits for the next engineering sprint.
## Which platform is better for you: Retell AI or delight.ai?
Retell's real advantage is depth in one place. A team that wants full control over its voice stack, is comfortable maintaining prompts and telephony logic, and only needs to solve for phone calls will get a genuinely fast, well-engineered voice engine out of it. That's a legitimate use case.
The tradeoff is real too:
- **No shared memory across channels.**
- **No governance layer** over what the agent is allowed to say or do.
- **No visibility** into a conversation once it moves off the phone.
[Delight.ai](http://delight.ai)'s tradeoff runs the other way, but that doesn't mean a slower start. CX teams typically configure and launch it themselves within weeks, owning setup from day one instead of waiting on an engineering queue for a single feature. What it carries alongside that speed is [Trust OS](https://delight.ai/trust-os), the governance and observability layer that gives a team visibility into why the AI did what it did, backed by the compliance certifications (GDPR, CCPA, ISO 27001:2022, SOC 2 Type II, among others) a security review actually checks for.
## Should you build on voice infrastructure or buy a full AI CX platform?
For most tech and B2B SaaS teams, [delight.ai](http://delight.ai) is the stronger choice. It covers a wider range of CX needs than a voice-only tool, and CX teams can configure and launch it themselves within weeks, without waiting on an engineering queue. [Delight.ai](http://Delight.ai)’s Trust OS layer also provides transparency into how the AI agent is making decisions, something that Retell AI doesn’t offer with their product. Nearly two-thirds of organizations name security and risk, not raw capability, as the top barrier to scaling agentic AI, according to [McKinsey's 2026 AI Trust Maturity Survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era). That finding applies as much to a voice agent as to any other AI system.
### For CX leaders and directors
If your team is already fielding cases across chat, email, and voice, and you're the one accountable for CSAT, you need one place to see what happened across all of them. A system with shared memory and an audit trail solves more of your actual job than a voice-only tool, no matter how good that tool sounds on a call.
### For founders and CEOs
If your calculus is engineering time versus a resolution-based bill, run the math honestly. Retell's per-minute cost is real and may work for a narrow, well-defined voice use case if you have in-house engineering to maintain it. If you're fielding cases across channels, a per-resolution model tied to a platform built for [B2B customer support](https://delight.ai/industries/b2b) tends to scale more predictably than stacking engineering hours on top of a per-minute bill. Before committing either way, run the math with open eyes: only 20% of organizations are capturing the real economic value from AI today, according to [PwC's 2026 AI Performance Study](https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-performance-study.html). The gap captures whether the team behind these AI tools are set up to use them well.
**The real difference between Retell AI and** [**delight.ai**](http://delight.ai) **is whether voice is the whole job or one part of a bigger one.** If you're a tech or B2B SaaS team fielding cases across channels, you need the second answer more often than the first. If Retell is one of several vendors on your shortlist, it's worth seeing how [delight.ai](http://delight.ai) stacks up against the others already in that conversation. [See how delight.ai compares to Sierra](https://delight.ai/sierra-vs-delight), another platform-versus-point-solution decision teams run through the same way.
- [What is Retell AI?](#what-is-retell-ai)
- [What is delight.ai?](#what-is-delightai)
- [Is Retell AI good for customer service, or just a voice tool?](#is-retell-ai-good-for-customer-service-or-just-a-voice-tool)
- [How does Retell AI pricing compare to a full CX platform?](#how-does-retell-ai-pricing-compare-to-a-full-cx-platform)
- [Do you need developers to run Retell AI?](#do-you-need-developers-to-run-retell-ai)
- [Which platform is better for you: Retell AI or delight.ai?](#which-platform-is-better-for-you-retell-ai-or-delightai)
- [Should you build on voice infrastructure or buy a full AI CX platform?](#should-you-build-on-voice-infrastructure-or-buy-a-full-ai-cx-platform)
## Frequently asked questions
**Is Retell AI a full customer service platform?**
No, Retell AI is not a full customer service platform. It's infrastructure for building voice AI phone agents specifically. It doesn't include chat, SMS, or email channels, and it has no shared memory that carries a conversation from a call into another channel. Teams that need voice plus other channels typically pair Retell with separate tools, or choose a platform like [delight.ai](http://delight.ai) that covers all of them under one memory layer from the start.
**How much does Retell AI cost per minute?**
Retell advertises $0.07 per minute for its voice-infrastructure layer. Once text-to-speech, the LLM, and telephony are added, independent cost breakdowns put the real rate at $0.13 to $0.31 per minute, depending on which providers a team chooses. New accounts get $10 in free credits, covering roughly 67 to 90 minutes of testing.
**Do you need engineers to build a voice agent on Retell?**
Yes, you need engineers for anything beyond a basic template on Retell. Retell markets itself as low-code, but prompt writing, telephony configuration, and webhook logic still require engineering resources, and there's no full no-code visual builder. Support runs through a community Discord channel rather than a dedicated account team.
**Does delight.ai replace Retell AI, or work alongside it?**
Yes, [delight.ai](http://delight.ai) replaces Retell rather than running alongside it, since both solve the same underlying job of handling a customer conversation. Retell handles the voice channel alone. [Delight.ai](http://delight.ai) handles voice as one channel inside a system that also covers chat, SMS, email, and WhatsApp, carried by the same memory across all of them.
**Which is better for a growing tech or B2B SaaS company?**
For most growing tech or B2B SaaS companies, [delight.ai](http://delight.ai) is the better fit because it covers more of the CX job without a heavier setup.
## Keep reading
[View more](/blog "View more")
[
6 real MCP examples, and what people actually do with them](/blog/ai-agent/6-real-mcp-examples)
[
Best Retell AI alternative for customer service](/blog/ai-agent/best-retell-ai-alternative-for-customer-service)
[
How to reduce cart abandonment](/blog/ai-for-cx/how-to-reduce-cart-abandonment)
[
Should AI Handle Customer Complaints?](/blog/ai-for-cx/should-ai-handle-customer-complaints)
[View more](/blog "View more")
### Ready to unleash the power of
AI customer service agents?
[Contact sales](/contact-sales)
## 6 real MCP examples, and what people actually do with them
Source: https://delight.ai/blog/ai-agent/6-real-mcp-examples
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- 6 real MCP examples, and what people actually do with them
## 6 real MCP examples, and what people actually do with them
[
Ishaan Bansal
AI engineer, internal operations](https://delight.ai/author/ishaan-bansal)
August 6 2026
### Key takeaways:
- **MCP is a connector, not a chatbot feature.** It lets an AI assistant like Claude read and act on live data in tools you already use, instead of just talking about them.
- **The task matters more than the connection.** Below are six popular MCP servers and the specific, everyday thing each one lets you do.
- **You don't need to be a developer to benefit.** Several of these examples run inside tools support, ops, and sales teams already use every day.
Most explanations of what an MCP connector can do stop at the same vague line, something like "connect this tool to Claude and it can access your data." That's true, but it leaves out the specific tasks people actually perform once it's connected.
Here's what MCP actually is, and six examples of real tasks people are handing off to an [AI assistant](https://delight.ai/ai-agent) because of it.
### What is MCP, in plain English?
The [Model Context Protocol](https://delight.ai/learn/what-is-an-mcp-model-context-protocol), or MCP, is an open standard that lets an AI assistant connect to outside tools and data instead of working only from what it already knows. Anthropic introduced it, and it's now supported by [Claude, Cursor, and a growing list of other AI clients](https://modelcontextprotocol.io/).
Before MCP, giving an AI assistant access to your [Slack](https://docs.slack.dev/ai/slack-mcp-server/connect-to-claude/) messages or your [GitHub](https://github.com/github/github-mcp-server) repository meant custom code for every single tool. MCP standardizes that connection, so one server can plug into any MCP-compatible assistant. That's part of why the number of available servers grew so quickly once major platforms started publishing their own.
Think of an MCP server as a translator. Your assistant asks a question in plain language. The server translates that into the specific commands a piece of software understands, and hands the answer back. You get the conversation. The server handles the plumbing.
### 6 MCP examples, and what you can do with them
MCP server | What it connects to | What you can actually do
---|---|---
GitHub MCP | Your code repositories | Ask about an open pull request and have it reviewed or merged from chat
Delight.ai's Delight Agent MCP | Your live customer service data | Ask "Why did customer satisfaction drop this week?" and get an answer backed by live metrics
Slack MCP | Your workspace's channels and threads | Find the one thread you're thinking of and get it summarized
Notion MCP | Your pages and databases | Turn raw meeting notes into a structured page with action items
Stripe MCP | Your payments and customer records | Check whether a refund actually went through, without logging in
Playwright MCP | Any website, through a real browser | Have your assistant click through a site and report back what it found
### 1. GitHub MCP reviews a pull request without you opening the repo
[GitHub's own MCP server](https://github.com/github/github-mcp-server) connects Claude, Cursor, VS Code, and Windsurf directly to your repositories. Instead of switching tabs to check a pull request, you can ask your assistant what changed, whether the tests passed, and what still needs review, all inside the same chat where you're already working.
The real value shows up on a busy day. A developer juggling five open pull requests can ask their assistant to summarize every one that's waiting on their review, flag any that touch a shared file, and draft a comment on the one that looks risky. That research and triage task used to mean five separate tab switches, but now it's one request.
### 2. Delight.ai's Delight Agent MCP turns your support data into a question you can just ask
[Delight.ai](https://delight.ai/integrations) launched its [own MCP](https://delight.ai/blog/product/introducing-delight-agent-mcp) in August 2026, and it points in the opposite direction from most examples on this list. Instead of an AI assistant reading a third-party tool, Delight Agent MCP lets Claude, Codex, Cursor, and other MCP-compatible assistants query live data straight out of a company's own [delight.ai](http://delight.ai/) workspace.
In practice, that means a support lead can ask things like:
- Why did customer satisfaction drop this week?
- Which conversations got flagged for a low-confidence response?
- What's our resolution rate compared to last week?
Each answer comes from real resolution rates, CSAT scores, and conversation transcripts, not a stale export somebody pulled last Tuesday. The launch shipped with more than 35 operational tools, covering everything from checking category breakdowns and flagging low-confidence responses to updating an agent's own configuration directly from a chat window.
### 3. Slack MCP finds the thread you're thinking of, not just keyword matches
The [Slack MCP server](https://docs.slack.dev/ai/slack-mcp-server/connect-to-claude/) lets Claude read and search channel history, follow threads, and post replies inside the permissions you already have. That sounds abstract until you've spent ten minutes scrolling a channel trying to find who said what.
Here's a concrete example. Someone on your team remembers a bug being discussed in #bug-reports last week but not which thread. Ask your assistant to find the thread where a login error was reported, and it can:
- Pull the exact message
- Summarize the fix that got proposed
- Reply with a link to the troubleshooting doc
All without you touching the scroll bar.
### 4. Notion MCP turns messy notes into a real page
[Notion's own MCP](https://developers.notion.com/guides/mcp/overview) server reads and writes pages, updates databases, and creates new content blocks from plain language. The everyday version looks like this. You come out of a call with a page of raw, half-typed notes.
Instead of manually formatting them, you can ask your assistant to:
- Create a page in your team's meeting notes database
- Structure the notes into attendees, discussion points, and decisions
- Spin up an individual task entry for each action item from a meeting transcript
What used to be fifteen minutes of copy-paste and formatting becomes one request.
### 5. Stripe MCP answers "did that refund go through" without logging in
[The Stripe MCP](https://docs.stripe.com/mcp) server gives an assistant structured access to customers, subscriptions, invoices, and payments, based on whatever permissions your API key actually has. For a support or finance team, that turns a login-and-search task into a question.
A support or finance teammate can ask things like:
- Did the refund for order 48213 process?
- What plan is this customer currently on?
- Which invoices are still unpaid this month?
Instead of opening Stripe, finding the customer, and checking the charge history, the answer comes back in the same window. With the right write permissions, the same assistant can issue a refund directly, no dashboard required.
### 6. Playwright MCP gives your assistant a real browser to click around in
[Microsoft's Playwright MCP server](https://playwright.dev/mcp/introduction) gives an assistant a real browser it can navigate, click through, and read, using the page's own structure instead of a screenshot. As of mid-2026, public usage rankings [put it as the single most-used MCP server](https://www.totalum.app/blog/best-mcp-servers-2026) in the community, ahead of both GitHub and Figma.
Instead of handing a developer a ticket, you describe the task to your assistant, and it can:
- Check whether a competitor changed their pricing page
- Fill out a form that has no API
- Confirm a signup flow still works after a design change
### How do you start using one?
Most MCP servers follow the same basic pattern:
1. **Add the server** to your AI client's settings, usually with a short config snippet or a one-line install command.
2. **Authorize it** with the account you actually want it reading from, nothing more.
3. **Start asking**, in plain language, once it's connected.
The permissions are worth checking before you start. An MCP server can only do what your existing account is allowed to do. Connecting a read-only Stripe key means the assistant can look things up, but can't issue a refund. That could be a safeguard or a limitation.
Some servers, like GitHub's and Slack's, are officially maintained by the company whose product they connect to. Others are community-built. Both can work well, but an official server is usually the safer starting point if you're new to this.
### Why this reaches past developers
It's easy to read a list like this and assume MCP is a developer tool that happens to also work for Slack and Notion. The pattern underneath it says something bigger. AI assistants are moving from answering questions about your work to actually doing pieces of it, inside the tools you already have open.
That shift already reaches well beyond engineering:
- **Support and success teams** ask their own operational data direct questions instead of exporting a report.
- **Operations and finance teams** check a payment or a refund without a separate login.
- **Anyone on a team** turns a messy set of meeting notes into a structured page without touching the formatting themselves.
Enterprise software is starting to look less like a dashboard you log into and more like a conversation you have. You don't export a report and read it. You ask, and the report answers back.
### What's next as more tools add support
New MCP servers are shipping every week, and the pattern keeps repeating. A platform that used to require exports, dashboards, and manual lookups adds a server, and the same questions that used to take five minutes take one. Google Workspace, Figma, and dozens of smaller SaaS tools have already followed the same path GitHub, Slack, and Stripe took.
These six examples aren't a finish line. They're a fair sample of what's already possible with tools most teams already use, and a preview of what "using software" is starting to mean.
If your team already fields customer conversations, the same shift applies to your own operational data. [Delight.ai](http://delight.ai/)'s own [Delight Agent MCP](https://delight.ai/blog/product/introducing-delight-agent-mcp) is what that looks like from the other side, letting your live support data answer a direct question instead of waiting for someone to open a dashboard. See how [delight.ai](http://delight.ai/)'s [AI Agent Builder](https://delight.ai/builder) puts that kind of access to work, or browse more breakdowns like this one on the [delight.ai blog](https://delight.ai/blog).
### Frequently asked questions
**What does MCP stand for?**
MCP stands for Model Context Protocol, an open standard that lets AI assistants connect to outside data and tools using a shared, consistent format instead of one-off custom code for every integration.
**Do I need to know how to code to use an MCP server?**
No, you don't need to code to use an MCP server. Once it's connected to your AI client, you interact with it entirely in plain language, and plenty of official servers, including Slack's and Notion's, are designed for a non-technical setup process.
**Is MCP only for Claude?**
No, MCP works with more than just Claude. It's an open protocol that Cursor, Codex, and a growing list of other AI clients also support, and the servers themselves work across any compatible client.
**What's the difference between an MCP server and a regular API integration?**
A regular API integration is usually custom-built for one specific tool and one specific AI product. An MCP server is built once, against the open standard, so any MCP-compatible AI assistant can use it without custom code on either side.
**Can an MCP server make changes, or does it only read data?**
An MCP server may be able to make changes, but it depends on the server and the permissions you grant it. Some, like a read-only Stripe connection, can only look things up. Others, including [delight.ai](http://delight.ai/)'s Delight Agent MCP, support write operations like updating an Actionbook or agent configuration directly, if you've authorized that level of access.
### Keep reading
[View more](/blog "View more")
[
Retell vs. delight.ai: Which fits your CX team?](/blog/ai-for-cx/retell-vs-delight-ai)
[
Best Retell AI alternative for customer service](/blog/ai-agent/best-retell-ai-alternative-for-customer-service)
[
How to reduce cart abandonment](/blog/ai-for-cx/how-to-reduce-cart-abandonment)
[
Should AI Handle Customer Complaints?](/blog/ai-for-cx/should-ai-handle-customer-complaints)
[View more](/blog "View more")
### Ready to unleash the power of
AI customer service agents?
[ Contact sales](/contact-sales)
## Best Retell AI alternative for customer service
Source: https://delight.ai/blog/ai-agent/best-retell-ai-alternative-for-customer-service
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- Best Retell AI alternative for customer service
[
Ishaan Bansal
AI engineer, internal operations](https://delight.ai/author/ishaan-bansal)
August 6 2026
**Key takeaways**
- **Retell AI is a fast, voice tool for developer teams,** not a full customer service platform.
- [**Delight.ai**](http://Delight.ai) **is the best overall alternative for CX teams that want one platform** across voice, chat, email, and SMS, with a memory layer that carries context between them.
- **The other five alternatives below each solve a narrower version of the problem**: plain-English configuration (Decagon), autonomous action-taking (Sierra), staying on your current helpdesk (Zendesk), coaching human agents (Cresta), or regulated enterprise scale (Cognigy).
[Retell AI](https://retellai.com/) has a real following among developer teams building voice agents from scratch. It's fast, its pricing is published, and it allows for more customization if you already have engineers who want to choose their own LLM, voice engine, and telephony provider.
However, Retell AI only builds phone agents. It doesn't unify that phone conversation with the chat, email, and SMS history sitting in your helpdesk. It also doesn't give a CX team a way to update agent behavior without a developer in the loop. For teams evaluating Retell specifically for customer service, not a voice proof of concept, that gap is usually what sends them looking for alternatives.
This guide covers why teams look past Retell AI, what actually matters when you evaluate an alternative, and how six platforms, [delight.ai](https://delight.ai/), [Decagon](https://decagon.ai/), [Sierra](https://sierra.ai/), [Zendesk](https://www.zendesk.com/), [Cresta](https://cresta.com/), and [Cognigy](https://www.cognigy.com/), compare on the criteria CX leaders weigh most.
## What is Retell AI, and why do CX teams look for alternatives?
Retell AI is a developer-first platform for building real-time, voice-based AI phone agents. You assemble an agent from modular pieces, your choice of large language model, voice engine, and telephony provider, and Retell orchestrates the pieces into a working phone bot, with a no-code builder layered on top for testing and monitoring.
Three frictions tend to surface once a CX team, not just an engineering team, starts evaluating it for actual customer service work.
### Retell is voice-only
Retell AI doesn't natively cover chat, email, or SMS. A customer who calls and later emails starts over with no shared history.
### Retell is built for developers, not CX teams
Maintaining a production agent means ongoing engineering time. Someone has to tune the LLM, the voice engine, and the telephony layer, then keep all three from drifting as each one changes independently. A CX team that lacks in-house engineering resources and needs one platform it can run by itself will hit a wall with Retell fast.
### Retell’s published cost isn't the real rate
Retell AI advertises a per-minute base price of $0.07, but that figure covers only the voice engine layer. Once you add an LLM ($0.003 to $0.08 per minute), telephony ($0.015 per minute), and any international calling, the real all-in cost typically runs $0.13 to $0.31 per minute, with a common mid-range setup on GPT-4.1 landing around $0.13 per minute, according to [CloudTalk's pricing breakdown](https://www.cloudtalk.io/retell-ai-pricing/) and [Retell AI's own cost analysis](https://www.retellai.com/blog/ai-voice-agent-pricing-full-cost-breakdown-platform-comparison-roi-analysis).
## What to evaluate before choosing a Retell AI alternative for customer service
CX leaders and founders comparing platforms after Retell AI tend to weigh the same handful of criteria, regardless of company size.
| Criterion | What to check |
|---|---|
| Channel coverage | Does the platform handle voice alongside chat, email, and SMS, or is voice still a separate system you'd have to stitch in? |
| Customer memory | When a conversation moves from one channel to another, does context carry over, or does the customer repeat themselves? |
| CX-team operability | Can your own team update agent behavior, or does every change route through a developer or a vendor's services team? |
| Pricing predictability | Is the rate published and stable, or does the real cost only become clear once your LLM, voice, and telephony bills stack up? |
| Governance and compliance | Are there audit logs, role-based access controls, and the certifications your security or legal team will ask for before a contract gets signed? |
| Implementation time | How long from signed contract to a live agent, and does that estimate hold once integrations are involved? |
## 6 best Retell AI alternatives for customer service
### Retell AI alternatives at a glance
| Platform | Channel coverage | G2 rating | Starting price | Best for |
|---|---|---:|---|---|
| delight.ai | Voice, chat, email, SMS, WhatsApp | 4.6/5 (126 reviews) | Usage-based, quote-only | One platform with unified memory |
| Decagon | Chat, email, voice | 4.7/5 (25 reviews) | Per-conversation (~$0.99) or per-resolution, quote-only | Plain-English control without engineering |
| Sierra | Voice, chat, email, SMS, WhatsApp | 4.4/5 (58 reviews) | Outcome-based, ~$150K/year+ | Autonomous, revenue-driving agents |
| Zendesk | Ticketing plus AI Copilot add-on | 4.3/5 (7,012 reviews) | $19–$115/agent/month + $50/agent/month Copilot | Staying on your existing helpdesk |
| Cresta | Voice, layered onto an existing CCaaS platform | 4.2/5 (43 reviews) | ~$60K–$150K/year | Coaching human agents |
| Cognigy | Voice, chat, enterprise-wide | 4.6/5 (13 reviews) | ~$100K–$350K+/year (pilots from ~$2,500/month) | Regulated, contact-center-heavy enterprises |
### Best overall: delight.ai
Where Retell AI hands you a voice pipeline to assemble, [delight.ai](https://delight.ai/ai-agent) hands you a working platform. Voice, chat, email, SMS, and WhatsApp run on one shared memory layer, and a CX team can run it day to day without a developer.
**Notable features**
- **Unified customer memory.** The [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) carries context across every channel and session, so a customer who calls, then emails twenty minutes later, never has to reintroduce themselves.
- **Governance without a developer bottleneck.** [Trust OS](https://delight.ai/trust-os) gives a CX team audit logs, role-based access control, and a real-time view into why the agent did what it did, backed by SOC 2 Type II, GDPR, and CCPA certifications.
- **Built on a proven scale.** Sendbird, [delight.ai](http://delight.ai)'s parent company, runs over 7 billion conversations a month across 4,000+ apps, including DoorDash, Match Group, and Noom.
**Considerations**
- **Pricing is quote-only**, the same opacity as most of the field below. You can't model exact costs before a sales conversation, unlike Retell AI's published per-minute rate.
- **Ramp time.** Because the memory layer compounds context over time, peak performance builds over weeks and months rather than day one.
**Evidence**
- Mixpanel's support team used to spend roughly 10 minutes per interaction just verifying who they were talking to, before AMP authenticated the customer and pulled account context automatically. Read the [Mixpanel case study](https://delight.ai/customers/mixpanel).
- Norse Atlantic Airways' AI agent Freya grew containment from 60% to 80% within two weeks of launch, and Hanssem's resolution rate climbed from 48% to roughly 86% over five months as its memory layer accumulated more context. Read the [Norse Atlantic Airways case study](https://delight.ai/customers/norse-atlantic-airways).
**Best fit:** CX teams and founders who want one platform, not a voice tool bolted onto a separate helpdesk, and are comfortable with usage-based, quote-only pricing in exchange for unified memory and CX-team ownership.
### Best for plain-English control without engineering: Decagon
[Decagon](https://decagon.ai/) builds cross-channel agents for chat, email, and voice, configured through its own Agent Operating Procedures (AOPs), a plain-English layer built specifically to remove the 'complex configuration languages that slow iteration, inflate costs, and drain engineering time.'
**Notable features**
- **Agent Operating Procedures.** Define and adjust agent behavior in natural language instead of a scripting or flow-builder language, so a CX team can ship changes without an engineering ticket.
- **Always-on QA and experimentation.** Watchtower and Experiments give a team continuous testing and live A/B testing on conversation design, not just a pre-launch check.
- **Cross-channel memory.** Chat, voice, and email run on a single intelligence layer, so context carries across a customer's full history with the brand.
**Considerations**
- **Usage-based, quote-only pricing.** Third-party teardowns estimate annual contracts in the roughly [$95K to $590K+ range](https://www.happyrobot.ai/hub/decagon-pricing-and-review), with a reported median near $400K, which is hard to forecast before a sales conversation.
- **No built-in ticketing.** Decagon needs a separate helpdesk to sit on top of, since it doesn't own ticket management itself.
- **Enterprise-weighted deployment.** Its strongest documented results come from high-volume consumer and fintech deployments, not lighter-touch SMB setups.
**Evidence**
- Duolingo reported an 80% deflection rate after deploying Decagon, per the [Duolingo case study](https://decagon.ai/case-studies/duolingo).
- ClassPass saw 10x higher deflection at launch than it had projected from its own Voice of the Customer program, per the [ClassPass case study](https://decagon.ai/case-studies/classpass).
**Best fit:** Fast-moving software and consumer teams that want a CX team, not engineering, driving day-to-day changes to agent behavior across chat, email, and voice.
### Best for autonomous, revenue-driving agents: Sierra
[Sierra](https://sierra.ai/) positions itself around outcomes, not just automation. Its agents take real actions across backend systems (refunds, order updates, upgrades) and it prices accordingly, charging only for resolutions it delivers.
**Notable features**
- **Real action-taking.** Ghostwriter builds production-ready agents from SOPs, transcripts, or plain-English goals, with guardrails built in from the start rather than bolted on after launch.
- **Outcome optimization.** Horizon's Context Engine ties agent decisions to business outcomes like lifetime value, not just resolution counts.
- **Enterprise-grade trust posture.** SOC 2, ISO 27001, ISO 42001, HIPAA, GDPR, and FedRAMP coverage, aimed squarely at large, regulated buyers.
**Considerations**
- **Outcome-based pricing requires agreeing on what counts as a 'resolution' before you can forecast a bill.** Reported starting points run around $150K/year, with year-one costs commonly landing between $200K and $350K+ ([The AI Agent Index](https://theaiagentindex.com/agents/sierra)).
- **No self-serve tier or free trial.** Every engagement goes through Sierra's enterprise sales process.
**Evidence**
- Rocket Mortgage's VP of Product Management credits Sierra's agent with driving client engagement directly, per [Sierra's customer stories](https://sierra.ai/customers/rocket-mortgage).
- CLEAR's CEO Caryn Seidman-Becker points to Sierra as core to how the company cares for members 'every day,' per [Sierra's customer stories](https://sierra.ai/customers/clear).
**Best fit:** Enterprise teams with dedicated engineering support and budget for a sales-led, outcome-priced engagement who need agents that take real action, not just answer questions.
### Best for staying on your existing helpdesk: Zendesk
[Zendesk](https://www.zendesk.com/) is the legacy helpdesk most support teams already run, now layering an AI Copilot (via its Forethought acquisition) onto its existing ticketing system rather than asking you to replace it.
**Notable features**
- **Native fit with infrastructure you already run.** If your team lives in Zendesk today, Copilot adds AI without a migration project.
- **Broad, mature integration ecosystem.** Zendesk's app marketplace and native connectors cover most tools a support team already relies on.
- **Incremental adoption.** AI capability can be added agent-by-agent rather than requiring a platform-wide switch.
**Considerations**
- **Cost scales with headcount, not just automation.** Copilot runs $50/agent/month on top of base Suite plans priced $19 to $115/agent/month; a fully-equipped 10-agent team (Copilot, QA, and workforce management add-ons) can run roughly $2,250 to $3,300/month all-in ([Voiceflow's 2026 Zendesk pricing breakdown](https://www.voiceflow.com/blog/zendesk-pricing)).
- **Ticket-scoped, not memory-native.** Copilot is a layer added to an existing ticketing system, not a platform built around unified conversational memory from day one.
- **Voice is a separate bundle**, priced on top of Copilot rather than included.
**Evidence**
- Zendesk's AI layer holds a 4.3/5 rating across 7,012 G2 reviews and 4.4/5 across 4,079 Capterra reviews, among the largest review volumes in this comparison ([eesel's 2026 Zendesk review roundup](https://www.eesel.ai/blog/zendesk-review)).
- Zendesk reports Copilot resolves and deflects tickets automatically across its existing customer base, though specific named-customer figures for the AI layer are less publicly documented than the platform's overall review volume suggests should exist.
**Best fit:** Teams that don't want to migrate off Zendesk and are comfortable adding AI as an incremental line item rather than replacing the underlying platform.
### Best for coaching human agents: Cresta
[Cresta](https://cresta.com/) doesn't replace your contact center, it coaches the humans working in it. Real-time agent guidance, automated QA scoring, and conversation intelligence layer onto a CCaaS (contact-center-as-a-service) platform you already operate.
**Notable features**
- **Real-time coaching during live calls.** Agents get in-the-moment guidance rather than after-the-fact review.
- **100% automated QA scoring.** Every interaction gets scored, not a manual sample.
- **Additive deployment.** Cresta layers onto an existing CCaaS platform without a rip-and-replace migration.
**Considerations**
- **Requires an underlying CCaaS platform.** Cresta isn't a standalone customer service platform, it needs a contact center to sit on top of.
- **Built for scale, not lighter teams.** Enterprise-tier pricing runs roughly $60K to $150K/year, with minimum seat thresholds around 50 to 100 agents and multi-week procurement cycles typical ([eesel's Cresta pricing breakdown](https://www.eesel.ai/blog/cresta-pricing)).
- **No published pricing.** Every product page routes to a demo request, per Cresta's own G2 listing.
- **Doesn't own the customer relationship.** It improves human performance within an existing system rather than replacing it.
**Evidence**
- Cresta holds a 4.2/5 rating across 43 G2 reviews, with 87% of reviewers from mid-market or enterprise organizations and real-time coaching as the most-praised capability across nearly every review ([Cresta's G2 profile, via eesel's summary](https://www.eesel.ai/blog/cresta-pricing)).
- One mid-market reviewer described watching 'B players rising up the ranks' after deployment, a coaching-specific result Cresta's own positioning leads with.
**Best fit:** Teams that already run a CCaaS platform and want to improve human-agent performance and QA, not replace the platform itself.
### Best for regulated, contact-center-heavy enterprises: Cognigy
[Cognigy](https://www.cognigy.com/), now part of NiCE, positions itself around enterprise scale and analyst credibility.
**Notable features**
- **Deep contact-center integrations.** Native connectors for Genesys, Avaya, AWS Connect, NiCE CXone, and Microsoft, built for enterprises that already run heavy contact-center infrastructure.
- **Agent Copilot for human agents.** Real-time coaching and instant knowledge access alongside its autonomous AI agents, covering both automation and human-assist in one platform.
- **Enterprise-grade throughput.** Cognigy reports over 1 billion annual interactions across its customer base, with 99% routing accuracy and a 70% average handle time reduction.
**Considerations**
- **Enterprise-tier pricing.** Reported figures run from roughly $2,500 to $5,000/month for limited pilots up to $100,000 to $350,000+/year for full enterprise deployments ([GetVocal's Cognigy pricing breakdown](https://www.getvocal.ai/blog/cognigy-pros-cons)).
- **Implementation depth.** Enterprise rollouts with heavy contact-center integration typically run longer than a lightweight CX-team setup.
- **Built for scale, not simplicity.** Its strongest fit is large, regulated organizations already running (or adjacent to) NiCE contact-center infrastructure.
**Evidence**
- Lufthansa runs 16+ AI agents automating over 16 million conversations a year for rebookings and refunds, with real-time translation built in, per [Cognigy's Lufthansa case study](https://www.cognigy.com/en/case-study/lufthansa).
- Toyota's deployment covers 25+ AI agents for customer care and in-car voice assistance, with a 95% acceptance rate on AI-booked appointments, per [Cognigy's Toyota case study](https://www.cognigy.com/en/case-study/toyota).
**Best fit:** Large, regulated enterprises already inside or adjacent to the NiCE ecosystem that can absorb enterprise-tier cost and integration overhead.
## How to choose the right Retell AI alternative for your team
For most CX teams evaluating Retell AI for actual customer service, not a voice proof of concept, [**delight.ai**](http://delight.ai) **is the strongest overall pick**. It's the only platform in this set built around a single unified memory layer across every channel your customers use, and it's designed for a CX team to run without a developer in the loop.
That said, a few situations point to a different platform being the better fit.
- **You want plain-English control over autonomous agent logic, without dedicated engineering, across chat, email, and voice.** Decagon's AOPs are built specifically for this.
- **You have engineering depth and need autonomous, action-taking agents at enterprise scale.** Sierra's outcome-based model and action-taking depth fit large, sales-led deployments.
- **You're not ready to leave Zendesk and want AI added incrementally.** Zendesk's Copilot add-on avoids a migration project.
- **You already run a contact center and want to coach human agents, not replace the platform.** Cresta is purpose-built for that layer.
- **You're a regulated enterprise already inside the NiCE ecosystem.** Cognigy's analyst recognition and contact-center integrations are built for that scale.
- **You genuinely just need a voice pipeline and have developer time to run it.** Retell AI remains a reasonable choice for that narrower job.
[Gartner reported](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) that 91% of customer service leaders are under pressure to implement AI in some form in 2026, part of why this evaluation keeps landing on more CX leaders' desks even when the original trigger was a voice-specific tool like Retell AI.
## See it against your own stack
[Talk to our team](https://delight.ai/contact-sales) and ask specifically how Agent Memory Platform would carry context across the channels your customers already use, and how your CX team would manage it without a developer in the loop. For a broader view of how [delight.ai](http://delight.ai) compares across the full field of enterprise AI CX platforms, the [Sierra AI alternatives guide](https://delight.ai/blog/ai-agent/sierra-ai-alternatives) covers several of the same vendors from a different starting point, and [delight.ai](http://delight.ai)'s own [Decagon](https://delight.ai/decagon-vs-delight), [Sierra](https://delight.ai/sierra-vs-delight), and [Cresta](https://delight.ai/cresta-vs-delight) comparison pages go deeper on each individual matchup.
- [What is Retell AI, and why do CX teams look for alternatives?](#section-anchor-id-0)
- [What to evaluate before choosing a Retell AI alternative for customer service](#section-anchor-id-1)
- [6 best Retell AI alternatives for customer service](#section-anchor-id-2)
- [How to choose the right Retell AI alternative for your team](#section-anchor-id-3)
- [See it against your own stack](#section-anchor-id-4)
## Frequently asked questions
**Is Retell AI good for customer service, or just for building voice agents?**
Retell AI is built for developer teams assembling voice agents from modular components, not for CX teams running day-to-day customer service. It doesn't natively cover chat, email, or SMS, and production use requires ongoing developer time to maintain the LLM, voice, and telephony pieces it's built from.
**What does Retell AI actually cost?**
Retell AI publishes a per-minute base cost of $0.07, but that figure covers only the voice engine. Once you add the LLM, telephony, and any international calling, the real end-to-end cost typically runs $0.13 to $0.31 per minute, according to third-party pricing breakdowns from CloudTalk and Retell AI's own blog.
**What's the biggest difference between Retell AI and a full CX platform like delight.ai?**
Retell AI covers one channel, voice, while a full CX platform like [delight.ai](http://delight.ai) unifies voice with chat, email, SMS, and WhatsApp on one memory layer, so a customer's history carries across every channel instead of resetting each time they switch.
**Do I need a developer to run a Retell AI alternative?**
Retell AI, Sierra, and Cognigy typically need ongoing developer or services support for configuration and integration work. [delight.ai](http://delight.ai) and Decagon are built for a CX team to operate day to day, with engineering needed mainly for initial setup.
**Does switching away from Retell AI mean losing my existing voice setup?**
Switching away from Retell AI does not necessarily mean losing your existing voice setup. Most full CX platforms, including [delight.ai](http://delight.ai), run voice on the same infrastructure as chat and email rather than requiring a separate migration project for the voice channel specifically. Confirm your specific telephony and CRM integrations before assuming full coverage.
## Keep reading
[View more](/blog "View more")
[
[Retell vs. delight.ai: Which fits your CX team?](/blog/ai-for-cx/retell-vs-delight-ai)
[
[6 real MCP examples, and what people actually do with them](/blog/ai-agent/6-real-mcp-examples)
[
[How to reduce cart abandonment](/blog/ai-for-cx/how-to-reduce-cart-abandonment)
[
[Should AI Handle Customer Complaints?](/blog/ai-for-cx/should-ai-handle-customer-complaints)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## AI cart abandonment recovery: How Delight.ai wins carts back
Source: https://delight.ai/blog/ai-for-cx/how-to-reduce-cart-abandonment
## AI cart abandonment recovery: How Delight.ai wins carts back
- [Blog](/blog)
- [AI for CX](/blog/ai-for-cx)
- AI cart abandonment recovery: How Delight.ai wins carts back
[](https://delight.ai/author/yeji-yoon)
[Yeji Yoon
Chief of staff](https://delight.ai/author/yeji-yoon)
August 6 2026
**Key takeaways**
- **AI cart abandonment recovery** gives each shopper a personalized answer to the specific reason they stopped, in real time and without waiting on a person.
- **Delight.ai uses one AI agent with continuous memory**, so the follow-up a shopper gets after leaving picks up from the question they asked at checkout.
- **When you compare AI agents for cart recovery**, check for channels that share context, live cart data, rules your team controls, and a clear handoff to a person.
AI cart abandonment recovery uses an AI agent to bring shoppers back to the carts they left by answering the specific reason they stopped. If a shopper stalls because checkout feels complicated, the agent can recognize it and walk them through the rest in real time, without anyone on your team stepping in.
Delight.ai's [AI sales agent](https://delight.ai/ai-sales-agent) handles this on the page and after the shopper leaves, with the same memory of the conversation in both places.
That coverage pays off because of how many carts are at stake. Shoppers abandon about 70% of online carts, according to [Baymard Institute's average of 50 studies](https://baymard.com/lists/cart-abandonment-rate). Many of those shoppers were close to buying, which is why [AI agents for retail](https://delight.ai/industries/retail) now work on both sides of the moment a shopper leaves.
## What is cart abandonment?
**Cart abandonment is when a shopper adds an item to their online cart but leaves the site before completing the purchase.** It's an earlier, broader moment than checkout abandonment, which describes a shopper who has already started paying and leaves before finishing. **Checkout abandonment usually points to a specific friction point, like a payment error, a surprise fee, or a form that asks for too much information.**
Cart abandonment happens earlier, like when a shopper who was only comparing prices abandons their cart. Because adding a product to a cart is a stronger buying signal than a page visit, it gives an AI agent a clear moment to start a recovery conversation.
## Why do shoppers abandon their carts?
Most shoppers abandon a cart over a single moment of hesitation, and the same causes tend to show up across studies. Each of those causes is also a question an AI agent can answer while the shopper is still deciding. Among shoppers who abandoned during checkout, Baymard Institute found these top reasons:
| Why shoppers left (share of shoppers, Baymard) | How Delight.ai's agents help in the moment |
|---|---|
| **Extra costs** like shipping, tax, and fees (40%) | Explains each fee, shows the full total before payment, and offers an incentive like free shipping if your rules allow it. |
| **Slow delivery** (20%) | Checks the shopper's delivery options and points them to faster options. |
| **Trust concerns** about entering card details (19%) | Answers with your own security and payment policies and links to the page on your site the answer came from. |
| **A required account** (18%) | Points the shopper to guest checkout, or collects their account details inside the conversation. |
| **A long or complicated checkout** (17%) | Walks the shopper through the remaining steps and answers questions about any field that stops them. |
Checkout fixes still come first, and [Baymard's checkout research](https://baymard.com/research-articles/current-state-of-checkout-ux) recommends making guest checkout the most prominent option and showing delivery dates instead of shipping speeds. An AI agent then responds to the hesitation a static checkout page can't, one shopper at a time.
## How do you calculate your cart abandonment rate?
Cart abandonment rate is the share of started carts that never turn into a completed order. To calculate it, divide completed purchases by carts created, subtract that result from 1, then multiply by 100.
**Abandonment rate = (1 − completed purchases ÷ carts created) × 100**
Here's what that looks like with sample numbers:
Abandonment rate = (1 − completed purchases ÷ carts created) × 100
Carts started
1,000
↓
Purchases completed
300
Carts abandoned
700
↓
Abandonment rate
70%
Example based on 1,000 carts started, 300 completed purchases, and 700 abandoned carts this month.
**The average cart abandonment rate across ecommerce is 70.22%**, according to Baymard Institute. It's worth calculating your own rate before adding AI recovery, so you have a baseline to measure improvements against.
## How does Delight.ai's AI cart abandonment recovery work?
With Delight.ai, one AI agent runs cart recovery from the first sign of hesitation to the follow-up. It spots a shopper hesitating, answers what stopped them, and follows up in their preferred channel if they leave, all within rules your team sets.
### 1. Spot the hesitation while the shopper is still on the page
When a shopper lingers on the shipping step or skips a field, Delight.ai's [Nudge Message](https://delight.ai/product-release/nudge-message) shows a short note above the chat in real time. The shopper knows help is there before they close the tab, and the agent can answer their question before it turns into an abandoned cart.
### 2. Answer the question that stopped them
A shopper asking whether a size 9 in a narrow-fitting brand will work for wide feet gets a straight answer and the best-fitting option. Delight.ai's Guided Selling, powered by [For You Conversations (FYC)](https://delight.ai/for-you-conversations-fyc), matches the shopper to the right product using what they've told the agent, so they can complete the purchase.
### 3. Follow up in the shopper's preferred channel after they leave
Delight.ai runs the follow-up through [Actionbooks](https://delight.ai/product-release/actionbook-editor), the step-by-step instructions your team writes and a person approves before anything goes live. With Delight.ai's Actionbooks, the shopper hears back in their preferred channel 15 minutes after they leave, with an answer to whatever made them pause. That follow-up can arrive by SMS, WhatsApp, email, or an outbound voice call.
### 4. Stay within the rules your team sets
Your team decides what the agent says, what it asks, and whether an incentive like free shipping is ever offered. Operations teams edit Actionbooks without engineering tickets, so the agent only offers what the business has signed off on.
## Why is continuous memory important for cart abandonment recovery?
Continuous memory lets the follow-up pick up exactly where the shopper left off, so the shopper experiences one consistent conversation with your brand. That consistency builds trust, and it makes a store look more professional than competitors whose follow-ups start from scratch.
Say a shopper asks on the checkout page whether a jacket will arrive by Friday, then leaves. When their follow-up text answers the Friday question directly, they have what they need to finish the purchase with confidence.
Delight.ai is built around one agent that covers on-site and in-app chat, SMS, WhatsApp, email, and voice. [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) carries the conversation's context across those channels, so the shopper never has to repeat the question. Follow-up calls can also open with who the shopper is and where their customer journey left off.
**Here's how Delight.ai's agent carries that same shopper from the product page through recovery:**
| Stage | What the shopper does | What Delight.ai's agent does |
|---|---|---|
| Product page | Adds a jacket to the cart and asks about delivery | Confirms which shipping options arrive by Friday |
| Checkout | Stalls on the shipping step | Shows a Nudge Message and answers the shipping question |
| 15 minutes after leaving | Hasn't come back to the cart | Follows up by SMS with the Friday delivery answer |
| Shopper replies | Asks whether returns are free | Answers from the store's returns policy in the same thread |
| Handoff | Asks to talk to a person | Hands off to your team with the full conversation attached |
## What makes Delight.ai effective for AI cart abandonment recovery?
Delight.ai is built around 6 capabilities that cart recovery depends on. Together, they answer the question that stopped each shopper, in the channel they use, within rules your team controls.
| Delight.ai capability | How it recovers carts |
|---|---|
| **Shared context across channels** | One agent runs chat, SMS, WhatsApp, email, and voice, and AMP carries memory between them, so a shopper who chatted on the website doesn't start over when the follow-up arrives by text. |
| **Live cart and product data** | Delight.ai connects to your inventory, payment, and internal systems without a migration project, so answers about stock, price, and delivery stay current. |
| **Answers instead of reminders** | Guided Selling answers the shopper's open question about fit or delivery and matches them to the right product, which a generic reminder can't do. |
| **Rules your team controls** | Your team writes Actionbooks without code and approves every one before it goes live, so discounts, timing, and messaging follow your margins and brand. |
| **A clear handoff to a person** | Delight Desk routes high-value or complicated carts to your team with the full conversation attached. |
| **Consent and compliance** | Automated calls and texts are regulated, and Delight.ai holds GDPR and CCPA compliance and a SOC 2 Type II report, with Safeguards that handle PII automatically. |
Email and SMS platforms are a strong fit for scheduled reminders, and voice-only agents can reach high-value carts by phone. A gap shows up when a shopper moves between those channels and the conversation fails to move with them.
## Takeaway
An abandoned cart usually comes down to one unanswered question about cost, fit, or timing. Delight.ai answers it on the page and follows up with context after the shopper leaves, giving more of those shoppers a reason to finish checkout. See how cart recovery fits into a full retail setup in the [retail AI blueprint](https://delight.ai/industry-blueprint-retail).
Then read how [proactive customer service](https://delight.ai/blog/ai-for-cx/proactive-ai-customer-service) works across the rest of the customer journey.
- [What is cart abandonment?](#section-anchor-id-0 "What is cart abandonment?")
- [Why do shoppers abandon their carts?](#section-anchor-id-1 "Why do shoppers abandon their carts?")
- [How do you calculate your cart abandonment rate?](#section-anchor-id-2 "How do you calculate your cart abandonment rate?")
- [How does Delight.ai's AI cart abandonment recovery work?](#section-anchor-id-3 "How does Delight.ai's AI cart abandonment recovery work?")
- [Why is continuous memory important for cart abandonment recovery?](#section-anchor-id-4 "Why is continuous memory important for cart abandonment recovery?")
- [What makes Delight.ai effective for AI cart abandonment recovery?](#section-anchor-id-5 "What makes Delight.ai effective for AI cart abandonment recovery?")
- [Takeaway](#section-anchor-id-6 "Takeaway")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## FAQ
**How does AI cart abandonment recovery work?**
AI cart abandonment recovery uses an AI agent to spot a shopper hesitating, answer the question that stopped them, and follow up after they leave. The follow-up can go out by SMS, WhatsApp, email, or phone. With Delight.ai, Nudge Message starts the conversation on the page, and Actionbooks handle the follow-up within rules your team sets.
**Should an AI agent offer a discount to recover a cart?**
An AI agent should offer a discount to recover a cart only if your business wants it to. Many shoppers leave over shipping costs, delivery timing, or sizing, and answering that question can recover the cart without giving away margin. With Delight.ai, your team decides in the Actionbook whether an incentive like free shipping is offered and when, and the agent follows those rules.
**Which channels should AI cart recovery use?**
AI cart recovery should use the channels your shoppers already respond to. Often, that's on-site chat and text messages, plus a phone call for high-value carts. The bigger question is whether those channels share context. A follow-up text should already know what the shopper asked on the page, so they never have to repeat themselves.
**How soon should a cart recovery message go out?**
A cart recovery message should go out while the purchase is still on the shopper's mind, which means minutes or hours, not days. With Delight.ai's Actionbooks, the shopper hears back in their preferred channel 15 minutes after they leave, with an answer to whatever made them pause.
**When should cart recovery hand off to a person?**
Cart recovery should hand off to a person when a cart is high-value, when the shopper's question involves a complex return or dispute, or when the shopper asks for someone. With Delight.ai, the person who takes over sees the full conversation, so the shopper doesn't have to explain again.
**Is cart abandonment the same as checkout abandonment?**
No, cart abandonment and checkout abandonment aren't the same thing, though they're related. Cart abandonment covers any shopper who adds an item and leaves before paying, while checkout abandonment specifically means a shopper started the payment process and left before finishing it. Checkout abandonment is the narrower, later, and usually more fixable of the two.
## Keep reading
[View more](/blog "View more")
[
Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[
Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[
Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[
Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## Who is responsible when AI makes a mistake?
Source: https://delight.ai/blog/ai-agent/who-is-responsible-ai-mistakes
## Who is responsible when AI makes a mistake?
[](https://delight.ai/author/ishaan-bansal)
[Ishaan Bansal](https://delight.ai/author/ishaan-bansal)
AI engineer, internal operations
August 7 2026
### Key takeaways
- Consumers don't separate a brand from the AI it deploys, which means that when the AI is wrong, **they blame the company**.
- **72% of consumers say mistakes or incorrect decisions are their top concern** about autonomous AI agents, ahead of privacy and control.
- A brand that wants credit for good AI has to accept the blame for bad AI. There's no third option, and [the 2026 Delight AI Index](https://delight.ai/ai-index) shows why.
A customer doesn't call an AI model's engineering team when a chatbot cancels the wrong order; they call the company they ordered from.
## What does it mean to be responsible when AI makes a mistake?
Brand responsibility when AI makes a mistake works less like a legal concept and more like a commercial one. Whoever a customer holds accountable for a bad outcome is, functionally, responsible for it, regardless of what a contract or a court eventually decides.
New consumer research backs this up directly. In [delight.ai's 2026 AI Index](https://delight.ai/ai-index), a survey of 1,000 U.S. consumers who had used customer service in the past year, when AI does something incorrectly, consumers blame the brand, not the AI vendor, the model provider, or the algorithm itself. When AI performs well, the same consumers credit the brand too, reading strong AI as a sign the company is modern and competent.
AI doesn't have a reputation of its own in a customer's mind, it simply borrows the brand's.
## Who do customers blame when AI gets it wrong?
**The Index found that 72% of consumers list mistakes or incorrect decisions as their top concern about autonomous AI agents**, well ahead of data privacy (62%) and lack of control (56%). That concern attaches to whichever company put the AI in front of them.
This is also why the usual excuses don't hold up in practice:
- **"The vendor built it."** Customers never see the vendor behind the AI. They see the brand's app, the brand's number, the brand's name on the confirmation email.
- **"The model made an error, not us."** A model error inside a branded product is a brand error the moment it reaches a customer.
- **"We disclosed the limitations."** A disclaimer buried in terms of service doesn't survive contact with a customer who was refused a necessary refund.
## Why "it depends" is the wrong answer
Most coverage of AI accountability lands on some version of "it depends". “It depends” on the contract, the jurisdiction, the specific failure, the vendor's terms of service. That answer might satisfy a legal brief, but it fails the person standing in front of the actual mistake.
Customers don't wait for a legal liability framework to assign blame before deciding how they feel about a company. They decide in the moment, based on what just happened to them and whose name was attached to it. A brand that waits for legal clarity before taking responsibility has already lost the trust it was trying to protect.
The practical answer is simpler than the legal one. If a brand deploys the AI, profits from the AI, and puts its name in front of the AI, the brand owns what the AI does.
## Where does trust in AI models break down for the consumer?
Brands tend to assume the fix for AI mistakes is a smarter model, but consumers are asking for something more basic. Consumers want the ability to see what the AI is doing and step in or summon human guidance when it's wrong.
Among consumers who used [AI customer service](https://delight.ai/ai-customer-service) in the past year, 57% had positive experiences and 48% found it easier to resolve their issue. But trust has a ceiling, and that ceiling is control:
- **65%** of consumers surveyed want human oversight on autonomous agents.
- **59%** of consumers surveyed want the power to stop or override an AI action at any time.
- **57%** of consumers surveyed want to approve AI actions before they happen.
- **47%** of consumers surveyed want the ability to correct mistakes and reverse decisions.
- **84%** of consumers surveyed want data privacy and security safeguards, the highest bar of all.
A brand's push toward maximum automation, running everything through AI with no visible checkpoint, is out of step with what most consumers want.
## What accountable AI looks like in practice
Being responsible for an AI's mistakes shows up as specific operational choices a brand either makes or doesn't.
That ownership shows up in the data outside [delight.ai](http://delight.ai)'s own research too. [McKinsey's 2026 AI Trust Maturity Survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found organizations with explicit, assigned ownership for responsible AI scored 2.6 on its maturity scale, against 1.8 for organizations without clear accountability.
### Give a human a way to see what the AI decided, and why
An AI action that no one can understand the reasoning behind is a liability that a brand can't manage in real-time. [Trust OS](https://delight.ai/trust-os) is [delight.ai](http://delight.ai)'s governance layer built for exactly that. It gives teams a reasoning trail behind AI decisions, an audit log of every configuration change, and automatic flagging of low-confidence responses before a customer ever sees one.
### Build in a real off switch
**59% of consumers want the power to override or stop an AI agent at any time**, per the Index. A brand that can't pause its own AI mid-interaction has already decided its customers don't get that control, whether that was the brand’s intention or not.
## The bottom line for any brand running AI
A brand that wants credit when its AI gets something right should already accept that it owns the moment the AI gets something wrong too. There's no version of AI customer service where the upside belongs to the brand and the downside belongs to the technology.
The 72% of consumers worried about AI mistakes and the 65% who want a human standing by for the moment that matters aren't asking brands to slow down on AI. They're asking brands to stop treating accountability as a footnote. See the [full 2026 Delight AI Index](https://delight.ai/ai-index) for the complete data behind where that trust gap sits today, industry by industry.
- [What does it mean to be responsible when AI makes a mistake?](#section-anchor-id-0)
- [Who do customers blame when AI gets it wrong?](#section-anchor-id-1)
- [Why "it depends" is the wrong answer](#section-anchor-id-2)
- [Where does trust in AI models break down for the consumer?](#section-anchor-id-3)
- [What accountable AI looks like in practice](#section-anchor-id-4)
- [The bottom line for any brand running AI](#section-anchor-id-5)
## Frequently asked questions
**Who is legally responsible when AI makes a mistake?**
Legal responsibility for an AI mistake varies by contract, jurisdiction, and the specific harm, and that variation is exactly why it's the wrong question for most brands to lead with. Commercially, the brand that deployed the AI and put its name in front of the customer is the one the customer holds responsible, regardless of how the legal question eventually resolves.
**Can a company blame its AI vendor for a mistake?**
A company can try to blame its AI vendor for a mistake in a contract dispute, but a customer never sees that contract. They see the brand's product failing them, which means the reputational cost lands on the brand no matter what the vendor agreement says behind the scenes.
**Does AI accountability mean using less AI?**
AI accountability doesn't mean using less AI. The 2026 Delight AI Index found consumers are comfortable with AI handling routine, low-risk tasks and actually expect it. Accountability means knowing which moments belong to AI and which ones need a human, then building the handoff so it happens before a mistake, not after a complaint.
**What do consumers want most after an AI mistake?**
After an AI mistake, consumers want the ability to fix it: the Index found 47% want the ability to correct the AI's mistake and reverse the decision, and 59% want the power to stop or override an AI agent at any time. Consumers are just asking for a way back when something goes wrong.
**Why do customers blame the brand instead of the AI technology?**
Customers blame the brand instead of the AI technology because they interact with a brand's product, not with the model or vendor behind it. The 2026 Delight AI Index found that when AI performs well, consumers credit the brand, and when it fails, they blame the brand the same way, which means the brand's reputation absorbs both outcomes either way.
## Keep reading
- [ How does real-time customer engagement impact your business?](/blog/ai-for-cx/real-time-customer-engagement)
- [ Retell vs. delight.ai: Which fits your CX team?](/blog/ai-for-cx/retell-vs-delight-ai)
- [ 6 real MCP examples, and what people actually do with them](/blog/ai-agent/6-real-mcp-examples)
- [ Best Retell AI alternative for customer service](/blog/ai-agent/best-retell-ai-alternative-for-customer-service)
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## How does real-time customer engagement impact your business?
Source: https://delight.ai/blog/ai-for-cx/real-time-customer-engagement
## How does real-time customer engagement impact your business?
[](https://delight.ai/author/sk-kim)
[SK Kim](https://delight.ai/author/sk-kim)
Head of engineering, client platform
August 7 2026
**Key takeaways**
- **Real time customer engagement is usually measured by how fast you respond**, not by who reaches out first.
- Most "real-time" tools, live chat, social replies, push notifications, **still wait for the customer to make the first move**.
- [Delight.ai](http://delight.ai)'s Nudge Message lets an AI agent open the conversation first, proactively reaching out the moment something goes wrong on the page.
## What is real time customer engagement?
Real time customer engagement means **responding to a customer's action the moment it happens**, not hours or days later.
- [**Genesys**](https://www.genesys.com/definitions/what-is-real-time-customer-engagement) defines it as the ability to interact with customers instantly across channels like live chat and social media.
- [**EBI.AI**](https://ebi.ai/blog/real-time-customer-engagement/) frames it as seizing a moment to connect with a buyer right when they need you most.
- [**SAS**](https://www.sas.com/en_us/insights/articles/marketing/real-time-customer-engagement.html) ties it to knowing a customer's past purchases and situational context well enough to meet them there.
Every one of these definitions agrees on the "real-time" part in that the response has to be instant, but most of them skip *who makes the first move*.
## Why most "real-time" engagement is still reactive
Nearly every tactic on a typical real-time engagement list is reactive by design:
- **Live chat** — waits for the customer to click the bubble open.
- **Social media replies** — waits for the customer to post or message first.
- **Push notifications** — waits for a trigger, but rarely one the customer can see or understand.
A handful of tools pop up a chat window when someone lingers on a page, but most live chat still just sits there, waiting to be opened.
That's not a flaw specific to any one tool, but instead how the category got defined. Real-time engagement got measured by response speed because for years, speed was the only thing a support team could actually control. It could answer a call the second it rang, but it couldn't watch what a customer was doing before they picked up the phone.
An [AI customer service](https://delight.ai/ai-customer-service) agent changes what's technically possible, because it can track the moment itself, not just wait to respond.
## What's the difference between proactive and reactive customer engagement?
### Reactive engagement
Reactive engagement is anything that starts because the customer acted first. They opened a chat, sent an email, or called in, and the business's job is to notice, then respond.
### Proactive engagement
Proactive engagement reverses that order. The business, or its AI agent, reaches out first, based on something it observed, like a form filled out halfway and then abandoned or a page read three times in a row. This happens even though the customer hasn't asked for anything yet.
Neither mode is real time customer engagement on its own. Reactive engagement can be instant and still miss the moment that actually mattered, the ten minutes before the customer gave up and left. Proactive engagement is what closes this gap.
## What are the benefits of proactive, real-time engagement?
People who feel emotionally connected to a brand **spend about twice as much** as people who don't, according to a 2018 [Adweek analysis](https://www.adweek.com/brand-marketing/3-steps-to-build-a-lasting-emotional-connection-with-customers/), still the most-cited figure of its kind. That connection gets built moment by moment, and a business that only shows up after being asked misses most of those moments.
### Examples of real time customer engagement in practice
Most real-time engagement strategies fall into a handful of recognizable patterns, whether the business is reacting fast or reaching out first:
- **Proactive live chat triggers** — a chat window opens on its own when a visitor lingers on a pricing page, rereads the same FAQ, or scrolls to the bottom of a product page, before they ask anything.
- **Cart and form abandonment recovery** — a message on-site, by SMS, or by email goes out within minutes of a customer stalling partway through checkout or a signup form, while the moment is still fresh.
- **Real-time order and service updates** — shipping alerts, delivery windows, appointment reminders, or ticket-status changes sent the moment something changes, instead of making the customer check back.
- **Social media monitoring and response** — a brand replies to a mention, complaint, or question on X, Instagram, or Reddit within minutes, before it turns into a public thread.
- **In-app nudges tied to behavior** — a message appears the moment a user hits a usage limit or stalls in an onboarding flow, right when the friction happens, not in a follow-up email days later.
The first four are still reactive at their core in that something has to happen before the system responds. The last one flips that with an AI agent who notices the trigger itself and starts the conversation, moving engagement from a fast response to a first move.
[Delight.ai](http://delight.ai) [customer gther](https://delight.ai/customers/gther) is a live example of what showing up first can do. Its AI concierge "acts as a proactive salesperson as much as it answers questions," nudging event visitors toward registering while it answers their questions, in the same conversation. That upsell happens inside the moment of engagement itself, instead of arriving later as a separate campaign.
[Mixpanel's](https://delight.ai/customers/mixpanel) support team tells a similar story from the retention side. Once [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) authenticates a customer and pulls their account context before a conversation starts, the team stops spending ten minutes figuring out who it's talking to and starts solving the actual problem. Support that resolves faster, and remembers who someone is, is support customers come back to.
## How AI makes real-time engagement proactive at scale
No support team can watch every visitor's behavior across thousands of sessions at once. A person can watch one screen, while an AI agent can watch all of them.
Agentic AI has made it possible for a system to **notice a stalled form or a lingering pause on its own**, at a scale no team could staff for. McKinsey's 2026 "State of AI Trust" survey (https://mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found organizations are moving past experimenting with AI and into scaled deployment of agentic systems across core business functions, which is the always-on monitoring that proactive engagement depends on.
None of this would be possible without trust and transparency though. An AI agent that reaches out at the wrong moment, or says the wrong thing when it does, does more harm than one that stays quiet. Getting the moment and the message right is the hard part of proactive engagement.
## How delight.ai's Nudge Message can increase customer loyalty and retention
[delight.ai](http://delight.ai)'s own AI agent, Harper, runs a feature built around that exact trigger problem. It's called Nudge Message, and it **lets the AI agent speak first**. A short message appears above the chat the moment something goes wrong on the page, instead of waiting for the customer to open the messenger and explain a situation from scratch.
Harper already uses Nudge Message to catch customers who start a form and don't finish it.
A customer who abandons a form is a customer the business would otherwise never hear from again. Catching them before they leave the page is **what retention looks like at the moment it actually happens**.
### Two ways Nudge Message can greet a customer
- **Standard welcome** — the chat opens with the usual greeting, as if the customer had opened it themselves.
- **Mid-conversation** — the chat opens already referencing what the AI agent noticed, so the customer never has to explain a situation the business already watched happen.
### Two types of Nudge messages
- **Fixed message** — a set message built for the fastest response.
- **AI-written message** — the AI agent writes a message tailored to the specific page and customer moment.
Either way, the message is scoped to something concrete the AI agent observed, not a generic "need help?" popup.
## What tools enable real time customer engagement today?
Real time customer engagement typically runs through two different categories of tool, and they solve different halves of the problem.
| Category | What it does | Examples |
|---|---|---|
| Marketing personalization platforms | Trigger a message from behavioral or purchase data, usually for a marketing or lifecycle campaign | Braze, Klaviyo, Salesforce Marketing Cloud |
| AI customer service agents | Act on that same moment inside a live support conversation, not a campaign | delight.ai and similar AI agent platforms |
A personalization platform is built to decide what offer to send. An AI customer service agent is built to have the actual conversation once someone is already engaging, or about to disengage, with support.
[Omnipresence](https://delight.ai/omnipresent) carries that conversation across channels (chat, SMS, email, voice, WhatsApp) so a nudge that starts in chat doesn't reset if the customer calls in later. That's **the difference between sending a message and holding a conversation**.
Real time customer engagement stops being about reaction speed once an AI agent can notice the moment itself, a stalled form, a lingering page, and say something before being asked. That's the shift proactive tools like Nudge Message make possible. Less time spent answering after a customer already gave up, more time spent catching them before they do. [Read more about how agentic AI is changing customer experience](https://delight.ai/blog/industry/agentic-ai-companies).
- [What is real time customer engagement?](#section-anchor-id-0 "What is real time customer engagement?")
- [Why most "real-time" engagement is still reactive](#section-anchor-id-1 "Why most \"real-time\" engagement is still reactive")
- [What's the difference between proactive and reactive customer engagement?](#section-anchor-id-2 "What's the difference between proactive and reactive customer engagement?")
- [What are the benefits of proactive, real-time engagement?](#section-anchor-id-3 "What are the benefits of proactive, real-time engagement?")
- [How AI makes real-time engagement proactive at scale](#section-anchor-id-4 "How AI makes real-time engagement proactive at scale")
- [How delight.ai's Nudge Message can increase customer loyalty and retention](#section-anchor-id-5 "How delight.ai's Nudge Message can increase customer loyalty and retention")
- [What tools enable real time customer engagement today?](#section-anchor-id-6 "What tools enable real time customer engagement today?")
## Frequently Asked Questions
**What is real time customer engagement?**
Real time customer engagement is responding to a customer's action the moment it happens, across whatever channel they're using. Most definitions focus on response speed, chat, social, and phone replies that arrive instantly. The gap in that definition is proactivity. Instant reaction still waits for the customer to act first, while proactive engagement has the business, or its AI agent, notice the moment and reach out before being asked.
**What's the difference between proactive and reactive customer engagement?**
Reactive engagement starts when the customer acts first, opening a chat or picking up the phone. Proactive engagement starts when the business notices something first, a stalled form, an abandoned cart, a lingering pause, and reaches out before the customer asks. Both can happen in real time; only one closes the gap before the customer gives up.
**Does real time customer engagement require AI?**
No, but AI is what makes the proactive half of it possible at scale. A person can watch one screen at a time, but an AI agent can watch thousands of sessions at once and notice a stalled form or a lingering page the moment it happens, which is what turns real-time engagement from a fast reply into a first move.
**Can real time customer engagement work for a small business?**
Yes, real time customer engagement works for a small business. The mechanics, noticing a customer's behavior on a page and reaching out at the right moment, don't require enterprise scale to matter. A small business with one product page and one chat widget can catch the same abandoned form or lingering visit an enterprise site can, using the same kind of AI agent trigger.
**What is delight.ai's Nudge Message?**
Nudge Message is a [delight.ai](http://delight.ai) AI agent feature that lets the agent speak first. A short message appears above the chat the moment something goes wrong on the page, before the customer opens the messenger. It can greet with a standard welcome or open mid-conversation, and it can send a fixed message or one the AI agent writes for the specific moment.
## Keep reading
[View more](/blog "View more")
[](/blog/ai-agent/who-is-responsible-ai-mistakes)
[Who is responsible when AI makes a mistake?](/blog/ai-agent/who-is-responsible-ai-mistakes)
[](/blog/ai-for-cx/retell-vs-delight-ai)
[Retell vs. delight.ai: Which fits your CX team?](/blog/ai-for-cx/retell-vs-delight-ai)
[](/blog/ai-agent/6-real-mcp-examples)
[6 real MCP examples, and what people actually do with them](/blog/ai-agent/6-real-mcp-examples)
[](/blog/ai-agent/best-retell-ai-alternative-for-customer-service)
[Best Retell AI alternative for customer service](/blog/ai-agent/best-retell-ai-alternative-for-customer-service)
[View more](/blog "View more")
### Ready to unleash the power of
AI customer service agents?
[Contact sales](/contact-sales)
## The 8 best AI tools for ecommerce in 2026
Source: https://delight.ai/blog/ai-for-cx/best-ai-tools-for-ecommerce
- [Blog](/blog)\n- [AI for CX](/blog/ai-for-cx)\n\n## The 8 best AI tools for ecommerce in 2026\n\n[](https://delight.ai/author/ishaan-bansal) \n[Ishaan Bansal \nAI engineer, internal operations](https://delight.ai/author/ishaan-bansal)\n\n\n\nAugust 10 2026\n\n### Key takeaways\n\n- **Most "best AI tool for ecommerce" searches are really about one of seven jobs**, but customer service is the one every ecommerce business eventually needs, since it's the highest-volume.\n- [**delight.ai**](https://delight.ai/ai-customer-service) **is the best overall AI tool for ecommerce customer service** because it unifies chat, voice, email, SMS, and WhatsApp on one memory layer instead of a single channel or a ticket-scoped tool.\n- **The other six categories below each solve a narrower job**: Shopify-native support (Gorgias), personalization (Nosto), email (Klaviyo), pricing (Prisync), forecasting (Netstock), content (Jasper), and reviews/loyalty (Yotpo).\n\nEcommerce operators searching for "the best AI tools for ecommerce" are usually trying to solve one specific problem. A support team wants faster resolution. A merchandiser wants sharper recommendations. A finance lead wants pricing that reacts to the market instead of a quarterly spreadsheet.\n\nCustomer service is where most of that searching starts, as it's a component of every ecommerce business, regardless of size, and it's the most visible to shoppers.\n\nThis guide covers what to evaluate before picking any AI tool for ecommerce, why [delight.ai](https://delight.ai/ai-customer-service) is the strongest overall pick for the customer-service job specifically, and how seven other tools compare on price, G2 rating, and coverage across the categories retailers actually shop in.\n\n### What to evaluate before choosing an AI tool for ecommerce\n\nRetailers comparing AI tools tend to weigh the same handful of criteria, regardless of which category they're shopping in.\n\n- **Category fit** — Does the tool solve the specific job costing your business the most right now?\n- **Channel or system coverage** — Does it cover every channel or system your CS team touches (chat, voice, email; or your full catalog and ERP)?\n- **Team operability** — Can your own team run it day to day, or does every change route through a developer or the vendor's services team?\n- **Pricing predictability** — Is the rate published and stable?\n- **Third-party proof** — What does the tool's G2 or Capterra rating and review count actually show?\n- **Named-customer proof** — Is there a real, dated result from a named customer, or only generic marketing claims?\n\n### 8 best AI tools for ecommerce, at a glance\n\n| Tool | Category | G2 rating | Starting price | Best for |\n|---|---:|---:|---|---|\n| delight.ai | AI customer service | 4.6/5 (126 reviews) | Usage-based, quote-only | Best overall: unified memory across every channel |\n| Gorgias | AI customer service | 4.6/5 (555 reviews) | $10–$900/mo by ticket volume | Shopify-native support without a platform change |\n| Nosto | Personalization | 4.6/5 | Revenue share, custom quote | Real-time product recommendations |\n| Klaviyo | Email/lifecycle marketing | 4.6/5 (1,325+ reviews) | Priced by active profiles | Segmentation and automated flows |\n| Prisync | Pricing/merchandising | 4.7/5 (166 reviews) | $99–$399/mo by catalog size | Competitor tracking and repricing |\n| Netstock | Inventory/forecasting | 4.6/5 (156 reviews) | From $900/mo, custom quote | Demand forecasting on top of an ERP |\n| Jasper | Content/copy | 4.7/5 (1,270+ reviews) | $59–69/mo Pro; ~$900/mo Business | Product and marketing copy at volume |\n| Yotpo | Reviews/loyalty | 4.3/5 (262 reviews) | Free tier; Pro from $199/mo | Review, loyalty, and UGC management |\n\n### Best overall: delight.ai\n\n[delight.ai](https://delight.ai/ai-customer-service) is an AI concierge built on [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp), which carries context across chat, SMS, email, voice, and WhatsApp, so a shopper never has to repeat themselves between channels. It holds a 4.6/5 rating on G2 across 126 reviews.\n\n**Notable features**\n\n- **Unified customer memory.** AMP carries context across every channel and session, so a shopper who chats, then emails a day later, never starts over.\n- **Governance without an engineering bottleneck.** [Trust OS](https://delight.ai/trust-os) gives a CX team audit logs, role-based access control, and a live view into why the AI did what it did, backed by SOC 2, HIPAA, and GDPR compliance.\n- **A helpdesk with no seat fee.** Delight Desk bundles ticketing, routing, and the AI agent on one platform, priced per successful AI resolution instead of per agent.\n\n**Considerations**\n\n- **Pricing is usage-based and quote-only.** You can't model an exact cost without a sales conversation.\n\n**Evidence**\n\n- [Retail furniture company Hanssem](https://delight.ai/customers/hanssem) raised its resolution rate from 48% to roughly 86% over five months and cut human transfers by 50% after deploying [delight.ai](https://delight.ai).\n\n**Best fit:** ecommerce businesses, from growing DTC brands to enterprise retailers, that want one platform handling support across every channel a shopper actually uses.\n\n### Best Shopify-native alternative for customer service: Gorgias\n\n[Gorgias](https://www.gorgias.com/) is an ecommerce-specific helpdesk and AI agent built primarily for Shopify stores. It holds a 4.6/5 rating on G2 across 555 reviews.\n\n**Notable features**\n\n- **Deep Shopify-native workflows**, built specifically for order, return, and shipping questions inside a single ecommerce platform.\n- **Fast setup** for a store that’s already fully built on Shopify.\n\n**Considerations**\n\n- **Ticket-scoped, not memory-native.** Its automation resolves the ticket in front of it rather than carrying persistent context across a shopper's full history.\n- **Plans range from $10 to $900 a month by ticket volume**, plus overage charges near $0.36 to $0.40 per ticket beyond the plan limit.\n- **No named-customer result** is publicly documented beyond aggregate review data.\n\n**Best fit:** single-platform Shopify stores that want ecommerce-specific support workflows without a broader platform change.\n\n### Best for personalization and product recommendations: Nosto\n\n[Nosto](https://www.nosto.com/) personalizes product recommendations, on-site search, and email content in real time based on shopper behavior. It holds a 4.6/5 rating on G2.\n\n**Notable features**\n\n- **Real-time personalization engine.** Tests thousands of experience variations before automatically promoting the best performers across recommendations, search, and email.\n- **Behavior-based targeting.** Tailors product discovery to each shopper’s real-time actions rather than static rules.\n\n**Considerations**\n\n- **Revenue-share pricing rather than flat tiers.** Reviewers note cost can climb quickly at scale.\n- **No named-customer result** is publicly documented beyond aggregate review data.\n\n**Best fit:** mid-market and larger catalogs where product discovery, not support volume, is the conversion bottleneck.\n\n### Best for email and lifecycle marketing: Klaviyo\n\n[Klaviyo](https://www.klaviyo.com/) automates segmentation and lifecycle flows, including welcome series, abandoned-cart, and win-back campaigns, across email and SMS. It holds a 4.6/5 rating on G2.\n\n**Notable features**\n\n- **Deep segmentation and lifecycle automation.** Triggers welcome series, abandoned-cart, and win-back flows automatically based on real shopper behavior.\n- **Unified email and SMS.** Runs both channels from one platform rather than stitching together separate tools.\n\n**Considerations**\n\n- **Priced by active profiles rather than send volume.** A large, mostly inactive list can cost more than actual usage suggests.\n- **No named-customer result** is publicly documented beyond aggregate review data.\n\n**Best fit:** stores whose biggest untapped revenue sits in an existing list that isn't segmented or triggered well today.\n\n### Best for dynamic pricing and competitor tracking: Prisync\n\n[Prisync](https://prisync.com/) tracks competitor prices and stock, then applies repricing rules automatically. It holds a 4.7/5 rating on G2.\n\n**Notable features**\n\n- **Automated repricing.** Applies competitor-based pricing rules across the catalog without manual review.\n- **Continuous competitor tracking.** Monitors prices and stock levels so repricing reflects real-time market conditions.\n\n**Considerations**\n\n- **Plans run $99 to $399 a month** by catalog size. Larger catalogs of 5,000 or more tracked products sit at the top of that range.\n- **No named-customer result** is publicly documented beyond aggregate review data.\n\n**Best fit:** any retailer competing on price in a category where competitors change prices often.\n\n### Best for inventory and demand forecasting: Netstock\n\n[Netstock](https://www.netstock.com/) sits on top of an existing ERP and turns sales history into demand forecasts and reorder recommendations. It holds a 4.6 rating on G2 across 156 reviews.\n\n**Notable features**\n\n- **ERP-native integration.** Sits on top of an existing ERP rather than requiring a separate inventory system.\n- **Automated demand forecasting.** Turns sales history into reorder recommendations without manual spreadsheet work.\n\n**Considerations**\n\n- **Starts at $900 a month with no free tier.**\n- **No named-customer result** is publicly documented beyond aggregate review data.\n\n**Best fit:** retailers whose biggest AI opportunity is capital tied up in the wrong stock, not customer-facing volume.\n\n### Best for AI-generated product content: Jasper\n\n[Jasper](https://www.jasper.ai/) generates product descriptions, ad copy, and marketing content trained on a brand’s own voice. It holds a 4.7 rating on G2 from over 1,270 reviews.\n\n**Notable features**\n\n- **Brand-voice product descriptions.** Trained on a brand’s own voice to produce on-brand copy automatically.\n- **Ad copy and marketing content.** Extends the same brand voice across ad copy and broader marketing content, not just product pages.\n\n**Considerations**\n\n- **The Pro plan runs $59 to $69 a month**, and a **Business tier starts near $900** a month.\n- **No named-customer result** specific to ecommerce is publicly documented beyond aggregate review data.\n\n**Best fit:** catalogs large enough that writing product copy by hand is a real bottleneck.\n\n### Best for reviews, loyalty, and UGC: Yotpo\n\n[Yotpo](https://www.yotpo.com/) manages review collection, loyalty programs, and SMS marketing from one platform. It holds a 4.3 rating on G2 across 262 reviews.\n\n**Notable features**\n\n- **Review collection and loyalty programs.** Manages both from one platform rather than separate tools.\n- **Integrated SMS marketing.** Extends the same platform to SMS campaigns rather than requiring a separate tool.\n\n**Considerations**\n\n- **A free tier covers up to 50 orders a month, and Pro starts at $199,** scaling to **$599 or higher** with volume.\n- **No named-customer result** is publicly documented beyond aggregate review data.\n\n**Best fit:** stores building repeat-purchase and referral loyalty on top of an already-solid checkout experience.\n\n### How to choose the right AI tools for your ecommerce business\n\nFor most ecommerce businesses, [**Delight.ai**](https://delight.ai) **is the strongest overall pick**, because customer service is the category every retailer eventually needs, it's the most customer-facing, and [Delight.ai](https://delight.ai) is the only tool here built around one memory layer across every channel rather than a single point feature.\n\nThat said, a few situations point to a different, more specialized tool.\n\n- **You're fully built on Shopify and want ecommerce-native support workflows without a platform change.** Gorgias fits that specific setup.\n- **Product discovery, not support volume, is your conversion bottleneck.** Nosto's real-time recommendations are built for that job.\n- **Your biggest untapped revenue sits in an existing, underused email list.** Klaviyo's segmentation and lifecycle flows target exactly that.\n- **You compete on price in a category where competitors reprice often.** Prisync's competitor tracking earns its cost there.\n- **Capital tied up in the wrong inventory is the real problem.** Netstock's ERP-integrated forecasting is purpose-built for it.\n- **Writing product copy by hand has become a real bottleneck.** Jasper covers that volume.\n- **You're building repeat-purchase and referral loyalty on an already-solid checkout.** Yotpo owns that layer.\n\n[McKinsey](https://www.mckinsey.com/featured-insights/week-in-charts/ai-at-work-but-not-at-scale) and Stord found that 89% of retailers have adopted AI in some form, but only 7% have scaled it past a pilot. The best strategy is to pick the single category costing the most, prove a tool there, and expand from that base rather than collecting tools across every category at once.\n\n### See it against your own stack\n\n[Talk to our team](https://delight.ai/contact-sales) and ask specifically how [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) would carry context across the channels your shoppers already use, and how your team would run it day to day without a developer in the loop. For a deeper look at the customer-service category specifically, [read the related guide to AI chatbots for ecommerce](https://delight.ai/blog/industry/best-ai-chatbot-ecommerce).\n\n### Frequently asked questions about the best AI tools for ecommerce\n\n#### Is delight.ai the best AI tool for ecommerce?\n\nFor customer service and support specifically, yes: [delight.ai](https://delight.ai) is the strongest overall pick for an AI tool for ecommerce because it unifies chat, voice, email, SMS, and WhatsApp on one memory layer. For a narrower job like personalization, email, or pricing, one of the specialist tools above is a better fit, since [delight.ai](https://delight.ai) isn't built to cover those categories.\n\n#### What is the best AI tool for ecommerce customer service?\n\n[delight.ai](https://delight.ai/ai-customer-service) is the strongest fit for cross-channel and enterprise retailers, since Agent Memory Platform carries context across every channel a shopper uses. Gorgias is the best alternative for Shopify-only stores that want ecommerce-specific workflows without changing platforms.\n\n#### How much do AI tools for ecommerce typically cost?\n\nAI tools for ecommerce typically range from under $100 a month to a custom enterprise quote, since pricing varies by category. Entry-level support and content tools often start under $100 a month, pricing and forecasting tools frequently run $99 to $900 a month or require a custom quote, and [delight.ai](https://delight.ai)'s consumption-based pricing scales with resolution volume rather than a flat seat fee.\n\n#### Can one AI tool cover customer service, personalization, and marketing at once?\n\nOne AI tool can rarely cover customer service, personalization, and marketing to standard, since each tool on this list is purpose-built for a single job. Retailers who get the most value tend to prove one category, usually customer service, before adding a second.\n\n#### Which AI tool for ecommerce has the best reviews?\n\nJasper and Prisync carry the highest review-site ratings compared here, both at 4.7/5 on G2, though rating alone doesn't account for category fit or the specific job you need solved.\n\n- [Key takeaways](#section-anchor-id-0 "Key takeaways")\n- [What to evaluate before choosing an AI tool for ecommerce](#section-anchor-id-1 "What to evaluate before choosing an AI tool for ecommerce")\n- [8 best AI tools for ecommerce, at a glance](#section-anchor-id-2 "8 best AI tools for ecommerce, at a glance")\n- [How to choose the right AI tools for your ecommerce business](#section-anchor-id-3 "How to choose the right AI tools for your ecommerce business")\n- [See it against your own stack](#section-anchor-id-4 "See it against your own stack")\n- [Frequently asked questions about the best AI tools for ecommerce](#section-anchor-id-5 "Frequently asked questions about the best AI tools for ecommerce")\n\n### Keep reading\n\n[View more](/blog "View more")\n\n[](/blog/ai-for-cx/customer-service-mcp-ecommerce) [Customer service MCP for ecommerce](/blog/ai-for-cx/customer-service-mcp-ecommerce)\n\n[](/blog/ai-agent/who-is-responsible-ai-mistakes) [Who is responsible when AI makes a mistake?](/blog/ai-agent/who-is-responsible-ai-mistakes)\n\n[](/blog/ai-for-cx/real-time-customer-engagement) [How does real-time customer engagement impact your business?](/blog/ai-for-cx/real-time-customer-engagement)\n\n[](/blog/ai-for-cx/retell-vs-delight-ai) [Retell vs. delight.ai: Which fits your CX team?](/blog/ai-for-cx/retell-vs-delight-ai)\n\n[View more](/blog "View more")\n\n### Ready to unleash the power of AI customer service agents?\n\n[Contact sales](/contact-sales)\n
## Customer service MCP for ecommerce
Source: https://delight.ai/blog/ai-for-cx/customer-service-mcp-ecommerce
## Customer service MCP for ecommerce
[](https://delight.ai/author/shailesh-nalwadi)
[Shailesh Nalwadi](https://delight.ai/author/shailesh-nalwadi) — Head of product management
August 10 2026
### Key takeaways
- **A customer service MCP connects an AI assistant to your store's live support data**, so someone on your team can ask a question and get an answer instead of exporting a report.
- **It's a different tool than a commerce MCP**, which handles product search and checkout. Mixing the two up is the most common mistake in this space right now.
- **You don't need to be a large retailer to use one.** [delight.ai](http://delight.ai)'s own Delight Agent MCP shows what this already looks like, shipped and in use.
Search "MCP for retailers" today and most of what comes back is written for developers, not the person running the store. It's also, more often than not, about a different problem than customer service. Here's what a customer service MCP is, who it's for, and what it looks like already working.
## What is a customer service MCP, and why does it matter for your store?
The [Model Context Protocol](https://delight.ai/learn/what-is-an-mcp-model-context-protocol), or MCP, is an open standard that lets an AI assistant connect to outside tools and data instead of working only from what it already knows. Anthropic introduced it in November 2024, and Claude, ChatGPT, and a [growing list of other AI clients](https://modelcontextprotocol.io/) now support it.
A customer service MCP applies that idea to your support operation specifically. It connects an AI assistant to your live support and order data: resolution rates, ticket history, order status, and the knowledge articles your team actually relies on. Instead of exporting a report and reading through it by hand, you ask a question in plain language and get an answer built from what's happening in your store right now.
Most retail-facing MCP content today is actually about a different category entirely, which is worth sorting out before going further.
## Customer-service MCP vs. commerce MCP: which one are you actually looking for?
Most articles that mention MCP and retail in the same sentence are describing a **commerce MCP**: a connection that lets a shopper's own AI assistant [search your product catalog, check pricing, and start a checkout](https://shopify.dev/docs/apps/build/storefront-mcp) on your customer's behalf. Shopify, Stripe, and a handful of other platforms already ship this kind.
A customer service MCP does the opposite job:
| Who it's for | What it connects to |
|---|---|
| Commerce MCP | An outside shopper's AI assistant — Your product catalog, so they can find and buy something |
| Customer service MCP | Your own team's AI assistant — Your operational data, so your team can understand and act on what's happening in support |
Both are useful, but they solve different problems, and treating them as the same thing is where most of the confusion starts. The scenarios below are about the second kind. (For a broader tour of MCP outside retail specifically, see [6 real MCP examples](https://delight.ai/blog/ai-agent/6-real-mcp-examples).)
## Do you need to be a big retailer for a customer service MCP to matter?
A customer service MCP gives your business a direct line to its own support data. Instead of pulling a report and reading through it by hand, whoever runs support can ask a plain-language question, about WISMO volume, a CSAT dip, or a return-policy gap, and get an answer built from what's actually happening right now. That's what makes it worth having at any size of team: it turns an afternoon of digging through tickets into one question.
Some cautious MCP coverage tells merchants not to worry about MCP yet, but that caution is aimed at commerce MCP specifically, the checkout-and-catalog kind, where building an agent-ready storefront is a real project. Customer service MCP is a lighter decision: it reads data your support tool or helpdesk already has, so there's nothing new to build.
Company size doesn't determine the value here. A two-person shop fielding order questions out of one shared inbox can ask the same kind of question a fifty-person support team asks, just at a smaller scale. The real requirement is having live support data worth asking a question about, and almost every ecommerce business already does.
## What a customer service MCP actually catches
Here's what that looks like in scenarios a retail team would actually run into:
- **A "where's my order" spike during a promo.** Instead of digging through a week of tickets by hand, ask why WISMO questions jumped this week and get the pattern back in one answer.
- **A return-policy knowledge gap nobody caught.** Ask which knowledge article on returns is actually driving repeat questions, and whether it needs a rewrite.
- **A peak-season handoff surge.** Ask what's pushing more conversations to a human this week, before the queue backs up further.
- **A wave of questions after a carrier delay.** Ask which orders are affected and send a proactive update instead of waiting for every customer to ask separately.
- **A quiet misunderstanding.** Ask about a specific conversation and get walked through exactly where the AI lost the thread, instead of reading the transcript line by line.
Each of these used to mean exporting a report, then cross-referencing it against something else by hand. A customer service MCP turns it into one question.
## delight.ai's Delight Agent MCP: what this looks like already shipped
[delight.ai](http://delight.ai) launched its own MCP, called [Delight Agent MCP](https://delight.ai/blog/product/introducing-delight-agent-mcp), in August 2026. It connects Claude, ChatGPT, Cursor, and Codex to a store's live agent data, so a support lead can ask a direct question instead of opening a dashboard.
At launch, it shipped with more than 35 operational tools:
- **Read tools**: resolution rate, CSAT, category breakdowns, full conversation transcripts, and flags on any low-confidence response.
- **Write tools**: updating an actionbook, a tool, or a knowledge source from inside the same conversation where the problem turned up.
Changes to an agent's instructions or safeguards apply to a [development environment](https://delight.ai/trust-os) first, so a team can test a fix without it touching what customers see right away.
Mixpanel, an early adopter, put it plainly in [delight.ai's launch announcement](https://delight.ai/blog/product/introducing-delight-agent-mcp): piecing together dashboards and transcripts used to eat up an afternoon most weeks, and being able to just ask, in the tool they already work in, is the unlock. For retail credibility specifically, [delight.ai](http://delight.ai)'s own proof point is Hanssem, a furniture retailer whose resolution rate climbed from 48% to roughly 86% over five months on [delight.ai](http://delight.ai)'s platform. That's a separate result from the MCP itself, not an MCP-specific case study.
Put the two together, and the picture is simple. The WISMO-spike and handoff-surge questions from the last section are the kind of question Delight Agent MCP is built to answer directly, in whatever AI tool a retail team is already using.
## Is it safe to connect an MCP to live customer data?
It can be safe, but only if you check permissions first, and that question is worth asking before "what can it do," not after. An MCP server only sees what you authorize, so confirm whether a given server is read-only or has write access before connecting it to anything that touches customer data. A read-only connection can look things up. A write-enabled one can change something, which is a bigger decision.
## How to get started with a customer service MCP
1. **Name the problem first.** If you're trying to help shoppers find and buy products through their own AI assistant, that's commerce MCP. If you're trying to understand and fix what's happening in your own support operation, that's customer service MCP.
2. **Check what you already have.** Support tools are shipping MCP options now. Intercom already has an official MCP server, and Freshdesk's is in early access. Check with your own helpdesk before assuming you need something new.
3. **Start read-only.** If you're not sure yet, begin with a connection that can look things up but not change anything.
4. **Ask a real question.** Skip the demo question, and ask something tied to a problem you have this week.
If your team already runs an AI agent for customer service, a customer service MCP turns that same live data into a question you can just ask. See how [delight.ai](http://delight.ai)'s own [Delight Agent MCP](https://delight.ai/integrations) works, or browse more breakdowns like this one on the [delight.ai blog](https://delight.ai/blog).
- [What is a customer service MCP, and why does it matter for your store?](#section-anchor-id-0 "What is a customer service MCP, and why does it matter for your store?")
- [Customer-service MCP vs. commerce MCP: which one are you actually looking for?](#section-anchor-id-1 "Customer-service MCP vs. commerce MCP: which one are you actually looking for?")
- [Do you need to be a big retailer for a customer service MCP to matter?](#section-anchor-id-2 "Do you need to be a big retailer for a customer service MCP to matter?")
- [What a customer service MCP actually catches](#section-anchor-id-3 "What a customer service MCP actually catches")
- [delight.ai's Delight Agent MCP: what this looks like already shipped](#section-anchor-id-4 "delight.ai's Delight Agent MCP: what this looks like already shipped")
- [Is it safe to connect an MCP to live customer data?](#section-anchor-id-5 "Is it safe to connect an MCP to live customer data?")
- [How to get started with a customer service MCP](#section-anchor-id-6 "How to get started with a customer service MCP")
## Frequently asked questions
**What's the difference between a customer service MCP and a commerce MCP?**
A commerce MCP connects a shopper's AI assistant to your product catalog for search and checkout. A customer service MCP connects your own team's AI assistant to your support and order data, so your team can ask questions about what's happening in your operation.
**Do small ecommerce businesses need an MCP, or is this only for large retailers?**
No, a customer service MCP isn't only for large retailers, and small ecommerce businesses can use one too. It reads data you likely already have in a helpdesk or support tool, so a small shop can use the same pattern a large support team does, just at a smaller scale.
**How does an MCP help with customer service, compared to product search or checkout?**
An MCP helps with customer service by answering operational questions directly from your live support data, things like why a certain question type spiked this week or which knowledge article is causing confusion. Product search and checkout are a separate, commerce MCP job; this is about understanding and fixing your own support operation instead of exporting a report and piecing the answer together yourself.
**Is it safe to connect an MCP to live customer or order data?**
Yes, it can be safe to connect an MCP to live customer or order data, but only if you check the permissions first. An MCP server can only do what you authorize, so confirm whether it's read-only or has write access before connecting it to anything involving customer data, and start read-only if you're unsure.
**Does delight.ai have its own MCP?**
Yes, [delight.ai](http://delight.ai) has its own MCP, called Delight Agent MCP, launched in August 2026. It connects Claude, ChatGPT, Cursor, and Codex to a store's live agent data for both reading operational metrics and making direct updates.
## Keep reading
[View more](/blog "View more")
[](/blog/ai-for-cx/best-ai-tools-for-ecommerce-in-2026)[The 8 best AI tools for ecommerce in 2026](/blog/ai-for-cx/best-ai-tools-for-ecommerce-in-2026)
[](/blog/ai-agent/who-is-responsible-ai-mistakes)[Who is responsible when AI makes a mistake?](/blog/ai-agent/who-is-responsible-ai-mistakes)
[](/blog/ai-for-cx/real-time-customer-engagement)[How does real-time customer engagement impact your business?](/blog/ai-for-cx/real-time-customer-engagement)
[](/blog/ai-for-cx/retell-vs-delight-ai)[Retell vs. delight.ai: Which fits your CX team?](/blog/ai-for-cx/retell-vs-delight-ai)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Reduce WISMO with proactive alerts from Delight.ai
Source: https://delight.ai/blog/insights/wismo-retail-guide
- [Blog](/blog)
- [Insights](/blog/insights)
- Reduce WISMO with proactive alerts from Delight.ai
## Reduce WISMO with proactive alerts from Delight.ai
[](https://delight.ai/author/leighanne-manwiller)
[LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller)
Product marketing manager
August 13 2026
**Key takeaways**
- **WISMO ("where is my order?") came up in about 1 in 7 retail accounts we spoke with this year**, and shoppers still ask this when a tracking page exists, because the page can't tell them what to do next.
- **Delight.ai reduces WISMO by reaching out when the order event happens**, such as a stalled carrier scan or a backorder, on the channel the shopper already uses.
- **A delay alert only stops the follow-up call if the shopper can act on it.** When the shopper can reply to change the address or cancel a delayed item in one conversation, they have no reason to call or email support.
Retailers can reduce WISMO by sending the update before the shopper thinks to ask, then resolving whatever the shopper asks next in the same conversation. A faster answer to "where is my order?" still means the shopper had to reach out. A delay alert the shopper can reply to, backed by an AI agent that can change the order, means the shopper never has to experience a multi-step customer support thread.
This is also where an [AI CX agent](https://delight.ai/ai-cx-agent) protects revenue. When a shopper can reroute a late package or cancel a backordered item, the rest of the order ships instead of being canceled or refunded. For [retail](https://delight.ai/industries/retail) teams, Delight.ai handles that full exchange, from the first delay alert to handoff when a person needs to take over.
## What is WISMO?
WISMO stands for **"where is my order?"** It's retail and ecommerce shorthand for the moment a shopper reaches out to ask about shipping status or delivery timing. Each of those calls, emails, or chats is a WISMO request, and each one lands on your support team.
The question usually hides 1 of 3 worries:
- **Has my order shipped?**
- **Where is my order now?**
- **When will my order arrive?**
Its counterpart at the other end of the order, **WISMR**, means "where is my return?" or "where is my refund?" and follows the same pattern.
For retail leaders, WISMO is a repeat-purchase problem before it's a support-cost problem. A shopper who has to chase one order across email and phone trees remembers that effort the next time they're deciding where to buy. Among the retailers Delight.ai spoke with this year, **about 1 in 7 brought up WISMO** or order status as a use case.
## Why do shoppers ask about WISMO even when a tracking page exists?
Shoppers ask about WISMO even when a tracking page exists because the page answers "where" but rarely answers "what now." One retailer the Delight.ai team spoke with described the pattern plainly: Its shoppers could check order status in several places online, but still found it more straightforward to message the care team.
The table below shows the gap between what a standard tracking page shows and what the shopper is trying to find out.
| What the shopper wants to know | What a tracking page shows | What's still missing |
|---|---|---|
| Has my order shipped? | "Label created" | Whether the package has left the warehouse |
| Where is it now? | The last carrier scan, which may be days old | Why it stopped moving |
| When will it arrive? | The carrier's estimate | A new date the shopper can plan around |
| Can I still change it? | Nothing | Whether the address or items can still change |
| What if it's lost? | Nothing | Who will fix it, and how fast |
The bottom 2 rows drive most of the frustration. A shopper who sees a stalled scan 2 days before a birthday doesn't want a summary of the tracking page's updates. They want to know whether to reorder, but a tracking page can't make that call for them.
Disconnected systems widen the gap. When the order system and the carrier feed don't share current data, the tracking page and the support team may give 2 different answers. The shopper then reaches out again to find out which one is right.
## How does Delight.ai reduce WISMO before the shopper asks?
Delight.ai reduces WISMO before the shopper asks by starting the conversation the moment the order event happens. Your team defines the events that count, Delight.ai's [proactive engagement](https://delight.ai/omnipresent) watches for them, and the shopper gets an update with a clear next step before they even think to reach out.
### Which order events start the outreach?
Proactive outreach in Delight.ai runs on real-time triggers from system events, delivery status, or an external API or webhook. A retailer might set a trigger for order events like these:
- **A package with no carrier scan** 48 hours after "label created."
- **An item that moves to backorder** after checkout.
- **A carrier delivery date that slips** past the date the shopper was promised.
Your team sets the trigger and the copy in one place, so the alert follows your policies instead of reading like a generic carrier email.
### Which channel does the delay alert go out on?
The delay alert goes out on the channel the shopper already uses, whether that's a text message or a voice call. For high-value or time-sensitive orders, [Proactive Voice](https://delight.ai/blog/product/voice-v2) can place an outbound call when an order status changes. Every call is logged and tied back to the original order.
### What does a delay alert need to say?
A delay alert needs to answer the shopper's next question, or it creates a new WISMO request instead of preventing one. In plain words, that means 3 things:
- **The new date**, or the date your team will update the shopper about their delayed order.
- **What changed**, such as a missed carrier scan or a backorder on one item.
- **What the shopper can do in response to the update**, such as reply to change the delivery address or cancel the delayed item.
Delight.ai packages this flow as a reusable set of steps that tells the AI agent what to say and which system to call. We call that set of steps an Actionbook, and the "[Proactive Delivery" Actionbook](https://delight.ai/industry-blueprint-retail) is built to get ahead of WISMO and resolve delivery issues in the same conversation.
## What happens when the shopper replies to the delay alert?
When the shopper replies to the delay alert, the AI agent handles their request in the same thread, with the order history already in view. The illustrative example below walks through that exchange step by step.
1. **The alert goes out.** On Tuesday, a shopper gets a text that her 2-item order is delayed because 1 item is on backorder.
2. **She replies with 2 requests at once.** She asks to send the in-stock item to her office and cancel the backordered one.
3. **The AI agent checks what can still change.** It reads the order system and confirms the in-stock item hasn't left the warehouse. Then it follows the retailer's Actionbook to update the address and cancel the backordered item.
4. **She calls to follow up 2 days later.** She asks when the package will arrive, and the voice agent already has Tuesday's thread, so she doesn't repeat the story.
5. **The package stops moving.** After 4 days with no carrier update, the case moves to a person with the full conversation attached.
Steps 3 and 4 depend on your AI agent's memory. Delight.ai's [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) carries context across every channel transition, so a shopper who starts in SMS and calls later never has to re-explain the order. Delight.ai also supports multiple active conversations at once, so a quick WISMO question doesn't get stuck behind a longer return that the same shopper has open.
Shoppers expect this level of continuity. In the [Delight AI Index](https://delight.ai/reports/ai-index), **70% of U.S. consumers said they'd feel more delighted if AI remembered their prior interactions**, and 49% prefer AI for checking order status, compared with 34% who prefer a human.
## Which systems does Delight.ai need to answer WISMO accurately?
Delight.ai needs to read from the system that owns the order to answer WISMO accurately. Retailers the Delight.ai team spoke with about WISMO pointed to their order platform as that source of truth, with their help desks only holding a copy.
Delight.ai's [integrations](https://delight.ai/integrations) cover the 4 connections behind most WISMO conversations:
- **Your order management system or commerce platform**, so the AI agent sees what shipped and what can still change.
- **Carrier tracking data**, so the delay alert fires on a missed scan, before the shopper complains.
- **Your help desk**, such as Zendesk or Salesforce, so any case that needs a person arrives with the conversation attached and no migration project.
- **Identity checks for guests and logged-out shoppers**, which Delight.ai's interactive forms collect and verify inside the conversation before any order details are shared.
The last connection is easy to miss. Retailers the Delight.ai team spoke with also flagged guest and logged-out shoppers as a reason order status questions end up with a live agent, because nothing confirms who's asking.
## When does Delight.ai hand a WISMO case to a person?
Delight.ai hands a WISMO case to a person when the problem needs judgment that a status update can't provide. A lost package or a "delivered but not received" claim falls in that group.
Shoppers draw the same line. In the Delight AI Index, 54% of consumers want a person when they dispute a price, and only 30% would hand that task to AI. Retail scored 59 out of 100 on the Index's trust scale, second only to tech and media.
[Forrester's 2026 customer service predictions](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) warn that overautomating complex or emotional inquiries can erode satisfaction. So the handoff has to be planned ahead of time, and 3 controls make that possible:
- **Your team sets the boundary.** [Trust OS](https://delight.ai/trust-os) gives your team control over what the AI agent handles on its own and when it escalates, with an audit log of every change.
- **The person starts with full context.** In [Delight Desk](https://delight.ai/desk), the human agent sees the replies and every action already taken.
- **Nothing reaches shoppers without review.** Actionbooks are drafted and reviewed by your team before they go live, so a new WISMO flow is tested before any shopper sees it.
## How do you measure whether WISMO actually went down?
You measure whether WISMO went down by counting how many orders still lead a shopper to ask "where is my order?", instead of how fast those requests get answered. [Gartner found that 91% of customer service leaders surveyed reported pressure from executive leadership to implement AI in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). Fewer WISMO requests per order is the result that holds up when those executives ask what the AI changed.
The table below shows the 4 numbers worth tracking before and after you turn on proactive delay alerts.
| Metric | How to calculate it | What it tells you |
|---|---|---|
| WISMO rate | WISMO requests ÷ orders shipped in the same period | Whether fewer orders lead a shopper to reach out |
| Repeat requests per order | WISMO requests ÷ orders with at least 1 WISMO request | Whether one answer settles the question |
| First-conversation resolution | WISMO conversations resolved without the shopper reaching out again ÷ all WISMO conversations | Whether the reply flow closes the loop |
| Orders saved | Delayed orders delivered or reshipped ÷ all delayed orders | Whether proactive outreach protects revenue |
Delight.ai gives you the raw material for each one. Conversation Search finds every "where is my order" message across thousands of conversations, and customized CSAT surveys show how shoppers felt after the alert. The Data Export API then sends full conversation data to your own BI tools.
## What's the first step to reduce WISMO with Delight.ai?
The first step to reduce WISMO with Delight.ai is to turn on a proactive delay alert for the 1 or 2 order events behind most of your "where is my order?" requests. Good candidates are a missed carrier scan or a backorder. Once shoppers can reply to that alert to change or cancel an order in the same conversation, they have their answer before they think to ask.
WISMO is one of several retail moments where an AI agent can protect a sale. The [retail AI agent use cases playbook](https://delight.ai/ebooks/retail-ai-agents) maps where AI agents fit across the shopper journey, from pre-sale discovery to post-sale care like WISMO and returns.
- [What is WISMO?](#section-anchor-id-0 "What is WISMO?")
- [Why do shoppers ask about WISMO even when a tracking page exists?](#section-anchor-id-1 "Why do shoppers ask about WISMO even when a tracking page exists?")
- [How does Delight.ai reduce WISMO before the shopper asks?](#section-anchor-id-2 "How does Delight.ai reduce WISMO before the shopper asks?")
- [What happens when the shopper replies to the delay alert?](#section-anchor-id-3 "What happens when the shopper replies to the delay alert?")
- [Which systems does Delight.ai need to answer WISMO accurately?](#section-anchor-id-4 "Which systems does Delight.ai need to answer WISMO accurately?")
- [When does Delight.ai hand a WISMO case to a person?](#section-anchor-id-5 "When does Delight.ai hand a WISMO case to a person?")
- [How do you measure whether WISMO actually went down?](#section-anchor-id-6 "How do you measure whether WISMO actually went down?")
- [What's the first step to reduce WISMO with Delight.ai?](#section-anchor-id-7 "What's the first step to reduce WISMO with Delight.ai?")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## FAQ
**What does WISMO stand for?**
WISMO stands for "where is my order?" It's shorthand for post-purchase questions about shipping status or delivery timing. These questions reach retail and ecommerce support teams on every channel they offer, and they tend to spike during peak shopping periods when carrier delays are more common.
**Why do shoppers ask "where is my order?" so often?**
Shoppers ask "where is my order?" so often because the update they get doesn't answer their next question. A tracking page shows the last scan but not why the package stopped, when it will arrive, or whether the order can still change. Stale carrier data and disconnected order systems push shoppers to reach out for an answer only a person or an AI agent can give.
**How do you reduce WISMO?**
You reduce WISMO by sending a delay alert as soon as an order event happens, then resolving the shopper's reply in the same conversation. The alert should include a new date, what changed, and what the shopper can still do. Accurate order data and a clear handoff for lost packages keep the alert from leading to a second call or email.
**Can AI reduce WISMO?**
Yes, AI can reduce WISMO for routine cases, because an AI agent connected to current order and carrier data can answer status questions and reach out about delays before the shopper asks. With memory across channels, it can also handle the follow-up, like an address change or a canceled item. Lost packages and disputes still go to a person with the full conversation.
**How much does WISMO cost?**
How much WISMO costs depends on channel and season, so there's no single reliable industry figure. WISMO calls generally take more agent time than a routine status check handled in an automated digital channel. For retail leaders, the more useful measure is how many requests a proactive update prevented and how many delayed orders were saved.
**What's the difference between WISMO and WISMR?**
The difference between WISMO and WISMR is which end of the order the shopper is asking about. WISMO means "where is my order?" and covers a shipment in progress, while WISMR means "where is my return?" or "where is my refund?" and covers a return already underway. In both cases, a clear, proactive update with a next step can prevent the shopper from having to ask.
## Keep reading
[View more](/blog "View more")
[](/blog/ai-agent/best-ai-voice-agents)
[Best AI voice agents in 2026, tested firsthand](/blog/ai-agent/best-ai-voice-agents)
[](/blog/industry/voice-ai-appointment-booking-home-services)
[Getting started with voice AI for home services](/blog/industry/voice-ai-appointment-booking-home-services)
[](/blog/insights/how-to-hire-your-first-cx-engineer)
[How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[](/blog/product/introducing-secure-caller-keypad-input)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[View more](/blog "View more")
## Ecommerce customer service automation: How to roll it out
Source: https://delight.ai/blog/ai-agent/ai-ecommerce-automation-retailers
## Ecommerce customer service automation: How to roll it out
[](https://delight.ai/author/shailesh-nalwadi)
[Shailesh Nalwadi
Head of product management](https://delight.ai/author/shailesh-nalwadi)
August 13 2026
## Key takeaways
- **Ecommerce customer service automation works best when it starts from your own resolved tickets.** On Delight.ai, historical tickets become draft workflows that a person reviews before anything goes live.
- **A staged rollout with an instant off switch** limits a bad answer to a few shoppers during a peak like Black Friday.
- **Resolution and saved orders show whether automation is working.** [Hanssem's resolution rate](https://delight.ai/customers/hanssem) rose from 48% to about 86% over 5 months as its team kept improving the agent.
Ecommerce customer service automation uses an artificial intelligence (AI) agent to resolve shopper requests like order status, order changes, and returns inside the systems that hold the order. For a retail customer experience (CX) team that has already decided to automate, the harder question is how to roll it out without putting peak-season revenue at risk.
Most of that risk sits in the rollout itself. The [Delight AI Index](https://delight.ai/reports/ai-index), a survey of 1,001 U.S. consumers, found that 71% already use AI customer service but only 57% like it. Teams that roll out [Delight.ai's AI agent for retail](https://delight.ai/industries/retail) in stages, starting from their own ticket history, start closing that gap before shoppers see a misstep.
## What can ecommerce customer service automation handle?
Ecommerce customer service automation handles the requests that follow a shopper from checkout to delivery, like order status, order changes, and returns or exchanges. [McKinsey](https://www.mckinsey.com/capabilities/operations/our-insights/the-contact-center-crossroads-finding-the-right-mix-of-humans-and-ai) found that 50% to 60% of customer interactions remain transactional, and that AI can already solve simple transactional issues through chat and voice assistants. Those transactional requests are where most rollouts begin.
The line between automation that helps and automation that frustrates is whether the AI agent can complete the request or only describe it. A shopper who wants to change a shipping address an hour after checkout needs the address changed before the order leaves the warehouse. They are not looking for a policy page about the cutoff.
Where-is-my-order (WISMO) questions are usually the largest share of that volume. The rest of a rollout depends on how well the agent's first workflows reflect the way your team already resolves repetitive requests.
## How do you turn resolved tickets into Delight.ai's first automated workflows?
The most accurate first workflows come from tickets your team has already resolved, because those tickets show how your policies work in practice. On Delight.ai, resolved tickets become draft workflows that define what the agent says, what it asks, and which system it calls to finish the job.
A person on the operations team reviews and edits every draft before it ships, and no draft deploys on its own. That reviewed workflow is what Delight.ai calls an [Actionbook](https://delight.ai/product-release/openbook), and operations teams author and edit Actionbooks directly, without engineering tickets.
### Which tickets should become the first workflows?
The best candidates combine high volume, a clear written policy, and an answer that lives in a system the agent can access. A return request for an unworn item inside a 30-day window clears all three. A damage claim with disputed photos fails on policy clarity, so it stays with a person.
For a broader map of where AI fits across a store beyond service, [AI in ecommerce use cases](https://delight.ai/blog/industry/ai-use-cases-ecommerce) covers product discovery, pricing, and forecasting.
### What if your ticket history lives in Zendesk?
Teams moving off Zendesk can bring that history with them to Delight.ai. [Delight Desk's Zendesk migration](https://delight.ai/zendesk-migration-desk) imports past tickets and help center content. It then drafts Actionbooks from past resolutions, so the agent starts from what the team already knows instead of a blank page.
Desk carries a $0 seat fee. Billing is per successful AI resolution, so adding people to the helpdesk during peak season doesn't add seat costs.
### Which order and helpdesk systems need to be connected before launch?
An AI agent can only fix an order in a system it can access. The table below lists the most common post-purchase requests and the systems Delight.ai's AI agent needs access to for each one, what the agent can complete, and when to escalate to a human agent.
| Shopper request | Systems the agent needs to access | What the agent can complete | When to escalate to a human agent |
|---|---|---|---|
| Where is my order? | Order and carrier tracking data | Live status and a delivery estimate | The carrier shows the package lost or delivered but the shopper disputes it |
| Change or cancel an order | Order management system, through an integration or internal API | The change, before the order ships | The order has already left the warehouse |
| Return or exchange | Returns policy, order history, and payments | An eligible return label or an exchange | The item is outside policy or arrived damaged |
| Product or sizing question after purchase | Product catalog and inventory | An answer, or an exchange for the right size | The shopper is asking for an exception |
Delight.ai's [integrations](https://delight.ai/integrations) connect the agent to helpdesk, customer relationship management (CRM), payments, inventory, and internal systems without a migration project. For a wider look at how an agent sits across point-of-sale, CRM, and loyalty systems, [conversational AI for retail](https://delight.ai/blog/ai-for-cx/conversational-ai-for-retail) covers the full stack.
With the workflows drafted and the systems connected, the next job is proving both before shoppers see them.
## How do you test Delight.ai's AI agent before shoppers interact with it?
Testing before launch means running the agent against the conversations your shoppers will send and checking that each one ended with the right outcome. [Forrester](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/) expects service quality to dip in 2026 as companies wrestle with the complexity of AI deployment. Testing is the step where a team catches a drop in service quality before shoppers notice it.
A store that launches without clear policies or representative test conversations ends up correcting wrong answers after shoppers have already seen them. Fixing a refund promise that breaks policy after a shopper has seen it costs far more than catching it in a test run.
### What should a pre-launch test set include?
A useful test set mirrors the requests your team handles every week, plus the edge cases that break policy or need a person. It should include:
- **A standard request phrased 5 different ways**, like a shopper asking where an order is with and without the order number.
- **An out-of-policy return**, like a worn item on day 45 of a 30-day window.
- **A frustrated shopper**, whose conversation should reach a person with the history attached.
- **A request the agent shouldn't act on alone**, like a refund on an order flagged for review.
Delight.ai's [Test Center](https://delight.ai/ai-cx-agent) runs this test set before any change reaches shoppers, one case at a time or as a [scheduled health check](https://delight.ai/product-release/end-to-end-ai-agent-testing-build-trust-before-you-go-live) that runs daily, weekly, or monthly. [Outcome-based testing](https://delight.ai/product-release/outcome-based-testing) checks whether the agent understood the intent and completed the right action, so a harmless change in wording doesn't break the test.
### Where does testing happen?
Delight.ai provides separate [development, staging, and production environments](https://delight.ai/product-release/staging-environment), and changes move from one to the next through validation gates before they reach shoppers. Delight.ai also tracks configuration changes over time, so the team can compare two versions side by side and [roll back to an earlier deployment](https://delight.ai/product-release/selective-deployment-and-rollback) from the past 30 days.
These controls are part of [Trust OS](https://delight.ai/trust-os), the governance and observability layer that shows the team what the agent did and why. Once Delight.ai's AI agent passes testing, the next step is planning when each stage of the rollout goes live.
## How do you roll out Delight.ai's AI agent without risking peak season?
A safe rollout happens in stages, by request type and by share of traffic, and is complete before peak traffic arrives. [Gartner](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) found that 91% of customer service and support leaders surveyed report pressure from executive leadership to implement AI. Pressure to move fast is the reason to roll out gradually, with safeguards in place, before a high-stakes week like Black Friday.
### What are the rollout stages?
1. **Launch one tested request type on one channel**, such as order status in web chat.
2. **Split traffic between the agent and the current flow.** Delight.ai's [A/B testing](https://delight.ai/product-release/a-b-testing) divides live conversations between the two versions and tracks each one's metrics separately. When the test ends, it rates how reliable the result is as high, medium, or low.
3. **Expand to more traffic only when resolution and customer satisfaction (CSAT) hold**, using gradual rollout to limit how many shoppers see each change while it ships.
4. **Add the next request type**, such as returns, and repeat the split.
5. **Choose exactly which changes go live.** [Selective deployment](https://delight.ai/product-release/selective-deployment-v2) shows a side-by-side comparison of what changed in each Actionbook, knowledge file, or setting, so the team picks which edits reach production.
6. **Freeze workflow updates prior to peak seasons**, leaving only fixes that have passed testing.
### What happens if Delight.ai's AI agent makes a mistake during peak season?
If Delight.ai's AI agent starts giving wrong answers during peak season, the team can pause it instantly without losing its setup. Delight.ai's Deactivate AI switch pauses every automated response at once, and the configuration stays intact for when the fix is ready to apply. Conversations can also be escalated to a human agent in one click.
For a smaller mistake, like one Actionbook change that causes problems on a Saturday, the team can roll back that change alone and keep everything else running. Deciding whether to expand, freeze, or roll back depends on reading the right numbers.
## How do you know Delight.ai's AI agent is working?
Delight.ai's AI agent is working when shopper requests are resolved, shoppers don't return with issues about the same order, and orders that could have become refunds are saved. Containment rate counts every conversation that never reached a person. That means **containment alone can hide failure**, because a shopper who gives up counts the same as one whose problem was solved.
The table below pairs each metric with what it reveals about a rollout and the trap to watch for.
| Metric | What it tells you | Watch out for |
|---|---|---|
| Resolution rate | The share of requests the agent completed without a person | Requests marked resolved that come back within days |
| Containment rate | The share of conversations that never reached a person | Shoppers who left frustrated also count as contained |
| Recontact within 7 days | Whether resolved requests stayed resolved | A rise right after a workflow change usually points to that change |
| Exchanges vs. refunds on eligible returns | Revenue kept when a return comes in | Policy limits what the agent can offer, so compare eligible returns only |
| CSAT, AI vs. human conversations | Whether shoppers trust the agent's answers | Compare the same request types, not overall averages |
| Handoff quality | Whether people receive the full context | Shoppers repeating their order number after a handoff |
[Delight Analytics](https://delight.ai/analytics) surfaces the inputs behind these numbers. Shoppers can rate individual answers with a thumbs up or down, and customizable CSAT surveys run after each conversation.
For the revenue metrics, Delight.ai's [Data Export API](https://delight.ai/product-release/data-export-api) exports conversation-level data, including resolution type, handoff markers, and abandonment markers, in JSON or CSV. The team can join that data to order records in its own business intelligence (BI) tool to see which conversations ended in an exchange instead of a refund.
## How do you improve Delight.ai's AI agent after launch without losing human control?
After launch, the conversations the agent handles become the source of its improvements, and the team decides how much of that improvement needs a person's sign-off. On Delight.ai, that loop runs on proposed fixes from production failures, flagged low-confidence answers, and human review.
### How does Zero-Touch Improvement propose fixes?
When conversations fail in production, an automated review groups the failures by root cause and proposes an edit to the Actionbook behind them. It then tests the fix against the conversations that failed, with a feature called [Zero-Touch Improvement (ZTI)](https://delight.ai/product-release/zero-touch-improvement).
ZTI runs at graduated levels of autonomy. At the first level, a person approves every proposed change. At the most autonomous level, the team sets boundaries ZTI can't change on its own, such as financial thresholds, compliance requirements, and sensitive topics.
### How do reviewers catch the answers ZTI doesn't?
Delight.ai flags low-confidence answers, the ones most likely to be wrong, and sends them to a [review queue](https://delight.ai/product-release/ai-hallucination-detection-alerts). A reviewer can approve, correct, escalate, or dismiss each flagged answer, then update the knowledge behind it so the next shopper with the same question gets the right answer.
For a broader quality check, Delight.ai's [human evaluators](https://delight.ai/product-release/humans-as-ai-evaluators) feature assigns reviewers by role, business function, or language. A Spanish-speaking team lead, for example, can filter the evaluator console to Spanish-language return conversations and score them on tone, clarity, and whether the issue was solved.
### Which conversations should Delight.ai's AI agent escalate to a human agent?
The team decides which requests the agent handles at all, since only reviewed Actionbooks go live, and role-based access controls who can edit, deploy, or delete those configurations. Delight.ai's AI agent should escalate to a human agent for:
- **An upset shopper** who needs empathy and careful attention.
- **An unusual dispute**, like a wrong item that doesn't match any standard return reason.
- **A judgment call**, like a one-off discount or exception that isn't written into any policy.
When one of these conversations comes up, the agent hands it to [Delight Desk](https://delight.ai/desk), the helpdesk where the human team works, as a ticket with the full conversation attached. The person sees what the agent tried, which systems it checked, and why it stopped, so the shopper doesn't have to repeat the issue.
### What does the improvement loop look like in practice?
[Hanssem](https://delight.ai/customers/hanssem), the South Korean furniture retailer, raised its resolution rate from 48% to about 86% over 5 months in 2025 with Delight.ai, with 50% fewer transfers to human agents. Hanssem's IT Division Director, Hyeong-Tak Kim, described what made the difference.
> "AI doesn't seem perfect from day one. What matters is continuous improvement and clear accountability."
>
> Hyeong-Tak Kim, IT Division Director, Hanssem (2025)
## Getting started with ecommerce customer service automation
Getting started with ecommerce customer service automation means picking one high-volume request, building it from tickets your team has already resolved, and testing it before a single shopper sees it. From there, a staged rollout and the right metrics show when the agent is ready for more traffic, ideally well before peak season. If your team is planning that first rollout, [talk to our team](https://delight.ai/contact-sales) about how Delight.ai would work with your order systems and policies.
- [What can ecommerce customer service automation handle?](#what-can-ecommerce-customer-service-automation-handle)
- [How do you turn resolved tickets into Delight.ai's first automated workflows?](#how-do-you-turn-resolved-tickets-into-delightais-first-automated-workflows)
- [How do you test Delight.ai's AI agent before shoppers interact with it?](#how-do-you-test-delightais-ai-agent-before-shoppers-interact-with-it)
- [How do you roll out Delight.ai's AI agent without risking peak season?](#how-do-you-roll-out-delightais-ai-agent-without-risking-peak-season)
- [How do you know Delight.ai's AI agent is working?](#how-do-you-know-delightais-ai-agent-is-working)
- [How do you improve Delight.ai's AI agent after launch without losing human control?](#how-do-you-improve-delightais-ai-agent-after-launch-without-losing-human-control)
- [Getting started with ecommerce customer service automation](#getting-started-with-ecommerce-customer-service-automation)
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## Frequently asked questions
**How do you roll out ecommerce customer service automation for a retail store?**
A successful ecommerce customer service automation rollout starts with one high-volume request type that has a clear policy, like order status, on one channel. The workflow is built from tickets your team has already resolved and tested against everyday and edge-case conversations, then traffic is split between the AI agent and your current flow. The rollout expands to more traffic and request types only when resolution and CSAT hold, and it's complete before peak season so the last weeks before Black Friday are for fixes, not expansion.
**Will ecommerce customer service automation work with our existing helpdesk and fulfillment systems?**
Ecommerce customer service automation works with your existing helpdesk and fulfillment systems as long as the AI agent can access the systems that hold the order. Delight.ai connects to helpdesks like Zendesk, CRMs like Salesforce, and payments, inventory, and internal systems through integrations and APIs, without a migration project. The check before launch goes request by request, since order status needs tracking data, order changes need the order management system, and returns need policy, order history, and payments.
**When should the AI agent hand off to a person, and who decides what it can do on its own?**
The AI agent should hand off to a person whenever policy, risk, emotion, or uncertainty calls for judgment, like a damage dispute or an upset shopper. On Delight.ai, the team decides which requests the agent handles, since only reviewed Actionbooks go live, and role-based access controls who can edit or deploy those configurations. Delight Desk receives the escalation with the full conversation attached, so the shopper doesn't have to repeat the issue.
**How is ecommerce customer service automation priced on Delight.ai?**
Ecommerce customer service automation on Delight.ai is priced with conversation-based usage fees, and unit costs decrease at scale. Delight Desk, the helpdesk where people and the AI agent work together, has a $0 seat fee and bills per successful AI resolution rather than per agent. That means adding people to cover a peak season doesn't add seat costs, and spend follows the requests the agent resolves.
**How long should a person review the AI agent's conversations after launch?**
A person should review the AI agent's conversations closely after launch until the agent handles routine requests reliably and escalates exceptions correctly, and lighter review never fully stops. Early on, that means daily checks for wrong answers and policy gaps. After that, the low-confidence review queue and human evaluators point reviewers to the conversations that need them, and Zero-Touch Improvement proposes specific fixes for a person to approve.
## Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-secure-caller-keypad-input)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[](/blog/product/introducing-ivr-navigation)
[Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[](/blog/product/introducing-universal-telephony)
[Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[](/blog/product/delight-ai-agent-guardrails-in-action)
[Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## Where to find top AI agents for industrial firms
Source: https://delight.ai/blog/industry/ai-agents-industrial-firms
## Where to find top AI agents for industrial firms
[](https://delight.ai/author/cheryl-chai)
[Cheryl Chai](https://delight.ai/author/cheryl-chai)
Product marketing lead
August 13 2026
**Key takeaways:**
- **Three different jobs answer to "AI agent for industrial firms":** back-office and ERP automation, supply chain planning, and field-service coordination. Pick the vendor that matches the job, not the pitch.
- **Seven vendors are profiled below**, each broken into what it is, what it does, its proof points and G2 rating, and exactly who it's best for, so the right fit is obvious at a glance.
- **Delight.ai is the only vendor built specifically for the multi-party chase that starts once a field job stalls** — parts depot, OEM, and client, contacted at once, not one call at a time.
**The shortlist:**
- **UiPath:** best for manufacturers with an established RPA practice extending into unstructured document work.
- **Automation Anywhere:** best for distributors and manufacturers processing high volumes of supplier paperwork.
- **Workato:** best for manufacturers and logistics networks running several disconnected systems at once.
- **Blue Yonder:** best for logistics operators and manufacturers managing multi-site supply chains.
- **ServiceMax (PTC):** best for teams that need a full FSM system of record for scheduling and technician history.
- **Agentforce Field Service and Operations:** best for Salesforce-native field service teams wanting AI-assisted scheduling inside their existing CRM.
- **Delight.ai:** best for teams whose FSM already flags a stalled job but can't act on it.
## AI agents for industrial firms at a glance
| Vendor | Category | G2 rating | Pricing model | Best fit |
|---|---|---|---|---|
| UiPath | Back-office / ERP automation | 4.6 (7,700+ reviews) | Enterprise, consumption-based | RPA teams extending into unstructured documents |
| Automation Anywhere | Back-office / ERP automation | 4.5 (5,500+ reviews) | Enterprise, consumption-based | High-volume supplier paperwork |
| Workato | Back-office / ERP automation | 4.7 (777 reviews) | Credit-based; free/Pro tiers, sales-led at enterprise scale | Manufacturers running multiple disconnected systems |
| Blue Yonder | Supply chain / logistics planning | 4.1 (30 reviews, Demand Planning) | Enterprise, quote-only | Multi-site supply chain operators |
| ServiceMax (PTC) | Field service management | 4.2 (140+ reviews) | Enterprise, quote-only | Teams needing an FSM system of record |
| Agentforce Field Service and Operations | Field service management, AI-assisted | 4.4 (1,000+ reviews) | $165/user/mo (Dispatcher/Technician); from $50/user/mo (Contractor) | Salesforce-native field service teams |
| Delight.ai | Field-service coordination agent | 4.6 (126 reviews, via Sendbird) | Conversation-based, $0 seat fee | Teams whose FSM already tracks stalls it can't resolve |
## What kind of AI agent does an industrial firm actually need?
An industrial firm needs an AI agent that matches the operational problem it must solve. Most use cases fall into three categories:
- **Back-office and ERP automation agents** read documents, apply business rules, and enter approved information into enterprise resource planning (ERP) systems without anyone retyping it by hand.
- **Supply chain and logistics planning agents** reroute shipments, rebalance inventory, and absorb supplier delays before they ever reach the plant floor.
- **Field-service and customer-operations coordination agents** step in after equipment is already out in the world and something goes wrong: a technician finds a missing part, a customer asks whether a service-level agreement (SLA) deadline will hold, or a parts depot needs an answer before its doors even open.
[Delight.ai's field-service coordination agent](https://delight.ai/field-services) sits squarely in this third category, built around the moment a job can't close on the first visit.
These three jobs need different tools built by different kinds of vendors. Blending them without saying so sends a buyer to evaluate the wrong platform for months before finding that out. The sections below keep the three separate.
## Back-office and ERP automation agents
### UiPath
**What it is**
[UiPath](https://www.uipath.com/) is an RPA-to-agent platform: existing robotic process automation (RPA) bots extended with AI agents that read documents, apply business rules, and escalate exceptions to a person instead of failing silently.
**What it does**
- Wraps existing RPA bots into agents that read unstructured documents, including invoices, purchase orders, and contracts.
- Validates data against business rules and enters approved information directly into the ERP.
- Routes purchase requests, purchase orders, and invoice exceptions through built-in approval and audit workflows (UiPath Maestro).
- Connects on top of existing procurement stacks, including Coupa, SAP, Oracle, Ariba, and JAGGAER, without a rip-and-replace.
**Proof points**
- **G2 rating:** 4.6 out of 5 on [G2](https://www.g2.com/products/uipath-agentic-automation/reviews), across more than 7,700 reviews.
- [**Johnson Controls**](https://www.uipath.com/resources/automation-case-studies/johnson-controls-is-unlocking-substantial-savings-with-intelligent-automation) automated 200,000 monthly transactions, reached 89% processing accuracy, and cut vendor costs by 75%.
- [**Bayer**](https://www.uipath.com/solutions/department/finance-and-accounting-automation/procure-to-pay) reported 70% fewer manual errors and 70% faster purchase order creation.
**Best fit**
Manufacturers and industrial firms with an established RPA practice who want to extend it into unstructured document work. Reviewers consistently flag pricing as high next to smaller, purpose-built tools, and configuring validation rules well still takes RPA expertise.
### Automation Anywhere
**What it is**
[Automation Anywhere](https://www.automationanywhere.com/) is an agentic automation platform built for document-heavy back-office work: invoices, purchase orders, shipping manifests, and inspection reports.
**What it does**
- Extracts and validates data from invoices, purchase orders, and shipping documents, learning rules from examples rather than requiring every rule hand-coded.
- Automates end-to-end accounts payable, from invoice receipt to ERP posting.
- Flags exceptions and unusual document formats for human review instead of guessing.
**Proof points**
- **G2 rating:** 4.5 out of 5 on [G2](https://www.g2.com/products/automation-anywhere/reviews), across more than 5,500 reviews.
- [**Stant**](https://www.automationanywhere.com/resources/customer-stories/stant-automotive-supplier) processes 94% of targeted supplier invoices through the platform, with 80% straight-through invoice processing and zero data-entry errors.
- A mining and heavy-equipment manufacturer working with [EY](https://www.automationanywhere.com/resources/customer-stories/ey-mining-customer) saw 3x greater efficiency, a 29% productivity increase, and zero errors.
**Best fit**
Distributors and manufacturers processing high volumes of supplier paperwork who need straight-through invoice and order processing across ERP workflows. Agent behavior can be harder to inspect on unusual document formats, and legacy ERP integrations sometimes need custom development.
### Workato
**What it is**
[Workato](https://www.workato.com/) is an ERP-agnostic integration and orchestration platform. Instead of specializing in one workflow, it connects agents across whatever mix of ERP, warehouse, and transportation systems a company already runs.
**What it does**
- Orchestrates a single agent across order-to-cash or procure-to-pay workflows spanning multiple systems, without custom code for each connection.
- Automates cross-system business processes for manufacturing, logistics, and finance teams running disconnected tools.
- Won 45 different #1 rankings in G2's Winter 2026 integration-platform reports.
**Proof points**
- **G2 rating:** 4.7 out of 5 on [G2](https://www.g2.com/products/workato/reviews), across 777 reviews.
- **Pricing:** credit-based, with a self-service free tier and Pro tier available; enterprise deployments involve sales engagement.
**Best fit**
Large manufacturers and logistics networks running several disconnected systems at once who need one agent that can span all of them. Reviewers say the platform itself is easy to use, but setup costs climb fast for bespoke, multi-system workflows.
## Supply chain and logistics planning agents
### Blue Yonder
**What it is**
[Blue Yonder](https://blueyonder.com) builds AI agents into its supply chain and logistics planning suite, automating demand sensing, shipment routing, and warehouse decisions.
**What it does**
- Senses demand shifts and rebalances inventory before a stockout or overstock hits the plant floor.
- Reroutes shipments and rebalances warehouse capacity in response to supplier delays or network disruptions.
- Escalates exceptions outside a planner's preset rules for a human decision instead of acting blind.
**Proof points**
- **G2 rating:** Blue Yonder Demand Planning holds 4.1 out of 5 on [G2](https://www.g2.com/products/blue-yonder-demand-planning/reviews), across 30 reviews.
- [**DHL**](https://blueyonder.com/customers/dhl) used Blue Yonder's network design tools to cut transportation costs by 7%.
**Best fit**
Logistics operators and manufacturers managing multi-site supply chains, not a single plant's order intake. Migrating its agents to a different planning system later is difficult once a company is running on it.
## Field service and customer-operations coordination agents
### ServiceMax
**What it is**
[ServiceMax](https://www.servicemax.com), now part of PTC, is a field service management (FSM) platform: the system of record for scheduling, dispatching, and technician history, with AI features layered on top.
**What it does**
- Schedules and dispatches technicians based on skill, location, and parts availability.
- Tracks technician history, work orders, and service contracts in one system of record.
- Layers AI-assisted scheduling on top of the FSM foundation through ServiceMax's AI service tools.
**Proof points**
- **G2 rating:** 4.2 out of 5 on [G2](https://www.g2.com/products/ptc-servicemax/reviews), across more than 140 reviews.
- Reviewers describe it as powerful but expensive, with a long implementation and a real learning curve for teams new to it.
**Best fit**
Industrial firms that need a full FSM system of record for scheduling and technician history. It tracks the work well; it doesn't place the outbound calls that get a stalled job moving again once a part is missing.
### Agentforce Field Service and Operations
**What it is**
Agentforce Field Service and Operations (Salesforce Field Service) layers AI-driven scheduling, routing, and dispatch automation on top of its FSM foundation.
**What it does**
- Schedules and routes technicians with AI-assisted optimization.
- Surfaces AI-generated safety assessments alongside technician job briefs.
- Integrates natively with the broader Salesforce ecosystem for account and case data.
**Proof points**
- **G2 rating:** 4.4 out of 5 on [G2](https://www.g2.com/products/agentforce-field-service-formerly-salesforce-field-service/reviews), across more than 1,000 reviews.
- [**Engie**](https://www.salesforce.com/customer-stories/engie/) uses it to surface AI-generated safety assessments for technicians.
- **Pricing:** Dispatcher and Technician licenses list at $165 per user per month; Contractor plans start from $50 per user per month.
**Best fit**
Salesforce-native field service teams that want AI-assisted scheduling and dispatch inside their existing CRM. Like ServiceMax, it tracks and schedules work well — it doesn't place the calls that resolve a stalled job once a part is missing or a diagnostic is pending.
### Delight.ai
**What it is**
[Delight.ai](https://delight.ai/), built by Sendbird, is the AI coordination agent for the moment a field job stalls. It isn't another system of record. It's the layer that acts the second a dependency breaks.
**What it does**
- **Contacts every party at once.** The parts depot, the OEM support line, and the client facility get reached in parallel, voice first with SMS and email as fallback, instead of one call at a time during business hours.
- **Owns the case end to end.** [Agent Steward](https://delight.ai/autonomous-agent) holds a case open across every party and channel until each dependency clears, so a second truck roll only gets scheduled once everything is actually confirmed.
- **Remembers everything.** [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) keeps a multi-day case coherent, so a client calling back on day three never has to re-explain what already happened.
- **Logs every action.** [Trust OS](https://delight.ai/trust-os) creates an immutable audit trail of every call, message, and escalation, tied to the escalation rules a customer's own operations team sets.
**Proof points**
- **G2 rating:** Sendbird, Delight.ai's platform, holds 4.6 out of 5 on [G2](https://www.g2.com/products/sendbird/reviews), across 126 verified reviews.
- [**Norse Atlantic Airways**](https://delight.ai/customers/norse-atlantic-airways) grew AI containment from 60% to 80% in two weeks running on Delight.ai's platform.
- [**Dr. Cool Services**](https://delight.ai/customers/dr-cool-services), a Las Vegas HVAC and plumbing company, uses Delight.ai's voice AI agent to handle unlimited concurrent calls in English and Spanish and book appointments end to end, with zero hold queue during peak summer demand.
- Runs on a platform built for enterprise scale: 7 billion-plus monthly conversations, with SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliance.
**Best fit**
Industrial and field-service operations whose FSM already flags a stalled job but has no way to act on it. Delight.ai is the coordination layer that takes a case from flagged to resolved, working alongside the FSM already in place, not replacing it.
## How do I choose the right AI agent for an industrial operation?
Industrial workflows can't tolerate an opaque decision on order entry, shipment routing, or a client-facing commitment. Three checks separate a well-built agent from one that isn't ready for this environment.
### Validation and escalation
Confirm the agent has explicit validation rules and a visible escalation path, not a black-box decision process. Ask any vendor to show exactly when their agent hands off to a person and why.
### Replace versus activate
A back-office agent replaces manual data entry. A coordination agent doesn't replace an FSM or a dispatcher; it activates outreach an FSM was never built to place.
### Integration proof
Ask for a real reference running your specific ERP or FSM, not a generic architecture diagram. A vendor that can't produce one usually means several more months of implementation risk than advertised.
## How does AI protect SLAs and build an audit trail for field-service work?
An SLA dispute gets resolved by whoever has the record, not by whoever remembers the timeline best. A field-service coordination agent's audit trail typically covers:
- **Every call and message** placed to a parts depot, OEM, or client, with a timestamp.
- **Every confirmation received**, including who confirmed it and when.
- **Every escalation to a human**, and the reason it happened.
None of this gets reconstructed after the fact from memory and email threads; it's logged as it happens.
This is what [Trust OS](https://delight.ai/trust-os) is built to do inside Delight.ai: an immutable log of every action taken on a case, tied to the escalation rules a customer's own operations team sets. When a case does need a human, it arrives with the full case history already attached.
None of this replaces the judgment a dispatcher or a service manager brings to a disputed bill or an out-of-policy exception. The agent handles the volume of confirmations and follow-ups; the person still handles the calls that need real judgment.
Choosing among back-office, supply chain, and field-service agents comes down to naming which of the three problems is actually costing time today. From there, evaluate vendors inside that category on its own terms, not against a generic checklist that doesn't distinguish them.
For a field-service operation specifically, [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) is what keeps a multi-day case coherent across every party and channel, so a client calling back on day three never has to re-explain what already happened. See how Delight.ai's [field-service coordination agent](https://delight.ai/field-services) fits alongside the FSM already in place.
## Contents
- [AI agents for industrial firms at a glance](#ai-agents-for-industrial-firms-at-a-glance)
- [What kind of AI agent does an industrial firm actually need?](#what-kind-of-ai-agent-does-an-industrial-firm-actually-need)
- [Back-office and ERP automation agents](#back-office-and-erp-automation-agents)
- [Supply chain and logistics planning agents](#supply-chain-and-logistics-planning-agents)
- [Field service and customer-operations coordination agents](#field-service-and-customer-operations-coordination-agents)
- [How do I choose the right AI agent for an industrial operation?](#how-do-i-choose-the-right-ai-agent-for-an-industrial-operation)
- [How does AI protect SLAs and build an audit trail for field-service work?](#how-does-ai-protect-slas-and-build-an-audit-trail-for-field-service-work)
## Frequently asked questions
**Can AI handle customer service for field service and repair companies?**
AI can handle defined coordination tasks for field service and repair companies after a job stalls, such as contacting a parts depot or OEM support line and sending customer status updates. Depending on the system, those contacts may happen in parallel rather than one at a time. Delight.ai's Agent Steward is designed to open a case, coordinate with each party until dependencies clear, and hand decisions that require human judgment to a person.
**How do I choose the right AI agent for an industrial or field-service operation?**
Confirm that it has explicit validation rules and a visible escalation path. Then determine whether it replaces a manual task, such as data entry, or activates a task that was not previously being performed, such as proactive multi-party outreach.
**Does AI replace dispatchers or field technicians?**
No. A coordination agent can handle routine confirmation calls and follow-ups, while a dispatcher or service manager retains responsibility for disputed bills, out-of-policy exceptions, and decisions the agent is not authorized to make.
**How does AI protect SLAs and create an audit trail for field-service or warranty work?**
AI can log actions, calls, confirmations, and escalations with timestamps as they happen. Delight.ai says Trust OS maintains an immutable, exportable log that operates within customer-defined escalation rules. Buyers should verify retention, export, access-control, and immutability requirements during evaluation.
**How can AI help field service companies reduce truck rolls?**
AI can help by confirming dependencies before a return visit is scheduled, such as a part's estimated arrival time, an OEM diagnostic, or a client's access window. This reduces the risk of sending a technician back before the job is ready, though impact depends on workflow design, data quality, and adoption.
## Keep reading
[View more](/blog)
[](/blog/insights/wismo-retail-guide)
WISMO: What "Where Is My Order?" Costs Retail Leaders
[](/blog/ai-agent/ai-ecommerce-automation-retailers)
How important is AI ecommerce automation for first-time online retailers?
[](/blog/ai-for-cx/how-to-evaluate-ai-agents-for-customer-service)
How to evaluate AI agents for customer service
[](/blog/ai-for-cx/best-ai-tools-for-ecommerce)
The 8 best AI tools for ecommerce in 2026
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## How to evaluate AI agents for customer service
Source: https://delight.ai/blog/ai-for-cx/how-to-evaluate-ai-agents-for-customer-service
## How to evaluate AI agents for customer service
[
Caroline Adamec
Content engineer](https://delight.ai/author/caroline-adamec)
August 13 2026
### Key takeaways
- A demo shows how an AI agent performs on its best day, but testing for **genuine resolution**, safe failure, and clean human handoff shows how it performs on a normal one.
- The **certifications a vendor holds today** are what get a deal through security review.
- Delight.ai grounds its own numbers in **containment and resolution rate** rather than a deflection rate, which only measures whether a conversation left the queue, not whether the problem actually got solved.
91% of customer service leaders say they are under pressure to implement AI in 2026: https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026. Most teams start evaluating AI agents for customer service by watching demos, and every demo looks good because a demo is built to succeed, not to reflect the reality of an actual support queue.
Buying an AI agent that resolves customer issues, not just handles a scripted conversation, takes more than one persuasive call with a vendor.
The finance leader signing the contract needs different proof than the customer service leader who will own the resolution numbers. A small business owner evaluating the same category cares about neither's spreadsheet, just whether the phone gets answered at 9pm on a Saturday.
This guide walks through how to evaluate AI agents for customer service so everyone in that room, whichever seat they sit in, ends up testing for the same five things.
## What is an AI agent for customer service?
An AI agent for customer service is software that understands a customer's request, decides what to do about it, and takes action. That distinguishes it from a scripted chatbot, which matches a customer's words to a pre-written decision tree and breaks the moment a question falls outside that tree.
The difference between a chatbot and an AI agent comes down to reasoning versus keyword matching.
A chatbot can tell a customer their order shipped because it matched the words in their message to a script, while an AI agent can check the order status, notice it is delayed, rebook the delivery window, and message the customer before they ask, because it reasons through the situation instead of just matching keywords. That shift is exactly why customer service leaders are under so much pressure to evaluate this category well, rather than assume any AI vendor is close enough to any other.
## What does it mean to evaluate an AI agent for customer service?
Evaluating an AI agent for customer service means testing it against conditions a sales demo is not designed to show you. A demo runs on curated questions, a clean knowledge base, and a vendor in the room to catch anything unexpected. Production needs to run on your own customers, your live systems, and the edge cases nobody scripted for, with no supervision.
### The 5 things a demo won't show you
1. Whether a resolution is genuine.
2. Whether the agent fails safely when it does not know an answer.
3. Whether its compliance posture is current rather than aspirational.
4. Whether escalation to a person preserves context.
5. Whether a proof of concept was built to resemble production.
## How to evaluate AI agents for customer service
Evaluating AI agents for customer service comes down to five checks, and they work best in order rather than picked at random. A vendor that resolves conversations well but fails silently the moment it does not know an answer will look strong on a scorecard and still damage a customer relationship. The next five sections walk through:
- Genuine resolution
- Safe failure
- Current compliance
- Careful escalation
- Production-realistic proof of concept
It's worth working through each one before signing, rather than accepting an adjective where a vendor should give you a specific number.
## Defining a genuine resolution
A resolution rate is only as honest as its definition. Most customer success teams define a genuine resolution as a conversation where the customer's actual issue was identified, addressed, and confirmed fixed, not just one where the conversation ended.
Some vendors blur that definition by counting a conversation as resolved the moment a customer does not explicitly ask for a human, instead of requiring the customer to confirm the issue is actually fixed. Those are two different numbers wearing the same label, and the gap between them is usually where a disappointing deployment starts.
### Ask for the re-contact rate, not just the resolution rate
A good way to test a resolution number is to ask for the re-contact rate alongside it. A vendor confident in its numbers shares both; one that won't is measuring deflection, not resolution.
The sharper question is the re-contact rate, the percentage of 'resolved' conversations that bring the same customer back with the same issue within a few days. A high re-contact rate exposes a resolution rate that is really measuring how a customer gave up, not how well their problem got solved.
In its own voice, Delight.ai favors containment and resolution framing over a deflection rate, because deflection only measures whether a conversation left the queue, not whether the customer's problem actually got fixed.
Part of what makes an honest resolution rate possible is context. A support agent that already knows a customer's order history and past conversations, like Delight.ai's Agent Memory Platform (AMP) (https://delight.ai/agent-memory-platform-amp), does not have to guess at intent the way a stateless chatbot does.
When Hanssem (https://delight.ai/customers/hanssem) deployed an AI agent to genuinely resolve conversations rather than just deflect them, its resolution rate moved from 48% to roughly 86% over five months, with transfers to a human agent cut in half over the same period.
## How do you know an AI agent won't go off-script?
An AI agent that cannot show you why it said what it said is not ready for customer-facing autonomy, however smooth its demo sounds. A platform that hallucinates confidently is worse than one that says it does not know, and your evaluation needs to surface that difference. The National Institute of Standards and Technology's AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) names four functions every trustworthy AI system needs:
1. Govern how the system is allowed to behave.
2. Map its risks to the specific use case.
3. Measure those risks on an ongoing basis.
4. Manage the response when something goes wrong.
### What to check before you trust an agent with a live conversation
Ask a vendor to show you the reasoning behind a specific answer, not just the answer itself. A platform built for this will have an audit trail that traces a response back to the knowledge source it came from, plus a way to flag a low-confidence answer before a customer ever sees it.
Delight.ai's governance layer, called Trust OS (https://delight.ai/trust-os), gives teams that kind of visibility into why the agent said what it said, along with an emergency switch to pause automated responses without losing any configuration.
## Which compliance and security certifications should you require?
The certifications a vendor holds today are what clear your security review. A missing certification does not just slow down procurement, it can disqualify a vendor once security or legal actually reviews the paperwork, and it leaves your business carrying the compliance risk of a partner that has not proven its own controls. At minimum, a customer-facing AI agent should hold:
- **SOC 2 Type II**, which confirms security controls operated effectively over a period of time, not just that they were designed correctly at a single point.
- **HIPAA**, required if any customer data touches healthcare information.
- **GDPR and CCPA**, required for EU and California customer data respectively.
Watch for a Type I report presented as if it carries Type II rigor. A Type I attests that controls are designed correctly at one point in time. A Type II attests that they operated effectively over a period, which is the stronger claim. Delight.ai holds a HIPAA Type I report and a SOC 2 Type II report, along with GDPR and CCPA compliance, and is explicit about which report is which.
### Ask for the report, not the badge
A good way to test a vendor's compliance claims is to ask for the actual SOC 2 Type II report, not a mention on their website. A vendor with current certifications sends it within a day; one relying on marketing language stalls or offers only a badge.
## How should escalation to a human work?
The moment an AI agent hands a conversation to a person is the moment most deployments either build trust or lose it, and the deciding factor is whether context comes with the handoff. A customer who has to re-explain their issue after being escalated experiences that escalation as a failure, even if the agent's first 90% of the conversation went well.
### What should transfer when a case escalates
A clean handoff carries three things to the human agent:
- The full conversation history, not a one-line summary.
- Whatever information the AI agent already gathered or confirmed.
- A plain note on what has already been tried.
Delight.ai's AI-native helpdesk, called Desk (https://delight.ai/desk), keeps the AI agent and human agents on one shared platform for exactly this reason, so escalation is a handoff within one system instead of a transfer between two. None of this replaces your team. The point of a good AI agent is freeing your team to spend their time on the cases that need a person's judgment, not routing every case to a human by default.
### Test the handoff, don't just ask about it
A good way to test escalation is to abandon a conversation mid-way, then return later. A good agent's handoff carries full context to the human agent; a weak one restarts the conversation from scratch.
## How do you run a demo or POC that predicts production performance?
A proof of concept only tells you something useful if it is built to resemble production. Most POCs run on clean data, a narrow set of questions, and close vendor support, which is exactly why they tend to outperform the deployment that follows them.
Forrester's 2026 customer service predictions expect service quality to dip industry-wide this year as companies run into exactly this gap between AI's promise and the operational work production actually requires: https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/.
### Build a POC that resembles production, not a demo
A good way to test a POC's realism is to load it with your own knowledge base and a random sample of real customer queries, not a curated set. A platform built for production handles the messy sample as well as the clean one; one tuned for demos starts stumbling the moment the questions stop being predictable.
It also helps to include the requests that break scripted systems alongside the simple ones any AI agent can answer:
- Multi-step requests, like a refund tied to a delayed shipment.
- Account changes that require verifying identity first.
- Exceptions that fall outside your standard policy.
### Watch for graceful failure, not just correct answers
A good way to test the AI agent is to ask an ambiguous question on purpose. A good agent will ask a clarifying question or make a reasonable inference, while a weak one either guesses with confidence or loops the customer in circles.
That distinction matters because neither the agent, nor your own team, can offer the right solution without first understanding the customer's actual problem, and an agent that fails gracefully asks for the context it's missing instead of guessing past it. How a platform fails when it does not know something tells you more than how it performs when it does.
## What else should the buying committee ask?
A handful of criteria round out the evaluation and belong in one shared scorecard instead of five separate vendor conversations.
| Criterion | What to ask | What's at stake |
|---|---|---|
| Pricing model | Per seat, per conversation, or per resolution? | A per-seat model can cost more at scale than an outcome-based one, even at a lower headline rate. |
| Implementation time | Days, weeks, or months from contract to first live conversation? | Every week in implementation is a week of unresolved conversations and delayed return on investment. |
| Integration depth | Can the agent take real actions in your systems, or only answer questions? | An agent that can see an order is late but cannot rebook it is answering, not resolving. |
| Channel coverage | Does the same agent work across chat, voice, SMS, and email? | Customers switch channels mid-conversation, and context should travel with them. |
| SLA and uptime | What is the documented uptime over the last 12 months, and how are outages handled? | A vendor that will not share this number usually has a reason not to. |
For the engineering side of testing an agent your own team is building, rather than evaluating one to buy, see the complete guide to AI agent evaluation: https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide.
## The takeaway
The teams that choose well do not get lucky with AI vendors. They ask the same five questions of every platform on their shortlist. Use this as your checklist for evaluating an AI agent for customer service:
1. What counts as a resolution?
2. What happens when the agent does not know an answer?
3. Which certifications are actually current?
4. What transfers at handoff?
5. Whether the proof of concept looked anything like production.
Bring this checklist into your next Delight.ai demo: https://delight.ai/demo-request, and see how Delight.ai answers each one: https://delight.ai/ai-customer-service.
- What is an AI agent for customer service?
- What does it mean to evaluate an AI agent for customer service?
- How to evaluate AI agents for customer service
- Defining a genuine resolution
- How do you know an AI agent won't go off-script?
- Which compliance and security certifications should you require?
- How should escalation to a human work?
- How do you run a demo or POC that predicts production performance?
- What else should the buying committee ask?
- The takeaway
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Frequently asked questions
**How is AI customer service usually priced?**
AI customer service is usually priced per seat, per conversation, or per resolution. A per-seat model charges a fixed fee regardless of AI usage. A per-conversation model charges for every interaction, resolved or not. A per-resolution model charges only when a conversation is genuinely resolved, which ties cost directly to value delivered.
**How long does it take to implement an AI customer service agent?**
Implementing an AI customer service agent typically takes anywhere from days to several months, depending on how much engineering support the vendor requires. A self-service platform that a CX team can configure directly tends to go live faster than one that needs a vendor's engineering team for every change.
**Can I opt out of having my conversation history used by the AI?**
Whether you can opt out of having your conversation history used by the AI varies by vendor, and it is worth asking directly rather than assuming. Ask specifically how much conversation history the agent uses, where it is stored, and whether a customer can request it not be retained.
**What SLA or uptime guarantee should I expect?**
The SLA or uptime guarantee you should expect is documented uptime over the last 12 months, not a forward-looking promise. A vendor confident in its infrastructure will share the actual number, along with how outages get communicated and resolved.
**Will an AI agent replace my customer service team?**
No, a well-deployed AI agent does not replace a customer service team. It resolves routine, high-volume requests end to end and hands off anything ambiguous, high-stakes, or emotionally sensitive to a person, with full context attached. The goal is freeing your team for the cases that actually need their judgment, not shrinking the team itself.
## Keep reading
[View more](/blog)
[ Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[ Introducing Intelligent Voice Handoff: A complete path from AI to human](/blog/product/introducing-intelligent-voice-handoff)
[ Patient engagement solutions for consumer health brands](/blog/industry/patient-engagement-solutions)
[ The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[View more](/blog)
## What is a hotel chatbot? Overview, use cases, and more
Source: https://delight.ai/blog/ai-for-cx/hotel-chatbot-ai-concierge
## What is a hotel chatbot? Overview, use cases, and more
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec — Content Engineer](https://delight.ai/author/caroline-adamec)
August 14 2026
**Key takeaways**
- **An FAQ-scoped hotel chatbot** handles routine pre-stay questions and leaves more complex guest needs to other workflows.
- **Journey coverage shapes the business case** because disruptions, in-stay requests, and post-stay follow-up influence guest loyalty.
- **An AI concierge carries context forward** across sessions and channels, creating one continuous guest conversation.
A hotel chatbot typically answers website questions before a guest books. Its business case ends when a guest asks for help beyond the routine requests covered by its scripted answers. The [travel and hospitality](https://delight.ai/industries/travel-and-hospitality) guest journey continues after booking and across future stays.
In Gartner's 2026 survey of 321 customer service and support leaders, **91% reported pressure from executive leadership to implement AI** ([Source: Gartner](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026)). Hotel groups need to target high-volume workflows that reduce support overhead, with governance tying the agent's decisions to measurable business results.
## What does a hotel chatbot do?
A hotel chatbot usually handles a defined set of routine guest questions and booking tasks. A guest might use one to check room availability or confirm an arrival time. The same interface can surface property details and handle configured reservation changes.
FAQ automation gives guests fast answers and frees staff from repetitive questions. Its value remains concentrated in the workflows and systems the hotel has configured.
An AI concierge has a broader service goal. **One continuous conversation carries the guest's context** from the first question through post-stay follow-up.
| Hotel chatbot (FAQ-scoped) | AI concierge (journey-spanning) |
|---|---|
| **Pre-stay** — Answers configured questions about rates, availability, and amenities | **Pre-stay** — Adds personalized recommendations and booking guidance |
| **During the stay** — Supports defined in-stay workflows | **During the stay** — Coordinates disruptions, requests, and relevant offers |
| **After checkout** — Sends configured surveys or follow-ups | **After checkout** — Uses trip context for re-engagement and loyalty conversations |
| **Guest context** — Session and channel continuity depends on product design | **Guest context** — Designed to preserve context across sessions and channels |
## What happens when a guest's request isn't a simple FAQ?
When a guest's request exceeds a simple FAQ, the hotel needs a workflow that can read the reservation, take an approved action, or bring in a team member. These moments expose the practical difference between scripted answers and journey-spanning service.
### During the stay: disruptions and mid-trip requests
During the stay and the trip around it, an AI concierge can use connected systems and approved hotel policies to turn a guest's problem into a specific next step:
- **Delayed arrival:** Record the revised arrival time, provide late check-in instructions, and notify the front desk.
- **Cancelled travel plans:** Check the reservation policy, offer eligible dates from connected inventory, and complete an approved booking change.
- **Room issue:** Gather the details, open a service request, share status updates, and escalate room moves or compensation decisions to staff.
### After checkout: rebooking and re-engagement
After checkout, an AI concierge can continue the conversation with actions tied to the guest's stay. PwC's 2025 Customer Experience Survey found that **52% of consumers had stopped using or buying from a brand after a bad product or service experience**, and 29% had left because of poor customer experience online or in person ([Source: PwC](https://www.pwc.com/us/en/services/consulting/commercial-excellence/library/2025-customer-experience-survey.html)).
- **Service recovery:** Follow up on a recorded room or service issue, confirm whether it was resolved, and route an open case to the right team.
- **Review collection:** Ask for feedback after a positive stay and direct the guest to the hotel's preferred review flow.
- **Personalized rebooking:** Use authorized stay history to suggest a relevant property, room type, or travel window for the next trip.
- **Loyalty re-engagement:** Remind the guest about available benefits or points when they can support a future booking.
## Does a hotel chatbot work with your existing systems?
Yes, a hotel chatbot can work with existing hotel systems when the vendor supports the required integrations and data access. The key test is whether the AI can retrieve current guest information and write approved actions back to the system that owns the workflow.
Suppose a returning guest changes an arrival time in chat and later reports a room issue by phone. The hotel's follow-up arrives by SMS. [Delight.ai](https://delight.ai/)'s [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) brings the conversation history and connected guest records into one profile, so the AI concierge can continue the case without making the guest reconstruct it at every touchpoint.
That context also determines whether the next action is useful. A production demonstration should show the AI reading the relevant hotel record, applying the property's rules, writing the approved update, and handing the case to a person when authority or judgment is required.
## Does AI replace your front-desk and guest-service team?
No, AI supports the front-desk and guest-service team by handling routine volume and escalating work that needs human judgment, empathy, or authority.
Deloitte reports that **81% of hoteliers prioritize increasing employee productivity**, while 49% list integrating AI-powered solutions among their priority technology initiatives ([Source: Deloitte](https://www.deloitte.com/us/en/industries/consumer/articles/future-of-hospitality-ai-innovation.html)). This approach gives hotel teams more capacity for complex requests and personal service.
Moving routine questions about arrival times or amenity hours through automated workflows gives staff more time for service recovery and the personal touches that shape a memorable stay.
PwC's 2025 survey found that **86% of consumers consider human interaction moderately or very important** to their overall brand experience. A strong operating model protects that human role with clear escalation criteria and complete conversation context.
## What's the real ROI case for AI in hotel guest service?
The strongest ROI case for AI in hotel guest service begins with lower support overhead and extends to revenue protected by a better guest experience. Hotels can track resolved-request rate, cost per resolution, recovered-booking revenue, and repeat-stay rate.
[Delight.ai](https://delight.ai/)'s consumer research found that **71% of consumers have used AI customer service, while 57% report positive experiences** ([Source: Delight AI Index](https://delight.ai/ai-index)). That 14-point experience gap gives hospitality brands a clear opportunity to distinguish their service.
Norse Atlantic Airways replaced its legacy chatbot with [Delight.ai](https://delight.ai/)'s AI agent, Freya, in under two months. **Containment climbed from 60% to 80% in the first two weeks** after launch.
The [Norse Atlantic Airways result](https://delight.ai/customers/norse-atlantic-airways) provides directional evidence from aviation, a neighboring travel category. Hotel leaders should set property-specific baselines and targets before using any vendor result in their own financial model.
## Takeaway
Evaluating a hotel chatbot on journey coverage connects technical scope to business outcomes. A useful scorecard follows each priority guest workflow from first contact through resolution and measures the effect on both guest experience and hotel performance.
An FAQ-scoped hotel chatbot can answer routine pre-stay questions efficiently. A journey-spanning approach builds recognition and service recovery into the path to the next stay.
The [AI Agents for Travel & Hospitality playbook](https://delight.ai/resources/ai-agents-for-travel-hospitality) shows how these workflows extend across four stages:
- **Planning and booking** with personalized recommendations and abandoned-booking recovery
- **Disruption management** for cancellations and changing travel plans
- **In-stay concierge service** for local activities and guest requests
- **Post-trip engagement** through guest support, follow-up, and rebooking
For hospitality leaders evaluating a hotel chatbot, journey coverage offers a clearer way to compare service value and long-term fit. [**Get the AI Agents for Travel & Hospitality playbook →**](https://delight.ai/resources/ai-agents-for-travel-hospitality)
## Common questions hospitality leaders ask about hotel chatbots
### What is the difference between a hotel chatbot and an AI concierge?
A hotel chatbot and an AI concierge differ mainly in scope and continuity. An FAQ-scoped chatbot answers configured questions or completes defined tasks. An AI concierge is designed to preserve context across the guest journey, connect with relevant systems, complete approved workflows, and involve a person when the request requires judgment.
### Does a hotel chatbot work across every guest channel, not just the website?
A hotel chatbot can work across multiple guest channels when its architecture shares context among them. Buyers should verify coverage across web, messaging, email, and voice based on the channels their guests use. An omnichannel design should let a guest move between supported channels with the conversation history intact.
### How long does it take to implement AI for hotel guest service?
Implementation time for AI in hotel guest service depends on workflow scope, integrations, governance, and rollout requirements. A credible plan begins with the hotel's priority use cases and defines validation and escalation before launch. Vendors should provide a timeline tied to that scope and comparable production deployments.
### Can AI support multiple languages for international guests?
Yes, AI can support multiple languages for international hotel guests. Hotels should test the languages their guests use most for accuracy, tone, policy handling, and human handoffs. The evaluation should also cover voice and text separately because performance can differ by channel.
### What happens when the AI cannot resolve a guest's request?
When the AI cannot resolve a guest's request confidently, it should escalate the conversation to a person with the history and relevant context attached. [Trust OS](https://delight.ai/trust-os) provides response review, factual-error and unsafe-content detection, and one-click human escalation controls. Hotel teams can define which requests remain automated and which require human ownership.
### Why choose a journey-spanning AI agent over a hotel-specific chatbot tool?
A journey-spanning AI agent is the stronger choice when a hotel wants continuity across planning, the stay, service recovery, and post-stay engagement. Its value comes from preserving context and completing connected workflows across those moments. An FAQ-scoped tool remains a focused option for hotels whose current goal is routine question handling within a defined channel.
- [What does a hotel chatbot do?](#what-does-a-hotel-chatbot-do)
- [What happens when a guest's request isn't a simple FAQ?](#what-happens-when-a-guests-request-isnt-a-simple-faq)
- [Does a hotel chatbot work with your existing systems?](#does-a-hotel-chatbot-work-with-your-existing-systems)
- [Does AI replace your front-desk and guest-service team?](#does-ai-replace-your-front-desk-and-guest-service-team)
- [What's the real ROI case for AI in hotel guest service?](#whats-the-real-roi-case-for-ai-in-hotel-guest-service)
- [Takeaway](#takeaway)
- [Common questions hospitality leaders ask about hotel chatbots](#common-questions-hospitality-leaders-ask-about-hotel-chatbots)
## Keep reading
[View more](/blog "View more")
[](/blog/ai-for-cx/ai-shopping-assistant-comparison) What is an AI shopping assistant? How top options compare
[](/blog/ai-agent/healthcare-virtual-assistant) What Is a Healthcare Virtual Assistant? A Leader's Guide
[](/blog/ai-agent/membership-management-software) Membership Management Software: The Gap AI Agents Fill
[](/blog/insights/wismo-retail-guide) WISMO: What "Where Is My Order?" Costs Retail Leaders
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## What is an AI shopping assistant? How top options compare
Source: https://delight.ai/blog/ai-for-cx/ai-shopping-assistant-comparison
## What is an AI shopping assistant? How top options compare
[Cheryl Chai](https://delight.ai/author/cheryl-chai)
August 14 2026
**Key takeaways**
- An AI shopping assistant gives shoppers **conversational product guidance** grounded in a retailer's data.
- The most useful differences between platforms are **channel coverage, workflow depth, pricing, and continuity** across the customer journey.
- At Delight.ai, we connect shopping and post-purchase care through **persistent memory and one conversation thread** across channels.
**At a glance**
- [**Alhena AI**](https://alhena.ai/products/ai-shopping-assistant) is best for brands seeking a proactive shopping and support concierge with published conversion proof.
- [**Gorgias AI Agent**](https://www.gorgias.com/ai-agent) is best for ecommerce teams that already use the Gorgias helpdesk.
- [**Rep AI**](https://www.hellorep.ai/) is best for brands focused on proactive selling, product discovery, and support automation.
- [**Ringly.io**](https://www.ringly.io/) is best for Shopify brands that want an AI phone agent.
- [**Delight.ai**](https://delight.ai/) is our recommendation for retailers that want persistent context across shopping and post-purchase care on chat, SMS, email, voice, WhatsApp, and in-app messaging.
Retail teams often evaluate an AI shopping assistant as a storefront chat tool, but the real buying question is what happens when a shopper's needs move beyond that chat window. Can the platform understand who they're talking to, act on live business data, and carry the context into whatever happens next? That's the lens we'll use to compare five current options across capabilities, customer proof, ratings, pricing, and continuity.
### What is an AI shopping assistant?
An **AI shopping assistant is software that helps shoppers find, compare, and buy products through natural-language conversations**. Because it's connected to product, inventory, order, and customer data, it can answer questions about fit, availability, and price, then help the shopper take the next step.
Older store search tools work best when shoppers type the exact product words they're looking for, and basic chat widgets tend to follow a preset script. AI shopping assistants can understand open-ended requests such as "I need a carry-on that will fit under the seat" or "Which of these moisturizers is better for sensitive skin?" What happens next depends on the data and workflows the assistant can access, so it may answer the question, recommend a product, update a cart, check an order, or begin a return.
That range is why "AI shopping assistant" now describes several kinds of software, from on-site product guides to ecommerce helpdesk agents and phone-based assistants. As you compare them, look at the part of the journey each one covers and how much work it can complete once it understands what the shopper needs.
### How is an AI shopping assistant different from a chatbot?
An AI shopping assistant understands more of the shopper's intent and can do more with that understanding. Traditional chatbots usually match a question to a scripted answer or help article, while shopping assistants can pull current product or order information and complete approved tasks.
You can see the difference when a shopper is ready to act. A polished answer might help someone choose a product, but the purchase can still stall if the tool can't add the item, apply an eligible discount, or carry the conversation into checkout.
Once an AI shopping assistant can move from answering a question to completing a task, it becomes part of what's often called agentic commerce. That term gives retail teams a more useful way to compare products because the conversation is only the starting point; the real test is how far the assistant can carry the shopper across discovery, purchase, and care before a person needs to step in.
### Is an AI shopping assistant the same as a virtual assistant?
No, an AI shopping assistant and a human virtual assistant serve different roles, even though search results often blur the two. A human virtual assistant typically handles scheduled operational work such as catalog maintenance, order entry, customer email, or merchandising support; an AI shopping assistant serves shoppers in real time and can manage many conversations at once.
| Factor | AI shopping assistant | Human virtual assistant |
|---|---:|---:|
| Primary role | Guides shoppers and completes connected customer-facing tasks | Handles assigned operational, administrative, or customer-service work |
| Availability | Runs continuously and manages many conversations at once | Works scheduled hours and handles a limited number of tasks at a time |
| Best suited for | Product guidance, order updates, routine returns, and repeatable workflows | Unfamiliar tasks, sensitive cases, judgment calls, and flexible back-office support |
| Access to data | Retrieves approved catalog, inventory, order, and customer information through integrations | Uses the tools and account access provided by the retailer |
| Escalation | Hands exceptions to a person based on configured rules | Makes judgment calls directly or escalates to a manager |
Many retailers can use both. AI can cover high-volume storefront conversations as they happen, while a human virtual assistant handles work that calls for flexibility, context, or judgment.
### What should a retail business evaluate before choosing an AI shopping assistant?
A retail business should evaluate data grounding, channel coverage, human handoff, and post-purchase continuity before choosing an AI shopping assistant. Feature lists can tell you what a product claims to support, but these four questions reveal how it's likely to behave inside your actual operation.
#### Is the AI shopping assistant grounded in your actual catalog?
Yes, an effective shopping assistant should be grounded in current catalog and operational data. Ask how often the platform synchronizes product details, inventory, pricing, promotions, and order status, then test what it does when that information is missing, conflicting, or stale. A confident answer isn't useful if the product it recommends sold out an hour ago.
#### Does the AI shopping assistant cover the channels your shoppers already use?
Yes, the assistant should cover the channels your shoppers already use. Confirm support for your mix of web chat, email, SMS, WhatsApp, social messaging, and voice, and pay close attention to what happens when a shopper changes channels. Channel coverage means less if the shopper has to start over each time.
#### Does the AI shopping assistant provide a clear human handoff?
Yes, the AI shopping assistant should provide an explicit, configurable handoff with rich context. You'll want to know what triggers an escalation, where the conversation lands, and what history or reasoning the human agent receives, because a transfer that drops the context asks the shopper to pay for the system's limitation with their time.
#### Can the AI shopping assistant help after checkout?
Yes, an AI shopping assistant can help after checkout if its integrations and workflows support post-purchase service. Many current products now cover order tracking, returns, refunds, cancellations, and subscription changes, so the sharper question is whether the shopper's identity, memory, and conversation history survive across those tasks.
### What are the best AI shopping assistants, and how do they compare?
The best AI shopping assistant depends on the channels, workflows, and systems your retail team already uses. Alhena AI, Gorgias AI Agent, Rep AI, Ringly.io, and Delight.ai each have a credible place in the market; for retailers that care most about persistent memory and cross-channel continuity, we believe Delight.ai has the strongest fit.
#### Alhena AI
**Best for**
Brands seeking a proactive shopping and support concierge with strong published conversion proof.
**What it does**
Alhena combines shopping and support functions, including product recommendations, product cards, agentic checkout, and human transfer. In its Tatcha deployment, the assistant connects with Salesforce Commerce Cloud and customer-data systems, so the conversation can move from a skin assessment to a prefilled checkout.
**Customer proof**
[Tatcha reported](https://alhena.ai/case-studies/tatcha) a **3x conversation-to-purchase conversion rate versus its site average, a 38% AOV uplift, and influence on 11.4% of site revenue**.
**G2 rating**
[4.9 out of 5 from 35 reviews](https://www.g2.com/products/alhena-ai/reviews).
**Pricing**
[A free plan includes 25 conversations per month](https://alhena.ai/pricing), and the Essentials plan costs $199 per month with annual billing for 200 conversation credits.
#### Gorgias AI Agent
**Best for**
Ecommerce teams that already run customer service through Gorgias.
**What it does**
Gorgias AI Agent works inside the Gorgias helpdesk, which makes it a natural extension for teams already using that system, and it supports product recommendations, order edits, returns, refunds, and subscription management across connected ecommerce workflows.
**Customer proof**
[Psycho Bunny reported](https://www.gorgias.com/customers/psycho-bunny) that AI Agent resolved 26% of tickets during its initial deployment and achieved a 4.67 CSAT score during the first 2 months.
**G2 rating**
[4.6 out of 5 from 557 reviews](https://www.g2.com/products/gorgias/reviews).
**Pricing**
[Gorgias helpdesk plans begin at $10 per month](https://www.gorgias.com/pricing), while AI Agent is a separate add-on that costs $0.90 per fully resolved interaction on most annual plans or $1 on monthly plans.
#### Rep AI
**Best for**
Ecommerce brands focused on proactive selling, product discovery, and support automation.
**What it does**
Rep AI offers sales, support, and combined concierge plans, so ecommerce brands can start with the side of the journey they care about most. Across those plans, the platform supports product recommendations, cart recovery, order status, returns, cancellations, human routing, and additional channels through add-ons.
**Customer proof**
[Rep AI reports that Bikes Online](https://www.hellorep.ai/case-studies/how-bikes-online-uses-rep-ai-to-improve-sales-cx-and-operational-efficiency) generated 8.4% of revenue through the platform, increased AOV by 48%, and reached a 92.53% AI resolution rate.
**G2 rating**
[4.9 out of 5 from 61 reviews](https://www.g2.com/products/rep-ai/reviews).
**Pricing**
[Plans start at $99 per month](https://www.hellorep.ai/) and scale with traffic and selected modules, with higher sample pricing published for stores with 80,000 monthly sessions.
#### Ringly.io
**Best for**
Shopify brands that want AI phone support for product, order, and return calls.
**What it does**
Ringly brings the shopping assistant to the phone, where it can identify callers, read Shopify order history, search the product catalog, check stock, request returns, send emails, and transfer unresolved calls.
**Customer proof**
[Ringly's live site](https://www.ringly.io/) shows a product example with a 75% resolution rate and $6,942 in attributed revenue. These figures are vendor-presented interface examples, not a named customer case study.
**G2 rating**
[G2 lists Ringly.io but does not display a score](https://www.g2.com/products/ringly-io/reviews) because the product has too few reviews for buying insight.
**Pricing**
[The Grow plan costs $349 per month](https://www.ringly.io/) for 1,000 minutes, while Pro costs $799 per month for 2,500 minutes and includes a 65% resolution guarantee after 90 days.
#### Delight.ai
**Best for**
Retailers that want one AI concierge to carry context across shopping and post-purchase care on multiple channels.
**What it does**
At Delight.ai, we connect [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp), [For You Conversations](https://delight.ai/for-you-conversations-fyc), and [Omnipresence](https://delight.ai/omnipresent) so the shopper's context doesn't disappear when the need or channel changes. The same platform supports personalized discovery, proactive cart recovery, order updates, returns, and refunds across chat, SMS, email, voice, WhatsApp, and in-app messaging.
**Customer proof**
[Furniture retailer Hanssem increased its resolution rate from 48% to about 86% over 5 months](https://delight.ai/customers/hanssem) and reduced transfers to humans by 50%. This is approved Delight.ai retail support evidence and is not presented as a shopping-conversion result.
**G2 rating**
[Sendbird, Delight.ai's parent company, is rated 4.6 out of 5 from 126 reviews on G2](https://www.g2.com/sellers/sendbird).
**Pricing**
We use conversation-based fees, and [Delight Desk has a $0 seat fee](https://delight.ai/desk). Customers pay for successful AI resolutions, so the helpdesk doesn't add a per-agent charge as the team grows.
### Comparison table
| Vendor | Channels | G2 rating | Pricing | Best fit |
|---|---|---|---|---|
| Alhena AI | Web, email, social, WhatsApp, voice | 4.9/5, 35 reviews | Free; Essentials $199/mo annually | Brands seeking proactive discovery and published conversion proof |
| Gorgias AI Agent | Email, chat, text; broader helpdesk channels available | 4.6/5, 557 reviews | Helpdesk from $10/mo; AI from $0.90 per resolved interaction annually | Existing Gorgias teams |
| Rep AI | Web plus optional email, social, and WhatsApp | 4.9/5, 61 reviews | From $99/mo; scales with traffic and modules | Brands focused on proactive selling and support automation |
| Ringly.io | Voice | no score displayed | Grow $349/mo; Pro $799/mo | Shopify brands prioritizing phone support |
| Delight.ai | Chat, SMS, email, voice, WhatsApp, in-app | 4.6/5, 126 Sendbird reviews | Conversation-based; Desk has $0 seat fee | Retailers seeking persistent context across the customer journey |
The overlap between shopping and support is growing, which is good news for retailers and a reason to look beyond a generic feature checklist. We've built Delight.ai for the team that values persistent memory, cross-channel context, and one operating layer across the relationship, especially when a shopper's next question may arrive on a completely different channel.
### Where do AI shopping assistants fall short after checkout?
AI shopping assistants fall short after checkout when their data connections and approved workflows cover only the buying stage, though that boundary is moving because every vendor in this comparison now publishes support for post-purchase tasks.
Coverage alone doesn't guarantee continuity. A platform may be able to process a return and still treat the person requesting it as a stranger, so retailers need to verify whether the same identity, preferences, and conversation history carry across channels and teams.
This is where a realistic test tells you more than a demo. Try a scenario that crosses the purchase boundary, such as a sizing recommendation followed by a delivery update, a return, and a replacement request, and see how much context survives from one step to the next.
### How does Delight.ai connect shopping and service?
Delight.ai connects shopping and service by using the same memory, business context, and governance across discovery, purchase, and care. We build that continuity through [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp), which combines customer memory with business intent, and [For You Conversations](https://delight.ai/for-you-conversations-fyc), which adapts each interaction to the shopper's history, preferences, goals, timing, and channel.
That understanding stays useful because [Omnipresence](https://delight.ai/omnipresent) carries the context when a shopper moves from chat to a call, then continues the follow-up through SMS or email. We can also reach out after a cart is abandoned or a delivery issue appears, while Interactive Forms collect structured information inside the conversation so the shopper doesn't get bounced to another screen.
For retailers, the result is one connected operating model for AI-led shopping and service, with human teams still in control of escalation rules. When judgment is required, the conversation reaches a person with the context attached, which means the handoff can continue the experience the shopper already started.
### Do AI shopping assistants improve conversion?
Yes, AI shopping assistants can improve conversion in specific deployments, but retailers need a clean baseline before the headline number means much.
[Alhena reports that Tatcha achieved 3x the site-average conversion rate](https://alhena.ai/case-studies/tatcha) for assistant conversations, while [Rep AI reports a 21.36% conversion rate for engaged shoppers at Vertical Spice](https://www.hellorep.ai/case-studies/how-vertical-spices-ai-sales-agent-converts-21-of-shoppers).
Both are vendor-published customer results, so they show what's possible in those deployments without establishing a category-wide benchmark.
#### How should retailers measure conversion lift?
Retailers should measure conversion lift by tracking conversion rate, average order value, assisted revenue, containment or resolution, escalation rate, CSAT, and repeat purchase. You'll also want to define attribution before launch and compare equivalent traffic and time periods, since an assistant-influenced session isn't the same thing as the store-wide average.
[The Delight AI Index](https://delight.ai/ai-index) found that **71% of consumers have used AI customer service, while 57% say they like it**, based on 1,001 U.S. respondents. That adoption and satisfaction gap makes experience quality a core part of the business case.
#### What should retailers take away?
Retailers should take away that choosing an AI shopping assistant means deciding how much of the customer relationship one platform should support. If persistent memory and cross-channel continuity are central to that decision, we believe Delight.ai is the strongest fit in this set. [See how we connect carts to care](https://delight.ai/blog/industry/retail-ai-agents).
- [What is an AI shopping assistant?](#section-anchor-id-0 "What is an AI shopping assistant?")
- [How is an AI shopping assistant different from a chatbot?](#section-anchor-id-1 "How is an AI shopping assistant different from a chatbot?")
- [Is an AI shopping assistant the same as a virtual assistant?](#section-anchor-id-2 "Is an AI shopping assistant the same as a virtual assistant?")
- [What should a retail business evaluate before choosing an AI shopping assistant?](#section-anchor-id-3 "What should a retail business evaluate before choosing an AI shopping assistant?")
- [What are the best AI shopping assistants, and how do they compare?](#section-anchor-id-4 "What are the best AI shopping assistants, and how do they compare?")
- [Where do AI shopping assistants fall short after checkout?](#section-anchor-id-5 "Where do AI shopping assistants fall short after checkout?")
- [How does Delight.ai connect shopping and service?](#section-anchor-id-6 "How does Delight.ai connect shopping and service?")
- [Do AI shopping assistants improve conversion?](#section-anchor-id-7 "Do AI shopping assistants improve conversion?")
### Frequently asked questions
**What is an AI shopping assistant, and how does it work?**
An AI shopping assistant is software that helps shoppers discover, compare, and buy products through natural-language conversations. It interprets the request, retrieves relevant catalog or customer data, and produces a grounded answer; more capable systems can then complete approved actions such as adding products, checking orders, or starting returns.
**What's the difference between an AI shopping assistant and a chatbot?**
An AI shopping assistant differs from a traditional chatbot through data access, reasoning, and action depth. Where a traditional chatbot commonly retrieves scripted answers, a shopping assistant can use live retail data, understand open-ended intent, recommend products, and execute connected workflows with human escalation when it's needed.
**What should a retail business evaluate before choosing an AI shopping assistant?**
A retail business should evaluate catalog grounding, channel coverage, workflow depth, human handoff, governance, pricing, and continuity across the customer journey. Go beyond the standard demo by testing real edge cases and confirming how the platform handles stale data, uncertain answers, system failures, and channel changes.
**How does an AI concierge differ from a point tool?**
A point-tool shopping assistant focuses on a defined channel or workflow, while a full AI concierge uses shared memory, business context, and governance across multiple stages and channels. At Delight.ai, we've designed the architecture to preserve context from product discovery through post-purchase care and human escalation.
**Which AI shopping assistants support post-purchase service?**
Delight.ai, Gorgias AI Agent, Rep AI, and Ringly all publish support for post-purchase tasks, and Alhena combines shopping with support capabilities. The real differences lie in channels, integrations, workflow depth, memory, and how context moves across touchpoints. We've designed Delight.ai around one persistent thread across chat, SMS, email, voice, WhatsApp, and in-app messaging.
**How should retailers measure AI shopping assistant ROI?**
Retailers should measure AI shopping assistant ROI through conversion rate, AOV, assisted revenue, support resolution, escalation, and CSAT against a pre-launch baseline for comparable traffic. Named vendor case studies show gains in specific deployments, but clear attribution and controlled testing will tell you far more about your own program than a vendor's headline metric on its own.
### Keep reading
[View more](/blog "View more")
[
What is a hotel chatbot? Overview, use cases, and more](/blog/ai-for-cx/hotel-chatbot-ai-concierge)
[
What Is a Healthcare Virtual Assistant? A Leader's Guide](/blog/ai-agent/healthcare-virtual-assistant)
[
Membership Management Software: The Gap AI Agents Fill](/blog/ai-agent/membership-management-software)
[
WISMO: What "Where Is My Order?" Costs Retail Leaders](/blog/insights/wismo-retail-guide)
[View more](/blog "View more")
### Ready to unleash the power of
AI customer service agents?
[Contact sales](/contact-sales)
## What Is a Healthcare Virtual Assistant? A Leader's Guide
Source: https://delight.ai/blog/ai-agent/healthcare-virtual-assistant
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- What Is a Healthcare Virtual Assistant? A Leader's Guide
## What Is a Healthcare Virtual Assistant? A Leader's Guide
[
Jin Ku
CTO](https://delight.ai/author/jin-ku)
August 14 2026
## Key takeaways
- **An AI-powered healthcare virtual assistant can manage patient conversations and administrative workflows**. The term can also refer to a remote human contractor.
- **Support across the patient journey:** The strongest platforms connect scheduling, coverage, coordination, and follow-up workflows before, during, and after a visit.
- **A practical evaluation resource:** Delight.ai's healthcare use case playbook shows how these workflows can fit across the patient journey.
Healthcare leaders encounter two meanings for the term "healthcare virtual assistant." One describes AI software, and the other describes a remote contractor who handles scheduling and paperwork for a clinic. This article focuses on the AI-powered healthcare virtual assistant.
## What is a healthcare virtual assistant?
An AI-powered healthcare virtual assistant is software that manages patient-facing conversations and administrative tasks across the [patient experience](https://delight.ai/industries/healthcare) for a hospital, clinic, payer, or health system. It can answer common questions, book appointments, check insurance coverage, and coordinate follow-up through configured workflows and connected systems.
A human medical virtual assistant is a remote contractor who may handle scheduling, billing, or transcription for a practice. The two categories share a search term, yet they serve different operating models and buying needs.
| | AI virtual assistant | Human virtual assistant |
|---|---:|---:|
| What it is | Software | A person |
| How it works | Uses connected systems, digital channels, and configured workflows | Works as a remote member of an administrative team |
| Availability | Can operate continuously, subject to system and channel availability | Depends on staffing and scheduled hours |
This guide focuses on AI-powered software and what a health system's leadership team should know before evaluating it.
## What can a healthcare virtual assistant do?
An AI-powered healthcare virtual assistant can handle many repetitive, high-volume conversations that occupy front-desk and contact-center teams. A well-designed system uses natural language processing to understand a patient's request, retrieve authorized information, and take an approved action.
For example, an AI-powered assistant can answer coverage questions, reschedule an appointment, check prior authorization status, or send a prescription alert. In a survey conducted in late 2024, McKinsey found that 85% of 150 healthcare leaders from payer, health system, and healthcare services and technology organizations were exploring or had adopted generative AI capabilities ([McKinsey, 2025](https://www.mckinsey.com/industries/healthcare/our-insights/generative-ai-in-healthcare-current-trends-and-future-outlook-2025)). The survey examined adoption plans, anticipated benefits, use cases, and ROI expectations.
The assistant's ability to complete these administrative workflows depends on integration and governance. A useful assistant retrieves current information, such as a patient's coverage or a provider's open appointment slots, and acts within the permissions the healthcare organization has approved.
## Where does a healthcare assistant fit across the patient journey?
An AI-powered healthcare assistant can support patients before a visit, during active care, and after the visit. Connecting those moments gives patients a consistent way to get administrative help and helps care teams maintain context across the relationship.
### Before the visit
Before the visit, an assistant can help patients confirm the practical details of their care.
- Appointment scheduling and rescheduling
- Insurance eligibility and coverage verification
- Pre-visit intake forms and instructions
### During care
During care, the assistant can provide timely administrative support within approved workflows.
- Check-in and arrival support
- Insurance authorization and eligibility checks
- Facility navigation and wait-time updates when connected data is available
### After the visit
After the visit, the assistant can help close the communication loop and keep follow-up tasks moving.
- Prescription alerts and pharmacy coordination
- Billing and payment questions
- Follow-up and preventive care scheduling
Supporting patients across all three stages requires the AI-powered assistant to carry context from one interaction to the next. [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp), Delight.ai's intelligence layer, unifies conversation history and structured data into an evolving profile. Through Delight.ai's connected channel capabilities, memory can persist across chat, SMS, email, and voice, so a patient who returns in March can continue a conversation that began in January.
## What should healthcare leaders demand for compliance and trust?
Healthcare leaders should demand documented privacy, security, governance, and escalation controls before an AI-powered healthcare virtual assistant handles protected health information. Patient data carries legal and operational obligations that require evidence beyond a general compliance statement.
- **A written business associate contract or other qualifying arrangement.** When a HIPAA-covered entity engages a business associate, HHS says the covered entity must establish a written contract or other arrangement that defines the work and requires protection of PHI ([HHS](https://www.hhs.gov/hipaa/for-professionals/covered-entities/index.html)).
- **Named certifications and reports.** Ask the vendor to identify the exact privacy frameworks, security certifications, and independent assessment reports it maintains, including their scope and report type.
- **Human escalation and governance controls.** Teams need clear rules for handoff, review, access, testing, and intervention when a request becomes sensitive or falls outside the AI-powered assistant's approved scope.
These requirements create a concrete standard for evaluating an AI-powered healthcare virtual assistant. Delight.ai maps its compliance and governance evidence to that standard through GDPR and CCPA compliance, a HIPAA Type I report, and a SOC 2 Type II report.
The evidence extends into operational controls. Delight.ai's [security program](https://delight.ai/security) includes role-based access controls and audit logs. [Trust OS](https://delight.ai/trust-os) records conversations, decisions, and tool calls for review and gives teams controls for testing, escalation, and intervention.
## Does a healthcare virtual assistant replace clinical staff?
An AI-powered healthcare virtual assistant supports clinical staff and does not replace them. It routes, schedules, and coordinates approved administrative workflows. Diagnosis, treatment recommendations, and clinical judgment remain with licensed professionals.
This boundary should be explicit in a vendor's scope, escalation rules, and testing program. The table below separates appropriate administrative workflows from decisions that require licensed clinical expertise.
| AI-powered healthcare virtual assistant can handle | Licensed professionals should handle |
|---|---|
| Appointment scheduling, rescheduling, check-in, and reminders | Clinical triage and decisions about the urgency of care |
| Insurance eligibility, authorization status, and billing questions | Diagnosis and interpretation of symptoms, tests, or imaging |
| Prescription alerts, refill-status updates, and pharmacy coordination | Prescribing, changing, or discontinuing medication |
| Delivery of approved instructions and routing to the appropriate care team | Treatment recommendations and patient-specific care plans |
By handling routine calls, forms, reminders, and status checks, the AI-powered assistant can give staff more time for complex cases and patient care. Clinical teams retain responsibility for decisions that require licensure, medical expertise, or patient-specific judgment.
## How do you evaluate a healthcare virtual assistant before implementing one?
Healthcare leaders can evaluate an AI-powered healthcare virtual assistant by checking journey coverage, compliance evidence, and human handoff quality against specific workflows.
- **Full-journey coverage.** Which pre-visit, active-care, and post-visit tasks can the AI-powered assistant complete with your current systems and data?
- **Provable compliance.** Can the vendor provide the relevant contracts, certifications, reports, security controls, and audit evidence for review?
- **Clean human handoff.** When a conversation becomes complex or sensitive, can the AI-powered assistant transfer it with the full context and a clear record of prior actions?
A structured use-case playbook can help teams compare these requirements before committing time to vendor demonstrations.
Healthcare leaders need an AI-powered system that follows the administrative patient journey from scheduling through follow-up. The system's permissions, integrations, evidence, and escalation controls determine whether it can deliver that continuity responsibly.
Delight.ai's healthcare use case playbook covers appointment scheduling, insurance verification, care-team support, and follow-up coordination. [Get the healthcare AI agent playbook](https://delight.ai/resources/healthcare-ai-agents) to identify the workflows and evaluation questions that deserve priority.
- [What is a healthcare virtual assistant?](#section-anchor-id-0 "What is a healthcare virtual assistant?")
- [What can a healthcare virtual assistant do?](#section-anchor-id-1 "What can a healthcare virtual assistant do?")
- [Where does a healthcare assistant fit across the patient journey?](#section-anchor-id-2 "Where does a healthcare assistant fit across the patient journey?")
- [What should healthcare leaders demand for compliance and trust?](#section-anchor-id-3 "What should healthcare leaders demand for compliance and trust?")
- [Does a healthcare virtual assistant replace clinical staff?](#section-anchor-id-4 "Does a healthcare virtual assistant replace clinical staff?")
- [How do you evaluate a healthcare virtual assistant before implementing one?](#section-anchor-id-5 "How do you evaluate a healthcare virtual assistant before implementing one?")
## Healthcare virtual assistant FAQs
### What is a healthcare virtual assistant?
An AI-powered healthcare virtual assistant is software that automates patient-facing conversations and administrative workflows such as scheduling, insurance verification, and follow-up coordination. A human medical virtual assistant is a remote staff member or contractor who performs administrative work. This guide uses the term for the AI-powered software category.
### Can a healthcare virtual assistant provide clinical decision support?
An AI-powered healthcare virtual assistant should not provide clinical decision support when its approved role is administrative coordination. Delight.ai routes patients and supports workflows such as scheduling and insurance checks; it does not diagnose conditions, recommend treatment, or replace a licensed provider's judgment. Clinical questions require an appropriate handoff to qualified staff.
### Is a healthcare virtual assistant HIPAA-compliant?
An AI-powered healthcare virtual assistant can support HIPAA-compliant workflows when the vendor and healthcare organization implement the required contracts, safeguards, access controls, and operating procedures. HIPAA applies to covered entities and business associates, so leaders should review the vendor's exact role, written agreements, security evidence, data handling, and audit controls.
### Does a healthcare virtual assistant replace front-desk or call center staff?
An AI-powered healthcare virtual assistant supports front-desk and contact-center staff and does not replace their judgment. It can absorb repetitive requests such as appointment changes and status checks. Staff remain responsible for sensitive conversations, exceptions, and cases that require human discretion, with boundaries defined before launch.
### How long does it take to implement a healthcare virtual assistant?
Implementation time for an AI-powered healthcare virtual assistant depends on workflow scope, security review, data readiness, system integrations, testing, and approval requirements. Scheduling platforms, insurance eligibility sources, billing systems, and escalation paths can each affect the timeline. A credible vendor should provide a plan tied to the organization's actual systems and launch criteria.
## Keep reading
[View more](/blog "View more")
[
What is a hotel chatbot? Overview, use cases, and more](/blog/ai-for-cx/hotel-chatbot-ai-concierge)[
What is an AI shopping assistant? How top options compare](/blog/ai-for-cx/ai-shopping-assistant-comparison)[
Membership Management Software: The Gap AI Agents Fill](/blog/ai-agent/membership-management-software)[
WISMO: What "Where Is My Order?" Costs Retail Leaders](/blog/insights/wismo-retail-guide)
[View more](/blog "View more")
## Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Membership Management Software: The Gap AI Agents Fill
Source: https://delight.ai/blog/ai-agent/membership-management-software
[](https://delight.ai/author/yeji-yoon)
Yeji Yoon
Chief of staff
August 14 2026
### Key takeaways
- **Membership management software runs the program.** It gives your team one place to manage member records, payments, renewals, benefits, and communications.
- **Members still need help in the moment.** When a discount fails or a renewal is unclear, they need an answer grounded in their account information.
- **Delight.ai adds AI-powered member care.** When connected through approved integrations, it uses membership data and conversation history to help resolve questions across channels.
If you are searching for membership management software, you probably want one reliable place to run a membership, loyalty, or wholesale-club program. Membership management software is that administrative system, giving your team a central record for each member and a way to organize recurring work such as payments, renewals, and benefit access.
**The software can show that a member's renewal is overdue or that an account qualifies for a discount, and an AI agent can use that information to help the member resolve the issue.** This guide explains how the two systems work together, with membership management software maintaining the program data and the AI agent helping members act on it. You can explore the wider retail context on the [Delight.ai blog](https://delight.ai/blog).
## What is membership management software?
Membership management software is the system a team uses to administer a membership program. It brings the member database and the recurring work around that database into one place.
Consider a wholesale-club member who upgrades to a higher tier. The membership management software records the new tier, applies the correct renewal date, and identifies the benefits attached to the account. When that member contacts the retailer about a missing benefit, the same record gives the service team accurate information to work from.
When a retailer connects its membership management software to Delight.ai through approved integrations, the AI agent can use authorized membership data to answer loyalty questions and help with redemptions. The membership management software continues to run the program, and [Delight.ai's retail AI agent](https://delight.ai/industries/retail) helps the member get an answer.
## Vendor comparison
This table compares software that administers membership programs. Delight.ai serves the connected member-care role, using authorized data from membership management software to help resolve member questions across channels.
| Vendor | Public entry price | How pricing scales | Included tools highlighted by the vendor |
|---|---:|---|---|
| WildApricot | $66 per month for up to 100 contacts, or $59.40/month with a prepaid 1-year term | Contact limit | Member database, renewals, payments, website builder, events, email, reporting, and mobile apps |
| Join It | $29 per month for Starter, plus a 3% service fee on paid memberships and events | Plan, member capacity, administrator seats, and transaction fee | Member database, recurring payments, renewal reminders, event registration, digital cards, directory, and higher-plan API access |
| MembershipWorks | Free for up to 50 accounts; paid plans start at $35 per month for up to 300 accounts | Account limit | Membership management, online payments, member-only content, directory, events, forms, and multiple administrators on paid plans |
| ClubExpress | $0.42 per active member per month for up to 200 members, subject to a $24 monthly minimum | Active-member count, with lower rates at higher volumes; setup starts at $150 | Membership database, signups, renewals, dues, website, event registration, email, mobile app, and chapter tools |
## What membership management software actually covers
Membership management software gives program owners a consistent view of each account and the work attached to it. From that shared record, the team can manage several parts of the membership lifecycle:
- **Member records.** When a member calls, the team can see the account status, tier, join date, renewal date, and related accounts in one profile.
- **Recurring billing and renewals.** The software can schedule charges, record payments, send reminders, and show the team which renewals need attention.
- **Event and access management.** A member's tier can determine which events, content, or benefits appear in the member portal.
- **Communications.** Email or SMS updates can reach the right group based on membership status or renewal timing.
- **Reporting.** Program owners can follow retention, renewals, engagement, and revenue without reconciling separate files.
Together, these functions give the team one reliable record of the membership program. Program owners can find accurate account information, confirm that billing is running as expected, and apply access rules consistently as the member base grows.
## Where membership management software hits its limit for retail members
Membership management software can record what happened to an account, yet the record alone cannot guide a member through the problem. Imagine a shopper reaching checkout and seeing that a tier discount has disappeared. The software holds the tier and renewal status; the shopper needs help interpreting that information and completing the purchase.
The same need for a clear, immediate response appears in situations such as these:
- A tier discount fails to apply at checkout.
- A member needs help understanding renewal status.
- A wholesale-club shopper needs an explanation for a payment or account issue.
In each case, the membership management software supplies the account facts. A connected AI agent can use those facts to explain what happened, guide the member through an approved next step, or bring in a person when the issue needs review.
A 2026 Gartner survey reported strong executive pressure to implement AI and identified customer satisfaction, operational efficiency, and self-service success as leading priorities that AI can help address.
## How an AI agent closes the gap membership software leaves open
An AI agent closes the member-care gap by turning authorized data from the membership management software into useful help during a conversation. When a member asks why a benefit is missing, the AI agent can reference the account context, follow the retailer's approved rules, and guide the member through the next step.
The relationship between the two systems stays clear. Membership management software continues to hold the tier, billing, renewal, and benefit information. Delight.ai uses approved information from that software to support the conversation, then carries the context into a human handoff when the case calls for judgment.
### What carries into the conversation
Membership management software holds the operational record for the program. It tells the retailer whether a membership is active, when it renews, which tier applies, and which benefits belong to the account. Program teams continue to update and administer that information in the membership management software.
Delight.ai's **Agent Memory Platform** (AMP) gives the AI agent the context needed to use authorized information from that record during a conversation. Through an approved integration, AMP can bring the relevant membership data together with the member's previous conversations, purchase activity, stated preferences, and open issues. The AI agent can then respond using the current account information and the retailer's approved business rules.
Consider a member who asks in web chat whether a renewal payment restored a tier benefit. After the membership management software supplies the current payment, renewal, and tier information, AMP connects those facts with the conversation already in progress. If the member follows up by phone, Omnipresence keeps that combined context available so the AI agent can continue the issue across chat, SMS, email, WhatsApp, voice, social, or in-app conversations.
### What this doesn't replace
Membership management software remains responsible for the program itself, including member records, billing, renewals, benefit rules, and access. The AI agent uses authorized information from that software during member conversations.
Human teams continue to handle approval gates, disputed charges, sensitive situations, and decisions that require judgment. When a handoff occurs, the AI agent passes along the conversation context so the member and the employee can continue from the same point.
## What this looks like in retail today
Hanssem provides verified retail proof for this conversational model. According to the retailer's Delight.ai customer story, its AI agent's resolution rate rose from **48% to about 86%** over 5 months, transfers to human agents fell by **50%**, and accuracy on repetitive queries exceeded **90%**.
Hanssem measured general retail customer service, which makes the results proof of the broader approach. The customer story shows that an AI agent can resolve more conversations by applying business context while keeping human support connected.
Membership care uses the same operating pattern. The membership management software supplies the account facts, Delight.ai uses approved data and workflows to help the member, and the team steps in when policy or judgment calls for review.
## Membership management software vs. an AI agent for member care
Membership management software and an AI agent serve connected roles in the same member journey. Membership management software administers the program, and the AI agent helps members resolve questions about their accounts, renewals, and benefits.
| Membership management software | AI agent for member care |
|---|---|
| Primary job: Stores member data, bills dues, manages renewals, and administers events or access | Primary job: Answers and resolves live member questions using approved data and workflows |
| Where members encounter it: Member portal, account pages, renewal notices, and program communications | Where members encounter it: Chat, SMS, email, voice, social, and in-app conversations |
| Core strength: Consistent records, scheduled processes, and program reporting | Core strength: Context-aware responses, guided actions, and connected handoffs |
| System boundary: Requires a conversational layer for live, account-specific service | System boundary: Relies on source systems for authoritative membership and billing data |
### When you need both
Retailers need both when an accurate account record and a helpful conversation determine the same outcome. For example, membership management software may show that a renewal payment cleared, while the AI agent uses that record to explain when the member's benefits will become available.
Connecting Delight.ai through approved integrations keeps the program data in membership management software. Delight.ai adds memory and resolution workflows to the member conversation, giving CX and operations leaders a way to improve service without moving the membership program into a new system.
## What does this cost?
Membership management software costs vary according to the size of the member database, the features a team needs, and the vendor's pricing model. These public prices were checked on August 14, 2026:
- **WildApricot.** Plans start at $66 per month for up to 100 contacts, or $59.40 per month with a prepaid 1-year term; price rises with contact limit. See: https://www.wildapricot.com/pricing
- **Join It.** Starter begins at $29 per month with 3 administrator seats and a 3% service fee; Total starts at $99 with 5 seats and a 2% fee; Extra starts at $199 with 10 seats and a 1.5% fee. See: https://joinit.com/pricing
- **MembershipWorks.** The free plan supports up to 50 accounts; paid plans start at $35 per month for up to 300 accounts, and pricing reaches $259 per month for up to 10,000 accounts. See: https://membershipworks.com/pricing/
- **ClubExpress.** Organizations with up to 200 members pay $0.42 per member per month, subject to a $24 minimum monthly hosting fee, with lower per-member rates at higher volumes. See: https://www.clubexpress.com/pricing
Delight.ai uses conversation-based pricing, with unit costs decreasing at scale. [Delight Desk](https://delight.ai/desk) carries a $0 seat fee, and customers pay for successful AI resolutions. This model ties the human helpdesk cost to AI-resolved work while keeping human agent seats free.
A useful business case evaluates each system according to the work it performs. Membership management software pricing reflects the size and administrative needs of the program. An AI agent creates value through the member conversations it can resolve accurately and consistently.
Membership management software gives your team the accurate program data it needs. Delight.ai helps turn that data into responsive member care through persistent context, cross-channel continuity, and guided resolution workflows. The [Retail Use Cases ebook](https://delight.ai/resources/retail-ai-agents) shows how this approach supports membership questions alongside product discovery, cart recovery, in-store guidance, returns, and order support.
## Contents
- [What is membership management software?](#what-is-membership-management-software)
- [What membership management software actually covers](#what-membership-management-software-actually-covers)
- [Where membership management software hits its limit for retail members](#where-membership-management-software-hits-its-limit-for-retail-members)
- [How an AI agent closes the gap membership software leaves open](#how-an-ai-agent-closes-the-gap-membership-software-leaves-open)
- [What this looks like in retail today](#what-this-looks-like-in-retail-today)
- [Membership management software vs. an AI agent for member care](#membership-management-software-vs-an-ai-agent-for-member-care)
- [What does this cost?](#what-does-this-cost)
## Frequently asked questions
### What is membership management software?
Membership management software is the system a team uses to administer a membership, loyalty, or wholesale-club program. It keeps each member's record connected to the recurring work around that account, including payments, renewals, benefits, access, and communications. Retail leaders rely on it to maintain accurate program information.
### How does membership management software improve member retention and engagement?
Membership management software supports retention and engagement by helping the program run consistently. It can keep renewal reminders on schedule, maintain accurate payment and benefit records, and show program owners where engagement is changing. When a member needs help, a connected AI agent can use those records to provide a timely response.
### How much does membership management software cost?
Membership management software pricing varies by vendor and program size. The number of members often affects the price, and the selected package may determine administrative access, transaction fees, integrations, and support. Retail leaders should compare the total cost against the program requirements each option covers.
### How does AI improve member care and renewals in retail loyalty programs?
AI improves member care by using authorized data from membership management software during the conversation. The AI agent can answer routine questions about renewals or benefits, guide the member through an approved process, and bring in a person with the conversation history attached. That continuity gives members a clearer path through issues that could otherwise interrupt renewal.
### What's the difference between membership management software and an AI agent for member support?
Membership management software administers the program and holds the member data. An AI agent uses authorized information from that software to help members through chat, SMS, email, voice, social, or in-app conversations. Together, the two systems connect accurate program operations with responsive member care.
## Keep reading
[View more](/blog)
[](/blog/ai-for-cx/hotel-chatbot-ai-concierge) What is a hotel chatbot? Overview, use cases, and more
[](/blog/ai-for-cx/ai-shopping-assistant-comparison) What is an AI shopping assistant? How top options compare
[](/blog/ai-agent/healthcare-virtual-assistant) What Is a Healthcare Virtual Assistant? A Leader's Guide
[](/blog/insights/wismo-retail-guide) WISMO: What Where Is My Order Costs Retail Leaders
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## The Trust Stack: AI Agent Governance and Earned Autonomy
Source: https://delight.ai/blog/ai-agent/the-trust-stack-how-to-give-an-ai-agent-more-autonomy-without-losing-control
- [Blog](/blog)
- [AI agent](/blog/ai-agent)
- The Trust Stack: AI Agent Governance and Earned Autonomy
## The Trust Stack: AI Agent Governance and Earned Autonomy
[Kibeom Lee](https://delight.ai/author/kibeom-lee-machine-learning-lead)
Machine learning lead
August 14 2026
Every AI support agent that failed in public was caught by somebody outside the company. A customer, a reporter, or a tribunal. The failures worth worrying about are the ones nobody catches at all, because they close cleanly and score well.
This is the argument Kibeom Lee, Head of AI/ML at Delight.ai, makes to every team preparing to put an agent in front of customers. What follows is the framework his team applies when deploying agents into production across [retail](https://delight.ai/industries/retail), [logistics](https://delight.ai/logistics-and-supply-chain), [home services](https://delight.ai/home-services-and-warranty) and [field service](https://delight.ai/field-services) operations: the five ways an agent fails quietly, and the eight questions to answer before you hand any workflow more autonomy.
## Three failures that made the news
**Gap, late 2025.** Days after Gap launched an AI shopping assistant, users had steered it into conversations about sex toys and Nazi Germany. Its vendor said a coordinated effort had targeted more than a dozen customers' agents at once, and that Gap's guardrails had been inadvertently misconfigured. Reported by The Information in December 2025.
**Air Canada, February 2024.** In Moffatt v. Air Canada, a British Columbia tribunal held the airline responsible for what its chatbot told a customer. The bot said he could apply for a bereavement fare retroactively, within 90 days of booking. That policy did not exist. The tribunal found Air Canada had not taken reasonable care to make sure its chatbot was accurate, and awarded CA$650.88 in damages, roughly CA$812 in total.
**Cursor, April 2025.** Cursor's own support AI told users a login problem was caused by a one-device policy. There was no such policy. The agent had invented an explanation for a bug. Because the answer arrived through an official support channel it looked authoritative, and some users cancelled over a rule the company never had.
Three different failure modes. Gap was adversarial. Air Canada was a fabricated policy that became a legal commitment. Cursor was a confident explanation with no evidence behind it, from a company that understands AI as well as anyone.
They have one thing in common. In all three, the customer found the problem before the company did. That gap is becoming a regulatory one too. Singapore's IMDA released a Model AI Governance Framework for Agentic AI in January 2026, the first framework to require a verifiable digital identity and audit trail per agent. NIST launched its AI Agent Standards Initiative the following month. EU AI Act enforcement is already live, with fines running up to €35 million or 7% of global turnover for the highest-risk violations.
When your agent is quietly wrong, who finds out first: you, or your customer?
## The failure that looks like success
Three AI support conversations that resolved fast and scored well. All three contain a failure.
Look at three conversations from an AI support agent. In the first, a customer asks for a refund on a $2,100 order and gets it. In the second, a customer asks about returning an item after 60 days and is told they have a full 90 days. In the third, a customer threatens a chargeback and the agent immediately processes a refund and closes the conversation.
Every one of them resolved in under a minute. Two scored CSAT 5.0. None triggered an escalation.
Now look again. The agent approved a refund it was never cleared to approve. It invented a return window that does not exist, which is the same failure that cost Air Canada its tribunal case. And it closed a payment dispute that should have gone to a person.
If you measure only the outcome the agent recorded, failure looks exactly like success. Resolution rate, CSAT, and deflection will all report a good week.
## Every silent failure is one of five things
The five silent AI agent failure modes: hallucination, data disclosure, unauthorized action, prompt injection and missed escalation.
Silent failures fall into five categories. Each category needs a different control. A hallucination detector will not stop an unauthorized refund. Access rules will not tell you when the agent should have escalated. A strong test score will not protect customer data.
| Failure mode | What it looks like in your data | What catches it |
|---|---|---|
| Hallucination | A confident answer with no source behind it, usually with a good CSAT score attached | Observability: benchmark testing before release, and source tracing after |
| Data disclosure | An ordinary-looking reply that contains PII or another customer's record | Oversight: automated review of every conversation, not a sample |
| Unauthorized action | A completed refund, credit or cancellation the agent had no clearance to make | Control: role and action permissions, checked against the activity log |
| Prompt injection | An off-brand exchange, usually following unusual or adversarial input | Oversight: automatic flagging, and a kill switch when a pattern emerges |
| Missed escalation | A closed conversation that a person should have handled | Observability and Oversight together: scoring the outcome against expected policy |
Trust is not a single quality score. It is the ability to tell these five apart and respond to each one differently.
Once you sort failures this way, something becomes clear. This is not only a model problem. It is a systems problem, and a smarter model does not solve it.
## The Trust Stack: four layers of trust
The Trust Stack is a four-layer framework for operating an AI agent safely: Observability, Control, Oversight, and Self-Improvement. Each layer answers one question, and each depends on the layers beneath it.
1. Observability. Can you detect a problem and reconstruct exactly what the agent did?
2. Control. Can you define what the agent, and the people managing it, are allowed to change or do?
3. Oversight. Can humans and automated systems review the agent's behaviour and intervene?
4. Self-Improvement. Can the agent learn from its failures and get better without stepping outside those controls?
At Delight.ai these four layers are the product surface we call [Trust OS](https://delight.ai/trust-os), and the examples below are drawn from it.
It is a stack rather than a checklist because the dependencies run in one direction. You cannot review evidence you never captured. You cannot safely give an agent more authority if you cannot reverse what it does. And you cannot automate improvement if you cannot measure whether anything improved.
### Layer 1: Observability
Two capabilities carry this layer.
[Conversation benchmark testing](https://delight.ai/trust-os) runs before anything reaches a customer. You define realistic customer conversations and the outcome the agent should produce. The test is not checking for one exact sentence. It checks whether the agent followed the right policy, took the right steps, and reached the right outcome. Run continuously, it catches a regression before it ships rather than after.
[The activity log](https://delight.ai/trust-os) covers what happens once the agent is live. When a customer asks whether they can pause a membership instead of cancelling, the log shows which knowledge source was used, which workflow was triggered, which step was selected, and which API was called. Every configuration version leaves a trace too.
The practical difference is in how you respond to an incident. Instead of reverse-engineering what the agent might have done, you can see what it did and why.
### Layer 2: Control
AI agents change constantly. Prompts get updated, policies change, knowledge is refreshed, new tools get connected. Control means treating those changes the way you would treat changes to any production software.
**Versioning and rollback.** Every prompt and configuration change is recorded and can be compared side by side, so you can see exactly what was added and removed. Development and production stay separate, so a new version is proven in staging before a customer sees it. If the new behaviour is worse, you roll it back.
[Role-based access](https://delight.ai/security). The person drafting a prompt may need to create and test a change without having permission to approve or deploy it. Bounding each role to what it actually needs reduces accidental changes, limits the damage from compromised access, and leaves a clear record of who drafted, tested, approved and deployed each version.
The goal is not to slow change down. It is to make every change visible, testable and reversible.
### Layer 3: Oversight
Oversight has to work at two levels at once.
Humans provide judgment. An evaluator reviewing an individual conversation can score tone, clarity, completeness, correctness, and whether the customer's problem was actually solved. That kind of nuance does not automate well.
The system provides coverage. No human team can review every conversation an agent handles. Automated evaluators can, and they detect the patterns humans miss at volume: hallucinations, policy violations, banned phrases, harmful content, missed safeguards.
Neither works alone. Automated evaluation misses context, and human-only review cannot keep up with the volume.
And a [kill switch](https://delight.ai/security). Most issues can go through review. Some cannot wait. Taking the agent offline should not require a ticket queue or a deploy window. Autonomy should never mean the system becomes unstoppable.
### Layer 4: Self-Improvement
At this point the stack lets you detect problems, trace decisions, control changes, review outcomes and stop the agent. The remaining question is whether the system can also fix what went wrong.
Who found the failure pattern across thousands of conversations? AI can do that. Who proposed the fix? AI can do that. Who tested and shipped the improved version? Within the right controls, AI can do that too. Many of the governance checks can be automated as well.
The work can run end to end without a person completing every step. That does not remove the human. People still define the objective, set the boundaries, choose the level of autonomy, and decide when the system has earned permission to run on its own. The role moves from doing every step to steering and governing the system that does the work.
## Zero-Touch Improvement: a self-running loop
Delight.ai calls the fourth layer Zero-Touch Improvement. It runs as a continuous four-stage loop.
1. Self-Build. The system researches conversations where something went wrong, looking for patterns like low CSAT, repeated questions, or unusually long resolution times. It forms a hypothesis, proposes a precise change, and explains both its reasoning and the outcome it expects. A human can approve, reject or redirect that hypothesis.
2. Self-Test. The change runs in staging against real conversations the current version failed. In a representative scenario, a proposed fix is tested against a batch of previously failed conversations, iterating until it resolves all of them.
3. Self-Evaluate. The system scores whether the new version actually performed better.
4. Self-Improve. Only then does the version deploy, and only if it meets the deployment rules. Version history and one-click rollback remain available afterwards.
Nothing ships because the AI suggested it. A change has to earn its way into production through evidence. Zero-touch does not mean zero accountability. It means the repetitive work runs continuously while people set the direction and the boundaries.
## Autonomy is a ladder, not a switch
If a system can research a failure, propose a fix, test it and deploy it, the question becomes how much of that loop should run on its own. The answer is not all of it at once.
- Level 0, See it. The system surfaces failure patterns. Your team decides what to change and does the work.
- Level 1, Approve it. The AI proposes a fix and presents the evidence. A person decides whether it moves forward.
- Level 2, Steer it to get better. People define the objective and the boundaries. The system runs more of the research, testing and deployment loop.
- Level 3, Get better at getting better. Only after the loop has repeatedly proven itself. The system identifies new problems and improves continuously, inside boundaries the team already approved.
The goal is not to move every workflow to Level 3. A low-risk product answer and a high-value refund should not carry the same autonomy. What matters is the order: evidence comes before permission. Autonomy is not switched on, it is earned.
## Eight questions to answer before you give an agent more autonomy
Take one workflow, refunds or cancellations or account changes, and answer all eight with a clear yes or no.
1. Observability
1. Can you catch a regression before a customer does?
2. Can you trace every decision back to its cause?
2. Control
1. Can you roll back a bad change instantly?
2. Does access match exactly what each role needs, and no more?
3. Oversight
1. Is every response reviewed, by a human and by the system?
2. Can you shut it off the instant something is wrong?
4. Self-Improvement
1. Does it test its own fixes against its own real failures?
2. Can it prove it got better, rather than just claim it?
The first six are table stakes. The last two are what makes autonomy defensible. Every no is an item on your autonomy backlog. When every answer is yes, that workflow is ready to earn more autonomy.
If you are running this against a vendor rather than your own build, the same eight questions work as evaluation criteria. Our [AI CX platform RFP template](https://delight.ai/ai-cx-platform-rfp-template) turns them into a scorecard.
## The point
Go back to the question at the top.
When your agent is quietly wrong, who finds out first: you, or your customer?
The Trust Stack is how you make sure the answer is you. Observability, then Control, then Oversight. Only then does self-improvement make anything safer. That is how an AI agent gets more autonomy without you giving up control.
*Kibeom Lee is Head of AI/ML at Delight.ai, where he leads the team that builds and deploys Delight.ai's customer-facing AI agents.*
- [Three failures that made the news](#three-failures-that-made-the-news)
- [The failure that looks like success](#the-failure-that-looks-like-success)
- [Every silent failure is one of five things](#every-silent-failure-is-one-of-five-things)
- [The Trust Stack: four layers of trust](#the-trust-stack-four-layers-of-trust)
- [Zero-Touch Improvement: a self-running loop](#zero-touch-improvement-a-self-running-loop)
- [Autonomy is a ladder, not a switch](#autonomy-is-a-ladder-not-a-switch)
- [Eight questions to answer before you give an agent more autonomy](#eight-questions-to-answer-before-you-give-an-agent-more-autonomy)
- [The point](#the-point)
## Frequently asked questions
What is the Trust Stack?
The Trust Stack is a four-layer framework for operating AI agents safely: Observability, Control, Oversight and Self-Improvement. Each layer depends on the ones beneath it, so autonomy at the top is only safe when the three below it hold.
What is a silent AI agent failure?
A silent AI agent failure is one the agent records as a success. The conversation resolves, the customer scores it well, and no escalation is triggered, but the agent hallucinated a policy, exposed data, took an unauthorized action, was manipulated, or skipped an escalation it should have made. Standard support metrics do not surface these.
Why is a smarter model not enough?
A smarter model is not enough because every failure mode above is a systems failure rather than a reasoning failure. A better model may hallucinate less, but it will not tell you which conversations went wrong, restrict what actions it can take, provide a rollback path, or prove a fix worked.
What are the levels of AI agent autonomy?
The levels of AI agent autonomy are four: see it, approve it, steer it, and get better at getting better.
How do you measure whether an AI agent actually improved?
You measure it by testing the proposed change against the specific conversations the previous version failed, then score whether the outcome improved. Comparing aggregate CSAT before and after will not isolate the effect of the change.
Does an AI agent need a kill switch?
Yes, an AI agent needs a kill switch. Most issues can go through normal review, but taking an agent offline should not depend on a ticket queue or a deploy window. Autonomy should never mean the system cannot be stopped.
## Keep reading
[View more](/blog)
[ What is a hotel chatbot? Overview, use cases, and more](/blog/ai-for-cx/hotel-chatbot-ai-concierge)
[ What is an AI shopping assistant? How top options compare](/blog/ai-for-cx/ai-shopping-assistant-comparison)
[ What Is a Healthcare Virtual Assistant? A Leader's Guide](/blog/ai-agent/healthcare-virtual-assistant)
[ Membership Management Software: The Gap AI Agents Fill](/blog/ai-agent/membership-management-software)
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Conversational AI in healthcare: What it does and how to evaluate it
Source: https://delight.ai/blog/industry/conversational-ai-healthcare
- [Blog](/blog)\n- [Industry](/blog/industry)\n- Conversational AI in healthcare: What it does and how to evaluate it\n\n## Conversational AI in healthcare: What it does and how to evaluate it\n\n[\n\nShailesh Nalwadi\n\nHead of product management](https://delight.ai/author/shailesh-nalwadi)\n\n\n\nAugust 18 2026\n\n### Key takeaways\n\n- **Connected patient support** can cover scheduling, insurance questions, check-in, and follow-up through one coordinated system.\n- **A credible vendor evaluation** examines integration, compliance, channel continuity, and human escalation as one operating model.\n- [**Delight.ai**](https://delight.ai/resources/healthcare-ai-agents) **connects the patient journey** with persistent context, omnichannel coverage, and governance built for review.\n\nConversational AI in healthcare can support patients across the administrative journey, from booking through follow-up. For healthcare customer experience leaders, the central buying question is whether those exchanges share **context and controls** across systems and channels. A connected approach reduces repeated explanations for patients and gives staff a clearer handoff when a person needs to step in.\n\n## What is conversational AI in healthcare?\n\n**Conversational AI in healthcare is software that interprets spoken or written language and responds to patient requests in a natural exchange.** Healthcare organizations use it for administrative workflows such as appointment scheduling, insurance questions, check-in, billing support, and refill coordination.\n\nRules-based chatbots usually present fixed choices or follow decision trees. Conversational AI uses **natural language processing** to identify the request behind a patient's phrasing, connect the request with approved information or systems, and choose an appropriate next step.\n\nAgentic AI adds the ability to complete multi-step work across tools with limited human input. Conversational AI focuses on understanding and managing the exchange, and a **healthcare AI agent** may combine both capabilities. The boundaries remain important because administrative coordination, clinical judgment, and emergency care require different controls.\n\n## How does conversational AI support patients from booking to follow-up?\n\nConversational AI supports patients from booking to follow-up by carrying approved information and interaction context through **3 phases** of the healthcare journey. Distinct workflows form one continuous relationship when the systems connect.\n\n### Before care: scheduling and insurance verification\n\nBefore care, healthcare teams can use a [healthcare AI agent](https://delight.ai/industries/healthcare) to make routine access tasks available beyond staffed hours. Common workflows include appointment scheduling, rescheduling, insurance inquiries, prior authorization status checks, and pre-visit instructions.\n\nInsurance questions require deeper system access than a simple appointment request. A useful response may depend on plan details, referral status, or information held in a billing platform. The agent therefore needs **permissioned access** to the relevant source and a clear escalation path when the available data cannot support an answer.\n\nBefore-care work typically covers:\n\n- **Appointment access:** Scheduling, rescheduling, and cancellations.\n- **Coverage support:** Insurance questions and prior authorization status checks.\n- **Visit preparation:** Registration details and pre-visit instructions.\n\n### During care: check-in and real-time authorization\n\nDuring care, conversational AI can use information collected before the visit to support arrival, check-in, and authorization workflows. That continuity reduces duplicate intake and gives staff a more complete record when they take over.\n\n[Delight.ai](https://delight.ai/agent-memory-platform-amp) provides a memory layer that combines conversation history and business data into a unified profile. The **Agent Memory Platform (AMP)**, working through **Omnipresence**, carries memory across chat, SMS, email, and voice so patients can change channels without repeating earlier context.\n\nDuring-care work typically covers:\n\n- **Arrival support:** Check-in guidance and registration updates.\n- **Authorization workflows:** Insurance authorization and eligibility checks.\n- **Language access:** Responses in supported languages, with escalation available when needed.\n\n### After care: follow-up, refills, and lab results\n\nAfter care, conversational AI can help teams continue administrative support between appointments. Discharge instructions, refill coordination, lab-result notifications, billing questions, and preventive-care reminders all depend on timely outreach and reliable routing.\n\n[Delight.ai's playbook on healthcare AI agents](https://delight.ai/resources/healthcare-ai-agents) shows how organizations can handle insurance coverage questions and close the loop on ongoing care with proactive follow-ups. Those workflows help care teams maintain continuity across patient touchpoints and focus their time on requests that call for human attention.\n\nAfter-care work typically covers:\n\n- **Care instructions:** Discharge information and approved follow-up guidance.\n- **Ongoing coordination:** Prescription refills and lab-result notifications.\n- **Proactive outreach:** Appointment reminders and preventive-care prompts.\n\n## What are the benefits of conversational AI in healthcare?\n\nThe benefits of conversational AI in healthcare include:\n\n- **Easier access:** Scheduling, routine questions, and follow-up can move forward outside staffed hours when the agent has accurate information and a defined workflow.\n- **More consistent administrative support:** Teams apply approved guidance consistently across channels and hours, with a context-rich handoff when an issue needs a person.\n- **More staff time for complex patient needs:** Automating routine access and coordination work frees staff to focus on complex cases and direct patient care.\n\nAccess friction already carries a measurable cost for patients. A [2024 McKinsey survey of 2,133 U.S. consumers](https://www.mckinsey.com/industries/healthcare/our-insights/harnessing-ai-to-reshape-consumer-experiences-in-healthcare) found that **25%** could not get the care they needed when they needed it. The same analysis describes people spending hours researching and contacting providers or payers for answers, which makes patient-access workflows a practical place to evaluate automation.\n\nCentral knowledge, safeguards, and conversation history can also help teams apply approved guidance consistently across channels and hours. When an issue exceeds the agent's scope, a context-rich handoff lets staff begin with the request and prior exchange already visible.\n\n## What should you look for when evaluating a conversational AI vendor?\n\nWhen evaluating a conversational AI vendor, look for **verified integration paths**, a **specific compliance posture**, **continuity across patient channels**, and **reliable human escalation**. A polished demo shows the conversation; technical and operational review shows whether the system can support a production patient journey.\n\n### Ease of integration\n\nThe integration review should map each proposed workflow to the system that owns the required data or action. Scheduling, electronic medical record or electronic health record access, billing, identity, phone, and knowledge systems may all participate in one exchange.\n\nDelight.ai supports connections to existing enterprise tools, healthcare knowledge sources, and internal application programming interfaces (APIs). Buyers should validate every named system, data permission, write-back action, and failure path during technical review so the implementation scope is clear before launch.\n\n### Compliance that's current\n\nThe compliance review should cover:\n\n- **The proposed data flow**\n- **The business associate agreement (BAA)**\n- **Safeguards** **and** **access controls**\n- **Audit evidence**\n- **Division of responsibilities**\n\n[U.S. Department of Health and Human Services guidance](https://www.hhs.gov/hipaa/for-professionals/special-topics/health-information-technology/cloud-computing/index.html) says a cloud service provider that creates, receives, maintains, or transmits electronic protected health information is a business associate. The guidance requires an appropriate BAA and makes clear that the Office for Civil Rights does not certify specific products.\n\n[Delight.ai's security posture](https://delight.ai/security) includes:\n\n- **GDPR** and **CCPA** alignment\n- A **HIPAA Type I report**\n- A **SOC 2 Type II report**\n\nIts governance and observability layer, [Trust OS](https://delight.ai/trust-os), provides reviewable logs for conversations, decisions, and tool calls, along with controls for testing and monitoring agent behavior.\n\n### Coverage across every channel patients use\n\nChannel coverage should preserve context when a patient moves among chat, voice, SMS, and email. Separate channel histories create repeated explanations for patients and fragmented records for staff.\n\nAn **omnichannel memory layer** should carry context across supported channels. A useful cross-channel test begins in one channel, continues in another, and ends with a human handoff to reveal which context survives each transition.\n\n### What to check / Ask this / Red flag\n\n| What to check | Ask this | Red flag |\n|---|---|---|\n| Integration | Which systems can the agent read from and write to for this workflow? | A pilot depends on replacing core infrastructure |\n| Compliance | What BAA, safeguards, reports, certifications, and audit evidence apply to our deployment? | A generic HIPAA claim with no supporting detail |\n| Channel coverage | Does context carry from chat to voice to SMS and email? | Each channel opens a separate conversation history |\n\n## Does conversational AI replace clinical judgment or your front-desk team?\n\nConversational AI does not replace clinical judgment or your front-desk team. Its appropriate role is to support **administrative workflows**, route requests, surface approved information, and escalate work that requires clinical or human judgment.\n\nDiagnosis, treatment recommendations, and emergency decisions belong with qualified professionals and established clinical workflows. A sound deployment defines these boundaries in policy, tests them before launch, monitors live conversations, and makes escalation easy for patients and staff.\n\nFront-desk and care teams retain ownership of complex cases, sensitive conversations, and exceptions. The technology gives them more capacity by handling repetitive access and coordination work, with the prior exchange attached when a request reaches a person.\n\n## In closing\n\nConversational AI in healthcare can turn booking, insurance questions, and follow-up into a connected administrative journey. A strong selection process tests the integrations, safeguards, channel continuity, and escalation model against the systems your organization already runs.\n\n[Download Delight.ai's healthcare AI agent playbook](https://delight.ai/resources/healthcare-ai-agents) for more use cases and deployment guidance across the patient journey.\n\n- [What is conversational AI in healthcare?](#section-anchor-id-0 "What is conversational AI in healthcare?")\n- [How does conversational AI support patients from booking to follow-up?](#section-anchor-id-1 "How does conversational AI support patients from booking to follow-up?")\n- [What are the benefits of conversational AI in healthcare?](#section-anchor-id-2 "What are the benefits of conversational AI in healthcare?")\n- [What should you look for when evaluating a conversational AI vendor?](#section-anchor-id-3 "What should you look for when evaluating a conversational AI vendor?")\n- [Does conversational AI replace clinical judgment or your front-desk team?](#section-anchor-id-4 "Does conversational AI replace clinical judgment or your front-desk team?")\n- [In closing](#section-anchor-id-5 "In closing")\n\n## Frequently Asked Questions\n\n### Is conversational AI in healthcare HIPAA-compliant?\n\n**HIPAA compliance** for conversational AI in healthcare depends on the vendor, deployment, data flow, safeguards, and contracts. HHS does not certify specific technology products, so a buyer should verify the BAA, access controls, encryption, risk analysis, incident procedures, audit evidence, and responsibilities assigned to each party. Independent reports and certifications strengthen the review as supporting evidence within deployment-specific due diligence.\n\n### What's the difference between a healthcare chatbot and conversational AI?\n\nThe difference between a healthcare chatbot and conversational AI lies in how each system interprets and manages a request. A rules-based chatbot follows predetermined choices or decision trees. Conversational AI uses **natural language processing** to identify intent from varied phrasing, retrieve approved information, and route the exchange through a more flexible workflow.\n\n### How long does it take to implement conversational AI in a clinic or health system?\n\nImplementation time for conversational AI in a clinic or health system depends on workflow scope, **integration depth**, data readiness, security review, and testing. After-hours appointment coverage usually has a smaller scope than a deployment spanning scheduling, insurance, billing, and follow-up. Vendors should provide a timeline tied to named systems, approval steps, and launch criteria for the proposed use case.\n\n### Can conversational AI support patients in multiple languages?\n\nConversational AI can support patients in **multiple languages**, with coverage varying by vendor, channel, and deployment. Evaluation should test the languages your patient population uses, including medical and administrative terminology, fallback behavior, and handoff quality. A language listed on a product page still needs practical validation with representative patient requests.\n\n### What happens when the AI can't resolve a patient's request?\n\nWhen the AI cannot resolve a patient's request, it should route the exchange to the appropriate person with the conversation history and relevant context attached. Escalation rules should account for:\n\n- **Complexity**\n- **Sensitivity**\n- **Low confidence**\n- **Policy boundaries**\n- **Urgent language**\n\nTeams should test those triggers and confirm that patients receive a **clear next step** during the handoff.\n\n## Keep reading\n\n[View more](/blog "View more")\n\n[\n\nThe Trust Stack: AI Agent Governance and Earned Autonomy](/blog/ai-agent/the-trust-stack-how-to-give-an-ai-agent-more-autonomy-without-losing-control)\n\n[\n\nWhat is a hotel chatbot? Overview, use cases, and more](/blog/ai-for-cx/hotel-chatbot-ai-concierge)\n\n[\n\nWhat is an AI shopping assistant? How top options compare](/blog/ai-for-cx/ai-shopping-assistant-comparison)\n\n[\n\nWhat Is a Healthcare Virtual Assistant? A Leader's Guide](/blog/ai-agent/healthcare-virtual-assistant)\n\n[View more](/blog "View more")\n\n### Ready to unleash the power of AI customer service agents?\n\n[Contact sales](/contact-sales)\n
## AI customer onboarding software for B2B SaaS in 2026
Source: https://delight.ai/blog/insights/customer-onboarding-software
[Mike Bohnett — CRO](https://delight.ai/author/mike-bohnett)
August 18 2026
## Key takeaways
- **B2B onboarding stalls at a few predictable moments**, such as a team invited with view-only access, a first API call that fails, or 48 quiet hours after signup, and most onboarding software only records them.
- **Delight.ai's Onboarding Actionbook starts on those signals**, branches by role, and either fixes the blocker with an approved action or hands it to a person with the diagnosis attached.
- **The fastest way to judge any AI customer onboarding tool**, ours included, is to stage those stalls in a demo and watch what the agent does.
We build AI agents for B2B software companies, and onboarding is one of the clearest tests of what an agent can do. A project tracker can tell you that the "invite your team" task is complete. It can't tell you that the 5 people invited are staring at an empty dashboard because they were added as Viewers, and it can't change that for them.
If you're evaluating SaaS customer onboarding tools, the useful questions are where setup stalls, what an agent should do about each stall, and how to test that before you buy. Our [AI CX agent](https://delight.ai/ai-cx-agent) answers those questions one way for [B2B SaaS teams](https://delight.ai/industries/technology), and the 8 customer onboarding platforms compared further down answer them in others.
## What is AI customer onboarding?
**AI customer onboarding uses an AI agent to spot where a new B2B customer is stuck in setup and finish that step with them, inside the conversation.** It sits alongside customer onboarding software, which plans and tracks the work, and it differs from fintech onboarding, where the term usually means identity checks and KYC.
## Where does B2B customer onboarding stall?
Onboarding rarely fails in one dramatic moment. It stalls at a handful of predictable points, and each one looks like silence from the customer's side:
- **Workspace setup** stops halfway when the admin can't finish a setting like single sign-on and nobody else on their team owns it.
- **Invites and permissions** go wrong when a new admin adds the team and colleagues log in to an empty dashboard because they only have view access.
- **The first integration** breaks when a developer hits an authentication error on day 2 and parks the task behind other work.
- **A quiet week** follows signup, when no one on the customer side logs in and the account drifts before anyone notices.
Each of those stall points is an event a system can detect, so customer onboarding automation should be tied to the event rather than to a calendar. A message that arrives the day an integration fails, with the fix attached, lands very differently than a generic check-in email at day 14.
The channel deserves as much thought as the timing. For B2B buyers, in-app messages and email usually fit onboarding better than calls or texts, since the person setting up the account is typically at a desk inside the product.
## How does Delight.ai's Onboarding Actionbook work?
We handle AI customer onboarding with a set of instructions that tells the agent when to step in, what to check, and which actions it may take. Operations teams write and approve those steps before anything goes live, and that reviewed set of instructions is what we call an Actionbook. The Onboarding Actionbook below comes from our [B2B AI agent playbook](https://delight.ai/ebooks/b2b-ai-agents).
### When does the Onboarding Actionbook start?
The Actionbook starts when a customer shows a specific setup signal, not on a fixed schedule:
- **Incomplete workspace setup**, such as an account that's mostly configured but still missing single sign-on.
- **A question about team invites or permissions**, asked in chat or by email.
- **A request for help with a data connection or integration.**
- **48 or more hours of onboarding inactivity**, which prompts proactive outreach before the account goes cold.
### What steps does the agent follow?
Once the Actionbook starts, the agent works through 3 steps in order:
1. **Check onboarding status** to find the customer's current stage and what's blocking it.
2. **Branch by role**, guiding an admin through workspace configuration, giving a developer the API quickstart, or walking an end user through the feature they're trying to use.
3. **Diagnose from the setup logs** when a step is stuck, then resolve it with an approved action or escalate it to a person with the context attached.
Because the [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) carries context across channels, a developer who starts in chat and follows up by email 2 days later picks up where the conversation left off.
### What does the agent need connected?
Each step depends on a system the agent can read from or act in, so the Actionbook is only as useful as the connections behind it. Checking status needs your onboarding milestones or account setup state, branching by role needs the user's role and the right guide for it, and diagnosing needs setup and API logs plus permission to change approved settings such as user roles. Escalation needs your help desk, so the transcript and diagnosis reach the person who picks it up and nobody has to ask the customer again.
### What does a blocked setup look like from start to finish?
Two examples from the playbook show the difference between tracking a stall and clearing it.
**An admin's team can't see the dashboard.** A new admin writes in to say the 5 people they invited can't see the dashboard. The agent checks permissions, finds the team set as Viewers, explains that Viewer access excludes the dashboard, and changes them to Editors once the admin agrees, then offers to build the first report together.
**A developer's integration starts failing.** A developer reports 401 errors on the users endpoint the day after a product update. The agent checks the API logs, finds the request still using a deprecated API-key header that the new version replaced with a Bearer token, and gives the exact header change with a link to the migration guide.
In both cases, a project tool would have shown the task as done or in progress while the customer sat blocked.
## When should an AI onboarding agent hand off to a person?
An AI onboarding agent should hand off whenever setup needs a judgment call, a commercial decision, or access the agent wasn't granted. A contract question, a custom data migration, or a frustrated champion all call for a person, and the handoff should arrive with the full conversation and diagnosis attached so nobody asks the customer to start over.
The controls around the agent decide how much it can do on its own. On Delight.ai, [Trust OS](https://delight.ai/trust-os) gives teams those controls:
- **Reviewed Actionbooks** mean every action the agent can take was approved by a person before it went live.
- **Staged testing** across development, staging, and production lets a Test Center confirm the agent completed the right action before customers see a change.
- **Role-based access control** sets who can view, edit, or deploy the agent's configuration.
- **An audit log** records every configuration change, and an activity log traces the reasoning behind each AI decision.
## How do you test an AI onboarding agent before you buy?
The most reliable way to test an AI onboarding agent is to stage the stalls your customers hit and watch what the agent does in your own systems. Feature lists and slide decks can't show that, so the table below turns each stall into a demo script you can run with any vendor, including us.
| Test | How to stage it | What a pass looks like |
|---|---|---|
| Permissions | Invite 3 test users with view-only access, then ask the agent why they can't see the dashboard | It names the role problem, proposes the change, asks for approval, and logs the action |
| Broken integration | Send API calls with an expired or outdated credential, then ask why they fail | It reads your actual error and configuration and gives a fix specific to your setup |
| Quiet account | Leave a test account idle for 48 hours with one setup step unfinished | It reaches out in-app or by email about that exact step, not with a generic check-in |
| Channel switch | Start a setup question in chat and follow up by email the next day | It picks up the thread without asking you to repeat anything |
| CRM write-back | Close a test deal in your CRM, then complete a setup task | The CRM record updates on its own, with no re-entry by hand |
| Handoff | Ask a contract or pricing question mid-setup | It routes to a person in your existing desk with the transcript and diagnosis attached |
| Audit trail | Ask the vendor to show the log entry for the permissions change | You see who approved the action, what changed, and when |
If a vendor can't run these live in a sandbox connected to your stack, the AI in their pricing page isn't ready for your onboarding yet.
## How do you measure AI customer onboarding?
You measure AI customer onboarding by whether customers reach first value faster and with fewer stalls, not by how many messages the agent sends. The table below shows which metrics answer that question and what each one tells you.
| Metric | What it measures | What it tells you |
|---|---|---|
| Time to first value | Days from contract or signup to the customer's first meaningful outcome in the product | Whether onboarding is getting faster overall |
| Setup completion rate | Share of new accounts that finish the required setup steps | Whether customers get through the steps that matter |
| Activation rate | Share of new users who complete the product's key action, such as a first report or first integration | Whether setup turns into real use |
| Stall recovery rate | Share of stalled accounts that resume after outreach | Whether event-triggered outreach is working |
| Resolution rate | Share of onboarding requests resolved without a handoff | How much setup work the agent completes on its own |
| Escalations with context | Share of handoffs that arrive with the diagnosis attached | Whether people pick up the work or restart it |
Containment rate and resolution rate measure different things, so it helps to track both. An agent can keep a customer in the chat without fixing the setup problem, which looks fine on containment and still leaves the account stuck.
## Customer onboarding software compared
Most teams still need customer onboarding software alongside an agent, because a project platform remains the source of truth for milestones, portals, and in-app guides. Several of these platforms now ship AI of their own, such as Rocketlane's [Nitro agents](https://www.rocketlane.com/pricing), GuideCX's [AI forecasting and dispatching](https://www.guidecx.com/products/features/ai-customer-onboarding/), and OnRamp's [Aero agents](https://onramp.ai/onramp-launches-aero), and those capabilities stay centered on the projects each platform manages.
**Shortlist**
- [**Delight.ai**](https://delight.ai/) owns the customer conversation, completing onboarding and setup work across sessions and connected systems.
- [**Rocketlane**](https://www.rocketlane.com/) treats onboarding as professional-services delivery, pairing project and resource tools with Nitro AI agents.
- [**GuideCX**](https://www.guidecx.com/) standardizes implementation with shared plans, and its forecasting flags risk before it spreads.
- [**OnRamp**](https://onramp.ai/) turns onboarding into a workspace with repeatable playbooks, CRM-triggered projects, and embedded AI agents.
- [**Dock**](https://www.dock.us/) centralizes the customer view, so plans, files, and status all live in one branded portal.
- Small and mid-size teams pick [**Onboard.io**](https://onboard.io/) for its dedicated workspace and transparent pricing.
- Product-led teams lean on [**Appcues**](https://www.appcues.com/) to build personalized in-app onboarding and lifecycle moments.
- [**Pendo**](https://www.pendo.io/) puts product analytics and in-app guidance under one roof.
| Platform | Channels or workflow depth | Integrations | Pricing | Best fit |
|---|---|---|---|---|
| Delight.ai | Chat, voice, SMS, email, WhatsApp, and in-app with persistent context | Salesforce, Zendesk, Freshworks, ServiceNow, Sprinklr, knowledge sources, and APIs | Contact sales; Desk has a $0 seat fee and per-successful-resolution billing | Customer-facing setup work that spans conversations and systems |
| Rocketlane | Projects, client portal, resources, time, and financials | HubSpot, Jira, Slack, Salesforce, Workato, Snowflake, Zapier, and API access by plan | $49 or $69 per team member per month annually; Enterprise and Nitro quote-based | Structured implementations and professional-services delivery |
| GuideCX | Templates, tasks, milestones, reporting, and shared customer views | CRM and workflow integrations, open API, and MCP-ready access | Contact sales | Repeatable implementation projects with operational controls |
| OnRamp | Playbooks, guided tasks, portal, status, files, messaging, and reminders | Salesforce, HubSpot, Slack, Gainsight, Zapier, and an open API | Starts at $15,000 | Standardized onboarding with embedded AI engagement |
| Dock | Portals, plans, files, learning, order forms, and automation | HubSpot and Salesforce on paid plans; API on Enterprise | Free; $350 Standard; $1,000 Premium; custom Enterprise | A branded customer destination for onboarding and enablement |
| Onboard.io | Templates, tasks, portal, discussions, reports, and projects | Salesforce, HubSpot, Slack, and Zapier from Essentials; REST API on Pro | $25 Starter; $300 Essentials; $1,000 Pro per month | Small and mid-size teams seeking transparent pricing |
| Appcues | Web and mobile flows, checklists, tooltips, messages, and surveys | Product, analytics, CRM, and engagement integrations | Prices not published; MAU, installations, and published experiences shape price | Product-led onboarding and lifecycle engagement |
| Pendo | Analytics, guides, replay, feedback, and orchestration | CRM, support, marketing, collaboration, and analytics tools | Free up to 500 MAUs; custom paid plans | Analytics-led product adoption and in-app guidance |
### Delight.ai: the AI agent for onboarding and setup workflows
[Delight.ai](https://delight.ai/ai-agent) is a customer-facing AI agent for B2B SaaS teams that need setup work completed across conversations and connected systems. Teams build the agent's Actionbooks in a plain-language [builder](https://delight.ai/builder), [Agent Steward](https://delight.ai/autonomous-agent) owns multi-step cases, and Trust OS adds testing and oversight. Pricing for the AI agent platform is through sales, and [Delight Desk](https://delight.ai/desk) has a $0 seat fee with billing tied to successful AI resolutions.
Sendbird, Delight.ai's parent company, holds a 4.6/5 rating on [G2](https://www.g2.com/products/sendbird/reviews), and in an April 2026 support deployment, Mixpanel removed about 10 minutes of manual account verification per support interaction.
### Rocketlane: professional-services onboarding platform
[Rocketlane](https://www.rocketlane.com/) is a professional-services automation platform for teams running structured, milestone-based implementations. It centers onboarding on project delivery and a branded client portal, and its Nitro agents handle meeting recaps, governance risk flags, and, on Enterprise, migration work. Standard and Premium cost $49 and $69 per team member per month billed annually with a 5-member minimum, and its HubSpot connection ships on Standard while Salesforce waits for Premium.
Rocketlane holds a 4.7/5 rating from [878 G2 reviews](https://www.g2.com/products/rocketlane-corp/reviews), and [Navix](https://www.rocketlane.com/customers/how-navix-switched-from-monday-com-to-deliver-faster-more-configurable-customer-implementations) cut its implementations from 3 to 5 months to 4 to 8 weeks.
### GuideCX: project-based onboarding and implementation management
[GuideCX](https://www.guidecx.com/) is an implementation management platform for teams standardizing onboarding around repeatable plans and project controls. It runs templated implementation plans with customer collaboration and reporting, its AI features forecast completion dates and flag project warnings, and it pushes onboarding data back into the CRM. All plans are priced through sales.
On G2, GuideCX averages 4.6/5 across [462 reviews](https://www.g2.com/sellers/guidecx), and a December 2025 case study shows [Kount](https://www.guidecx.com/resource-center/case-studies/kount-cuts-onboarding-and-increases-capacity/) cutting its average implementation time by more than 43%.
### OnRamp: repeatable onboarding workflow automation
[OnRamp](https://onramp.ai/) is an onboarding and engagement platform for teams scaling repeatable playbooks with a customer portal and CRM-triggered projects. Status, files, and messages live in one guided task list, and its Aero agents, launched May 27, 2026, monitor engagements and flag risk. Pricing [starts at $15,000](https://onramp.ai/pricing).
OnRamp holds a 4.4/5 rating from [202 G2 reviews](https://www.g2.com/products/onramp-technology/reviews), and [A2Z](https://onramp.ai/a2z-customer-story) cut its time to activation by 75%, from more than 90 days to 22.
### Dock: client-facing onboarding portal
[Dock](https://www.dock.us/) is a client workspace for revenue and customer-success teams that want one branded home for plans, content, learning, files, and progress. Workspaces sync with Salesforce and HubSpot on paid plans, and an [AI agent builder](https://www.dock.us/library/agent-builder) arrived in June 2026 on Premium and Enterprise. [Dock's plans](https://www.dock.us/pricing) run from free (10 workspaces) to $350 a month for Standard and $1,000 a month billed annually for Premium, with custom Enterprise pricing.
On G2, Dock averages 4.6/5 across [481 reviews](https://www.g2.com/sellers/dock), and its [Loom story](https://www.dock.us/library/customer-story-loom) (2023, updated January 2026) reports about 2 hours saved per customer onboarding launch.
### Onboard.io: branded client onboarding workspace
[Onboard.io](https://onboard.io/) is a client onboarding workspace for small and mid-size B2B SaaS teams that want clear entry pricing. It pairs templates and task management with a customer portal that customers enter through magic links, so they never need a paid seat. [Pricing](https://onboard.io/pricing) is $25 a month for Starter (1 paid seat), $300 for Essentials (5 seats), and $1,000 for Pro (10 seats), with 10% off for annual prepayment.
Onboard.io holds a 5.0/5 rating from [48 G2 reviews](https://www.g2.com/sellers/onboard-software-inc), and [Curve Dental](https://onboard.io/case-studies/curve-dental) reported about a 90% year-over-year drop in onboarding-attributed churn.
### Appcues: in-app onboarding with an AI experience layer
[Appcues](https://www.appcues.com/) is an in-product adoption platform for product-led teams building onboarding, announcements, surveys, and lifecycle experiences inside web and mobile apps. Nontechnical teams build flows, checklists, and tooltips, and the Appcues 2.0 release in July 2026 added AI-assisted building. Appcues doesn't publish prices for its Start, Grow, and Enterprise plans, which are [priced on](https://www.appcues.com/pricing) monthly active users, installations, and published experiences.
On G2, Appcues averages 4.6/5 across [359 reviews](https://www.g2.com/sellers/appcues), and [MYOB](https://www.appcues.com/customer-stories/how-myob-increased-new-user-activation-by-21-with-thoughtful-personalized-onboarding) increased new-user activation by 21% with it.
### Pendo: in-product onboarding and engagement platform
[Pendo](https://www.pendo.io/) is a product experience and analytics platform for teams combining usage analytics, in-app guidance, session replay, and feedback. It joins product analytics with in-app guides, and its AI features surface patterns in usage data. [Pendo Free](https://www.pendo.io/pricing/) covers up to 500 monthly active users, and the Base, Core, and Ultimate plans are priced on request.
Pendo holds a 4.4/5 rating from [1,821 G2 reviews](https://www.g2.com/sellers/pendo-io), and an August 2025 case study shows [UserTesting](https://www.pendo.io/customers/how-usertesting-revolutionized-onboarding-and-engagement-with-pendo/) increasing onboarding completion by 29%.
## The right platform follows the work
Onboarding stalls between steps, so the tool that helps most is the one that's present when a customer gets stuck. Project-led teams can choose a dedicated implementation system, product-led teams can prioritize in-app guidance, and relationship-led teams may begin with a shared portal. For B2B SaaS teams that want an agent to clear those stalls across systems, the [B2B AI agent playbook](https://delight.ai/ebooks/b2b-ai-agents) walks through the full Onboarding Actionbook and 4 more examples, from technical support to churn prevention.
- [What is AI customer onboarding?](#section-anchor-id-0)
- [Where does B2B customer onboarding stall?](#section-anchor-id-1)
- [How does Delight.ai's Onboarding Actionbook work?](#section-anchor-id-2)
- [When should an AI onboarding agent hand off to a person?](#section-anchor-id-3)
- [How do you test an AI onboarding agent before you buy?](#section-anchor-id-4)
- [How do you measure AI customer onboarding?](#section-anchor-id-5)
- [Customer onboarding software compared](#section-anchor-id-6)
- [The right platform follows the work](#section-anchor-id-7)
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Frequently asked questions
Common questions about choosing customer onboarding software for B2B SaaS teams.
### How does AI improve customer onboarding?
AI improves customer onboarding by catching stalls as they happen and resolving them inside the conversation. Instead of waiting for a scheduled check-in, an AI agent can notice an incomplete setup step, a permissions question, or a quiet week, then guide the right person through the fix or complete an approved action for them. The result is a shorter path to first value, and the people on the onboarding team spend their time on judgment calls rather than routine setup questions.
### Can an AI agent complete onboarding tasks, or does it only guide users through them?
An AI agent can complete onboarding tasks when it's connected to the right systems and given permission to act. Delight.ai uses Actionbooks to define approved actions, such as changing a team member's access level, and Agent Steward to own multi-step cases. Rocketlane's Nitro agents, GuideCX's AI dispatching, and OnRamp's Aero agents focus on projects and operations, so a live workflow test is the clearest way to confirm how much each agent completes.
### Will AI customer onboarding feel impersonal?
AI customer onboarding doesn't have to feel impersonal, because the agent handles routine setup and hands the high-stakes moments to a person. A good setup answers a permissions question in seconds, remembers what the customer already tried, and escalates a contract question or a frustrated champion with the full context attached. Customers get faster help on simple steps, and the onboarding team gets more time for the conversations that build the relationship.
### Is customer onboarding software the same as a digital adoption platform?
Customer onboarding software and digital adoption platforms overlap across in-product guidance. A digital adoption platform such as [Whatfix](https://whatfix.com/) adds step-by-step flows, smart tips, and pop-ups inside live applications, while customer onboarding software can also manage implementation projects, client portals, files, milestones, and cross-team handoffs. Appcues and Pendo sit closest to the digital adoption side of this list.
### How much does customer onboarding software cost?
Customer onboarding software ranges from free entry plans to custom enterprise contracts. Dock offers a free plan with 10 workspaces, Onboard.io starts at $25 a month, Rocketlane starts at $49 per team member per month with a 5-member minimum, and OnRamp starts at $15,000, while GuideCX, Appcues, and paid Pendo plans don't publish prices. Before comparing prices, it's worth checking how many users the base plan covers, whether implementation and integrations cost extra, and whether AI usage is billed separately.
### What's the best customer onboarding software for B2B SaaS companies?
The best customer onboarding software for a B2B SaaS company matches its dominant onboarding motion. Rocketlane, GuideCX, and OnRamp fit structured implementations, Dock and Onboard.io fit shared customer workspaces, and Appcues and Pendo fit product-led adoption. Delight.ai is the recommended choice when onboarding depends on continuous customer conversations, persistent context, and governed actions across connected systems.
## Keep reading
[View more](/blog)
[Getting started with voice AI for home services](/blog/industry/voice-ai-appointment-booking-home-services)
[How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[View more](/blog)
## What vendor consolidation means for the modern customer service stack
Source: https://delight.ai/blog/industry/vendor-consolidation-customer-service
## What vendor consolidation means for the modern customer service stack
[](https://delight.ai/author/mike-bohnett)
[Mike Bohnett — CRO](https://delight.ai/author/mike-bohnett)
August 21 2026
- **Vendor consolidation reduces overlapping suppliers.** Customer service teams often accumulate separate vendors for chat, SMS, voice, and agent assistance, even when those tools serve the same customers.
- **Customer effort exposes vendor sprawl.** Count the systems a customer's question crosses before your team can resolve it.
- **A unified AI customer service platform connects memory, channels, and governance.** That shared foundation can replace several disconnected point solutions.
Vendor consolidation reduces the number of suppliers a company uses for overlapping work. Customer service teams are especially prone to vendor sprawl because they may buy separate tools for chat, SMS, voice, and agent assistance. [Gartner found that 91% of customer service and support leaders felt executive pressure to implement AI in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). When teams respond with isolated purchases, each new tool adds its own contract to renew, its own data boundary to secure, and its own workflow for a new team to learn, overhead that usually doesn't surface until the next renewal or security review.
For enterprise customer experience (CX), IT, and procurement leaders, vendor consolidation changes both cost and service delivery. The goal is to reduce overlap while preserving the systems your operation still needs. A unified [AI customer experience platform](https://delight.ai/ai-customer-experience-platform) can bring customer memory, channel coverage, and AI governance into one operating layer.
## What is vendor consolidation?
Vendor consolidation is the process of moving overlapping products or services to a smaller group of capable suppliers. The result is fewer software licenses to track, fewer contracts to renew, and fewer vendor relationships for finance, legal, and IT to manage day to day. In customer service, consolidation may replace separate chat, voice, SMS, and AI-assistance vendors with one platform that shares context across those functions.
Customer service vendor sprawl usually grows through a series of reasonable, disconnected decisions. A support team selects a chatbot, a marketing team signs an SMS platform, and a regional office adds a voice tool because the primary contract doesn't cover its market.
After several buying cycles, the company is managing a fragmented stack that no single team designed.
## What are the signs your customer service stack has too many vendors?
Your customer service stack has too many vendors when fragmentation creates avoidable work for customers, employees, or governance teams. The clearest signal is the number of systems a customer's question must cross before your team can resolve it.
### Different vendors for chat, voice, and SMS
Separate providers for chat, voice, and SMS often force customers to repeat information when they switch channels. The phone agent may have no visibility into the earlier chat, leaving the customer to reconstruct the conversation while the company pays multiple vendors to support one journey.
### No shared memory across tools
Disconnected tools rarely carry a customer's full context between them, even when basic integrations move selected fields or transcripts. [Forrester describes enterprise SaaS sprawl as hundreds of applications that don't communicate with one another](https://www.forrester.com/blogs/saas-as-we-know-it-is-dead-how-to-survive-the-saas-pocalypse/). Customer service teams feel that gap immediately because context determines whether the next person or system can continue the conversation.
### Procurement stalling deals over vendor count
Another point solution can create another security review, data-processing agreement, renewal, and budget request. As the vendor list grows, procurement and IT security teams have more third parties to assess and more dependencies to govern, which can slow approvals for new capabilities.
### Duplicate reporting and reconciliation work
Each vendor may use its own dashboard, export format, and definition of a resolved conversation. CX and analytics teams then have to reconcile multiple datasets before they can answer basic performance questions or compare results across channels.
## What are the benefits of consolidating customer service vendors?
Consolidating customer service vendors can reduce administrative work and create a more continuous customer experience. The strongest gains come from removing overlapping contracts, data boundaries, and channel-specific workflows.
### Fewer contracts, more negotiating leverage
[Forrester recommends reducing SaaS vendors to a set of strategic partners](https://www.forrester.com/blogs/saas-as-we-know-it-is-dead-how-to-survive-the-saas-pocalypse/) as companies rework their software investments for AI. Concentrating spend can reduce renewal negotiations and repeated security assessments while giving priority partners a larger share of the relationship.
### One customer experience across every channel
A shared platform can preserve the customer's context as a conversation moves between channels. hipages, an on-demand marketplace serving more than 4 million Australians, chose a unified Sendbird stack built on Delight.ai, Sendbird Chat, and Salesforce Connector. Its CTO, Jeremy Burton, explained the buying requirement clearly.
> "We weren't looking for a stack of point solutions. We needed a single, cohesive platform that could support the entire customer journey end to end," said Jeremy Burton, CTO of hipages ([hipages customer story, 2026](https://delight.ai/customers/hipages)).
That continuity gives customers a familiar experience and gives support teams the context to continue the work without another introduction.
## How does one AI platform replace a stack of customer service point solutions?
One AI platform can replace several customer service point solutions when it provides shared memory, cross-channel delivery, and centralized governance. Those capabilities let teams manage the customer relationship as one operating system while retaining integrations with systems of record.
### Memory that carries across every tool
A shared memory layer combines conversation history with account and business context so every interaction starts informed. Delight.ai's [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) unifies structured data, such as account records, with conversational signals in a customer profile that develops over time.
Mixpanel's support agents once spent the first 10 minutes of each interaction identifying the customer, checking account status, verifying permissions, and reviewing the implementation. With Delight.ai, authentication and account context are available before the response, removing that manual investigation ([Mixpanel customer story, 2026](https://delight.ai/customers/mixpanel)).
### One shared presence across chat, voice, SMS, and email
Delight.ai's [Omnipresence](https://delight.ai/omnipresent) carries customer context across web and in-app chat, SMS, email, voice, and WhatsApp. A customer can change channels and continue the same conversation. Burton described the operational effect at hipages: "Our agents never ask for context anymore—because they already have it. That alone changed our entire support operation" ([hipages customer story, 2026](https://delight.ai/customers/hipages)).
### One governance layer for every AI decision
Centralized governance gives teams one place to test AI behavior, review decisions, set escalation controls, and trace conversations. Delight.ai's [Trust OS](https://delight.ai/trust-os) provides observability, oversight, and control across the AI agent lifecycle, including logged decisions and tool calls, automated content detection, and human escalation controls.
### A fragmented stack versus a consolidated one
| Fragmented stack (several point solutions) | Consolidated platform (one system) |
|---|---|
| Customer memory: Resets or requires manual transfer at a channel switch | Customer memory: Carries across chat, voice, SMS, email, and WhatsApp |
| Oversight: Separate controls and activity records by vendor | Oversight: Shared governance, controls, and traceability |
| Reporting: Metrics and definitions vary by tool | Reporting: Shared reporting across the service operation |
| Renewals and security reviews: Repeated for each vendor | Renewals and security reviews: Managed across fewer strategic relationships |
## What are the risks of vendor consolidation?
Vendor consolidation introduces concentration, migration, and negotiating risks that require deliberate controls. Data portability, current security evidence, staged implementation, and a documented fallback path can reduce those risks before the company commits.
### Vendor lock-in and over-reliance
Concentrating workflows and data with one provider can make a future move more complex. Buyers should confirm contract terms, data ownership, export formats, integration dependencies, and configuration portability during evaluation, then document how the organization would transition if service quality or business needs changed.
### Compliance and security gaps during the switch
Migration can expose security and compliance gaps if teams move customer data before they validate controls and responsibilities. In McKinsey's 2026 AI Trust Maturity Survey, [nearly two-thirds of respondents identified security and risk concerns as the leading barrier to scaling agentic AI](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era). A consolidated platform places more customer interactions under one provider's [security](https://delight.ai/security) program, so current evidence deserves close review.
The review should cover at least these areas:
- **SOC 2 Type II** evaluates whether the relevant controls operated effectively over a defined period, within the [AICPA's System and Organization Controls framework](https://www.aicpa-cima.com/resources/landing/system-and-organization-controls-soc-suite-of-services).
- **ISO 27001** certifies an information security management system against [the standard's requirements](https://www.iso.org/standard/27001).
- **HIPAA evidence** should specify the report or attestation scope when customer interactions may include [protected health information](https://www.hhs.gov/hipaa/for-professionals/covered-entities/index.html).
Delight.ai's current approved certification set includes SOC 2 Type II, ISO 27001:2022, and a HIPAA Type I report. Buyers can review the public security summary and request the underlying evidence through the [Sendbird Trust Center](https://trust.sendbird.com/).
### Losing negotiating leverage by over-consolidating
Heavy concentration can reduce competitive pressure at renewal. A documented fallback path, periodic market checks, and clear service-level commitments help preserve leverage after a company standardizes on its primary platform.
## Fewer vendors, one system to hold accountable
Vendor consolidation works when it removes fragmented ownership from the customer journey. Customers keep their context across channels, and your teams gain a shared system for service delivery, governance, and measurement. [Request a walkthrough](https://delight.ai/contact-sales) to see how Delight.ai can consolidate customer service workflows around your conversations, policies, and existing systems.
- [What is vendor consolidation?](#what-is-vendor-consolidation)
- [What are the signs your customer service stack has too many vendors?](#what-are-the-signs-your-customer-service-stack-has-too-many-vendors)
- [What are the benefits of consolidating customer service vendors?](#what-are-the-benefits-of-consolidating-customer-service-vendors)
- [How does one AI platform replace a stack of customer service point solutions?](#how-does-one-ai-platform-replace-a-stack-of-customer-service-point-solutions)
- [What are the risks of vendor consolidation?](#what-are-the-risks-of-vendor-consolidation)
- [Fewer vendors, one system to hold accountable](#fewer-vendors-one-system-to-hold-accountable)
## Frequently asked questions
**Does vendor consolidation mean picking one vendor for everything?**
Vendor consolidation doesn't require one vendor for every business system. In customer service, it usually means reducing overlapping point solutions for chat, voice, SMS, email, help desk workflows, and AI orchestration. A consolidated platform can continue connecting to customer relationship management (CRM), enterprise resource planning (ERP), payments, identity, and other systems of record.
**How long does it take to consolidate onto a new customer service platform?**
The time required to consolidate onto a customer service platform depends on channel scope, integrations, data migration, testing, and governance requirements. Delight.ai customer gther launched its AI chatbot in 1 day for a focused use case, and Norse Atlantic Airways replaced its legacy chatbot in under 2 months for a broader deployment. These examples illustrate the range, and they don't establish a guaranteed timeline ([gther customer story, 2025](https://delight.ai/customers/gther); [Norse Atlantic Airways customer story, 2026](https://delight.ai/customers/norse-atlantic-airways)).
**What happens to our existing ticket history when we consolidate?**
Existing ticket history can move with the service operation when the migration path supports it. Delight.ai's [one-click Zendesk migration](https://delight.ai/zendesk-migration-desk) transfers tickets, history, knowledge base content, macros, users, and custom fields. It can also analyze historical resolutions and draft Actionbooks for human review, giving the AI agent workflows based on the team's prior work.
**Is vendor consolidation only about saving money?**
Vendor consolidation can save money and improve operational consistency. Fewer contracts and reviews reduce administrative work, while shared customer context, governance, and reporting can reduce repeated questions, manual reconciliation, and channel-specific handoffs.
**What should we ask any AI vendor before consolidating onto their platform?**
Ask AI vendors how they handle portability, security, governance, measurement, and implementation. Strong answers should include evidence and named responsibilities:
- What data, configurations, and activity records can we export if the relationship ends?
- Which compliance certifications and reports are current, and what scope does each cover?
- How can our team review, test, pause, and escalate AI decisions?
- How will the vendor migrate our history, integrations, and workflows?
- Which named, dated customer results support the vendor's performance claims?
## Keep reading
[View more](/blog "View more")
[](/blog/ai-agent/warranty-claims-management-software) Best warranty claims management software for administrators
[](/blog/industry/what-is-unified-commerce) What is unified commerce, and why does it matter for high-consideration retailers?
[](/blog/ai-for-cx/enterprise-conversational-ai) Enterprise conversational AI: What to look for before you buy
[](/blog/insights/customer-onboarding-software) Best Customer Onboarding Software for B2B SaaS in 2026
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Best warranty claims management software for administrators
Source: https://delight.ai/blog/ai-agent/warranty-claims-management-software
## Best warranty claims management software for administrators
[ Shailesh Nalwadi — Head of product management](https://delight.ai/author/shailesh-nalwadi)
August 21 2026
## Key takeaways
- **Warranty claims management software centralizes how a claim gets opened, checked, routed, and closed out**, across everyone with a stake in it, from the administrator to the contractor, parts supplier, and sometimes an insurance adjuster.
- **This comparison covers 5 platforms serving distinct buyers**, from mid-market home warranty administrators to global manufacturers and service networks. They are [WarrantyHub](https://warrantyhub.com/), [Claimlane](https://www.claimlane.com/), [Syncron](https://www.syncron.com/solutions/warranty-management), [PTC Warranty](https://www.ptc.com/en/products/ptc-warranty), and [ServiceBench](https://www.asurion.com/servicebench/service-provider/).
- **Delight.ai is an AI coordination layer that sits on top of a claims platform.** It adds human-approved coordination for decisions involving money, coverage disputes, or policy exceptions to whichever of the 5 platforms above an administrator already runs.
An administrator, third-party administrator (TPA), or insurer relies on this software daily to open, validate, route, and close claims. For a [home warranty operator](https://delight.ai/home-services-and-warranty), resolution can involve a homeowner, contractor, parts supplier, and adjuster across several days or visits. The strongest setup combines a reliable claims system of record with timely coordination across every party.
## Warranty claims management software: the shortlist
Use the shortlist to match each platform with its clearest fit.
- **WarrantyHub** is the fastest path to a working system for mid-market home warranty administrators, with published pricing and a 30–60 day rollout; the enterprise platforms further down this list typically run multi-month timelines.
- **Claimlane's** home base is brands and retailers handling product returns and repairs. It shows up in this comparison because its self-service intake and supplier-collaboration workflow solves a similar problem from the retail side.
- **Syncron's** strength shows up once supplier recovery becomes the bottleneck. Large OEMs and dealer networks use it to claw back money from suppliers after a claim closes, on top of logging the claim itself.
- **PTC Warranty** makes the most sense for manufacturers who already run other operations on PTC's product lifecycle software, since its warranty module plugs directly into that same system.
- **ServiceBench (Asurion)**, the legacy option here, wins on footprint and cost. It's the platform behind Asurion's service-provider network, and its starting price undercuts every other platform in this list, though its interface and review scores show its age.
## What is warranty claims management software?
**Warranty claims management software centralizes how a claim against a product or service warranty gets opened, checked against coverage rules, routed to the right person, and closed out.** The exact workflow depends on the operator.
An OEM (original equipment manufacturer) may resolve a claim with a single payout or return authorization, while a home warranty administrator's version of resolved takes longer, since a contractor has to be scheduled, a part tracked to the house, the homeowner kept in the loop the whole time, and the closeout documented once the work is actually done.
[Gartner reported that 91% of service and support leaders surveyed faced executive pressure to implement AI in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). The same survey identified customer satisfaction, operational efficiency, and self-service success as top priorities. Warranty administrators can use those priorities to evaluate whether a platform improves the complete claim journey.
## Why claim coordination matters as much as claim tracking
Claim coordination deserves the same attention as claim tracking because each open claim creates dependencies across people, systems, and timelines. A status such as 'in progress' records the state of a claim, but the operator also needs to manage the work that moves it forward.
### Parts and supplier delays
A covered repair can stall when a contractor discovers that a required part is unavailable. The administrator needs an updated arrival estimate, a revised service date, and a homeowner notification tied to the same claim record. A timely update protects the service team from avoidable status calls and gives the homeowner a clear next step.
### Rescheduled appointment windows
Contractor schedule changes require a confirmed new appointment window and an immediate customer update. When the claims system, dispatch tools, and communication channels share current data, the administrator can keep the repair moving and avoid a chain of manual calls.
### Slow adjuster follow-up
High-value repairs and coverage questions often pause for an adjuster's decision, so a coordinated workflow needs to surface the pending approval, provide the relevant evidence, and alert the appropriate person before the delay affects the contractor's schedule or the homeowner's experience.
## The best warranty claims management software, compared
The comparison below uses vendor pages for what each platform claims to do, current public pricing for what it actually costs, and live G2 or Capterra data for how existing customers rate the day-to-day experience, backed by named customer results as proof a platform performs at real scale, the kind a demo alone can't show. Review counts remain small for several of these products, so buyer fit and workflow coverage deserve equal weight alongside the star rating.
### WarrantyHub
- **Best for:** Mid-market home warranty administrators and TPAs that want a modern platform with a published rollout target.
- **What it does:** WarrantyHub runs claims and policy management in one system, then layers on its own coordination features. A built-in contractor portal handles job updates, homeowner self-service tracking keeps customers informed, and automated notifications fire without a rep having to remember to send them.
- **Customer proof:** Its [Home Warranty Inc. story](https://warrantyhub.com/) reports 9.1% faster claims and about 30% more renewals after onboarding.
- **Capterra rating:** [5.0 out of 5 from 6 reviews](https://www.capterra.com/p/253580/BidBoxPro/).
- **Pricing:** [Essentials costs $549 per month and Professional costs $849 per month](https://warrantyhub.com/pricing/); Enterprise pricing is customized.
### Claimlane
- **Best for:** Brands and retailers that manage product warranty claims, repairs, and returns across direct-to-consumer and business channels.
- **What it does:** Claimlane lets a customer file a claim themselves, then routes it to the right supplier for collaboration on the resolution, whether that's a refund, a replacement, or a prepaid shipping label, with analytics and integrations tying the workflow back to the rest of the tech stack.
- **Customer proof:** Its [Sports Connection and Skechers story](https://www.claimlane.com/resources/case-studies/skechers-warranty-claims) reports an 80% reduction in claim processing time, from about 2 weeks to 1–2 days.
- **G2 rating:** [4.8 out of 5 from 13 reviews](https://www.g2.com/products/claimlane/reviews).
- **Pricing:** Claimlane's Core plan costs $800 (about €700) per month for up to 2,000 tickets a year; teams needing more volume or extra modules move to a custom-quoted plan.
### Syncron
- **Best for:** Large OEMs and dealer networks managing claims, approvals, payout timelines, and supplier recovery.
- **What it does:** Syncron standardizes how a claim moves from submission to approval, automating sign-off steps that used to need manual review, and it structures the supplier-recovery process so an OEM can claw back cost once the claim itself has been paid.
- **Customer proof:** [Master Group reduced approved-claim payout time from as long as 90 days to less than 24 hours](https://www.syncron.com/hubfs/Customer%20Stories%20-%20Master%20Group.pdf) and cut its backlog from more than 2,000 claims to fewer than 10.
- **G2 rating:** [4.3 out of 5 from 29 product reviews](https://www.g2.com/products/syncron-service-lifecycle-management/reviews).
- **Pricing:** Syncron directs buyers to request a quote.
### PTC Warranty
- **Best for:** Large manufacturers that need centralized warranty rules, claims, returns, supplier recovery, parts orders, and analytics.
- **What it does:** PTC Warranty enforces one set of coverage and business rules across a manufacturer's warranty operation, and it feeds field failure data back to product teams so a recurring defect shows up as a design problem for engineering to fix, on top of showing up on the claims ledger.
- **Customer proof:** PTC identifies its warranty product as a Leader in the 2024 IDC MarketScape for worldwide warranty service management applications.
- **G2 rating:** [4.8 out of 5 from 2 reviews](https://www.g2.com/products/ptc-warranty/reviews); the sample is too small to carry a buying decision by itself.
- **Pricing:** PTC asks buyers to contact the company for pricing.
### ServiceBench
- **Best for:** Service providers that manage technicians, work in progress, parts, claims, invoicing, and customer communications.
- **What it does:** ServiceBench schedules the technician, tracks the job from a mobile app in the field, and processes the claim once the work is done, giving both the provider and the customer visibility into parts status without a phone call.
- **Customer proof:** ServiceBench underpins Asurion's national service-provider network, giving participating providers access to its scheduling software and mobile app; Asurion does not publish a current, independently verifiable call-volume figure for the platform.
- **Capterra rating:** [3.7 out of 5 from 35 reviews](https://www.capterra.com/p/138688/ServiceBench/).
- **Pricing:** Capterra lists a starting price of $119 per month; Asurion also provides ServiceBench access at no cost to participating service-network providers.
## Comparison summary table
| Platform | Deployment and operability | Pricing model | Best fit |
|---|---:|---|---|
| WarrantyHub | Vendor target of 30–60 days | $549–$849 per month; custom Enterprise | Mid-market home warranty administrators and TPAs |
| Claimlane | Vendor target of 2–4 weeks for simple setups and 4–8 weeks for complex setups | $800 per month for 2,000 tickets/year; custom above that | Brands and retailers |
| Syncron | G2 reports an average implementation time of 7 months | Custom quote | Large OEMs and dealer networks |
| PTC Warranty | Enterprise deployment scoped with PTC | Contact PTC | Large manufacturers with complex warranty operations |
| ServiceBench (Asurion) | Web and mobile tools for service providers | $119 per month starting price; network access terms vary | Service providers and repair networks |
The products take different approaches to coordination:
- **WarrantyHub** offers automated notifications and a built-in contractor portal
- **Claimlane** routes work among internal teams and outside suppliers
- **Syncron** connects warranty stakeholders across the approval and payout chain
- **ServiceBench** supports customer and technician communications through its scheduling app
These 5 platforms handle the claim itself. Delight.ai is a separate AI layer that sits on top of one of them, coordinating the homeowner, contractor, parts supplier, and adjuster across channels until the work is complete.
## Does your claims platform integrate with your contractor and CRM systems?
Yes, your claims platform should integrate with the contractor, customer relationship management (CRM), dispatch, and support systems that hold current claim data. Integration quality determines whether an update reaches every party or becomes another manual task.
[Delight.ai integrations](https://delight.ai/integrations) connect to the customer service platforms a warranty operation is likely already running, such as Salesforce and Zendesk for the support desk or ServiceNow and NICE CXone for the contact center, so a handoff to a human agent carries full conversation context with it.
Delight's [home services and warranty product](https://delight.ai/home-services-and-warranty) also connects with ServiceTitan and Housecall Pro on the dispatch side, plus custom CRMs for teams running something else. Buyers should confirm the exact connector, data direction, refresh behavior, and implementation scope for their stack.
## Can AI handle warranty claims on its own?
**AI can take on any step that follows a clear process and doesn't require a judgment call.** A person should still approve decisions that carry higher risk, involve money, a coverage dispute, or don't reduce to a fixed rule.
### What AI can take off your plate
AI can take on the repetitive follow-up work that doesn't require a judgment call:
- Chasing down a missing document from a homeowner or contractor
- Checking a parts status with a supplier
- Sending the homeowner a status update
- Putting together the evidence packet an adjuster needs to make a decision
Delight.ai packages steps like these into Actionbooks, which operate like standard operating procedures for the AI agent. Each Actionbook stays in draft until a person approves it, giving operations teams control over what reaches customers.
### Where a person still needs to sign off
A person should sign off when a claim involves:
- A disputed coverage decision
- A high-value repair
- A policy exception
- A compliance-sensitive action
[Trust OS](https://delight.ai/trust-os) keeps a full log of what the AI did and why, so a disputed decision can be traced back to the conversation that produced it. It also gives administrators role-based access controls, staged deployment, and a way to pause the AI immediately if something needs to stop. [McKinsey's 2026 AI Trust Maturity Survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found that nearly two-thirds of respondents viewed security and risk concerns as the leading barrier to scaling agentic AI, which makes visible approval rules and auditability essential selection criteria.
## What does warranty claims management software cost?
Warranty claims management software starts at $119 per month for ServiceBench, $549 for WarrantyHub, or $800 for Claimlane's Core plan, while Syncron and PTC use custom quotes; since each figure represents a different buyer and deployment scope, a direct license comparison can mislead. **The clearer number is the operational cost per closed claim**, which adds implementation, integrations, administrator time, and contractor follow-up on top of the license fee.
## Where an AI layer fits on top of your claims platform
An AI layer sits above the claims system of record. The platform stays the authoritative record, while the AI layer coordinates actions and communications across systems, channels, and parties and keeps every participant working from current context.
[Delight.ai](https://delight.ai/ai-customer-service) gives home warranty teams a persistent case for the homeowner, contractor, parts supplier, and adjuster, combining conversation history with CRM and contextual data through its [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) across chat, SMS, email, and voice. Human approval gates and Trust OS controls keep financial and policy decisions within the boundaries your team sets.
[Explore Delight.ai's customer service resources](https://delight.ai/blog) for practical guidance on adding this kind of coordination layer to an existing operation.
- [Warranty claims management software: the shortlist](#)
- [What is warranty claims management software?](#)
- [Why claim coordination matters as much as claim tracking](#)
- [The best warranty claims management software, compared](#)
- [Does your claims platform integrate with your contractor and CRM systems?](#)
- [Can AI handle warranty claims on its own?](#)
- [What does warranty claims management software cost?](#)
- [Where an AI layer fits on top of your claims platform](#)
## Frequently Asked Questions
### What is warranty claims management software?
Warranty claims management software is a system that centralizes claim intake, validation, routing, processing, documentation, and resolution. Manufacturers often use it for coverage rules, returns, payouts, and supplier recovery. Home warranty administrators also use it to coordinate contractors, parts, appointments, customer updates, and adjuster approvals.
### Which warranty claims software has the best reviews?
WarrantyHub has the highest rating in this comparison at 5.0 out of 5 on Capterra, based on 6 reviews. Claimlane has 4.8 out of 5 on G2 from 13 reviews, Syncron has 4.3 from 29 reviews, PTC Warranty has 4.8 from 2 reviews, and ServiceBench has 3.7 from 35 Capterra reviews. Small samples warrant a broader evaluation of workflow fit, integrations, proof, and implementation.
### How much does warranty claims management software cost?
Public starting prices in this comparison range from $119 per month for ServiceBench to $549 per month for WarrantyHub and $800 per month for Claimlane's Core plan. Syncron and PTC require custom quotes. Buyers should compare total implementation, integration, migration, and operating costs at the same claim volume.
### Can AI approve a warranty claim without a person reviewing it?
AI can approve a warranty claim within clearly defined rules and autonomy levels. For high-value, disputed, exceptional, and compliance-sensitive decisions, people retain approval through Delight.ai's staged autonomy and approval gates, which allow teams to automate low-risk work after performance meets their standards.
### Do I need a separate AI customer service layer if I already have a claims platform?
A separate AI customer service layer is valuable when your current stack leaves the team spending substantial time coordinating contractors, suppliers, adjusters, and homeowners. Delight.ai works with an existing stack and keeps context across channels. The claims platform remains the system of record, and the AI layer manages communication and follow-through.
## Keep reading
[View more](/blog)
[ What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service)
[ What is unified commerce, and why does it matter for high-consideration retailers?](/blog/industry/what-is-unified-commerce)
[ Enterprise conversational AI: What to look for before you buy](/blog/ai-for-cx/enterprise-conversational-ai)
[ Best Customer Onboarding Software for B2B SaaS in 2026](/blog/insights/customer-onboarding-software)
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## What is unified commerce, and why does it matter for high-consideration retailers?
Source: https://delight.ai/blog/industry/what-is-unified-commerce
- [Blog](/blog)
- [Industry](/blog/industry)
- What is unified commerce, and why does it matter for high-consideration retailers?
## What is unified commerce, and why does it matter for high-consideration retailers?
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec](https://delight.ai/author/caroline-adamec)
Content Engineer
August 21 2026
**Key takeaways**
- **Unified commerce** connects a retailer's sales channels, inventory, and customer data into one system, so a purchase started online and finished in a store draws on the same information either way.
- **Backend unification alone isn't enough.** Customer conversations have to carry that same context forward, or the shopper feels the seam anyway.
- **Whether unified commerce actually works comes down to four moments:** product discovery, assisted conversion, post-purchase support, and returns.
Unified commerce connects a retailer's sales channels, inventory, and customer data into one system, so online, in-store, and mobile touchpoints all read from the same live record instead of reconciling overnight. For [high-consideration retailers](https://delight.ai/industries/retail), where a single conversation with a customer can shift conversion, basket size, or a return decision, the promise is simple: a shopper never has to repeat themselves. However, the reality often falls short of that promise.
## What is unified commerce?
Unified commerce is a retail strategy that merges digital commerce, point of sale, and order and inventory management into a single, real-time system.
A store's inventory count, a website's product catalog, and a call center's order history typically live in three separate databases that sync overnight; unified commerce keeps all three current at once, updated continuously as sales happen.
The practical effect shows up in how employees can actually help a shopper, regardless of channel:
- **A store associate** can tell a shopper that an item is in stock two towns over, pulled from the same live inventory feed the website uses.
- **A support agent** can see that an online order already shipped before the shopper finishes asking about it.
None of that requires guessing, because the systems underneath are reading from the same source.
## Unified commerce vs. omnichannel retail
Omnichannel retail and unified commerce solve related problems, but they aren't the same thing, and retailers often use these terms interchangeably in ways that blur a meaningful distinction.
In simple terms, **omnichannel is the front-end presence**, a retailer's face to the shopper across chat, phone, a store, and an app. **Unified commerce is the back-end consistency underneath that presence**, the shared system all of those channels actually draw from.
| Omnichannel retail | Unified commerce |
|---|---|
| Multiple channels, each built to feel consistent | Multiple channels, reading from one shared system |
| Loyalty points may only post in the channel where they were earned | Loyalty points post once, visible everywhere |
| A return started online may need to restart in-store | A return started online can finish in-store without re-entry |
| Systems reconcile on a schedule | Systems reconcile in real time |
Here's why the distinction matters. A retailer can nail the front-end presence, matching fonts, tone, and hours of operation across every channel, without ever building the back-end consistency underneath it.
When that happens, a shopper's actual data stays stuck in silos, and the effects show up fast. For example, a return approved by chat gets treated as new information when it reaches a store associate, or a loyalty offer promised online never applies at checkout.
Unified commerce is the harder, less visible work of preventing exactly that, and it's usually where retailers hit friction after launch.
## Why unified systems don't guarantee a unified experience
A retailer can unify its backend completely, one shared inventory view, one customer record, one order management system, and shoppers can still feel like they're starting over every time they talk to someone new.
That happens because the systems being unified **are databases, not customer conversations**. A shopper might spend ten minutes on live chat comparing two mattresses, then call the same retailer an hour later to ask about financing, and land with an agent who has no idea the chat ever happened.
The gap shows up in a few predictable places:
- A shopper explains their situation to a chatbot, then repeats the same explanation to a phone agent twenty minutes later.
- A return started through a mobile app has to be re-described to a store associate who has no record of the original request.
- A shopper who asked a detailed sizing question online gets a generic, unrelated follow-up email a week later.
Every one of those breakdowns can happen inside a company whose backend systems are, by any technical measure, fully unified. The inventory count is accurate, the order history is complete, but the **customer conversation simply doesn't carry any of that forward**.
## Four moments where unified commerce either holds up or falls apart
For a high-consideration retailer, where customer conversations are part of the buying experience, a systems diagram can't prove unified commerce is working. Only four specific moments can, each one a place where a shopper's context either carries forward or doesn't.
### Product discovery and selection
A shopper researching a purchase rarely does it in one sitting. They might browse a category online, ask a chat widget a comparison question, then come back two days later expecting the retailer to remember what they were looking at.
When the systems are unified but the conversation isn't, that shopper starts from zero every time, describing the same preferences and budget they already gave.
### Assisted conversion, in-store and online
This is the moment a sales associate or a support agent actually closes the sale, and it depends on knowing what a shopper has already done elsewhere. A shopper who added an item to an online cart, then walked into a store to see it in person, shouldn't have to explain that from scratch.
The backend integration alone doesn't solve this. Instead, what solves it is a system that carries context from a web chat straight to the sales floor, like [delight.ai's Omnipresence](https://delight.ai/omnipresent). The associate has all of the shopper's buying history, which allows them to assist the shopper at a more personal level.
### Post-purchase order support
Once an order ships, the questions shift: Where is it? Can it be changed? What happens if it's late?
A shopper expects whoever answers to already know the order number in question. [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) keeps that order number, the questions already asked about it, and any promises already made visible across every channel a customer might use next, so a customer who emailed about a delayed shipment doesn't have to read their order number over the phone an hour later.
### Return prevention and exceptions
Returns are where fragmentation costs the most, because a frustrated shopper has the least patience for repeating themselves. A genuinely unified approach can catch some returns before they happen, flagging a sizing mismatch or a delivery delay early enough to fix it.
Complex cases, a damaged item, a disputed charge, will still escalate to a person. A good system makes sure that person has the full history the moment the case reaches them, so the shopper isn't repeating their story a third time to someone starting from a blank screen.
## What unified commerce looks like when it works
[BJ's Wholesale Club](https://delight.ai/customers/bjs) offers a concrete example of what unified commerce looks like when it actually works.
Its AI shopping assistant, Bev, handles the same conversation whether a member is comparing products, asking about a coupon, or checking a membership renewal date, and can hand a member to a live agent without losing that context. A member browsing for a grill, checking club hours, or asking about a renewal is talking to the same Bev throughout every one of those questions.
Members who engaged with Bev converted at a higher rate and showed greater purchasing activity per visit than those who didn't, and spanning the whole shopping journey in one continuous conversation is what changed how members actually shopped.
## A quick way to check whether your commerce is unified where it counts
A systems audit will tell you whether your inventory, order management, and point of sale are connected, but it won't tell you whether the customer conversations layered on top of them are.
A few questions get closer to that answer:
1. Can **a shopper who started a return by chat finish it by phone** without repeating the reason?
2. Does **the associate on your sales floor know what a shopper looked at online** this morning?
3. If a shopper emails about a late order, **does the reply reference the actual order**, or ask them to explain it again?
4. When a shopper **switches from your app to a phone call mid-conversation**, does the customer support agent answering the call know what happened?
A retailer that can answer yes to all four has unified commerce the way a shopper actually experiences it.
Unified commerce starts as a backend project, but it's only finished once customer conversations on top of it stop asking shoppers to repeat themselves. For a high-consideration retailer, that's the difference between a shopper who feels known and one who feels handled. [Explore how delight.ai works for retail](https://delight.ai/industries/retail) to see what that looks like across discovery, conversion, and everything after.
- [What is unified commerce?](#section-anchor-id-0 "What is unified commerce?")
- [Unified commerce vs. omnichannel retail](#section-anchor-id-1 "Unified commerce vs. omnichannel retail")
- [Why unified systems don't guarantee a unified experience](#section-anchor-id-2 "Why unified systems don't guarantee a unified experience")
- [Four moments where unified commerce either holds up or falls apart](#section-anchor-id-3 "Four moments where unified commerce either holds up or falls apart")
- [What unified commerce looks like when it works](#section-anchor-id-4 "What unified commerce looks like when it works")
- [A quick way to check whether your commerce is unified where it counts](#section-anchor-id-5 "A quick way to check whether your commerce is unified where it counts")
## Frequently asked questions
**What is unified commerce?**
Unified commerce is a retail strategy that connects sales channels, inventory, and customer data into one real-time system, so every channel reads and writes to the same data instead of syncing on a delay. The result is a single, current view of a shopper and their order, no matter which channel they're using.
**What's the difference between unified commerce and omnichannel retail?**
Omnichannel means a shopper gets a consistent experience across channels. Unified commerce means those channels draw from the same underlying system, so the experience is genuinely continuous. A retailer can be omnichannel without being unified if the channels look alike but the underlying customer data stays separate.
**Does unified commerce mean replacing our existing POS, OMS, or CRM systems?**
Not necessarily. Most unified commerce initiatives connect existing systems rather than replacing them outright, though the amount of integration work depends on how siloed those systems already are. The bigger question is usually whether the customer-conversation layer on top of those systems can see the same data the systems now share.
**How does AI fit into a unified commerce strategy?**
AI can carry the context a unified backend makes available into the actual customer conversation, so a shopper's history, preferences, and open questions follow them from chat to phone to a store associate's screen. That's a different job than unifying the backend systems themselves, and it's the layer most unified commerce strategies leave out.
**What's a good first step if we're starting from disconnected systems?**
The first step is usually mapping where a shopper's context currently breaks, before picking any new software. One journey worth tracing is a product question that turns into a purchase and then into a support request, marking every point where the shopper has to re-explain themselves. That map points directly at which systems, and which customer conversations, need to unify first.
## Keep reading
[View more](/blog "View more")
- [What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service)
- [Best warranty claims management software for administrators](/blog/ai-agent/warranty-claims-management-software)
- [Enterprise conversational AI: What to look for before you buy](/blog/ai-for-cx/enterprise-conversational-ai)
- [Best Customer Onboarding Software for B2B SaaS in 2026](/blog/insights/customer-onboarding-software)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Enterprise conversational AI: What to look for before you buy
Source: https://delight.ai/blog/ai-for-cx/enterprise-conversational-ai
## Enterprise conversational AI: What to look for before you buy
[ Kibeom Lee — Machine learning lead](https://delight.ai/author/kibeom-lee-machine-learning-lead)
August 20 2026
Key takeaways
- **Enterprise-grade is a checkable claim**, supported by performance under load, built-in security and compliance, and deep integration with the systems already running.
- **This comparison covers nine platforms**, from Zendesk and Salesforce Agentforce to Delight.ai.
- **Pricing model matters as much as price**, since per-seat, per-resolution, and credit-based platforms use different units.
If you already know what conversational AI is, the harder question is whether a particular platform can handle your conversation volume, compliance exposure, and existing systems. Third-party or vendor content often stops at feature lists. This guide follows the evaluation process CX and technology leaders face before a vendor review, when broad promises have to survive contact with legal, security, engineering, and operations.
The category definition is only the starting point. Enterprise buyers still need a way to tell which platforms can carry real responsibility at scale.
### What makes conversational AI enterprise-grade instead of just a bigger chatbot?
**Conversational AI earns enterprise-grade status in production, where failures carry operational and regulatory consequences.** The platform has to stay reliable as volume climbs, work inside the systems a team depends on, and give the business control over what happens when confidence drops.
Those claims should all be testable. Performance under load is one part of the evidence, but a serious review also follows customer data across systems and shows exactly who can approve, inspect, or stop the AI's work.
[Gartner found that 91% of customer service leaders felt pressure to implement AI in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). That pressure has put an AI feature on nearly every vendor roadmap. It has also made the gap between a product-page claim and production readiness much more important.
For the plain definition, see our [guide to conversational AI](https://delight.ai/learn/conversational-ai). The enterprise question begins once the definition is settled and the shortlist has to hold up under scrutiny.
### How should you evaluate an enterprise conversational AI platform?
You should evaluate an enterprise conversational AI platform by asking for evidence that connects the demo to your operating environment. The strongest vendors can make that connection concrete, using your policies, integrations, conversation patterns, and expected volume.
Six checks are worth running before a platform earns a shortlist spot:
- **Scale and reliability:** production-volume evidence backed by uptime, latency, and concurrency commitments.
- **Security and compliance:** the certifications, data controls, and contractual terms your legal and security teams require.
- **Integration depth:** customer authentication and meaningful actions inside the CRM, helpdesk, and business systems already in use.
- **Governance over autonomy:** visible approval gates, audit logs, human handoff controls, and role-based permissions.
- **Omnichannel continuity:** context that survives movement across chat, voice, SMS, email, and other required channels.
- **Pricing clarity:** a model your finance team can map to expected usage, headcount, implementation, and overages.
This exercise exposes the conditions under which a platform works well and the places where your team would still carry the risk. A long feature list cannot do that on its own.
### The 9 best enterprise conversational AI platforms, compared
Nine platforms come up often in enterprise customer service evaluations, and each approaches the problem from a different starting point:
- **Zendesk:** best for teams extending AI across an existing ticketing system.
- **Salesforce Agentforce:** best for CRM-native deployments built around Salesforce.
- **Fin:** best for teams focused on outcome-priced customer service automation.
- **Ada:** best for large, omnichannel AI customer service programs.
- **Cognigy:** best for voice-heavy contact center orchestration.
- **Freshdesk with Freddy AI:** best for teams seeking approachable helpdesk and AI packaging.
- **Kore.ai:** best for low-code, integration-heavy enterprise deployments.
- **Gorgias:** best for ecommerce support and shopping workflows.
- **Delight.ai:** best for memory-driven, governance-first deployments with an AI-native helpdesk.
The comparison below uses public product and pricing information. G2 ratings and review counts change continuously, and contract pricing can vary by volume and scope.
#### Quick-glance comparison table
| Platform | Channels & coverage | Pricing model (2026) | G2 rating | Best fit for |
|---|---|---:|---:|---|
| Zendesk | Email, messaging, voice, SMS, social (Zendesk Suite) | Support from $19/agent/month; Suite from $55/agent/month; Copilot $50/agent/month; AI resolutions use tiered allowances | 4.3/5 (≈7,000 reviews) | Extending existing Zendesk workflow |
| Salesforce Agentforce | Digital and voice inside Salesforce ecosystem | $2/conversation, $500 per 100,000 Flex Credits, or editions from $550/user/month | 4.3/5 (≈1,200 reviews) | Salesforce-centered operations |
| Fin (Intercom) | Live chat, email, WhatsApp, SMS, phone, Slack | $49/month includes 50 outcomes; $0.99/additional outcome | 4.5/5 (≈3,900 reviews) | Outcome-priced automation |
| Ada | Voice, email, chat, messaging, WhatsApp, SMS, social, in-app | Conversation-based pricing; resolution-based option; contract pricing | 4.6/5 (172 reviews) | Omnichannel enterprise CX |
| Cognigy | Voice and digital contact center channels | Contract pricing based on conversations, concurrent voice lines, knowledge usage | 4.6/5 (13 reviews) | Voice-heavy orchestration |
| Freshdesk (Freddy AI) | Email and ticketing; broader chat/voice by plan | $19-$89/agent/month; Copilot $29/agent/month; $49 per 100 AI Agent sessions | 4.4/5 (≈3,700 reviews) | SMB and mid-market helpdesks |
| Kore.ai | Voice and digital channels, 30+ prebuilt integrations | Contract pricing | 4.6/5 (474 reviews) | Integration-heavy deployments |
| Gorgias | Email, chat, SMS; social and voice optional | AI-resolved interactions $0.90 annually or $1 monthly on most plans | 4.6/5 (557 reviews) | Ecommerce support and sales |
| Delight.ai | Chat, SMS, email, voice, WhatsApp, in-app, social | Pay per successful AI resolution; Delight Desk human seats $0 | 4.6/5 (125 reviews) | Memory and governance-first deployments |
#### 1. Zendesk
**What to know:** Zendesk is a ticketing-first helpdesk with AI agents layered into its existing workflow. Support starts at $19 per agent per month, Suite starts at $55, and Copilot lists at $50 per agent per month. G2 rates Zendesk 4.3/5 across about 7,000 reviews.
Zendesk offers a familiar path for teams that already run support there. Email, messaging, voice, SMS, social, routing, and reporting sit in one agent workspace, which can reduce the organizational disruption of adding AI.
Its billing deserves a close read. On [May 18, 2026, Zendesk introduced a three-tier automated-resolution model](https://support.zendesk.com/hc/en-us/articles/9570369117338-About-automated-resolution-tiers): only a fully AI-resolved, unescalated conversation draws down the allowance, billed at $1.50 per resolution on a committed pack or $2.00 pay-as-you-go above 5 to 10 free resolutions per agent per month. The total still depends on the seat plan, any Copilot licenses, and the mix of resolutions the AI completes.
#### 2. Salesforce Agentforce
**What to know:** Agentforce is a CRM-native agent platform built around Salesforce data and workflows. Pricing starts at $2 per conversation or $500 per 100,000 Flex Credits, and Agentforce editions start at $550 per user per month. Its G2 rating is 4.3/5 across about 1,200 reviews.
For teams already committed to Service Cloud, Agentforce keeps customer records, workflow actions, and AI configuration inside the Salesforce ecosystem. Cost forecasting takes more work because available models meter different things: conversations, credits, and bundled editions.
#### 3. Fin (Intercom)
**What to know:** Fin focuses on customer service outcomes across chat, email, WhatsApp, SMS, phone, and Slack. The $49 monthly base includes 50 outcomes, with additional outcomes priced at $0.99 each. Fin holds a 4.5/5 G2 rating across about 3,900 reviews.
Fin defines an outcome as a successful resolution or a configured procedure handoff, and it charges at most once per conversation. Salesforce signed an agreement in June 2026 to acquire Fin, formerly Intercom, making roadmap and contracting questions especially relevant.
#### 4. Ada
**What to know:** Ada is an AI-native customer experience platform spanning voice, email, chat, messaging, SMS, social, and in-app support. It primarily uses conversation-based pricing and offers a resolution-based option for specific enterprise needs. Ada has a 4.6/5 G2 rating from 172 reviews.
Ada brings channels together through a shared reasoning layer, with Playbooks for multi-step procedures and a Performance Center for testing and improvement. Public dollar rates are not published—comparison begins with a proposal built around your expected volume and channels.
#### 5. Cognigy
**What to know:** Cognigy is built for enterprise voice and digital contact center orchestration. Its contracts meter conversations, concurrent voice lines, and knowledge usage. The platform holds a 4.6/5 G2 rating, based on 13 reviews.
Cognigy gives voice a central role, with orchestration across contact center systems and digital channels. [Forrester named Cognigy a Leader](https://www.forrester.com/blogs/the-tightrope-walkers-conversational-ai-must-bridge-modern-ai-and-contact-center-reality/) in its Q2 2026 Wave for conversational AI platforms for customer service. Because the G2 sample is small, references from comparable production environments can add useful context.
#### 6. Freshdesk (Freddy AI)
**What to know:** Freshdesk combines a helpdesk with Freddy AI. Annual plans run from $19 to $89 per agent per month, Freddy AI Copilot costs $29 per eligible agent, and additional AI Agent capacity costs $49 per 100 sessions. Freshdesk has a 4.4/5 G2 rating across about 3,700 reviews.
Freshdesk is an accessible entry point for teams that want ticketing, routing, knowledge, analytics, and AI in one product family. The broader omnichannel experience depends on the plan and whether the deployment uses Freshdesk or Freshdesk Omni. The first 500 AI Agent sessions are included once per account on Pro and Enterprise plans; Growth includes no free sessions.
#### 7. Kore.ai
**What to know:** Kore.ai is a low-code enterprise agent platform sold through contract pricing. Its AI for Service documentation covers more than 30 prebuilt integrations, and G2 rates the platform 4.6/5 across 474 reviews.
Kore.ai suits programs that must connect service workflows across systems such as Salesforce, ServiceNow, Zendesk, Microsoft products, and custom APIs. A proof of concept should update a real record, recover from failed authentication, and hand the conversation over with context intact.
#### 8. Gorgias
**What to know:** Gorgias combines an ecommerce helpdesk with an AI Agent for support and shopping workflows. Most plans price AI-resolved interactions at $0.90 with annual billing or $1 with monthly billing, on top of the helpdesk plan. Gorgias holds a 4.6/5 G2 rating across 557 reviews.
Gorgias is built around ecommerce work such as product questions, order changes, returns, and subscription updates. Its AI Agent supports email, chat, and SMS. An AI-resolved interaction can also count toward helpdesk usage, making the underlying ticket fee part of the total cost.
#### 9. Delight.ai
**What to know:** Delight.ai combines persistent memory, omnichannel continuity, operational governance, and an AI-native helpdesk. Delight Desk costs $0 per human-agent seat, and the business pays for successful AI resolutions.
Delight.ai carries customer context across chat, SMS, email, voice, WhatsApp, in-app, and social channels. [Trust OS](https://delight.ai/trust-os) gives teams visibility into conversations and tool calls, flags unsafe or low-confidence behavior, and adds staging, role-based access, gradual rollout, testing, and shutdown controls.
The proof is strongest where context changes the work. [Mixpanel's support team](https://delight.ai/customers/mixpanel) replaced roughly 10 minutes per interaction of manual verification before Delight.ai automated that step. [Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways) increased containment from 60% to 80% within two weeks, and the platform processes more than 7 billion conversations each month across 4,000+ apps.
Explore the full [AI Agent platform](https://delight.ai/ai-agent) or see how [Delight Desk](https://delight.ai/desk) changes the economics of human-agent seats.
### How enterprise conversational AI platforms handle security, compliance, and governance
Security, compliance, and governance determine whether an enterprise conversational AI platform can move beyond the first round of review. A security certificate can open the door, but the operating controls decide whether the system can stay in production once it begins taking action for customers.
Enterprise buyers typically check for:
- **Data protection coverage:** attestations, regulatory alignment, encryption, residency, retention, and subprocessor terms.
- **Visibility into AI decisions:** a traceable record of the knowledge, policies, tools, and actions behind an outcome.
- **Risk detection:** a way to surface hallucinations, policy violations, unsafe content, and low-confidence responses early.
- **Operational control:** role-based access, staging, approval gates, rollback, escalation, and a kill switch.
Delight.ai brings those controls together in Trust OS. Every conversation and tool call is logged, risky behavior can be flagged automatically, and teams can test changes in staging or through gradual rollout before committing all production traffic. Product-facts reference GDPR, CCPA, HIPAA, and SOC 2 Type II coverage.
[McKinsey's 2026 AI Trust Maturity Survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found organizations moving toward scaled AI deployment while reporting gaps in strategy, governance, and risk management.
### Why integration and channel continuity decide whether a platform reduces work or adds a channel
A conversational interface reduces work only when it can reach the systems where that work happens. If a human still has to identify the customer, retrieve account data, update the ticket, and reconstruct the history after a channel change, the AI has added another surface to manage.
Identity and context are the first hurdle. [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) authenticates the customer and retrieves account context before Delight.ai responds. At [Mixpanel](https://delight.ai/customers/mixpanel), that replaced roughly 10 minutes of manual verification per interaction.
Channel continuity carries the same context forward. [Omnipresence](https://delight.ai/omnipresent) connects in-app chat, web chat, SMS, email, WhatsApp, voice, and social channels, so a conversation can move without resetting the customer's history, preferences, or open case.
### How is enterprise conversational AI priced, and what does that mean for total cost?
Enterprise conversational AI pricing depends on what the vendor chooses to meter. A seat ties spend to team size. Conversations, sessions, outcomes, and credits move with customer activity or the work the system completes.
That difference can make two similar-looking proposals behave very differently after launch. Credit models may charge for each action inside a conversation. Outcome models depend on the vendor's definition of success, and contract minimums or implementation costs can change the first-year total.
The cleanest comparison uses the same 12-month scenario for every vendor. Ask each one to price expected volume, a peak month, the required human team, and the actions or channels the deployment will use. The result will be far more useful than comparing headline rates with different denominators.
### Closing takeaway
Choosing an enterprise conversational AI platform comes down to three checkable things:
- **Scale:** evidence that it holds up at real production volume.
- **Governance:** security and operational controls the business can stand behind.
- **Integration depth:** enough access and continuity to change what the human team spends time on.
Once those questions have specific answers, the shortlist becomes easier to defend across CX, technology, security, and finance. Explore [Delight.ai's approach to enterprise-grade AI](https://delight.ai/enterprise-grade), or use the [Delight AI Index](https://delight.ai/ai-index) to see where your organization stands today.
- [What makes conversational AI enterprise-grade instead of just a bigger chatbot?](#section-anchor-id-0 "What makes conversational AI enterprise-grade instead of just a bigger chatbot?")
- [How should you evaluate an enterprise conversational AI platform?](#section-anchor-id-1 "How should you evaluate an enterprise conversational AI platform?")
- [The 9 best enterprise conversational AI platforms, compared](#section-anchor-id-2 "The 9 best enterprise conversational AI platforms, compared")
- [How enterprise conversational AI platforms handle security, compliance, and governance](#section-anchor-id-3 "How enterprise conversational AI platforms handle security, compliance, and governance")
- [Why integration and channel continuity decide whether a platform reduces work or adds a channel](#section-anchor-id-4 "Why integration and channel continuity decide whether a platform reduces work or adds a channel")
- [How is enterprise conversational AI priced, and what does that mean for total cost?](#section-anchor-id-5 "How is enterprise conversational AI priced, and what does that mean for total cost?")
- [Closing takeaway](#section-anchor-id-6 "Closing takeaway")
### Frequently asked questions
Which enterprise conversational AI platforms handle multi-turn voice calls?
Multi-turn voice is available from Cognigy, Kore.ai, Ada, Salesforce Agentforce, and Delight.ai. The important distinction is how voice performs inside a live contact center. Delight.ai's [Voice AI Agent](https://delight.ai/voice) handles interruptions and background noise, then carries context into chat, SMS, or email when the conversation moves. Test a failed or interrupted call from start to handoff and watch what the human agent receives.
What does a failed conversational AI pilot actually cost an enterprise?
A failed conversational AI pilot costs whatever the company committed to vendor fees, implementation, internal labor, integration work, and security review. The larger loss may be time: re-scoping the use case, rebuilding support for the project, and delaying the outcome that justified the pilot. A useful estimate comes from the contract and actual staff hours.
What's the difference between a chatbot and an enterprise conversational AI platform?
A chatbot usually answers defined questions within a bounded flow. An enterprise conversational AI platform adds production scale, security controls, system actions, auditability, persistent context, and governed handoff. The practical difference appears when the software has to complete approved work across several systems and explain how it reached the result.
How do enterprises measure ROI from conversational AI?
Enterprises measure conversational AI ROI against a fixed baseline. Resolution rate, containment, average handle time, repeat contact, CSAT, revenue impact, and cost per successful resolution all help show what changed after deployment. Delight.ai's [ROI calculator](https://delight.ai/roi-calculator) can frame the estimate before launch, and a controlled pilot can replace assumptions with observed results.
How do enterprise conversational AI platforms handle customization?
Customization can happen through no-code workflows, APIs, or a mix. The more important question is who can safely keep the system current after launch. Operations leaders need room to tune day-to-day behavior; engineering and governance remain responsible for integrations, access, and production risk.
### Keep reading
[View more](/blog "View more")
[ What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service)
[ Best warranty claims management software for administrators](/blog/ai-agent/warranty-claims-management-software)
[ What is unified commerce, and why does it matter for high-consideration retailers?](/blog/industry/what-is-unified-commerce)
[ Best Customer Onboarding Software for B2B SaaS in 2026](/blog/insights/customer-onboarding-software)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## What is an answering service for home services?
Source: https://delight.ai/blog/industry/answering-service-for-home-services
- [Blog](/blog)\n- [Industry](/blog/industry)\n- What is an answering service for home services?\n\n## What is an answering service for home services?\n\n[](https://delight.ai/author/leighanne-manwiller) [LeighAnne Manwiller](https://delight.ai/author/leighanne-manwiller) \nProduct marketing manager\n\n\n\nAugust 24 2026\n\n**Key takeaways**\n\n- **[Speed to lead](https://delight.ai/blog/ai-for-cx/speed-to-lead-why-home-service-businesses-lose-jobs) decides the job.** A fast response gives a home service business the best chance to reach a new customer before that customer calls another provider.\n- **Live and AI answering services handle pickup differently.** The right fit depends on call volume, budget, and the amount of coordination required once the call ends.\n- **Booking is only the start.** Delight.ai carries the job through dispatch changes, parts delays, and follow-up, keeping the conversation connected after the first call.\n\nAn answering service helps a home service business reach new customers when its own team cannot pick up. A homeowner facing a leaking pipe or failed air conditioner may contact several providers, so response time directly affects your chance to win the work. When exploring options for answering services, operations leaders should choose a service that fits both their call volume and the work required after booking.\n\n## What is an answering service?\n\nAn answering service is a live or automated system that handles business calls when the company's team is unavailable. For a home service business, it can cover calls that arrive mid-job, after hours, or during a seasonal demand surge.\n\nThe category includes three common approaches:\n\n- **Live call center.** A trained operator uses your greeting and script, then takes a message or transfers the call.\n- **Interactive voice response.** A recorded menu routes callers through keypad or voice selections.\n- **AI voice agent.** AI-powered software that understands natural speech and can complete tasks such as checking availability or booking a repair visit.\n\nEach approach completes a different amount of work during the call. Message taking creates a callback task for the office whereas direct booking via an AI agent can place the caller on the schedule before the conversation ends.\n\n## Why speed to lead matters so much for home service businesses\n\nSpeed to lead matters because a faster response improves the chance of reaching a prospective customer when their need is still urgent. Homeowners dealing with a broken furnace, failed air conditioner, or plumbing leak often keep calling until they reach a provider who can help.\n\nResearch on more than 15,000 web-generated leads found that contacting a lead within 5 minutes produced a **900% increase in contact rates compared with waiting 10 minutes**. The same research found that only 4.7% of companies achieved the optimal 5-minute response window, according to the [Lead Response Management study](https://resources.insidesales.com/wp-content/uploads/2019/11/infograpic-_LeadRespMgmt.pdf). Customer experience also shapes retention. [A 2025 PwC analysis](https://www.pwc.com/us/en/services/consulting/commercial-excellence/future-customer-experience-is-supply-chain.html) reports that **55% of consumers would stop buying from a company after several bad experiences**.\n\nFor a business fielding more inquiries than its office can answer live, an [AI customer service](https://delight.ai/ai-customer-service) approach can absorb call spikes without adding equivalent staffing for peak periods. Pickup is one part of the experience. The call also needs to connect cleanly with booking, dispatch, and follow-up.\n\n## Live answering services vs. AI answering services for home service businesses\n\nLive and AI answering services both cover calls, but they scale differently: a live operator brings judgment no script can replace, while an AI agent can hold several calls at once and complete a task like booking without waiting for a person to free up. The right choice depends on call volume, budget, and the work that must happen during and after each conversation.\n\n### What a live, human-first answering service offers\n\nA live, human-first answering service offers a trained operator who follows your script, takes messages, and transfers urgent calls. This model can suit businesses that value a person on every call and have predictable call volume.\n\n[Centratel](https://www.centratel.com/), for example, markets human-first call capture to plumbers, electricians, HVAC companies, and other trade businesses. Its operators provide call capture and emergency dispatch, and the company states that its call processing uses neither AI nor offshore agents. Since each operator handles one conversation at a time, seasonal surges can increase usage and cost.\n\n### What an AI-native answering service offers\n\nAn AI-native answering service answers with a voice agent that can hold several calls at once, work around the clock without overtime, and complete a scripted task like booking an appointment directly into the calendar. **Gartner reports that 91% of customer service leaders feel pressure to implement AI in 2026**, and the answering-service category is following that shift. The tradeoff shows up in scope: most AI answering services still stop once the call ends, the same limit live services have.\n\n## How much does an answering service cost?\n\n**An answering service can start at about $30 per month** for a live-service base plan, but the real bill depends on how many minutes are included and what the overage rate is once a caller goes over them. In [Forbes Advisor's current comparison](https://www.forbes.com/advisor/business/software/best-answering-services/), the featured live services start at $30 to $359 per month, and listed additional-minute rates range from $1.37 to $2.60 among providers that publish them.\n\n| Approach | Typical starting price | What drives the bill |\n|---|---|---|\n| Live answering service | $30–$360+/month base | Included minutes, then $1.37–$2.60/minute overage |\n| AI answering service | Usage-based, varies by provider | Call volume, features like scheduling or bilingual support |\n| Home services operations platform | Conversation-based pricing | Call volume plus post-call coordination work |\n\nSticker price only tells part of the cost story. A service that ends with message taking leaves callbacks, rescheduling, and data entry with the office, so **the meaningful comparison is cost per job completed, not cost per minute answered**.\n\n## What happens after the call is the real test\n\nThe real test begins after the call, when the business must confirm the appointment, handle schedule changes, and keep the customer informed through completion. Every handoff creates another chance for context to disappear or follow-up to stall.\n\n**Most answering services, live or AI, stop once the appointment is booked.** Technician delays, backordered parts, and warranty coordination then return to the office for manual follow-up.\n\nThis is the gap [Delight.ai's Voice AI Agent](https://delight.ai/voice) is built to close. Its [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) carries the same conversation history forward instead of ending at the booking, so a mid-job update or a parts delay gets handled without the customer re-explaining themselves from scratch. Delight.ai can also coordinate rescheduling and proactive updates when parts are delayed.\n\n## What to look for in an answering service for home services\n\nLook for an answering service that matches the full workflow of a home service job, including booking, dispatch, customer updates, and follow-up. Four criteria help reveal how well a provider will fit: \n\n- **Live field-service integration.** The service should read technician availability and write confirmed bookings into supported tools. For example, Delight.ai's home services offering names ServiceTitan and Housecall Pro among its integrations.\n- **Bilingual coverage.** English and Spanish support on the same line can serve more callers without requiring a separate number.\n- **Proactive updates.** Timely outreach about a delayed part or shifted appointment window reduces uncertainty and avoidable inbound calls.\n- **Persistent memory across the job.** The service should carry the customer's address, issue, and prior conversation into later interactions.\n\n[Dr. Cool Services](https://delight.ai/customers/dr-cool-services), an HVAC and plumbing business, uses Delight.ai's Voice AI Agent to answer peak-season calls and book appointments end to end in English and Spanish. The case study also documents instant pickup, automated booking, and adaptations to appointment windows during extreme summer demand.\n\n> \"Our best people shouldn't spend their days reading addresses back and forth. They should be solving problems and calming frustrated customers.\" — Mark Farley, President, Dr. Cool Services\n\n## 3 answering services worth a look for home service businesses\n\nA home service business weighing its options is choosing between three approaches: \n\n- **[Centratel](https://www.centratel.com/).** A traditional, human-first live answering service built for trade businesses like plumbing and electrical work. No AI, no offshore agents, and pricing that's sales-gated rather than published.\n- **[AnswerConnect](https://www.answerconnect.com/).** A live, human-first answering service that markets itself as \"always people, not bots,\" offering 24/7 call answering, lead capture, and appointment scheduling, with named home-service partners like Mosquito Squad and Mister Sparky. Plans start around $350 a month for 200 minutes, with larger volumes requiring a custom quote.\n- **Delight.ai.** An AI-native approach that answers the call and then carries the job through dispatch changes, parts delays, and warranty follow-up on one shared thread. Sendbird, Delight.ai's parent company, is rated 4.6/5 on G2.\n\nThe best fit depends on how much of the job you want the service to own after each call.\n\nBusinesses win more jobs when fast pickup leads into clear, consistent coordination. An answering service closes the first response gap, and a connected workflow keeps the booking, dispatch, and follow-up moving as one job.\n\nFor teams fielding more calls than they can answer live, [see how Delight.ai works for home service businesses](https://delight.ai/home-services-and-warranty).\n\n- [What is an answering service?](#section-anchor-id-0 "What is an answering service?")\n- [Why speed to lead matters so much for home service businesses](#section-anchor-id-1 "Why speed to lead matters so much for home service businesses")\n- [Live answering services vs. AI answering services for home service businesses](#section-anchor-id-2 "Live answering services vs. AI answering services for home service businesses")\n- [How much does an answering service cost?](#section-anchor-id-3 "How much does an answering service cost?")\n- [What happens after the call is the real test](#section-anchor-id-4 "What happens after the call is the real test")\n- [What to look for in an answering service for home services](#section-anchor-id-5 "What to look for in an answering service for home services")\n- [3 answering services worth a look for home service businesses](#section-anchor-id-6 "3 answering services worth a look for home service businesses")\n\n## Frequently asked questions\n\nAre answering services still worth it now that AI answering services exist? \nYes, answering services remain valuable for home service businesses that miss calls, face seasonal spikes, or need after-hours coverage. Live services provide human call handling, and AI services can add concurrent call capacity and automated tasks. The best choice depends on the team's workflow, call volume, and expectations for booking and follow-up.\n\nHow fast should a home service business respond to a new lead? \nA home service business should respond within minutes whenever possible. Research on web-generated leads found that contact rates were 900% higher when a lead was called within 5 minutes versus 10, which shows how quickly responsiveness decays even outside the home-services context specifically. Immediate pickup also meets the urgency of many repair calls.\n\nDoes an answering service work with my field service software? \nAnswering services can work with field service software when the provider supports a direct integration with your platform. Confirm that the service can read live technician availability and write confirmed bookings into the schedule, not just relay a message for someone to enter by hand. Integration coverage varies, so confirm the exact platform and features before signing an agreement.\n\nCan an answering service handle bilingual (English/Spanish) callers? \nYes, many answering services offer English and Spanish coverage, although availability and plan terms vary. Dr. Cool Services uses Delight.ai's Voice AI Agent to conduct natural conversations and complete appointment booking in both languages on the same line. Ask each provider whether bilingual service covers the full workflow, including booking, transfers, and follow-up.\n\nWill an answering service replace my office staff or dispatcher? \nAn answering service should support office staff and dispatchers by absorbing routine call volume and completing defined tasks. Your team can then focus on exceptions, judgment calls, and customer relationships that benefit from human attention. The operating model works best when escalation rules and staff ownership remain clear.\n\nWhat does "speed to lead" mean? \n[Speed to lead](https://delight.ai/blog/ai-for-cx/speed-to-lead-why-home-service-businesses-lose-jobs) is the time between a new customer inquiry and the business's first response. For home service businesses, a short response time increases the chance of reaching the homeowner while the need is active. Fast pickup is especially valuable for urgent repair requests and after-hours calls.\n\n## Keep reading\n\n[View more](/blog "View more")\n\n[ Conversational AI for retail](/blog/ai-for-cx/conversational-ai-for-retail)
[ What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service)
[ Best warranty claims management software for administrators](/blog/ai-agent/warranty-claims-management-software-for-adm-tk51fe)
[ What is unified commerce, and why does it matter for high-consideration retailers?](/blog/industry/what-is-unified-commerce)\n\n[View more](/blog "View more")\n\n### Ready to unleash the power of AI customer service agents?\n\n[Contact sales](/contact-sales)\n
## Conversational AI for retail
Source: https://delight.ai/blog/ai-for-cx/conversational-ai-for-retail
- [Blog](/blog)
- [AI for CX](/blog/ai-for-cx)
- Conversational AI for retail
## Conversational AI for retail
[](https://delight.ai/author/ethan-hong)
[**Ethan Hong — Product manager**](https://delight.ai/author/ethan-hong)
August 24 2026
**One agent across the shopper's full journey**
- High-consideration retailers can use **one conversational AI agent across discovery, conversion, order support, and returns** to preserve each shopper's context throughout.
- Hanssem's resolution rate rose from **48% to about 86%** over five months with Delight.ai, alongside a **50% reduction** in transfers to human agents.
- Accurate product guidance can prevent avoidable returns, and automated workflows can make necessary returns easier to complete.
## Platform shortlist
- **Delight.ai** is the recommended fit for retailers that want persistent shopper memory and one agent across the customer journey.
- **Gorgias** works well for Shopify-centered ecommerce teams that want a helpdesk with an AI shopping and support agent built in.
- Larger service organizations already standardized on an omnichannel ticketing suite tend to reach for **Zendesk**.
- **Fin by Intercom** suits teams that want either a standalone AI agent or a native pairing with Intercom's helpdesk.
- For small and midsize ecommerce teams, **Tidio Lyro** offers a lower-cost entry point.
- Enterprises with complex contact center and voice environments are the clearest fit for **NiCE Cognigy**.
## What is conversational AI for retail?
Conversational AI for retail is software that understands shoppers' questions in everyday language, maintains context across a conversation, and **completes approved tasks through a retailer's systems**. It can check inventory, pull up an order, recommend a product, or start a return in the same conversation, instead of routing the shopper elsewhere to finish the task.
The category has become a priority for service leaders. In [a 2025 Gartner survey](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026), **91% of customer service and support leaders** reported executive pressure to implement AI, with customer satisfaction, operational efficiency, and self-service success among their top 2026 priorities.
Retail gives these customer conversations direct commercial value. A question about fit, availability, or delivery can shape the purchase, and a well-connected agent can support that decision across the full [retail customer journey](https://delight.ai/industries/retail).
## How is conversational AI different from a retail chatbot?
Conversational AI differs from a traditional retail chatbot in what it understands and what it is allowed to do next:
- **A rule-based chatbot** maps a message to a predefined flow, so it can only go where that flow already leads.
- **Conversational AI** reads varied phrasing, asks a useful follow-up when something is unclear, and carries what it learns into the rest of the conversation.
Consider a shopper who writes in to say the tracking information has not updated in a week and they are worried that their package is lost. A basic chatbot reads that as a where-is-my-order request and returns the same tracking link the shopper already has open, technically responsive but not actually helpful.
A connected conversational AI agent authenticates the shopper, pulls the live carrier status instead of the static tracking page, and recognizes that "hasn't updated in a week" is the real question, not the order number. It can explain what a stalled scan typically means, offer a replacement or refund if the shipment qualifies under policy, and complete that recovery in the same conversation instead of forwarding the shopper to a phone queue. This combination of dialogue and action is central to the broader definition of [conversational AI](https://delight.ai/learn/conversational-ai).
## Benefits of conversational AI in retail
Conversational AI shows up in a retailer's numbers in three places: how much shoppers buy, how much it costs to serve them, and whether they come back. [McKinsey's 2026 European retail analysis](https://www.mckinsey.com/industries/retail/our-insights/rewiring-retail-in-europe-the-ai-imperative) ties personalization and size recommendations to cutting return rates by **up to 7%**.
- **Higher conversion.** Real-time inventory and holds reduce comparison shopping and speed purchase decisions.
- **Larger baskets.** Contextual cross-sells (e.g., showing socks with hiking boots) feel like helpful advice rather than generic upsell.
- **Fewer avoidable returns.** Accurate fit and compatibility guidance reduces ordering multiple sizes.
- **Stronger retention.** Shared memory reduces repeated explanations across channels and sessions.
- **Peak coverage.** Automated answers to high-volume questions free agents for high-value calls.
## Conversational AI in retail examples
Four moments show how conversational AI supports the shopper journey. Effective models preserve the same customer context through each moment.
### Helping shoppers discover or choose products
When a shopper asks if boots run true to size for wide feet, an AI agent with access to fit notes and the customer's purchase history can recommend the right size based on similar customers.
Delight.ai's [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) unifies structured data, past purchases, and contextual signals into an evolving profile. This shared memory gives recommendations a consistent foundation while enabling the model to continuously learn. If the customer later returns a pair, the agent integrates that feedback to refine future recommendations.
### Moving a shopper from browsing to buying
Conversational AI can detect hesitation and address the specific blocker. For example, if a shopper asked about delivery speed and then abandoned the cart, a connected agent can follow up when shipping improves or stock becomes available nearby.
Delight.ai customer Hanssem reported a resolution rate increase from **48% to about 86% over five months**, and a 50% drop in transfers to human agents.
### Supporting customers after they buy
Post-purchase support benefits from live order data and continuity across channels. When a shipment is delayed, the agent can explain status, apply recovery policy, and keep the case active across later interactions.
Continuity across chat, SMS, email, WhatsApp, and voice depends on shared memory. Delight.ai's [Omnipresence](https://delight.ai/omnipresent) capability helps preserve context as customers change channels or return in later sessions.
### Facilitating and preventing returns
Conversational AI can gather return reasons, request supporting information, create labels, and initiate approved refunds or exchanges without human input. Delight.ai's retail platform supports returns and refund processing as part of post-purchase service.
Pre-purchase guidance (sizing, fit, compatibility) helps prevent avoidable returns by guiding shoppers to the right product the first time.
High-risk cases (charge disputes, suspected fraud, ambiguous damage claims) should follow escalation rules. Delight.ai's [Trust OS](https://delight.ai/trust-os) provides testing, monitoring, safeguards, and governed deployment for control and visibility.
## How does conversational AI fit into the systems a retailer already runs?
Conversational AI acts as an interaction and orchestration layer. Integrations connect the agent to systems that hold information and permissions required to complete tasks.
- **Point of sale and inventory.** Live access supports accurate store-level availability and pickup answers.
- **CRM and loyalty platforms.** Membership tier, purchase history, and preferences inform personalized responses.
- **Order and fulfillment systems.** Current shipment data enables precise order-status and delivery responses.
- **Returns, payments, and policy tools.** Approved workflows define the actions an agent may take and when to escalate.
This preserves systems of record while adding a conversational interface. Delight.ai provides [integrations](https://delight.ai/integrations) for platforms such as Salesforce and Zendesk, plus APIs for business-specific systems.
## What should a retailer ask before choosing a conversational AI platform?
Evaluate how a platform resolves real customer needs, manages risk, scales economically, and hands work to people. These operational questions reveal more than a feature checklist.
### Does it resolve or deflect?
Top platforms measure successful resolution separately from containment, closure, and transfer rate. Ask vendors to define each metric, show performance by retail intent, and explain how repeat contacts affect results.
### How does it handle a mistake?
The platform should surface low-confidence or incorrect responses, preserve a review trail, and support controlled improvements. Ask how teams test changes, monitor production behavior, and reverse problematic updates.
### What does it cost as volume grows?
Model cost at peak-season volume using the vendor's exact billing unit. Per-seat, per-ticket, per-conversation, and per-resolution models create different cost curves, especially as automation rates change.
### Where does the human take over?
Humans should take over when policy, risk, emotion, or uncertainty demand judgment. Retail teams should be able to configure handoff boundaries and receive the complete conversation, customer context, and action history at handoff.
## Shortlist of the best conversational AI for retail platforms
Delight.ai is recommended for retailers prioritizing persistent shopper memory, governed action, and continuity across discovery, purchase, service, and returns. Below is focused guidance for teams with different needs. Pricing and G2 data were checked on August 21, 2026.
#### Delight.ai
- Category and channels: AI concierge across chat, SMS, email, WhatsApp, voice, and in-app experiences
- Pricing: Conversation-based usage; Delight Desk has a $0 seat fee and charges for successful AI resolutions
- G2 rating: Sendbird (Delight.ai's parent) rated [4.6/5 on G2](https://www.g2.com/products/sendbird/reviews)
- Customer proof and best fit: Hanssem's resolution rate rose from 48% to about 86% in five months, with 50% fewer human transfers. Best for retailers wanting one agent across the whole customer journey.
#### Gorgias
- Category and channels: Ecommerce helpdesk and AI agent across email, chat, and text
- Pricing: Helpdesk from [$10/month](https://www.gorgias.com/pricing); AI Agent typically around $1.00 per resolved interaction monthly or $0.90 annually
- G2 rating: 4.6/5 (557 reviews)
- Customer proof and best fit: Jaxxon reported 17% lower live-chat volume and 6% higher conversion after self-service chat; strong fit for Shopify-centered DTC brands.
#### Zendesk
- Category and channels: Omnichannel service suite with AI agents, ticketing, messaging, voice, and knowledge
- Pricing: Suite Team $55 per agent/month; Suite Professional $115 per agent/month; Copilot $50 per agent/month
- G2 rating: 4.3/5 (7,021 reviews)
- Customer proof and best fit: Unity reported almost 8,000 self-service deflections and about $1.3 million in savings; fits organizations already standardized on Zendesk.
#### Fin by Intercom
- Category and channels: AI agent for chat, email, voice, social; available with Intercom or an existing helpdesk
- Pricing: Intercom helpdesk from $29 per seat/month plus $0.99 per outcome; specialized and voice pricing varies
- G2 rating: 4.5/5 (3,901 reviews)
- Customer proof and best fit: Lightspeed reports Fin resolves up to 65% of conversations autonomously; good for teams already using Intercom or seeking a flexible standalone AI agent.
#### Tidio Lyro
- Category and channels: AI agent and customer service suite for live chat, email, social, and ecommerce support
- Pricing: Lyro starts at $32.50/month; premium volume and managed service pricing is custom
- G2 rating: 4.6/5 (1,959 reviews)
- Customer proof and best fit: Your KAYA reported a 75% resolution rate after adding Lyro; a good fit for small and midsize ecommerce teams.
#### NiCE Cognigy
- Category and channels: Enterprise contact center AI with voice, chat, and agent assist
- Pricing: Contact sales for a custom quote
- G2 rating: 4.6/5 (13 reviews)
- Customer proof and best fit: Cognigy and Schwarz IT won a 2025 retail award for Lidl's LIVA voice assistant; suited for enterprise voice and contact-center deployments.
Every retailer should validate this shortlist against its own intents, systems, governance, and seasonal volume. For persistent customer memory across the relationship, Delight.ai is the clearest fit in this group.
## The bottom line
Retailers evaluating conversational AI should expect the platform to remember the shopper, complete approved work across connected systems, and preserve context through discovery, purchase, service, and returns. [See how Delight.ai works across the retail journey.](https://delight.ai/industries/retail)
- [What is conversational AI for retail?](#section-anchor-id-0)
- [How is conversational AI different from a retail chatbot?](#section-anchor-id-1)
- [Benefits of conversational AI in retail](#section-anchor-id-2)
- [Conversational AI in retail examples](#section-anchor-id-3)
- [How does conversational AI fit into the systems a retailer already runs?](#section-anchor-id-4)
- [What should a retailer ask before choosing a conversational AI platform?](#section-anchor-id-5)
- [Shortlist of the best conversational AI for retail platforms](#section-anchor-id-6)
- [The bottom line](#section-anchor-id-7)
## Frequently asked questions
#### Does conversational AI replace retail support and sales staff?
Conversational AI augments retail teams by handling repetitive, high-volume requests and escalating work that needs judgment. Human staff keep responsibility for sensitive disputes, complex exceptions, relationship management, and cases outside the agent's approved authority.
#### How much does conversational AI cost for a retailer?
Costs depend on billing unit, platform fees, seats, channels, integrations, and peak volume. Entry prices range from tens of dollars per month for small deployments to custom enterprise contracts. Model total cost at expected automation and seasonal demand.
#### How long does it take to deploy conversational AI in retail?
Deployment can take days for a narrow knowledge-based pilot and several weeks or longer for a governed rollout connected to inventory, CRM, order, and returns systems. A focused pilot on a high-volume intent helps establish data quality, workflow ownership, and success metrics.
#### Is conversational AI the same thing as an AI agent?
They overlap: conversational AI describes language-based interaction, while an AI agent describes a system that can pursue a goal and take approved actions. Retail platforms increasingly combine both.
## Keep reading
[View more](/blog)
[](/blog/industry/answering-service-for-home-services)
[What is an answering service for home services?](/blog/industry/answering-service-for-home-services)
[](/blog/industry/vendor-consolidation-customer-service)
[What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service)
[](/blog/ai-agent/warranty-claims-management-software-for-adm)
[Best warranty claims management software for administrators](/blog/ai-agent/warranty-claims-management-software-for-adm)
[](/blog/industry/what-is-unified-commerce)
[What is unified commerce, and why does it matter for high-consideration retailers?](/blog/industry/what-is-unified-commerce)
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## What is a HIPAA compliant AI agent?
Source: https://delight.ai/blog/industry/hipaa-compliant-ai-agent
- [Blog](/blog)\n- [Industry](/blog/industry)\n- What is a HIPAA compliant AI agent?\n\n## What is a HIPAA compliant AI agent?\n\n[](https://delight.ai/author/cheryl-chai) \n[Cheryl Chai](https://delight.ai/author/cheryl-chai) \nProduct marketing lead\n\n\n\nAugust 25 2026\n\n**Key takeaways**\n\n- **A HIPAA compliant AI agent needs a signed Business Associate Agreement** whenever the vendor acts as a business associate, along with safeguards for electronic PHI that actually hold up under review.\n- That means access controls that limit who sees what, audit logs that can reconstruct an incident, and a documented breach procedure with a real deadline attached.\n- **Delight.ai is a HIPAA compliant AI agent** for **healthcare customer support**, holding a HIPAA Type I report and a SOC 2 Type II report, with Trust OS giving your team visibility into every action the agent takes.\n\nGartner found that [91% of customer service leaders felt pressure from executive leadership to implement AI in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). In healthcare, that pressure immediately requires a compliance review, given the sensitive nature of the information AI agents handle. Leaders at health commerce businesses need to know whether an AI agent can support patient service without exposing protected health information (PHI) or slowing procurement.\n\nThe phrase "hipaa compliant AI agent" appears throughout vendor marketing, yet HIPAA compliance depends on the full deployment. Contracts, data flows, safeguards, policies, and the customer's own operating practices all shape the answer.\n\nFor proactive outreach and self-service booking, the evaluation starts with the agent's role and every system that receives patient data. [Delight.ai's healthcare AI agent](https://delight.ai/industries/healthcare) is designed for administrative patient interactions such as scheduling, insurance questions, billing support, reminders, and follow-ups, with security controls built for regulated environments.\n\n## What is a HIPAA compliant AI agent?\n\nA HIPAA compliant AI agent is an AI system configured and operated to meet applicable HIPAA Privacy, Security, and Breach Notification Rule obligations when it creates, receives, maintains, or transmits PHI. No model name or vendor label establishes compliance on its own. The organization, vendor, deployment, and workflow must work together to protect the data.\n\nAn AI agent enters HIPAA's scope when it handles PHI for a covered entity or business associate. A conversation can qualify when it connects identifiable information to care or payment, including:\n\n- A patient confirming an appointment associated with their provider record.\n- A patient asking about insurance coverage tied to their account.\n- A patient requesting a prescription refill through a provider's service channel.\n\nThe [U.S. Department of Health and Human Services (HHS)](https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/business-associates/index.html) explicitly names an AI chatbot that handles patient PHI for medical reminders or appointment scheduling as an example of a business associate, a HIPAA term for any vendor that creates, receives, maintains, or transmits PHI on a covered entity's behalf.\n\nThat status means the company selling or operating the AI agent, the vendor, must sign a Business Associate Agreement with the healthcare organization, safeguard the data under HIPAA's Security Rule, and notify the organization if a breach occurs, obligations that apply whether or not the vendor calls itself "HIPAA compliant."\n\n## What does HIPAA compliance require from an AI agent?\n\nHIPAA compliance requires administrative, physical, and technical safeguards suited to the risks of the deployment. The following 5 areas form a practical vendor review, though they do not replace a formal risk analysis or legal and security review.\n\n### A signed Business Associate Agreement (BAA)\n\nA Business Associate Agreement (BAA) is a written contract that governs a vendor's use and disclosure of PHI when the vendor acts as a business associate. It must define permitted uses, require appropriate safeguards, address incident reporting, and extend relevant obligations to subcontractors. The [HHS business associate guidance](https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/business-associates/index.html) explains when the relationship applies and what the agreement must cover.\n\nConfirm that the BAA covers the proposed AI workflows, data, subprocessors, and services. Commercial availability alone is insufficient if the contract excludes the product tier or data flow your team plans to use.\n\n### Encryption in transit and at rest\n\nEncryption in transit and at rest is a strong baseline for an AI agent that handles electronic PHI (ePHI). The current HIPAA Security Rule treats encryption as an addressable implementation specification, which means a regulated entity must assess whether it is reasonable and appropriate and document any equivalent alternative. A vendor should identify the protocols it uses, where encryption begins and ends, and how it manages keys.\n\nNaming the actual encryption specifics gives a security team something concrete to assess, rather than a compliance label taken on faith. [Delight.ai](https://delight.ai/industries/healthcare) is one example: it encrypts patient data at rest with AES-256 and in transit with TLS 1.3, per its published security practices.\n\n### Access controls and audit logging\n\nAccess controls and audit logging should show that authorized people and systems can reach only the ePHI their roles require. HIPAA requires access controls and authentication for any system touching ePHI, along with a way to record and examine activity after the fact, which for an AI agent means confirming it verifies who or what is asking before it hands over a record, not just that a password exists somewhere in the stack.\n\nDelight.ai's own [role-based access control for AI agents](https://delight.ai/blog/ai-agent/ai-agent-role-based-access-control) works this way, scoping exactly what each team or workflow can reach. For an AI workflow, reviewers should be able to trace the records accessed and the actions completed.\n\nA scheduling agent, for example, may need appointment availability and a verified patient identity. Giving that workflow unrestricted access to clinical notes would expand exposure without supporting the task.\n\n### Minimum-necessary PHI handling\n\nMinimum-necessary PHI handling limits uses, disclosures, and access to the information needed for the intended purpose. A booking confirmation generally does not need a patient's full visit history, so the workflow should request and expose a smaller data set.\n\nRetention deserves a separate answer because HIPAA's minimum-necessary standard does not establish one universal deletion period for every record. The vendor should explain which data it stores, why it stores it, where it resides, who can retrieve it, and how retention settings align with your legal and operational requirements. The [HHS Security Rule summary](https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations/index.html) provides the current framework for risk-based safeguards and access management.\n\n### Breach notification timelines\n\nHIPAA breach notification timelines depend on the affected party and the nature and size of the breach. A business associate must notify the covered entity after discovering a breach of unsecured PHI without unreasonable delay and no later than 60 calendar days. Contracts often require faster notice so the covered entity can investigate and meet its own obligations.\n\nThe BAA should spell out the contractual notification deadline and exactly who to call first, since a vendor that only commits to HIPAA's 60-day outer limit can still leave your team finding out about a breach weeks after the fact. HHS's [Breach Notification Rule guidance](https://www.hhs.gov/hipaa/for-professionals/breach-notification/index.html) explains the duties for covered entities and business associates.\n\n## How do you evaluate whether an AI agent platform is actually HIPAA compliant?\n\nYou evaluate whether an AI agent platform supports HIPAA compliance by reviewing the contract, independent reports, data architecture, and operational evidence for the exact deployment. These 4 questions move the review beyond a homepage claim.\n\n### Is a BAA offered by default, or gated behind a premium tier?\n\nA BAA must be available for the product, plan, and workflow that will handle PHI. Ask the vendor to identify any pricing, hosting, feature, or subprocessor conditions before procurement advances, then have legal and security teams confirm that the agreement covers the intended use.\n\n### Does the report cover how controls are designed, or how they perform over time?\n\nThe report's scope tells you whether an assessor reviewed control design at a point in time or tested control operation across a defined period.\n\n| Report | What it attests | Evidence window | What it tells a buyer |\n|---|---|---|---|\n| HIPAA Type I report | Controls were suitably designed at a specified date | A single point in time | The control design met the assessment criteria |\n| HIPAA Type II report | Controls were suitably designed and operated effectively during the review period | A defined period | The controls were tested across the stated period |\n\nAsk the vendor for the report itself, not just the label, so you can see which criteria the auditor tested against, what period it covered, and any exceptions noted, since a one-line compliance claim can't tell you whether the report actually covers the workflow you're deploying. Delight.ai holds a HIPAA Type I report alongside a SOC 2 Type II report, with each report described according to its scope.\n\n### Where does patient data flow?\n\nPatient data should flow only through systems and subprocessors approved for the intended HIPAA-regulated workflow. A useful architecture review answers 5 questions.\n\n- **Third-party access** — does PHI reach a third-party model provider, and under what terms?\n- **Model training** — can any of that data train future models?\n- **Data residency** — which regions store it?\n- **Retention window** — how long do records remain available?\n- **Deletion** — how does deletion actually work?\n\nThis review should follow the data from the first patient message through integrations, logs, analytics, backups, and exports. A gap anywhere in that path can undermine controls at the conversational layer.\n\n### How deep is the audit trail?\n\nThe audit trail should let your team examine system activity involving ePHI and investigate the agent's actions. HIPAA requires mechanisms that record and examine relevant information-system activity, while each organization determines the detail needed through its risk analysis.\n\nDelight.ai combines full audit logs with a governance and observability layer called [Trust OS](https://delight.ai/trust-os). Your team can review the agent's behavior and maintain control over what it may do, while a timestamped activity trail records actions during multi-step work. That trail supports internal review, incident response, and ongoing oversight.\n\n## How does this apply to proactive outreach and appointment booking?\n\nHIPAA applies to proactive outreach and appointment booking when those workflows involve PHI for a covered entity or business associate. Each message must fit the organization's privacy and security program, the approved purpose, the patient's communication preferences, and the vendor's contracted data handling.\n\nDelight.ai organizes administrative patient support across 3 stages:\n\n- **Connect** takes care of the pre-visit work — scheduling, insurance or prior-authorization verification, and registration.\n- Once care is underway, **Support** manages check-in and authorization status, and delivers approved care instructions.\n- After the visit ends, **Continue** closes the loop with order or lab-result notifications, follow-up scheduling, and refill coordination.\n\nChannel changes should preserve context and controls. [Omnipresence](https://delight.ai/omnipresent) carries conversation context across chat, SMS, email, voice, WhatsApp, and in-app experiences, allowing a patient interaction to continue without an unnecessary restart. Your team still determines which channels and message content are appropriate for each workflow.\n\n## The bottom line\n\nA credible HIPAA review connects the claim to evidence across contracts, controls, data flows, and auditability. Delight.ai gives healthcare support leaders a starting point most vendors can't match on paper alone, a HIPAA Type I report and a SOC 2 Type II report each scoped and described on its own terms, encryption and role-based access controls that actually get audited, and Trust OS's logs standing in for evidence instead of a promise.\n\nIts healthcare AI agent doesn't provide clinical guidance, clinical decision support, or medical recommendations, it handles the scheduling, order, and service side of care and hands off to your team when clinical judgment is needed.\n\nReview [Delight.ai's security and compliance practices](https://delight.ai/security) with your legal and security stakeholders as you evaluate the deployment.\n\n- [What is a HIPAA compliant AI agent?](#what-is-a-hipaa-compliant-ai-agent)\n- [What does HIPAA compliance require from an AI agent?](#what-does-hipaa-compliance-require-from-an-ai-agent)\n- [How do you evaluate whether an AI agent platform is actually HIPAA compliant?](#how-do-you-evaluate-whether-an-ai-agent-platform-is-actually-hipaa-compliant)\n- [How does this apply to proactive outreach and appointment booking?](#how-does-this-apply-to-proactive-outreach-and-appointment-booking)\n- [The bottom line](#the-bottom-line)\n\n## Frequently asked questions\n\n**What makes an AI agent HIPAA compliant?**\n\nAn AI agent supports HIPAA compliance when the regulated organization and its vendors apply the required contracts, policies, risk management, and safeguards to the full deployment. When the vendor is a business associate, the parties need an appropriate BAA. The environment also needs its own access and audit controls, authentication, and transmission security, plus incident procedures sized to whatever the organization's risk analysis actually turns up, not a generic checklist applied the same way to every deployment.\n\n**Which AI agent platforms are HIPAA compliant for healthcare customer support?**\n\nAI agent platforms suitable for HIPAA-regulated customer support should be able to produce evidence for your specific deployment, not just a badge, starting with a BAA that actually names your workflow, a data-flow picture that shows where PHI goes, and an independent report you can read rather than take on faith. [Delight.ai](https://delight.ai/industries/healthcare) supports administrative healthcare workflows and holds a HIPAA Type I report and a SOC 2 Type II report. Buyers should validate every shortlisted platform against their intended use with legal, privacy, and security stakeholders.\n\n**Is Delight.ai HIPAA compliant?**\n\nYes, Delight.ai supports HIPAA-regulated healthcare workflows and holds a HIPAA Type I report, along with a SOC 2 Type II report and ISO 27001:2022 and ISO 42001:2023 certifications. Its healthcare security controls pair AES-256 encryption at rest with TLS 1.3 in transit, layered under role-based access controls and audit trails that let a security team trace who touched a given record and when. The customer remains responsible for configuring and operating its deployment in line with its own HIPAA obligations.\n\n**Can an AI agent handle appointment booking and follow-ups and still stay HIPAA compliant?**\n\nYes, an AI agent can handle appointment booking and follow-ups within a HIPAA-compliant deployment when the organization and vendor apply the necessary agreements, safeguards, permissions, and communication policies. HHS specifically identifies appointment-scheduling AI chatbots that handle patient PHI as potential business associates, so the vendor relationship and workflow need the same diligence as other ePHI systems.\n\n**Does using an AI agent for patient communication create HIPAA risk?**\n\nYes, any system that creates, receives, maintains, or transmits ePHI introduces risks that the organization must identify and manage. The review usually turns up the same few gaps, access scoped more broadly than the workflow needs, a subprocessor never named in the BAA, or an activity log too thin to reconstruct what happened after the fact. A BAA, technical safeguards, documented data flows, and ongoing oversight reduce those risks within a broader HIPAA compliance program.\n\n## Keep reading\n\n- [What is an answering service for home services?](/blog/industry/answering-service-for-home-services) \n \n- [Conversational AI for retail](/blog/ai-for-cx/conversational-ai-for-retail) \n \n- [What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service) \n \n- [Best warranty claims management software for administrators](/blog/ai-agent/warranty-claims-management-software) \n \n\n[View more](/blog)\n\n### Ready to unleash the power of \nAI customer service agents?\n\n[Contact sales](/contact-sales)\n
## The best digital concierge platforms for hotels
Source: https://delight.ai/blog/industry/digital-concierge-for-hotels
- [Blog](/blog)
- [Industry](/blog/industry)
- The best digital concierge platforms for hotels
## The best digital concierge platforms for hotels
[](https://delight.ai/author/eloy-lee)
**Eloy Lee**
[AI-native brand designer](https://delight.ai/author/eloy-lee)
August 25 2026
### Key takeaways
- AI concierge platforms, hotel guest apps, and messaging tools can **look similar while solving different operational problems**
- **Workflow depth separates the field,** since the strongest platforms carry context across channels, connect to hotel systems, and move a guest request toward completion
- **Review counts, pricing, and customer results all come from different sources**, so they're worth reading with the same scope in mind rather than at face value
A digital concierge for hotels gives guests a conversational way to ask questions, make requests, and receive help throughout a stay. This guide focuses on AI and messaging platforms that can support those interactions. It excludes guestroom tablets and in-room television interfaces that lack a conversational service layer.
The timing is relevant for hotel leaders. In a survey of 321 customer service leaders, [Gartner found that 91% faced executive pressure to implement AI](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). The same research found that customer satisfaction, operational efficiency, and self-service success were their leading priorities.
For the category fundamentals, read [what a hotel chatbot is](https://delight.ai/blog/ai-for-cx/hotel-chatbot-ai-concierge). The comparison below is for hotel executives and general managers who already understand the category and need to evaluate a vendor.
- [**Delight.ai**](https://delight.ai/) best fits hotel groups that want an AI concierge with Agent Steward's grasp of the guest journey, so a message like "my room is cold" arrives with the context that the guest just checked in, not as a question from a stranger.
- [**Decagon**](https://decagon.ai) suits enterprise teams already evaluating a general AI customer experience platform that now includes a travel offering.
- [**Canary Technologies**](https://canarytechnologies.com) works well when a hotel wants guest messaging, digital check-in, upsells, and related guest management tools in one hospitality platform.
- For hotels that prioritize multilingual conversational AI, [**HiJiffy**](https://www.hijiffy.com) is the stronger match.
- [**Duve**](https://duve.com) centers its case on a branded guest app, online check-in, communications, and pre-arrival upsells, so it fits properties built around that journey.
- Hotels that want guest messaging, mobile check-in, and upsells inside familiar messaging channels are the target for [**Akia**](https://www.akia.com).
- [**Whistle by Cloudbeds**](https://www.cloudbeds.com/guest-engagement-software/) makes the most sense for properties that want guest experience workflows embedded directly in the Cloudbeds platform.
## What counts as a digital concierge for hotels?
A digital concierge for hotels is a conversational service layer that helps a guest obtain information or move a request forward. That definition covers several interfaces, but the buying decision becomes clearer when leaders separate the interface from the operational capability behind it.
- **Guestroom tablets** let guests order services and browse property information from an in-room device.
- **In-room television interfaces** present hotel services and content through the room television.
- **Branded guest apps** combine property information, check-in, upsells, and service requests on a mobile interface.
- **Two-way messaging platforms** connect guests and hotel teams through channels such as SMS, WhatsApp, web chat, email, and OTA messaging.
- **AI conversational agents** interpret natural-language requests, use connected data, and can trigger approved workflows across hotel or customer service systems.
The last two categories are the focus here. A hotel may need a simple messaging hub, a hospitality guest experience suite, or a broader AI agent platform. The best choice depends on the workflows the property wants to automate, the systems involved, and the level of control the operating team requires.
## How we evaluated these digital concierge platforms
We evaluated these digital concierge platforms on the same four dimensions:
- **Category and best fit** based on the vendor's published product scope and target customer.
- **Workflow depth** based on the channels, integrations, and actions the vendor publicly describes.
- **Commercial signal** based on published pricing or a clear quote-based model.
- **Public proof** based on current review profiles and named customer evidence.
Review figures are snapshots, and vendor case studies report results selected by the vendor, so it's worth treating both as initial evidence for a deeper look rather than a final answer. During a buying process, that deeper look often comes from seeing a vendor reproduce the relevant workflow with your own systems, policies, and guest scenarios.
## Digital concierge platforms compared
The platforms differ most in deployment model, pricing, and best fit. The table below summarizes those distinctions before the detailed profiles that follow.
| Vendor | Deployment and operability | Pricing model | Best fit |
|---|---|---|---|
| Delight.ai | Trust OS provides testing, review, auditability, and control | Conversation-based; $0 Delight Desk seat fee with per-resolution charges | Hotel groups seeking one governed AI concierge across channels |
| Decagon | Enterprise implementation with testing, guardrails, and versioning | Custom, based on conversations or resolutions | Enterprise teams with a mature CX stack |
| Canary Technologies | Hospitality suite with quote-based onboarding | Custom quote | Hotels seeking a broad hospitality operations suite |
| HiJiffy | Plan-based channel access and hospitality integrations | From €99 to €319 per month, plus setup | Hotels prioritizing multilingual guest communications |
| Duve | Modular plans with published minimums | From $120 to $200 per month | Hotels centered on a branded guest app and upsells |
| Akia | Hospitality deployment with PMS integrations | Custom quote | Hotels seeking mobile-first digital guest workflows |
| Whistle by Cloudbeds | Delivered through the Cloudbeds platform | Custom quote within Cloudbeds plans | Current or prospective Cloudbeds customers |
### Delight.ai
Delight.ai is an AI concierge platform built around [Agent Steward](https://delight.ai/autonomous-agent)'s grasp of where a guest actually is in their stay. If a guest who just checked in messages "my room is cold," Agent Steward already knows the reservation and the stage of the stay, so the response can skip the interrogation and go straight to a fix. [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) combines conversation history, customer data, and live signals into that evolving profile, and Omnipresence carries it across chat, voice, SMS, email, WhatsApp, and in-app conversations, so the context holds even when the channel changes.
Operations teams can govern the agent through [Actionbooks](https://delight.ai/product-release/action-books-management) and [Trust OS](https://delight.ai/trust-os), including approval controls, response review, and traceable activity, so a guest who starts in chat and calls later gets a next interaction that continues with full context instead of starting over.
- **Best fit:** Enterprise hotel groups that want a single AI concierge and governance layer across guest-facing channels.
- **Review signal:** Delight.ai's parent company, [Sendbird](https://www.g2.com/products/sendbird/reviews), holds a 4.6 out of 5 rating from 126 G2 reviews.
- **Pricing:** Conversation-based usage fees; Delight Desk has a $0 seat fee and charges per successful AI resolution.
- **Public proof:** [Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways) replaced its legacy chatbot in under 2 months, then increased containment from 60% to 80% within 2 weeks of launch.
The Norse result comes from aviation, a neighboring travel category. It shows the platform's ability to improve a live travel service workflow through structured tuning, while a hotel should establish property-level baselines for its own business case.
### Decagon
As an enterprise AI customer experience platform, Decagon runs agents across chat, email, voice, SMS, and custom channels. Its Agent Operating Procedures let CX teams define agent logic in natural language while technical teams control integrations, guardrails, and versioning.
Decagon now has a [dedicated travel and hospitality offering](https://decagon.ai/industry/travel-hospitality) that covers booking support, itinerary changes, loyalty workflows, and connected actions through systems such as Salesforce and Zendesk, with a published 6-week implementation example.
- **Best fit:** Enterprise travel or hotel organizations that want a horizontal AI agent platform and already operate a mature customer service stack.
- **Review signal:** [G2](https://www.g2.com/products/decagon/reviews) lists a 4.7 out of 5 rating from 31 reviews.
- **Pricing:** Custom pricing based on conversations or resolutions, according to Decagon's published comparison content.
- **Public proof:** The travel page publishes a 6-week implementation example and general case-study outcomes.
Decagon and Delight.ai overlap on enterprise channels, connected actions, and governance, though Delight.ai makes persistent customer memory and long-horizon case ownership central to its product architecture, giving hotel groups a clear basis for a workflow-level comparison.
### Canary Technologies
Canary Technologies packages AI guest messaging together with digital check-in, smart checkout, dynamic upsells, tipping, and other hotel workflows in one hospitality guest management platform, with a messaging product that consolidates SMS, WhatsApp, OTA channels, WeChat, LINE, and email into a unified inbox.
The platform has broad hospitality adoption. Canary reports use by more than 20,000 hotels in over 125 countries, including Wyndham, Four Seasons, and Choice Hotels. Its case-study library also gives buyers hotel-specific evidence across messaging, upsells, mobile check-in, and AI voice.
- **Best fit:** Hotels that want a hospitality suite spanning guest messaging, arrival, departure, and upsell workflows.
- **Review signal:** [G2](https://www.g2.com/products/canary-technologies/reviews) lists 4.8 out of 5 from 24 reviews.
- **Pricing:** Quote-based; Canary does not publish a dollar amount for its packages.
- **Public proof:** [Brøchner Hotels](https://canarytechnologies.com/press/brochner-hotels) reports nearly 4,000 guest messages per month and roughly 5 times more upsell revenue within 6 months of adoption.
Canary's hotel-specific workflows and customer base make it a strong operational point solution. A group comparing it with Delight.ai should decide whether it needs an all-in-one hospitality suite or an AI concierge that can span a wider customer experience stack.
### HiJiffy
HiJiffy runs its hospitality communications platform on its own Aplysia AI, supporting web chat, WhatsApp, social channels, email, and OTA messaging, with plan-dependent channel access. The company reports support for more than 130 languages and integrations with more than 100 hotel management systems.
Its public pricing creates a useful starting point for budget planning. The feature set expands from website chat in Basic to a broader channel mix and in-stay concierge workflows in higher tiers.
- **Best fit:** Hotels that prioritize multilingual conversational AI, direct booking support, and a hospitality-specific communications hub.
- **Review signal:** [Hotel Tech Report](https://hoteltechreport.com/marketing/hotel-chatbots/hijiffy-hotel-chatbot) lists a 4.5 out of 5 rating from 184 reviews.
- **Pricing:** Basic starts at €99 per month, Pro at €159, and Premium at €319, with setup fees based on property count.
- **Public proof:** HiJiffy reports that [Leonardo Hotels](https://www.hijiffy.com/success-stories/leonardo-hotels) automated 93% of about 281,000 conversations and saved an estimated 14,000 hours.
The Leonardo figures are vendor-reported and include HiJiffy's estimate of time saved. They still provide a concrete scale signal for a multi-property hotel group evaluating repetitive guest inquiry volume.
### Duve
Online check-in, a branded guest app, communications, and upsells anchor Duve's hotel guest experience platform, and its plans stack those capabilities so buyers can see clearly how the product expands from arrival workflows into messaging and revenue generation.
The guest app is central to Duve's experience. That makes it especially relevant for properties that want guests to complete pre-arrival tasks, receive tailored offers, and access property information from a branded mobile destination.
- **Best fit:** Hotels that want a branded guest app with online check-in, a communication hub, and structured upsell journeys.
- **Review signal:** [Hotel Tech Report](https://hoteltechreport.com/guest-experience/hotel-guest-apps/duve-app) lists a 4.8 out of 5 rating from 675 reviews.
- **Pricing:** Published minimums are $120 per month for Basic, $150 for Pro, and $200 for Premium.
- **Public proof:** Duve reports that [Sofitel Sydney Darling Harbour](https://duve.com/how-duve-increased-sofitel-sydney-darling-harbours-upsell-revenue-by-selling-over-35k-in-just-one-month) generated more than $35,000 in upsell revenue in 1 month.
Buyers should test Duve's revenue case against their own room count, arrival volume, offer mix, and guest adoption rate.
### Akia
Akia brings SMS, WhatsApp, web chat, email, and OTA messages into one inbox, then extends that same hotel guest experience platform into mobile check-in, digital registration, guest forms, and upsell experiences through its Mini Apps.
This channel mix gives Akia a broader scope than an SMS utility, while its guest experience remains rooted in short, mobile conversations. Hotels should examine channel availability by market and validate how each workflow connects to their property management system (PMS) — the software a hotel uses to handle reservations, check-in and checkout, room status, and guest folios.
- **Best fit:** Hotels that want mobile-first guest communication and lightweight digital workflows across familiar messaging channels.
- **Review signal:** [Hotel Tech Report](https://hoteltechreport.com/guest-experience/guest-messaging-platforms/akia) lists a 4.8 out of 5 rating from 121 reviews.
- **Pricing:** Akia does not publish a current first-party price; buyers need a custom quote.
- **Public proof:** [Hotel Van Zandt](https://akia.com/case-studies/hotel-van-zandt-replaces-3-tools-with-akia) reports that Akia replaced 3 tools and generated enough upsell revenue to cover the subscription cost.
The case study supports a consolidation and revenue story, though it does not disclose the subscription amount or upsell total. Buyers should request those inputs when building a comparable property-level model.
### Whistle by Cloudbeds
Whistle has evolved into Cloudbeds Guest Experience, a guest communications layer connected to Cloudbeds operations that supports multiple messaging channels, a unified inbox, automated translation, digital check-in, reviews, and upsell workflows.
Cloudbeds now presents Guest Experience inside its Experience plan, which also includes its PMS, payments, channel manager, and booking engine. This makes the platform most relevant when a property wants guest communication tied closely to the rest of its Cloudbeds environment.
- **Best fit:** Current or prospective Cloudbeds customers seeking an integrated guest experience and property management stack.
- **Review signal:** Only available for [Cloudbeds PMS](https://hoteltechreport.com/operations/property-management-systems/cloudbeds-myfrontdesk), which lists a 4.6/5, 1261 reviews.
- **Pricing:** Quote-based as part of the Cloudbeds platform; public plan pages do not list a standalone dollar amount for Guest Experience.
- **Public proof:** [Casetta Group](https://cloudbeds.com/customers/casetta-group) reports stronger guest engagement, additional upsell opportunities, and more streamlined housekeeping and amenity requests.
Cloudbeds also reports that Guest Experience now powers more than 4 million guest conversations per month across 13 channels. That platform-scale figure gives existing Cloudbeds customers a useful adoption signal.
## How should a hotel choose between these platforms?
A hotel should choose a digital concierge by testing the workflows that carry the most guest impact and operational risk. Price and review scores can narrow a shortlist. A live demonstration against the property's systems reveals whether the platform can support the real work.
### Does it integrate with your PMS and act on what it reads?
The platform should integrate with your PMS at the depth required by the target workflow. Reading a reservation may be enough for an availability question, while a late checkout, room move, or folio adjustment can require permissioned write access.
A live demo showing the exact data flow with a realistic test reservation is worth requesting from every vendor. The demo should show the system reading the correct fields, applying the property's rules, writing an approved change, and logging the result. Delight.ai's [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) can combine structured customer data with conversation history, but the integration still needs to be proven for each hotel's stack.
### Can it complete the request, or only describe one?
A capable AI concierge should move an approved guest request toward completion. A policy answer has value, yet the operational result comes from checking eligibility, updating the connected system, confirming the outcome, and preserving context for the next interaction.
A multi-step scenario in every vendor demo reveals more than a list of supported integrations, and a late checkout tied to availability, a fee, and a folio update is a good one to request. It also shows where the platform needs human approval and how it communicates that handoff to the guest.
### What happens when it doesn't know the answer?
A reliable platform should recognize uncertainty, apply the hotel's escalation policy, and preserve a reviewable record of what happened. [McKinsey's AI Trust Maturity Survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found that security and risk concerns were the leading barrier to scaling agentic AI, with inaccuracy and cybersecurity among the most frequently cited risks.
Testing an ambiguous policy question, an unavailable system, and a request outside the agent's permission is worth requesting from each vendor. The response should reveal how the system handles its own uncertainty, whether it hands off to a person, and whether the exchange leaves a log a manager can review later. [Trust OS](https://delight.ai/trust-os) supports this by grading responses for quality, flagging unsafe or off-policy content before a guest sees it, and logging every tool call so a manager can trace exactly what the AI did and why.
### In closing
A digital concierge earns operational scope by resolving routine work reliably and escalating the moments that require human judgment. That balance gives front-desk and guest-service teams more time for recovery, empathy, and high-touch service.
These three tests are enough to narrow the shortlist, after which the finalists are worth comparing against one property, one channel, and one measurable workflow. [See how Delight.ai supports hospitality](https://delight.ai/industries/travel-and-hospitality) as you build that evaluation.
- [What counts as a digital concierge for hotels?](#section-anchor-id-0 "What counts as a digital concierge for hotels?")
- [How we evaluated these digital concierge platforms](#section-anchor-id-1 "How we evaluated these digital concierge platforms")
- [Digital concierge platforms compared](#section-anchor-id-2 "Digital concierge platforms compared")
- [Delight.ai](#section-anchor-id-3 "Delight.ai")
- [Decagon](#section-anchor-id-4 "Decagon")
- [Canary Technologies](#section-anchor-id-5 "Canary Technologies")
- [HiJiffy](#section-anchor-id-6 "HiJiffy")
- [Duve](#section-anchor-id-7 "Duve")
- [Akia](#section-anchor-id-8 "Akia")
- [Whistle by Cloudbeds](#section-anchor-id-9 "Whistle by Cloudbeds")
- [How should a hotel choose between these platforms?](#section-anchor-id-10 "How should a hotel choose between these platforms?")
## Frequently asked questions
Common questions about digital concierge platforms for hotels.
**How is a true AI concierge different from a hotel chatbot?**
A true AI concierge interprets natural language, uses connected context, and can trigger approved actions across systems. A scripted hotel chatbot usually matches a guest's words to predefined answers or decision trees. The practical test is whether the system can complete a multi-step request, preserve context across a later interaction, and explain its handoff when human judgment is required.
**Does a digital concierge replace the front-desk and guest-service team?**
No, a digital concierge supports the front-desk and guest-service team rather than replacing it, handling repetitive requests and carrying context between interactions so staff can focus elsewhere. Human staff remain essential for service recovery, sensitive decisions, physical assistance, and moments that depend on empathy. Gartner reports that more than 80% of organizations plan to expand human agent responsibilities as AI reshapes frontline service roles.
**How long does implementation take?**
Implementation time depends mainly on how deeply the platform needs to integrate with hotel systems and how ready the property's own data and staff are to support it, not on the vendor alone. A focused first use case can launch quickly, while a multi-property rollout across channels and systems requires more validation. Deloitte's enterprise AI research identifies insufficient worker skills as the biggest barrier to integrating AI into existing workflows, which makes operating readiness part of the timeline.
**What's the realistic ROI case for a digital concierge?**
The realistic ROI case combines lower service effort, recovered staff capacity, and incremental guest revenue. Hotels can build that model around a handful of concrete numbers: what it costs to resolve a request, how quickly the guest gets an answer, how many staff hours come back, and how much upsell or recovered-booking revenue the platform actually drives. Vendor averages provide reference points, but a credible business case uses the property's own interaction volume, baseline performance, and approved revenue assumptions.
**Is a digital concierge the same thing as a virtual front desk?**
Not exactly — a digital concierge and a virtual front desk are distinct service models. A virtual front desk typically connects guests with remote human staff through phone or video. A digital concierge is software instead, handling information and approved workflows through an app, messaging channel, voice interface, or AI agent, with human escalation available when the request requires it.
## Keep reading
[View more](/blog "View more")
[](/blog/industry/hipaa-compliant-ai-agent)
What is a HIPAA compliant AI agent?](/blog/industry/hipaa-compliant-ai-agent)
[](/blog/industry/answering-service-for-home-services)
What is an answering service for home services?](/blog/industry/answering-service-for-home-services)
[](/blog/ai-for-cx/conversational-ai-for-retail)
Conversational AI for retail](/blog/ai-for-cx/conversational-ai-for-retail)
[](/blog/industry/vendor-consolidation-customer-service)
What vendor consolidation means for the modern customer service stack](/blog/industry/vendor-consolidation-customer-service)
[View more](/blog "View more")
## Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Delight.ai plumbing answering service for after-hours calls
Source: https://delight.ai/blog/industry/plumbing-answering-service
## Delight.ai plumbing answering service for after-hours calls
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec
Content engineer](https://delight.ai/author/caroline-adamec)
August 27 2026
### Key takeaways
- **About 1 in 3 calls to a plumbing company's Delight.ai voice agent came in after hours** or on weekends from July to September 2026.
- **Delight.ai sorts after-hours calls by urgency**, so a burst pipe goes straight to the on-call plumber and a running toilet gets booked for the morning without waking anyone up.
- **Agent Steward works your systems while the caller is still on the line**, checking open slots in ServiceTitan or Housecall Pro and booking the job before the call ends.
A plumbing answering service built on Delight.ai answers every call, triages the problem, books the visit, and follows the job through to completion. The [Bureau of Labor Statistics](https://www.bls.gov/ooh/construction-and-extraction/plumbers-pipefitters-and-steamfitters.htm) notes that plumbers are often on call for emergencies, so evening and weekend work is common. In practice, this means that the person who could answer the phone is usually under a sink on another job.
Delight.ai's [AI booking agent](https://delight.ai/ai-booking-agent) is built for those after-hours calls. **From July to September 2026, 33% of calls to one plumbing company's Delight.ai voice agent came in outside weekdays from 9am to 5pm PST, or on a weekend.**
Each of those calls is a homeowner who needs help and will likely try the next plumber on the list if nobody picks up. [CallRail's 2025 benchmark](https://www.businesswire.com/news/home/20250114793850/en/CallRail-Releases-Report-Benchmarking-Marketing-Efforts-for-Small-Businesses) found that up to 85% of callers whose calls go unanswered won't call back.
> We see the same pattern with Harper, the Delight.ai agent on our own website. From July to October 2026, **49% of Harper's U.S. leads started chatting on a weekend or outside business hours in every U.S. time zone.**
## The plumbing problems Delight.ai handles
Some after-hours callers are watching water spread across their kitchen floor, and others are finally have a chance to call about a toilet that's been running for a week. Both types of callers deserve a booked appointment, but only one should wake up your on-call plumber. A plumbing answering service built on Delight.ai tells them apart at any hour, so emergencies move fast and routine jobs get booked without anyone on your team touching the call.
### Active running water emergencies
Every hour of active running water spreads damage on to floors, drywall, and the rooms below. [Triple-I's homeowners loss data](https://www.iii.org/fact-statistic/facts-statistics-homeowners-and-renters-insurance) puts the average water damage and freezing claim at $15,400 for 2019 to 2023. The sooner a caller reaches the main shutoff and a plumber is dispatched, the less damage the crew has to deal with when they arrive.
That's why the Delight.ai [Voice Agent](https://delight.ai/voice) opens by asking whether water is still flowing, whether the caller can reach the shutoff, and whether anyone is in danger. Those answers tell the agent how urgent the call is, so an active leak goes straight to your on-call plumber with the address and details attached. That gives your plumber more time to focus on fixing the problem, and less time spent on administrative intake.
A slow drip gets the next open appointment instead of a 2am call to your emergency technician, meaning your on-call team is rested for the jobs that need them. However, these jobs still get booked, avoiding potential income loss. Small leaks add up too, since the [EPA's WaterSense program](https://www.epa.gov/watersense/fix-leak-week) estimates that an average household's leaks can waste more than 9,300 gallons of water a year.
### Closing the vocabulary gap between homeowners and plumbers
Plumbers and homeowners rarely use the same words for the same problem. In [HomeServe's 2025 State of the Home survey](https://www.homeserve.com/en-us/media/homeServe-survey-reveals-alarming-gap-between-homeowner-preparedness-and-reality-of-home-repair-emergencies/), blocked or overflowing toilets were among the most common home emergencies, at 26%. A homeowner with one might say "the toilet's backing up," so an intake script written in trade terms leaves that caller confused or the job gets booked with the wrong details.
A plumbing company working closely with Delight.ai ran into this while testing a voice agent greeting that asked callers what was going on "with your system or fixture." The company pointed out that a caller could interpret "fixture" as a light fixture, which it doesn't service, or a plumbing fixture, which it does. Delight's [Trust OS guardrails](https://delight.ai/trust-os) allowed the misunderstanding to be caught quickly, and rewritten to simply ask callers to describe the problem they were having, mitigating the confusion.
The fix was a one-line edit to the agent's instructions, which Delight.ai calls Actionbooks, made during testing before live callers heard the confusing version. Actionbooks are written in plain language, so operations teams can make changes like this as soon as they spot a problem instead of waiting on engineering. Every caller after that gets a clearer conversation and a more accurate booking.
### Unexpected follow-up appointments or rescheduled jobs
Plenty of plumbing jobs run past the first appointment. A camera inspection turns a drain clearing into a sewer line repair, or a water heater repair stalls for a week because the replacement part is on backorder. These are the jobs where homeowners lose patience, because they've already had a plumber out, but they still don't have hot water.
[Agent Steward](https://delight.ai/autonomous-agent), Delight.ai's feature for owning a multi-step case from start to finish, tracks the delay and sends the homeowner a revised arrival date as soon as the part slips. It also offers new appointment windows automatically, so the homeowner hears about the delay from you before they have to call to follow up.
If the homeowner does call back, [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) carries the address, the original problem, and the job status into the conversation. Making a frustrated customer repeat their story is how a delayed job turns into a bad review or a call to a different plumber next time. Picking up where the job stands keeps that customer with you and keeps routine status calls off your office's plate.
### Plumbing calls covered by a home warranty
Warranty plumbing calls involve the homeowner, the warranty company, and the plumber, and the homeowner usually ends up stuck in the middle. They just want to know whether the repair is covered and when someone is coming. A traditional answering service can't answer either question, so one leak turns into a chain of callbacks.
Delight.ai checks the homeowner's plan during the call, explains what's covered, and confirms it by text before booking the visit. Claim status updates then reach the homeowner and the warranty company automatically as the job moves. The homeowner gets a clear answer in one conversation, and your team skips the back-and-forth that slows every warranty job down.
### Estimates that never close
A repipe or water heater replacement quote often sits unanswered while the homeowner thinks it over or gets a second opinion. By then, the lead came in, a plumber drove out, and someone wrote up the quote, so an unsold estimate is a job you've already paid to win.
Delight.ai follows up on unsold estimates automatically across voice, text, and email, and sends service agreement renewal reminders before the busy season. Your team recovers those jobs without anyone keeping a follow-up list.
## What happens on a plumbing call with Delight.ai?
Most of the work on a Delight.ai call happens at the same time. While the homeowner is still talking, Agent Steward checks technician availability in ServiceTitan or Housecall Pro, texts open slots, and writes the booking back into your system. The call ends with a confirmed job instead of a promised callback, which is where a traditional answering service loses the customer.
| What's happening on the call | What Delight.ai does | Feature behind it |
|---|---|---|
| Homeowner calls at 11pm | Answers right away, in English or Spanish | Voice AI Agent |
| Caller gives name and address | Verifies identity and plan | Interactive forms |
| Caller describes the problem | Triages urgency and escalates emergencies | Actionbooks |
| Caller wants an appointment | Checks live availability and texts open slots | Agent Steward |
| Caller picks a slot | Books the job and confirms the technician | ServiceTitan or Housecall Pro sync |
| Caller asks about the visit | Shares the time, keeps technician notes internal | Actionbook sharing rules |
| A part is delayed | Sends a new arrival date and windows | Agent Steward and AMP |
The same flow holds under peak demand. [Dr. Cool Services](https://delight.ai/customers/dr-cool-services), an HVAC and plumbing company, uses the Delight.ai Voice AI Agent to answer every call instantly through peak season and book appointments in English and Spanish. When demand spikes, appointment windows widen from 4 to 6 hours, and callers hear why.
> "Our customers don't want to navigate phone trees or wait on hold. They want to talk to someone who can help."
> Mark Farley, President, Dr. Cool Services, 2026
## Delight.ai vs. a traditional plumbing answering service
The difference between a traditional plumbing answering service and Delight.ai is clearest on after-hours emergency calls, which are also some of a plumber's most valuable jobs. [Angi's emergency plumber cost guide](https://www.angi.com/articles/emergency-plumber-cost.htm) reports that emergency plumbers charge 1.5 to 3 times the standard rate. When an emergency call ends as a message instead of a booking, that premium job often goes to whoever answers next.
| Question | Traditional answering service | Delight.ai |
|---|---|---|
| What does the caller hang up with? | A message and a promised callback | A confirmed technician and time |
| What happens on a storm night? | Callers wait for a free operator | Every call gets answered |
| Who handles parts delays? | Your office, the next morning | Agent Steward, on the same case |
For the full breakdown of message-taking services, IVR menus, and AI booking agents, see [how an AI booking agent compares to answering services](https://delight.ai/ai-booking-agent#compare).
## Setting up Delight.ai for a plumbing company
Implementation takes 1 to 2 weeks for home services teams, and setup covers the systems and rules your office already runs on:
- **Field service software**, with ServiceTitan, Housecall Pro, and custom CRMs syncing appointments and job status in real time.
- **Intake and escalation rules**, written by your operations team as Actionbooks and reviewed before launch.
- **English and Spanish** on the same line, with support for 29+ languages from one agent.
- **Security**, backed by a SOC 2 Type II report and logged, timestamped interactions.
After-hours calls are where plumbing jobs are won or lost, because a homeowner calling at 11pm will hire whoever answers. With a plumbing answering service built on Delight.ai, every call gets triaged and booked while the caller is on the line. Your on-call plumber only hears about true emergencies, and every job stays on one case until it's done.
[See how Delight.ai works for home services](https://delight.ai/industries/home-services-and-warranty). For a look across other trades, read our guide to [answering services for home services](https://delight.ai/blog/industry/answering-service-for-home-services).
- [The plumbing problems Delight.ai handles](#section-anchor-id-0 "The plumbing problems Delight.ai handles")
- [What happens on a plumbing call with Delight.ai?](#section-anchor-id-1 "What happens on a plumbing call with Delight.ai?")
- [Delight.ai vs. a traditional plumbing answering service](#section-anchor-id-2 "Delight.ai vs. a traditional plumbing answering service")
- [Setting up Delight.ai for a plumbing company](#section-anchor-id-3 "Setting up Delight.ai for a plumbing company")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
## Frequently asked questions
### What's the best plumbing answering service?
The best plumbing answering service finishes the work during the call instead of leaving your team a callback list. It's worth looking for one that separates active leaks from routine repairs, books a confirmed window while the caller is on the line, and writes the job into ServiceTitan or Housecall Pro. Delight.ai covers that full path, from the first ring through parts delays and return visits.
### How does an AI answering service handle plumbing emergencies after hours?
An AI answering service handles a plumbing emergency by asking urgency questions first, such as whether water is still flowing and whether the caller can reach the main shutoff. With Delight.ai, an active leak routes to the on-call plumber right away with the address and answers attached, while a routine repair gets the next open appointment. A suspected gas leak can follow a safety script your team sets, telling the caller to leave the property first.
### How much does a plumbing answering service cost?
Traditional answering services often charge a monthly bundle of minutes plus per-minute overages, so a busy storm month can cost well past the advertised rate. Delight.ai uses conversation-based usage pricing, and unit costs decrease at higher volume. Cost per booked job is the more useful comparison, since a service that only takes messages leaves the booking work with your office.
### Does a plumbing answering service work with ServiceTitan or Housecall Pro?
It depends on the provider, since some integrations only log a lead for a callback. Delight.ai connects with ServiceTitan, Housecall Pro, and custom CRMs, reading live technician availability during the call and writing confirmed appointments back in real time. Appointment and job status stay in sync, so your dispatch board matches what the caller was told.
### Can a plumbing answering service book jobs during the call?
Some can, and others only take messages. With Delight.ai, open appointment windows are texted to the caller while the voice call continues, the caller confirms with one tap, and the booking is written back to your dispatch system before the call ends. English- and Spanish-speaking callers go through the same booking flow.
### Will a plumbing answering service replace my dispatcher or office staff?
No. A plumbing answering service frees your dispatcher and office staff from routine intake, booking, and status calls, so they can focus on the calls that need judgment. With Delight.ai, complex warranty questions, upset customers, and unusual situations go to your team with the full call history attached.
## Keep reading
[View more](/blog "View more")
[](/blog/ai-agent/best-ai-voice-agents)
[](/blog/industry/voice-ai-appointment-booking-home-services)
[](/blog/insights/how-to-hire-your-first-cx-engineer)
[](/blog/product/introducing-secure-caller-keypad-input)
[View more](/blog "View more")
## HVAC answering service: What to know before you choose one
Source: https://delight.ai/blog/industry/hvac-answering-service
## HVAC answering service: What to know before you choose one
[Caroline Adamec — Content Engineer](https://delight.ai/author/caroline-adamec)
August 27 2026
**Key takeaways**
- HVAC calls follow the weather, carry urgent safety questions, and often lead to multi-visit jobs. **An effective answering service needs workflows built around those realities.**
- **Live services provide a person on each call**, while **AI-native services add concurrent-call capacity and consistent intake** during surges.
- **Pricing varies across both AI and live categories**, so compare the cost of a confirmed booking and the work required after the call.
- A job-lifecycle service extends beyond intake and booking to coordinate dispatch changes, parts delays, warranty follow-up, and customer re-engagement, which is where Delight comes in.
## What is an HVAC answering service?
**An HVAC answering service handles calls for a heating and cooling business** when the in-house team is unavailable or already at capacity. It can cover evenings, weekends, overflow periods, and peak-season surges. Depending on the service, it may also qualify the request, schedule an appointment, transfer an emergency call, or create a job in field service software.
Not every provider marketed as an HVAC answering service does the same job, and the difference rarely shows up on a sales page. The useful distinction is operational depth:
- **Message-taking services** capture contact details for a callback.
- **Booking services** reserve an appointment directly.
- **Job-lifecycle service** retain context after the call and help coordinate the updates that follow diagnosis, parts ordering, rescheduling, and completion.
## What makes HVAC calls different from other home-service calls
**HVAC calls combine weather-driven demand, urgent comfort and safety concerns, and jobs that can span several visits**. Those conditions put more pressure on intake and dispatch than a generic call script can handle.
The caller may know only that the house is getting hotter or colder. The answering service has to capture symptoms, household risk factors, location, equipment details, and scheduling constraints without attempting a technical diagnosis. That information gives the on-call team a clearer starting point.
### Seasonal call spikes and no-heat/no-AC urgency
Heating and cooling demand follows a pronounced seasonal pattern. [The U.S. Energy Information Administration](https://www.eia.gov/todayinenergy/detail.php?id=42915) reports that electricity load is generally highest during summer afternoons as air-conditioning use rises, lower in the spring and fall, and shaped throughout the year by weather. That pattern helps explain why HVAC operators prepare for sharp changes in service demand during heat waves and cold snaps, even though electricity use is not a direct measure of service calls.
Peak demand can exceed the capacity of a fixed office team within hours. A useful HVAC answering service should show:
- How it handles concurrent calls
- How it preserves the order of urgent requests
- How it applies the operator's escalation rules
The [EIA's seasonal electricity analysis](https://www.eia.gov/todayinenergy/detail.php?id=42915) provides the broader demand context, and [Dr. Cool Services' experience](https://delight.ai/customers/dr-cool-services) shows how peak summer volume affects a working HVAC dispatch operation: during Las Vegas summers that regularly top 115°F, the company's call volume surged, leading to hold times increasing and calls slipping to voicemail, until a voice AI agent took over answering every call instantly and booking appointments without a queue.
### Multi-visit refrigerant and compressor jobs
**Complex repairs often continue after the first appointment**. A technician may diagnose a refrigerant leak or compressor failure, then have to confirm warranty or insurance coverage where it applies, locate a part, and schedule a return visit. Each change creates a customer communication task tied to the same job.
An answering service that retains job context can give a returning caller a useful status update and route exceptions with the relevant history attached. This [home-services guide](https://delight.ai/blog/industry/ai-receptionist-home-services) describes the multi-visit coordination problem across HVAC and related trades. The practical evaluation question is whether the answering service can recognize the existing job across calls and channels.
### Manufacturer warranty and maintenance-plan coordination
HVAC work involving a warranty adds equipment details, eligibility checks, approvals, and documentation to the job. Maintenance plans create a different cadence through seasonal tune-ups, renewal reminders, and follow-up. Both warranty and renewal workflows begin with accurate intake and continue well beyond the initial booking.
It can be helpful to research how the answering service stores warranty status and plan membership, which system holds the source record, and what happens when the information is incomplete. A strong workflow within an answering service surfaces the missing detail for a staff member and preserves the customer's place in the process.
## Which type of HVAC answering service fits your business?
The right type of HVAC answering service depends on your call mix, surge pattern, escalation needs, and field service workflow. Live services place a person on the call, which can be valuable when emotional judgment dominates the interaction, while AI-native services handle many calls concurrently and apply the same intake logic across every one of them.
| Evaluation point | Live answering service | AI-native answering service |
|---|---|---|
| Staffing model | Human receptionist handles each conversation | AI voice agent handles conversations concurrently |
| Pricing examples | Per-minute and custom plans are common | Subscription, usage, and custom models all exist |
| Human judgment | Present on every answered call | Available through a configured handoff path |
| Surge planning | Confirm reserved staffing and overflow rules | Confirm tested concurrency and escalation capacity |
The categories can also overlap with some vendors. Smith.ai, for example, offers AI-first, hybrid, and human-first models, so a label alone will not establish fit.
If you're looking for the best fit for your business, try testing each vendor with the same urgent call, routine maintenance request, existing-job status call, or bilingual scenario that your office handles today.
## How much does an HVAC answering service cost?
HVAC answering service cost depends on the provider's pricing model, included usage, call duration, workflow complexity, and add-on fees. AnswerForce states that its plans are charged by the minute and include a monthly minute allowance. AnswerPro advertises flexible pricing for different call volumes without publishing an HVAC rate on its service page.
AI-native pricing also varies. [NextPhone](https://www.getnextphone.com/answering-service-for-hvac) publishes a $199 monthly HVAC plan with unlimited calls, while many other providers require a quote. Treat any vendor's advertised rate as the beginning of the calculation.
A setup fee, an extra phone number, a live transfer, or a per-minute overage during a heat-wave can each add to that starting number, so ask for the fully loaded monthly cost rather than the base rate alone.
**Cost per booked job is a more useful operating measure than the plan price by itself.** Track qualified calls, confirmed appointments, transfer success, cancellations, and staff rework. These measures reveal whether the service captures revenue and reduces administrative effort.
## How does an HVAC answering service handle emergency calls?
An HVAC answering service handles emergency calls by collecting the right details, applying the contractor's escalation policy, and routing the call to the appropriate response. It should identify comfort urgency and immediate safety hazards as separate paths.
For a no-heat or no-AC call, useful intake includes:
- **Current conditions:** Capture indoor temperature when available, how long the system has been down, and whether the failure is complete or intermittent.
- **Household risk:** Ask whether infants, older adults, or people with relevant medical conditions are in the home. [The CDC identifies these groups](https://www.cdc.gov/carbon-monoxide/about/index.html) as more vulnerable.
- **Safety signals:** A gas odor, carbon monoxide alarm, symptoms of exposure, smoke, or fire should trigger the operator's emergency script. [CDC guidance](https://www.cdc.gov/natural-disasters/safety/index.html) advises people who suspect a gas leak to leave the building and contact emergency authorities. A service call can follow after the location is safe.
The service should then transfer the call to the on-call technician, create an urgent job, or provide the next approved step from the HVAC company's policy.
### How Delight.ai handles HVAC emergency calls
[Dr. Cool Services](https://delight.ai/customers/dr-cool-services) uses Delight.ai's Voice AI Agent to answer peak-season calls without hold times, book appointments end to end, and speak with callers in English and Spanish. The workflow preserves human judgment for complex warranty questions, upset customers, and unusual situations.
> "Our customers don't want to navigate phone trees or wait on hold. They want to talk to someone who can help."
>
> — Mark Farley, President, Dr. Cool Services, 2026
## Does an HVAC answering service integrate with ServiceTitan, Jobber, or Housecall Pro?
Yes, many HVAC answering services integrate with ServiceTitan, Jobber, or Housecall Pro, although the actions available through each connection vary. A vendor's integration might do no more than log a lead for someone to call back later, or it might read live availability and write a confirmed appointment during the call itself, and that gap is what actually determines whether the integration saves your office any work.
Ask the vendor to demonstrate the exact workflow in your system, and verify:
- Which fields the integration reads and writes
- How quickly data appears on your dispatch board
- How duplicate records are prevented
- What happens when the integration fails
A live booking claim should include a real-time availability check and a confirmed write-back during the call.
Delight.ai documents live ServiceTitan and Jobber connections in its [home-services guide](https://delight.ai/blog/industry/ai-receptionist-home-services), and it integrates with [ServiceTitan, Housecall Pro, and other custom CRM connections](https://delight.ai/industries/home-services-and-warranty). Confirm the required objects and actions during technical scoping because each HVAC operation configures its dispatch stack differently.
## What to look for in an HVAC answering service
Prioritize documented workflows that match your call types, dispatch rules, and software. A polished demo shows conversation quality, but a scenario-based test shows whether the service can support daily operations.
- **Emergency triage:** Confirm that the service separates comfort requests from safety hazards and follows your approved escalation policy.
- **Scheduling accuracy:** Test live availability, skill and service-area rules, appointment write-back, and failure handling.
- **Bilingual coverage:** Test the full booking flow in every language you promise customers, including transfers and confirmation messages.
- **Job memory:** Check whether a status call connects to the existing job, technician notes, parts status, and next appointment.
- **Human handoff:** Verify the trigger, response time, receiving team, and context transferred with the call.
- **Reporting:** Require visibility into answer rate, booked jobs, transfers, outcomes, errors, and staff interventions.
Use examples from your own call recordings and dispatch board during evaluation. A vendor that performs well on your hardest recurring scenarios gives you better evidence than a generic scripted demo.
## Answering services worth knowing about
These vendors represent several common service models.
| Vendor | Model | What it offers |
|---|---|---|
| [AnswerForce](https://www.answerforce.com/hvac-answering-service) | Live, human-staffed | 24/7 HVAC answering and emergency dispatch support, billed per-minute. |
| [AnswerPro](https://answerpro.com/industries/hvac-answering-service/) | Live, human-staffed | 24/7 HVAC answering and emergency dispatch support, with flexible pricing and no published HVAC rate. |
| [Nexa](https://www.nexa.com/industries/hvac/) | Live, human-staffed | Phone, text, and chat coverage around the clock, plus appointment scheduling and bilingual call answering. |
| [NextPhone](https://www.getnextphone.com/answering-service-for-hvac) | AI-native | Flat monthly price with ServiceTitan, Housecall Pro, Jobber, and FieldEdge connections. |
| [Smith.ai](https://smith.ai/) | AI-first, hybrid, or human-first | Configurable AI-first, hybrid, or human-first receptionist models, with human agents available for handoffs. |
| [Delight.ai](https://delight.ai/home-services-and-warranty) | AI-native, full job lifecycle | Inbound call answering plus booking over voice and SMS, contractor coordination, parts updates, warranty claims, and re-engagement, with live ServiceTitan, Jobber, and Housecall Pro connections. |
## Why Delight is the most comprehensive answering service for HVAC
Delight.ai has a broader job-lifecycle scope. Its [home-services and warranty platform](https://delight.ai/home-services-and-warranty) covers inbound calls, booking over voice and SMS, contractor coordination, parts updates, warranty claims, and re-engagement. In practice, that means the same platform that booked the original appointment can also text a homeowner an updated arrival window when a part is delayed, without a staff member re-explaining the job from scratch.
Choose an HVAC answering service by tracing what happens from the first ring through the completed job. Call pickup protects the initial opportunity, while accurate booking, informed handoffs, and timely updates protect the customer relationship that follows.
Explore how [Delight.ai supports the home-services job lifecycle](https://delight.ai/home-services-and-warranty) across those stages.
- [What is an HVAC answering service?](#section-anchor-id-0 "What is an HVAC answering service?")
- [What makes HVAC calls different from other home-service calls](#section-anchor-id-1 "What makes HVAC calls different from other home-service calls")
- [Which type of HVAC answering service fits your business?](#section-anchor-id-2 "Which type of HVAC answering service fits your business?")
- [How much does an HVAC answering service cost?](#section-anchor-id-3 "How much does an HVAC answering service cost?")
- [How does an HVAC answering service handle emergency calls?](#section-anchor-id-4 "How does an HVAC answering service handle emergency calls?")
- [Does an HVAC answering service integrate with ServiceTitan, Jobber, or Housecall Pro?](#section-anchor-id-5 "Does an HVAC answering service integrate with ServiceTitan, Jobber, or Housecall Pro?")
- [What to look for in an HVAC answering service](#section-anchor-id-6 "What to look for in an HVAC answering service")
- [Answering services worth knowing about](#section-anchor-id-7 "Answering services worth knowing about")
- [Why Delight is the most comprehensive answering service for HVAC](#section-anchor-id-8 "Why Delight is the most comprehensive answering service for HVAC")
## Frequently asked questions
Common questions about choosing an HVAC answering service.
### How much does an HVAC answering service cost?
HVAC answering service cost varies by pricing model, call volume, duration, integrations, and human support. AnswerForce uses per-minute plans with included monthly minutes, NextPhone publishes a $199 monthly HVAC plan with unlimited calls, and other providers use custom quotes.
Compare total monthly cost with qualified calls, confirmed bookings, transfer outcomes, and staff rework to understand the service's operational value.
### How does an HVAC answering service handle a no-heat or no-AC emergency?
An HVAC answering service handles a no-heat or no-AC emergency by collecting current conditions, household risk factors, equipment symptoms, and location details before applying the contractor's escalation rules. Safety signals follow a separate emergency script. A gas odor, carbon monoxide alarm, smoke, or symptoms of exposure should direct the caller to leave and contact emergency authorities according to approved safety guidance.
### Does an HVAC answering service integrate with ServiceTitan, Jobber, or Housecall Pro?
Yes, many HVAC answering services connect with ServiceTitan, Jobber, or Housecall Pro, though integration depth varies by vendor. Ask whether the service can read current availability, create or update the correct record, write a confirmed appointment during the call, and recover cleanly from a failed sync.
Test those actions in your configured environment before signing a contract.
### Can an HVAC answering service handle seasonal call surges?
Yes, an HVAC answering service can handle seasonal surges when the provider has enough concurrent capacity and a tested overflow plan. Live providers should explain staffing commitments, queue behavior, and peak pricing, while AI-native providers should document concurrency, latency, handoff capacity, and failure handling.
Use your busiest historical hour as the benchmark for a load test.
### Is a live or an AI-native answering service better for HVAC?
The better HVAC answering service is the one that performs reliably on your call mix and dispatch workflow. Live coverage places human judgment on every call, while AI-native coverage adds concurrent capacity and consistent structured intake, with human escalation configured for sensitive cases.
Evaluate both models against the same emergency, booking, status, bilingual, and integration scenarios.
## Keep reading
[View more](/blog "View more")
[](/blog/industry/plumbing-answering-service) [What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[](/blog/ai-for-cx/best-customer-experience-solutions) [Best customer experience solutions: 7 platforms compared](/blog/ai-for-cx/best-customer-experience-solutions)
[](/blog/ai-agent/conversational-ai-use-cases) [Conversational AI use cases: Real examples by industry](/blog/ai-agent/conversational-ai-use-cases)
[](/blog/industry/digital-concierge-for-hotels) [The best digital concierge platforms for hotels](/blog/industry/digital-concierge-for-hotels)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Best customer experience solutions: 7 platforms compared
Source: https://delight.ai/blog/ai-for-cx/best-customer-experience-solutions
## Best customer experience solutions: 7 platforms compared
[](https://delight.ai/author/cheryl-chai) Cheryl Chai — Product marketing lead
August 27 2026
## Key takeaways
- Customer experience software splits into distinct categories, from ticketing tools and CRM-native suites to contact-center platforms and AI-native agents. **The right fit depends on the job you actually need done**.
- The strongest shortlist starts with **the work your team needs to improve**, then weights key product features from different vendors based on your team's use cases for them.
- Delight.ai is the recommended fit for software companies that need **persistent customer context across channels and sessions**, supported by documented results from Mixpanel.
## Shortlist
- [**Zendesk**](https://www.zendesk.com) is the right fit for support teams already running a mature ticketing workflow who want AI layered on top, without a full platform switch.
- [**Salesforce Service Cloud**](https://www.salesforce.com/agentforce) is the strongest fit when service data already lives inside Salesforce.
- [**Delight.ai**](https://delight.ai/ai-customer-experience-platform) is built for software companies whose support conversations are too complex for a scripted bot to resolve alone.
- [**Genesys Cloud CX**](https://www.genesys.com) is a contact-center platform sized for high call volume, not support complexity.
- [**Qualtrics XM for Customer Experience**](https://www.qualtrics.com) is the right fit for teams that need to measure and act on customer sentiment across the business.
- [**HubSpot Service Hub**](https://www.hubspot.com) is the right fit for a company that already runs its CRM on HubSpot and hasn't outgrown a bundled, mid-market support tool yet.
- [**Fin**](https://fin.ai) (formerly Intercom) is the fastest path to a working AI agent for a small, product-led team.
If you're comparing the best customer experience solutions for your software company, the market splits faster than most buying guides admit. Zendesk and Salesforce dominate the general search results, but neither was built for a support conversation that spans three systems and two days. The seven platforms below cover the real range on the market, from ticketing tools to AI-native agents, so you can match the tool to how complex your support conversations are.
For software companies, complexity often comes from different context, like account permissions or billing data, that's spread across several systems. [Delight.ai's AI customer experience platform](https://delight.ai/ai-customer-experience-platform) addresses that environment with a memory layer that follows a customer from a web chat to an app session to a phone call, without making them start over.
## What is a customer experience solution?
**A customer experience solution is software that helps a company understand, manage, or improve customer interactions across the relationship.** The category includes five common platform types, each organized around a different operating job.
- **Helpdesk and service platforms**, such as Zendesk, organize support requests into a shared queue a human team works through.
- **CRM-native service platforms**, such as Salesforce Service Cloud and HubSpot Service Hub, connect support work to customer records used by other teams.
- **Contact-center platforms**, such as Genesys Cloud CX, coordinate voice and digital interactions at enterprise scale.
- **Experience management platforms**, such as Qualtrics, collect and analyze customer signals, then route insights and issues into action.
- **AI-native agents**, like Delight, handle conversations and workflows autonomously, with human escalation available when the case requires it.
The categories overlap as vendors expand. Each platform's center of gravity still shapes implementation, pricing, and ownership.
## What are the benefits and real-world use cases of customer experience solutions?
These five categories translate into concrete, everyday benefits once a team is actually set up.
- A **helpdesk and service platform** can centralize email, chat, social, and phone interactions, then route each case with its history intact.
- A **CRM-native service platform** can show an agent the customer's plan, purchases, account activity, and previous cases in the same workspace.
- A **contact-center platform** can direct high call and messaging volume to the right resource, support workforce planning, and monitor operational performance.
- An **experience management platform** can analyze surveys, calls, digital behavior, and feedback to identify friction and trigger recovery workflows.
- An **AI-native agent** can answer questions with natural language processing, take approved actions in connected systems, and escalate with context when human judgment is needed.
Which of these benefits your team needs most points directly at which of the seven platforms below actually fits.
## 7 best customer experience solutions to consider
These seven platforms cover the categories for mid-market and enterprise software companies.
### Zendesk
**Best for:** Established service teams that want a mature support platform with extensive workflows and integrations.
**What it does:** Zendesk organizes support requests into a shared queue, and its Copilot add-on drafts responses on top of that queue, while its AI agents can complete a multi-step request, like checking an order status in a connected system, before replying.
**Customer proof:** Action Property Management reports [nearly 80% AI automation for chat](https://www.zendesk.com/customer/action-property-management/), an **81% reduction in average first-response time**, and a 20-point increase in customer satisfaction.
**G2 rating:** [4.3 out of 5 from about 6,800 reviews](https://www.g2.com/products/zendesk-for-customer-service/reviews).
**Pricing:** [Suite Team costs $55 per agent monthly](https://www.zendesk.com/pricing/) with annual billing, Suite Professional costs $115, and Copilot adds $50 per agent monthly. Enterprise pricing requires a quote.
**Best fit:** A team that already runs its support operation inside Zendesk's ticketing workflow and wants AI layered on top of it. It's a weaker fit for a conversation that needs to span multiple systems in one continuous thread.
### Salesforce Service Cloud
**Best for:** Companies that use Salesforce as their CRM system of record and want service operations on the same platform.
**What it does:** Salesforce Service Cloud layers case management and routing on top of the same Salesforce instance that already holds a company's sales and account data, with Agentforce adding AI capabilities that act on that data directly.
**Customer proof:** Nexo reports a [**62% case resolution rate**](https://www.salesforce.com/customer-stories/nexo/) using Agentforce, with more than 2,600 hours saved on questions it now answers autonomously.
**G2 rating:** [4.4 out of 5 from about 7,300 reviews](https://www.g2.com/products/agentforce-service-formerly-salesforce-service-cloud/reviews).
**Pricing:** [Service Cloud starts at $25 per user monthly](https://www.salesforce.com/service/pricing/). Enterprise costs $175 per user, Agentforce for Service costs $125 per user, and Help Agent resolutions cost $2 each.
**Best fit:** A company that already runs its CRM on Salesforce and wants service data living in the same system as sales data, rather than a separate support platform bolted on the side.
### Delight.ai
**Best for:** Software companies that need persistent customer context, cross-channel continuity, and governed resolution across systems.
**What it does:** Delight.ai is an AI-native customer experience agent, built for software companies whose support conversations span multiple systems and sessions. Its [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) (AMP) authenticates a user and pulls their account context before a conversation even starts, and Omnipresence carries that same context with a customer as they move from chat to email to voice.
**Customer proof:** Mixpanel's agents previously spent **the first ten minutes of each interaction** identifying the customer and reviewing the account. In an [April 2026 story](https://delight.ai/customers/mixpanel), Delight.ai retrieves that context before responding.
"We're not just answering faster. We're answering smarter." — Kathleen Matthews, Global Support, Mixpanel
**G2 rating:** Delight.ai's parent company, Sendbird, holds a [4.6 out of 5 rating from 124 reviews](https://www.g2.com/products/sendbird/reviews); that rating reflects Sendbird's broader product portfolio.
**Pricing:** Delight.ai uses conversation-based enterprise pricing. [Delight Desk](https://delight.ai/zendesk-migration-desk) has a $0 seat fee, with charges tied to tickets the AI agent resolves.
**Best fit:** A software company whose support cases already outgrew what a ticket-scoped tool like Zendesk or a single-purpose bot can resolve on its own.
### Genesys Cloud CX
**Best for:** Enterprise contact centers that need voice, digital channels, routing, workforce capabilities, and AI on one cloud platform.
**What it does:** Genesys Cloud CX routes voice and digital interactions across a contact center, and its virtual agents and copilots can pick up a routine call or chat directly during a volume spike instead of leaving a caller on hold.
**Customer proof:** Genesys reports a [**20-point Customer Experience Index increase**](https://www.genesys.com/customer-stories/genesys), a 35% shift to chat, 34% faster routing, and 157,000 working hours saved over three years in its own support organization.
**G2 rating:** [4.4 out of 5 from about 1,500 reviews](https://www.g2.com/products/genesys-cloud-cx/reviews).
**Pricing:** [CX 1 costs $75 per named user monthly](https://www.genesys.com/pricing) and CX 2 costs $115 with annual billing. Concurrent and hourly licenses are also available.
**Best fit:** A call-heavy contact-center operation sized for named-user seat pricing, rather than a software company's smaller, more complex support conversations.
### Qualtrics XM for Customer Experience
**Best for:** Enterprises that need to unify customer signals, analyze experience drivers, and trigger closed-loop action.
**What it does:** Qualtrics pulls signals from surveys, calls, chat, and digital behavior into one view, and its Experience Agents can follow up on a specific complaint to clarify what actually went wrong before routing it to a team.
**Customer proof:** TruGreen expanded from 500,000 annual survey responses to [**500 million signals**](https://www.qualtrics.com/customers/trugreen/). It also reports 30% fewer escalations and 33% more human-agent capacity.
**G2 rating:** [4.3 out of 5 from about 750 reviews](https://www.g2.com/products/qualtrics-customer-experience/reviews).
**Pricing:** Qualtrics directs buyers to [request pricing](https://www.qualtrics.com/contact/) for its enterprise Customer Experience packages.
**Best fit:** A team that needs to understand how customers feel about the business, not one trying to resolve support tickets directly.
### HubSpot Service Hub
**Best for:** Growing companies that want service, customer success, and CRM context within HubSpot.
**What it does:** HubSpot Service Hub bundles ticketing and a knowledge base with the same CRM data that already holds a customer's purchase history, and its Breeze AI agent can resolve a routine request using that account context without a rep opening a second tool.
**Customer proof:** Sticos reports that HubSpot's Customer Agent handles [**41% of incoming support**](https://www.hubspot.com/case-studies/sticos), with 75% resolution and 91% chat deflection.
**G2 rating:** [4.4 out of 5 from about 2,900 reviews](https://www.g2.com/products/hubspot-service-hub/reviews).
**Pricing:** HubSpot offers some free tools. [Monthly pricing](https://www.hubspot.com/products/service) starts at $10 per seat for Starter, $100 for Professional, and $150 for Enterprise. Annual pricing and onboarding fees affect the total.
**Best fit:** A company that already runs its marketing and sales through HubSpot and hasn't outgrown a bundled, mid-market support tool yet.
### Fin (formerly Intercom)
**Best for:** Teams that want a quickly deployed AI agent on an existing helpdesk or within the Intercom helpdesk.
**What it does:** Fin resolves a conversation directly across chat, email, and a handful of other channels, following a set procedure to update a connected system like an order-status tool, and handing a case to a person when it hits the edge of what it's configured to do.
**Customer proof:** Anthropic reached a [50.8% resolution rate within about a month of deploying Fin](https://fin.ai/customers/anthropic), with 96% conversation participation and more than 1,700 support hours saved in month one. As that deployment matured, [**Fin reported Anthropic's resolution rate climbing to 79%**](https://fin.ai/customers/anthropic-transformation), with the agent handling 63% of total support volume end-to-end.
**G2 rating:** [4.5 out of 5 from about 3,900 reviews](https://www.g2.com/products/fin/pricing).
**Pricing:** Fin lists [$0.99 per outcome](https://fin.ai/pricing) with a 50-outcome monthly minimum when used with a current helpdesk. Fin with the Intercom helpdesk adds seats starting at $29 per month.
**Best fit:** A small, product-led team that wants AI deflection working quickly. A software company with longer, multi-step support cases will usually need more than Fin's current setup covers. Salesforce signed an agreement on June 15, 2026, to [acquire Fin for approximately $3.6 billion](https://www.salesforce.com/uk/news/press-releases/2026/06/15/salesforce-signs-definitive-agreement-to-acquire-fin/), subject to closing conditions, which is worth weighing for anyone comparing Fin against Salesforce itself.
## How the 7 platforms compare
| Platform | Channel and capability coverage | Pricing model | Best fit |
|---|---|---|---|
| Zendesk | Messaging, email, voice, social, knowledge | Per agent, add-ons, outcomes | Established service teams |
| Salesforce Service Cloud | Cases, digital, voice add-ons, knowledge | Users, add-ons, credits, resolutions | Salesforce-centered companies |
| Delight.ai | Web, app, SMS, email, WhatsApp, voice, social | Conversation pricing; $0 Desk seats | Context-heavy software support |
| Genesys Cloud CX | Voice, digital, routing, workforce tools | Named, concurrent, or hourly | High-volume contact centers |
| Qualtrics XM | Surveys, calls, chat, behavior, social | Custom quote | Signal-led CX programs |
| HubSpot Service Hub | Help desk, knowledge, feedback, success | Free and paid seat tiers | Growing HubSpot teams |
| Fin (formerly Intercom) | Tickets, email, chat, messaging, voice | Outcomes and optional seats | Fast AI-agent deployment |
Zendesk, Salesforce, Genesys, and HubSpot provide broad service platforms. Qualtrics leads with customer signals, while Delight.ai and Fin lead with customer-facing AI resolution.
Delight.ai's strongest fit appears when continuity is the hard problem. AMP builds an evolving customer profile, and Omnipresence **carries it across channels and sessions**. Mixpanel's removal of about ten minutes of account investigation per interaction provides concrete proof.
## How do I choose the right customer experience solution for my business?
Choose the right customer experience solution by defining the job, testing real customer work, and comparing the full commercial model.
1. **Which work should improve first?** Every one of these platforms is built around a different job, from clearing a ticket queue to resolving a conversation outright, so start by naming the specific bottleneck you're trying to fix.
2. **How much context does resolution require?** A billing question that also needs an account lookup takes a different platform than a password reset a script can handle alone.
3. **Which actions can the platform complete on its own?** Some tools only draft a reply for a human to approve; others can update a connected system and hand off with full context only when a case actually needs a person.
4. **How will the team govern it?** A regulated conversation needs an audit trail a compliance team can review, not just a transcript.
5. **What will the full year actually cost?** Seat fees, per-resolution pricing, and onboarding fees add up very differently once volume climbs, so model the total against your own case count, not the sticker price.
Test each finalist against a mix of routine requests and your hardest real cases, the ones that involve a policy exception or a failed integration, and score how each platform actually performs on resolution, context retention, and governance, not just on the demo.
## The bottom line
Support conversations at a software company regularly span multiple systems, like a billing question that also needs an account lookup. That's the specific problem [Delight.ai's Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) is built to solve, and it's worth testing against your own support logs instead of a generic demo, the way Mixpanel did before cutting roughly ten minutes of verification from every case.
Comparing customer experience platforms is about matching what each one is built for to the shape of your own support conversations. If yours are the complex, multi-system kind a scripted bot can't close alone, that's worth testing directly against [how Delight.ai's AI agent handles that complexity](https://delight.ai/ai-customer-experience-platform).
- [Key takeaways](#section-anchor-id-0 "Key takeaways")
- [Shortlist](#section-anchor-id-1 "Shortlist")
- [What is a customer experience solution?](#section-anchor-id-2 "What is a customer experience solution?")
- [What are the benefits and real-world use cases of customer experience solutions?](#section-anchor-id-3 "What are the benefits and real-world use cases of customer experience solutions?")
- [7 best customer experience solutions to consider](#section-anchor-id-4 "7 best customer experience solutions to consider")
- [How the 7 platforms compare](#section-anchor-id-5 "How the 7 platforms compare")
- [How do I choose the right customer experience solution for my business?](#section-anchor-id-6 "How do I choose the right customer experience solution for my business?")
- [The bottom line](#section-anchor-id-7 "The bottom line")
## Frequently asked questions
**Which customer service AI agent platforms receive the highest customer satisfaction ratings?**
Delight.ai's parent company, Sendbird, holds a 4.6 rating from 124 reviews across its broader portfolio. A high rating reflects overall customer satisfaction, not necessarily fit for a specific use case, so weigh it alongside the category and pricing details covered above.
**What's the best customer experience software for a growing software company?**
The best customer experience software for a growing software company matches its case complexity and systems. HubSpot suits teams growing within its CRM, Zendesk offers a mature service platform, and Salesforce offers deep CRM alignment. Delight.ai is the recommended fit for persistent account context across systems, sessions, and channels.
**What are the best alternatives to Zendesk for customer experience?**
Which Zendesk alternative fits best depends on what's driving the switch:
- **Salesforce Service Cloud** for CRM-native service.
- **Genesys Cloud CX** for contact-center scale.
- **HubSpot Service Hub** for connecting service and CRM.
- **Qualtrics** for turning signals into action.
- **Fin** (formerly Intercom) for working across several existing helpdesks.
- **Delight.ai** for persistent memory, channel continuity, and an AI-native desk with a $0 seat fee.
A team leaving Zendesk for cost reasons often lands on HubSpot's bundled tiers; a team leaving because Zendesk's ticketing model can't resolve multi-step conversations on its own is usually looking for an AI-native agent instead.
**Will AI replace traditional CRM and customer service software?**
AI is being added into existing CRM and customer service platforms rather than replacing them outright. Salesforce, HubSpot, and Zendesk have all layered AI agents on top of their existing ticketing and CRM workflows instead of rebuilding around AI from scratch. The bigger shift is toward AI-native platforms built around resolution from the start, which compete alongside the incumbents rather than eliminating the category.
**Is Delight.ai a good fit for B2B SaaS companies?**
Delight.ai is a strong fit for B2B SaaS companies whose support depends on account-level context. Mixpanel previously spent about ten minutes per interaction verifying the customer and reviewing account details; Delight.ai now retrieves that context before the first response. AMP pulls that account context before a conversation starts, Omnipresence carries it across channels, and Desk lets a human step in with the same history the AI agent already has.
**How do I choose between an AI-native platform and an incumbent helpdesk?**
Choose between an AI-native platform and an incumbent helpdesk by testing both architectures against your service operation. An incumbent offers established ticketing, reporting, workforce workflows, and integrations. An AI-native platform is worth evaluating once autonomous resolution, not just faster human handling, is actually the point.
## Keep reading
[View more](/blog "View more")
[ What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[ HVAC answering service: What to know before you choose one](/blog/industry/hvac-answering-service)
[ Conversational AI use cases: Real examples by industry](/blog/ai-agent/conversational-ai-use-cases)
[ The best digital concierge platforms for hotels](/blog/industry/digital-concierge-for-hotels)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Conversational AI use cases: Real examples by industry
Source: https://delight.ai/blog/ai-agent/conversational-ai-use-cases
[](https://delight.ai/author/ishaan-bansal)
Ishaan Bansal — AI engineer, internal operations
August 27 2026
**Key takeaways**
- Software support teams can **use conversational AI to verify an account** before a human ever joins the conversation.
- In healthcare and retail, the technology can **coordinate administrative requests like scheduling and billing with ongoing member care**.
- Mixpanel, Hanssem, and Norse Atlantic Airways report faster verification, higher resolution or containment, and fewer transfers **after deploying Delight.ai**.
## What is conversational AI, and how is it different from a chatbot?
A rules-based chatbot resets with almost every message, but conversational AI holds onto the thread of a conversation. It remembers what a customer already said, works through a request that takes more than one step, and stays with someone even if they switch channels or ask an unplanned follow-up question.
[**Conversational AI**](https://delight.ai/learn/conversational-ai) **is technology that holds a natural-language exchange with a person and uses what was said earlier to shape what it says or does next**, instead of treating each message as a standalone query. An [AI agent](https://delight.ai/ai-agent) adds the ability to work through a process, such as checking an order, authenticating an account, or routing a complex case with the conversation history attached.
This distinction has become urgent for customer service leaders. In a February 2026 survey of 321 service and support leaders, [Gartner found that 91% faced executive pressure to implement AI](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026). Clear use cases help teams focus that investment on customer outcomes and workable service processes.
### Conversational AI vs. a chatbot
Conversational AI and chatbots differ in how they understand requests, retain context, and move work forward.
| Capability | Rules-based chatbot | Conversational AI |
|---|---|---|
| Context | Matches inputs to predefined paths | Interprets open-ended language and uses earlier details in the exchange |
| Continuity | Treats each interaction as a new session | Carries relevant context through a longer exchange and, when supported, across channels |
| Action | Serves an answer from a script or knowledge base | Supports multi-step tasks through connected business systems and defined workflows |
| Escalation | Has no built-in way to recognize uncertainty | Recognizes uncertainty in a well-governed system and routes the case to a person with the available context |
## Conversational AI use cases in software and technology support
Examples show where conversational AI adds value in B2B and product support.
- Account verification for B2B support — [Mixpanel](https://delight.ai/customers/mixpanel) uses Delight.ai to authenticate users and retrieve account context before responding. About 10 minutes of manual account verification removed from each support interaction.
- Scheduling and care coordination (healthcare) — conversational AI can confirm appointments, answer billing questions, send reminders, and route clinical requests to qualified staff, enabling administrative service at any hour with clinical judgment kept in human hands.
- Connected shopping and member care (retail) — [BJ's Wholesale Club](https://delight.ai/customers/bjs) uses Bev for product discovery, coupon guidance, membership questions, order support, and live escalation; members using Bev showed higher conversion and purchasing activity.
- High-volume customer service — [Hanssem](https://delight.ai/customers/hanssem) improved its AI agent through continuous review and refinement: resolution rose from 48% to ~86% over five months, transfers fell 50%, and accuracy on repetitive queries exceeded 90%.
### How does conversational AI cut account-verification time for B2B support teams?
Conversational AI cuts account-verification time by **authenticating a user and retrieving the account details needed to answer the request**. The support conversation can begin with the customer's actual problem because the system has already assembled relevant tier, permissions, history, and configuration data. Before it ever responds, the system:
- Authenticates the customer and confirms identity
- Retrieves account details such as plan tier, permissions, and history
- Surfaces the customer's last troubleshooting step, if present
### Conversational AI outcomes at Mixpanel
At Mixpanel, preliminary administrative work consumed about 10 minutes of each interaction. Kathleen Matthews, Global Support at Mixpanel, described the process in 2026: "Our support agents were spending the first ten minutes of every interaction just figuring out who they were talking to. The actual problem-solving came second."
Mixpanel's deployment uses Delight.ai to authenticate users and retrieve context so that returning customers' plan tier and last troubleshooting step are already on record before the AI responds. That verification now happens immediately, giving customers a relevant answer earlier and giving human agents better context when they need to intervene.
The same mechanism applies across B2B software support: account-aware service distinguishes between permissions issues, unavailable features, and configuration errors before choosing next steps. That precision reduces repetitive lookup work and frees support teams for technical investigation and customer guidance.
## Conversational AI use cases in healthcare
### How does conversational AI support scheduling and care coordination without adding staff?
Conversational AI supports scheduling and care coordination without adding staff by handling high-volume administrative requests at any hour. Patients can confirm appointments, ask about billing, check authorization status, or complete intake steps through the same service channel.
The healthcare workflow spans three connected stages:
- Before the visit: appointment scheduling, reminders, insurance verification, and registration
- During the visit: check-in and real-time billing/authorization handling without pulling staff from care
- After the visit: follow-up scheduling, refill coordination, and escalation to appropriate teams when human judgment is needed
Continuity matters: the AI agent preserves administrative context across channels so staff receive appointment history and other details when cases escalate, saving patients from repeating information.
### What are the limitations of conversational AI in healthcare?
Conversational AI cannot perform actions that require clinical judgment. It supports administrative workflows and routes patients to the right resources, but clinicians retain responsibility for diagnosis, treatment recommendations, and interpreting clinical results.
This boundary requires inspectable governance: escalation rules that staff can point to, audit trails showing routing decisions, and access controls that protect PHI. McKinsey's 2026 AI Trust Maturity Survey highlighted security, risk, and inaccuracy as leading barriers to scaling agentic AI.
Practical boundary:
- Administrative support: appointment changes, billing/insurance questions, reminders, routing forms, and escalating urgent requests
- Clinical responsibility: diagnosis, interpretation of tests, treatment recommendations, and care decisions remain with clinicians
## Conversational AI use cases in retail and ecommerce
### How does conversational AI turn one assistant into the full shopper relationship?
By carrying customer context across discovery, purchase, and care, a conversational AI assistant can move a single conversation from product questions to coupon guidance, account help, order support, or live handoff without losing context.
### How BJ's Wholesale Club uses Bev to connect the shopper relationship
BJ's built Bev, an AI shopping assistant powered by Delight.ai, around continuity. Bev helps members find products, apply coupons, or check renewals and escalates to live agents with the full conversation attached when needed. Scott Vandegrift, VP of Product at BJ's Wholesale Club, said in 2026: "Whether a member is browsing for a grill, looking up club hours, or asking about a renewal, they're talking to the same Bev. That's how a real member-team member relationship is supposed to feel."
Members who engaged with Bev converted at a higher rate and showed greater purchasing activity per visit.
### What results do retailers see from conversational AI?
Retailers that refine conversational AI against real interactions see higher resolution, fewer transfers, and greater capacity. Hanssem's improvements illustrate this with separate measures for resolution, transfer rate, and accuracy.
Hanssem's results by the numbers:
- Resolution rate: rose from 48% to ~86% between May and October 2025
- Transfers to human agents: fell 50%
- Accuracy on repetitive queries: exceeded 90%
How Hanssem achieved this: weekly cycles of reviewing conversations, diagnosing failure modes, and shipping fixes to prompts and workflow logic. "AI doesn't seem perfect from day one. What matters is continuous improvement and clear accountability," said Hyeong-Tak Kim, IT Division Director at Hanssem.
## What business outcomes do companies see from conversational AI?
Companies report faster service, higher automated resolution, and better continuity when conversational AI has access to the right context and a clear operating scope. Key outcomes tied to specific workflows include:
- Faster verification: Mixpanel removed ~10 minutes of manual account research per interaction
- Higher resolution and containment: Hanssem raised resolution to ~86% over five months; Norse Atlantic raised containment from 60% to 80% in two weeks
- Fewer transfers: Hanssem cut transfers to human agents by 50%
- More connected journeys: BJ's uses one assistant across discovery, savings, membership guidance, and care
Broad adoption data: the [2025 Stanford AI Index](https://hai.stanford.edu/ai-index/2025-ai-index-report/economy) reported 78% of surveyed organizations used AI in 2024 (up from 55% in 2023). Generative AI use in at least one business function rose from 33% to 71%.
Customer outcomes depend on surrounding service processes: Mixpanel's account context, Hanssem's five months of refinement, and Norse Atlantic's focused cycle each connected technology to defined operational work and measurable metrics.
## In closing
The common thread is continuous context paired with operational control. Delight.ai's [AI Customer Experience Platform](https://delight.ai/ai-customer-experience-platform) ties this together: memory carries a customer's context from chat into a phone call, and governance shows why the AI acted instead of trusting it blindly.
- [What is conversational AI, and how is it different from a chatbot?](#section-anchor-id-0 "What is conversational AI, and how is it different from a chatbot?")
- [Conversational AI use cases in software and technology support](#section-anchor-id-1 "Conversational AI use cases in software and technology support")
- [Conversational AI use cases in healthcare](#section-anchor-id-2 "Conversational AI use cases in healthcare")
- [Conversational AI use cases in retail and ecommerce](#section-anchor-id-3 "Conversational AI use cases in retail and ecommerce")
- [What business outcomes do companies see from conversational AI?](#section-anchor-id-4 "What business outcomes do companies see from conversational AI?")
- [In closing](#section-anchor-id-5 "In closing")
## Conversational AI use cases FAQs
Explore frequently asked questions about conversational AI use cases across software, healthcare, and retail.
### What's the difference between conversational AI and a basic chatbot?
Conversational AI interprets open-ended language, maintains context, and supports multi-step work. Basic chatbots commonly follow predefined paths or retrieve fixed answers. Conversational AI can connect to business systems and route complex cases with history attached.
### What advantage does conversational AI offer in healthcare?
Conversational AI provides round-the-clock capacity for administrative requests (scheduling, reminders, billing checks, intake) and preserves context across requests while routing clinical issues to qualified staff. Clinicians retain medical judgment.
### Is conversational AI the same as generative AI?
Conversational AI is a system for natural dialogue; generative AI produces new text, images, or audio. A conversational system may use generative models alongside memory and safeguards as part of its design.
### What are the most common conversational AI use cases?
High-volume, repeatable requests: verifying an account before support, confirming appointments, tracking orders, or answering product questions while guiding toward checkout. Humans handle ambiguous or high-stakes cases.
### Does conversational AI replace human agents?
No. In these deployments, conversational AI handles defined, repetitive work and gives humans more time for technical, sensitive, or complex cases. A separate April 2026 [Gartner survey found that 85% of service and support leaders are expanding human agent responsibilities](https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-survey-finds-eighty-five-percent-of-service-and-support-leaders-are-expanding-human-agent-responsibilities-despite-expectations-of-mass-ai-layoffs) as AI handles routine work.
## Keep reading
[View more](/blog "View more")
[
What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[
HVAC answering service: What to know before you choose one](/blog/industry/hvac-answering-service)
[
Best customer experience solutions: 7 platforms compared](/blog/ai-for-cx/best-customer-experience-solutions)
[
The best digital concierge platforms for hotels](/blog/industry/digital-concierge-for-hotels)
[View more](/blog "View more")
### Ready to unleash the power of
AI customer service agents?
[Contact sales](/contact-sales)
## The complete guide to customer experience analytics
Source: https://delight.ai/blog/ai-for-cx/guide-to-customer-experience-analytics
[Ishaan Bansal](https://delight.ai/author/ishaan-bansal)
AI engineer, internal operations
August 28 2026
**Key takeaways**
- **Customer experience analytics** connects customer feedback, behavior, and operational data so you can see where an experience succeeds or breaks down.
- A useful measurement system combines **perception, effort, loyalty, and operational outcomes** across metrics like CSAT, NPS, and resolution rate.
- [**Delight.ai Analytics**](https://delight.ai/analytics) gives teams same-day visibility into resolution, handoffs, and conversation-level risk.
Customer experience analytics turns scattered signals from customer interactions into a clear view of what customers experience. Connect those signals quickly enough, and you can trace friction back to its cause and see whether a fix actually worked.
This guide walks through which data and metrics to track as well as how to turn them into a repeatable analytics program. It also covers how [AI customer experience platforms](https://delight.ai/ai-customer-experience-platform) expand the range of interactions teams can analyze.
## What is customer experience analytics?
**Customer experience analytics is the practice of analyzing data from customer touchpoints to understand and improve the experience.** It combines what customers say, what they do, and what happens when your team or an automated system helps them.
Each source answers a different question. A survey captures a customer's reaction to an interaction, behavioral data shows the path they took, and operational data shows how the business handled the request. Connecting those records often reveals a shared root cause, say, a product workflow failure showing up as both a low satisfaction score and a repeat support contact.
Most businesses already hold this data somewhere, scattered across a support inbox, a CRM, and a survey tool. A reliable program starts by connecting those records around one shared thread, a customer, an account, or a conversation, so teams can investigate a single experience across systems.
## What data actually feeds customer experience analytics?
Customer experience analytics draws on 3 main data types, each of which explains a different part of the customer journey:
- **Feedback data** captures what customers say through surveys, reviews, interviews, and support conversations.
- **Behavioral data** records what customers do, including the features they use, purchases they make, and steps where they abandon a task.
- **Operational data** shows how the business handles an interaction, including wait time, repeat contact, transfers, resolution status, and service-level performance.
The strongest analysis connects all 3 at the customer or account level. Picture a shopper who can't find a clear answer about return eligibility in the help center, abandons the cart, then comes back, completes the order, and contacts support twice about that same return policy. Only by connecting the abandonment, the unanswered question, and the repeat contacts to one shopper does the actual problem come into view, a return policy that isn't explained clearly enough to stop it from costing both a sale and two support tickets.
Data quality sets the ceiling for the analysis. Before teams compare trends across channels, identifiers, event definitions, timestamps, and resolution rules all need to line up. A shared definition of a resolved conversation, for example, prevents one team from counting a closed ticket and another from counting a completed customer outcome as the same event.
## What metrics and KPIs measure customer experience?
No single metric captures customer experience by itself. The right mix depends on the journey stage and the decision at hand, but it should always cover feeling, effort, and value, plus whether AI-assisted support is actually resolving anything.
### Customer satisfaction score (CSAT)
[Customer satisfaction score](https://theacsi.com/company/the-science-of-customer-satisfaction/), or CSAT, measures satisfaction with a specific interaction, product, or service. A common survey asks customers to rate the experience on a 1-to-5 scale, then reports the percentage who selected 4 or 5.
CSAT works well soon after a defined touchpoint because the experience is fresh. It also carries response-bias risk because people with strongly positive or negative experiences may be more likely to respond, so pair the score with response volume, representative sampling, and qualitative feedback.
### Net promoter score (NPS)
[Net promoter score](https://www.bain.com/contentassets/a0e49354bbe74390831ae2b899be35d1/loyalty_insights_1_introducing_the_net_promoter_system_update.pdf), or NPS, asks how likely someone is to recommend a company, product, or service on a 0-to-10 scale. Respondents who select 9 or 10 are promoters, those who select 7 or 8 are passives, and those who select 0 through 6 are detractors. Subtracting the percentage of detractors from the percentage of promoters produces the score.
NPS gives leaders a consistent relationship-level indicator, but the number does not identify its own cause. Segment the results by account type, tenure, product, or journey stage, then connect the score to open-ended feedback and behavior to find a useful explanation.
### Customer effort score (CES)
Customer effort score, or CES, gets at how much work an interaction actually took for the customer, whether that means resolving an issue, completing a task, or just getting an answer. It can expose friction such as repeated authentication, unnecessary transfers, or a support article that sends customers back to the contact queue.
CES scales vary, so the direction of a good score depends on the survey wording. Track the share of responses that indicate high effort and pair that result with the workflow step where customers struggled. This keeps the metric tied to a process your team can change.
### Customer lifetime value and churn rate
Customer lifetime value, or CLV, estimates the revenue or profit a customer represents across the full relationship, while churn rate tracks the percentage of customers or recurring revenue lost in a given period. Together, these metrics connect experience signals to retention and financial performance.
Use a consistent time window and customer definition when you compare these measures with CX data. For a B2B software company, account-level churn and recurring revenue may give a clearer view than individual-user churn because many users can belong to one buying account.
### Resolution, containment, and handoff rate
- **Resolution rate** measures the share of conversations or cases that reach the outcome your team defines as resolved.
- **Containment rate** measures the share completed within an automated channel without a live agent.
- **Handoff rate** measures the share transferred from an AI agent to a person.
These rates answer different operational questions, so teams need to agree on the definition for each internally. One team might close a case at first reply, another only once the customer confirms the fix, and each will report a different number for what looks like the same metric. A rising containment rate is useful only when resolution quality and customer satisfaction remain healthy.
[Hanssem reported](https://delight.ai/customers/hanssem) that its resolution rate increased from 48% to about 86% over 5 months after adopting a Delight.ai agent. Transfers to human agents fell by 50% during the same period. The paired numbers make the case for tracking outcomes and workload side by side, not just one or the other.
[Delight.ai Analytics](https://delight.ai/analytics) brings resolution, handoff, and satisfaction into one conversation-level view and updates the metrics in real time. Teams can trace a change in a rate back to the transcripts, topics, tool calls, and flagged responses that explain it.
## Which touchpoints in the customer journey should you measure?
You should measure the touchpoints that influence a specific customer decision or business outcome, from initial evaluation through support and renewal. Starting with a journey map helps teams choose points where a signal can lead to a concrete action.
- **Before purchase** includes website visits, search, product discovery, pricing evaluation, signup, and trial activation.
- **During use** includes onboarding, feature adoption, account changes, order status, billing, and routine service interactions.
- **When a problem occurs** includes self-service attempts, contact initiation, authentication, transfers, resolution, and follow-up.
Define the event and desired outcome for each selected touchpoint. For example, a warranty claim might include the first contact, identity verification, documentation request, decision, and final resolution. Measuring repeated explanations or transfers at each step gives the team a precise workflow to improve.
Not every touchpoint deserves equal attention. It's important to prioritize the ones with real volume behind them, a clear effect on the customer, or genuine commercial risk if they go wrong. A smaller set of well-defined measures will usually produce clearer action than a broad dashboard filled with events that no team owns.
## How do you build a customer experience analytics program?
You build a customer experience analytics program as a repeating cycle that connects data collection to a measurable operational change:
- **Collect** existing feedback, behavioral, and operational data from the systems that already record the journey.
- **Connect** records through consistent customer, account, conversation, and event identifiers.
- **Analyze** a defined outcome by segment, journey stage, and time period to find the pattern driving it.
- **Act** through an owned change, then compare the same measure before and after implementation.
Start with one decision, such as reducing repeat contacts for a high-volume support topic. That single choice does the rest of the scoping for you, naming the records to connect and the metric to track, plus who owns the change and how long to wait before judging the result. It also gives leaders a clearer return-on-investment case than a dashboard project with no defined action.
[Mixpanel's support team](https://delight.ai/customers/mixpanel) previously spent about 10 minutes at the start of each interaction identifying the customer and checking account context. Delight.ai now handles that identification step automatically, authenticating the user and pulling their account history and tier before it ever responds. It also checks permissions and workflow context, removing the manual verification step from the interaction entirely.
"We're not just answering faster. We're answering smarter," said Kathleen Matthews of Mixpanel's Global Support team in 2026.
Identity and account context now feed directly into the response itself, and Mixpanel can track whether that shift actually improves resolution, effort, and product adoption over time.
## What tools do you need for customer experience analytics?
The right tools for customer experience analytics aren't universal. Where your data already lives shapes the choice, and so does how consistently your team identifies customers across systems. The decisions your program is actually meant to support matter just as much.
| Tool category | Primary role | Best fit |
|-----------------------------------|----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|
| Support or contact-center analytics | Measures conversations, queues, transfers, and service outcomes where support occurs | Teams whose CX data is concentrated in one service platform |
| Standalone CX analytics platforms | Combines surveys, customer records, and journey signals across systems | Organizations with experience data spread across multiple business tools |
| Channel-specific analytics | Examines one touchpoint in depth, such as a website, product, app, or social channel | Teams improving a defined digital journey or channel |
| AI-agent analytics | Connects AI conversations with resolution, handoff, satisfaction, risk, and root-cause data | Teams that use AI agents for a meaningful share of customer interactions |
Integration and data access deserve as much scrutiny as dashboards. A tool that cannot export event-level data or explain how it calculates a metric leaves you unable to verify the numbers it shows you, or move that data into your own systems later.
[Delight.ai Analytics](https://delight.ai/analytics) is built directly into AI agent conversations. It tracks resolution rate, handoff rate, and CSAT together on every conversation as it happens, and flags a low-confidence or risky response before it turns into a customer complaint. [Delight Agent MCP](https://delight.ai/blog/ai-for-cx/customer-service-mcp-ecommerce) extends that further, letting a team ask a plain-language question, like which topic is driving the most handoffs this week, and trace the answer back to the conversations behind it.
## How is AI changing customer experience analytics?
AI is changing customer experience analytics by making conversation-level analysis available across every AI-assisted interaction as it happens. An AI agent creates a record of every conversation the moment it happens, at a scale no manual review process could match.
That scale supports faster root-cause analysis. When handoffs rise for one topic, a team can examine the affected transcripts, see which knowledge or tool calls the agent used, identify the failure pattern, and test a targeted change against the same metric.
[Gartner reported](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) that 91% of 321 customer service and support leaders surveyed in October 2025 felt executive pressure to implement AI. The combination of broad adoption and uneven customer sentiment raises the value of analytics that can distinguish a completed outcome from a fast transfer or an unresolved conversation.
## In closing
Customer experience analytics works best as an operating habit, not a one-time project. Clear definitions and connected records are the foundation, but they only pay off when someone actually owns the action that follows. Together, that combination turns feedback, behavior, and operational data into a same-day view of where customers struggle and whether a change is helping.
As AI-assisted conversations add richer data, teams can measure the customer outcome alongside the system behavior that produced it. Explore the [Delight.ai blog](https://delight.ai/blog) for more guidance on building an AI customer experience practice around measurable results.
- [What is customer experience analytics?](#section-anchor-id-0 "What is customer experience analytics?")
- [What data actually feeds customer experience analytics?](#section-anchor-id-1 "What data actually feeds customer experience analytics?")
- [What metrics and KPIs measure customer experience?](#section-anchor-id-2 "What metrics and KPIs measure customer experience?")
- [Which touchpoints in the customer journey should you measure?](#section-anchor-id-3 "Which touchpoints in the customer journey should you measure?")
- [How do you build a customer experience analytics program?](#section-anchor-id-4 "How do you build a customer experience analytics program?")
- [What tools do you need for customer experience analytics?](#section-anchor-id-5 "What tools do you need for customer experience analytics?")
- [How is AI changing customer experience analytics?](#section-anchor-id-6 "How is AI changing customer experience analytics?")
- [In closing](#section-anchor-id-7 "In closing")
## Frequently Asked Questions
### What's the difference between customer experience analytics and customer analytics?
Customer experience analytics and customer analytics differ mainly in scope. Customer analytics typically examines behavior and commercial activity, such as purchases, product use, and churn. Customer experience analytics connects those signals with direct feedback and operational records to explain how customers experienced a journey and how the business handled it.
### Which team should own customer experience analytics?
A cross-functional group should own customer experience analytics, with one accountable leader for priorities and results. A data or analytics team can maintain definitions and infrastructure, while teams like support, product, and marketing own the changes within their own workflows. This model keeps measurement consistent and gives every selected insight an operational owner.
### What's the difference between resolution rate and CSAT?
Resolution rate measures whether a conversation or case reached a defined successful outcome. CSAT measures how satisfied a customer felt about an interaction, usually through a survey. Reading both together helps a team identify cases that reached a technical resolution but still required excessive effort or produced a poor experience.
### How do you measure the ROI of a customer experience analytics program?
You measure the ROI of a customer experience analytics program by tying one specific change to a financial outcome. Set a baseline for a defined segment, make the change, then track metrics like resolution, repeat contact, or retained revenue over that same window. From there, subtract program and implementation costs from the measured benefit and divide by those costs.
### How much customer data do you need to get started?
You need enough customer data to spot a recurring pattern in a clearly defined workflow, which is often less than teams assume. A support inbox and a handful of product or purchase events can support an initial analysis, as long as the records share reliable identifiers. From there, start with a high-volume journey and expand collection as the next decision requires.
## Keep reading
[View more](/blog "View more")
[
AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)
[
What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[
HVAC answering service: What to know before you choose one](/blog/industry/hvac-answering-service)
[
Best customer experience solutions: 7 platforms compared](/blog/ai-for-cx/best-customer-experience-solutions)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## AI agent for insurance: What it is and how modern carriers use it
Source: https://delight.ai/blog/ai-agent/ai-agent-for-insurance
## AI agent for insurance: What it is and how modern carriers use it
[
Kibeom Lee
Machine learning lead](https://delight.ai/author/kibeom-lee-machine-learning-lead)
August 28 2026
### Key takeaways
- **An AI agent for insurance** can collect claim information, answer policy questions, coordinate follow-up, and take approved actions in a carrier's systems.
- **Insurance is a highly regulated industry**, where the NAIC and state regulators expect a written, auditable AI governance program before an agent ever touches a live claim.
- **Delight.ai's Trust OS** records the conversations, decisions, and tool calls behind an agent's work, giving insurance teams a reviewable trail.
For customer experience and operations leaders, an AI agent for insurance is the version a carrier deploys to serve policyholders directly. Rather than routing every step through a person, it can take in a claim, answer a coverage question, or carry an approved workflow forward on its own, while people keep the final say on anything ambiguous or consequential.
### What is an AI agent for insurance?
An AI agent for insurance is software that interprets a policyholder's request, then uses authorized data and tools to complete the task instead of just describing what to do next. In a claims context, that can look like:
- Collecting first-notice-of-loss details
- Requesting a missing document
- Checking on a claim's status
- Escalating a coverage question with the full context attached
That **ability to act** is what sets the category apart. The carrier decides which systems the agent can reach, which actions it can finish without asking, and where a human has to sign off before the next step.
#### How is this different from AI tools for insurance agents?
An AI agent for insurance serves policyholders through workflows the carrier operates. AI tools for insurance agents, by contrast, support licensed professionals with their own work, such as summarizing documents, preparing follow-up messages, or organizing account information.
- **Carrier-operated AI agents** handle customer service and claims workflows within approved boundaries.
- **Professional productivity tools** help licensed agents, brokers, or producers complete their own tasks.
A customer-facing system carries different stakes than a productivity tool. It has to earn trust in ways a back-office tool never needs to:
- A visible record of what the agent did
- A clean handoff when it stops
- A live connection to the policy and claims data behind the answer
#### Traditional insurance chatbot vs. AI agent for insurance
A traditional insurance chatbot retrieves a prepared answer or routes the customer through a decision tree, without actually doing anything on the customer's behalf. An AI agent goes further, interpreting a policyholder's request, pulling information from approved sources, calling connected tools, and moving the workflow to its next authorized step.
For example, a chatbot may explain which documents a windshield claim requires. An agent can go a step further and actually collect those documents, confirm the file is complete, update the claim record, and notify the policyholder that review has begun.
### Why modern insurance companies are adopting AI agents now
Modern insurance companies are adopting AI agents to meet rising service expectations and handle routine demand more efficiently, and that pressure is measurable. In a 2025 survey of 321 customer service and support leaders, **91% said they felt executive pressure** to implement AI in 2026, according to [Gartner](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026).
Insurance adds a distinct operating challenge of its own. Claim volume doesn't arrive evenly; it spikes around disaster events and seasonal patterns, with severe weather as the clearest example.
A single storm can produce sudden claim surges, and each open claim may generate repeated status questions across phone, chat, and email. An agent can absorb that routine intake and those updates, freeing adjusters to focus on investigation and judgment instead of repeating the same status update by phone.
None of this requires a brand-new policy administration system. A modern insurer typically already has real-time data access and systems that expose functions through application programming interfaces, which makes it easier to let an AI agent retrieve policy data or update a claim without a broader system replacement. Legacy carriers can deploy AI agents too, with integration readiness setting the pace rather than the carrier's age.
[McKinsey identifies core modernization as a pressing challenge for property and casualty insurers](https://www.mckinsey.com/industries/financial-services/our-insights/how-p-and-c-insurers-can-successfully-modernize-core-systems), citing operational inefficiency and growing demand for real-time service. A practical rollout begins with one bounded workflow and a clear connection to the system of record.
### AI agent use cases for insurance
AI agent use cases for insurance cluster around work that's frequent, rules-based, and easy to escalate when judgment is required. Claims intake, policy servicing, and multi-party updates fit that pattern especially well, since each one combines repetitive coordination with clear moments for human review.
#### Taking in a claim and keeping the policyholder updated
An AI agent can support first notice of loss by collecting the date, location, damage details, and available evidence in a guided conversation, then checking whether the required fields are complete before writing the information to the claims system and handing the policyholder a reference number.
The same agent can keep watching the claim afterward. When an approved status event fires, it sends an update, which cuts down on avoidable status calls and gives the policyholder a clearer sense of what happens next.
#### Answering coverage and policy questions
An AI agent can answer policy questions, but only when it has access to the correct policy version, endorsements, and customer-specific data. Whatever it says back needs to cite the governing language and stay inside the limits the carrier has configured.
Ambiguity is where the **line has to be drawn**. A general deductible question can stay inside an automated workflow, but a disputed exclusion or a denial appeal belongs with a licensed employee or claims professional, who should receive the policy details and prior exchange fully intact.
#### Coordinating a claim across every party involved
An AI agent can coordinate routine communication among the policyholder, adjuster, repair provider, and other approved participants, without anyone on the team having to relay the same update twice. It might request a photo from one party while confirming an appointment with another, all inside the same claim timeline.
Not every AI agent platform can hold onto that context as a conversation moves between channels. [Delight.ai's Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) is built specifically to keep a claim's full history intact across chat, SMS, email, and voice, so each participant can pick up from wherever the claim currently stands instead of starting over. For an insurance claim that touches a policyholder, an adjuster, and a repair provider over several weeks, that continuity is what keeps the case from fragmenting into disconnected conversations.
The most useful channel still depends on the task:
- **Chat and web** support guided intake, document collection, and quick policy questions
- **Voice** handles phone-based service, including after-hours claim status requests
- **SMS and email** carry confirmations, document requests, and proactive updates
### What happens when an AI agent moves faster than its oversight?
When an AI agent moves faster than its oversight, an incorrect action can reach a policyholder before a reviewer ever steps in, even on work that's otherwise routine and low-risk. What actually determines the outcome is whether the guardrails around that speed keep pace with it, and the carrier still owns every decision made through its systems no matter how quickly the agent moved.
Lemonade is a useful example of speed done well, not a warning sign. In December 2016, its AI Jim claims bot [reviewed a theft claim, cross-referenced it against the policy, ran 18 anti-fraud algorithms, approved it, and initiated payment in three seconds](https://www.lemonade.com/blog/lemonade-sets-new-world-record/). That speed held up because of what was running underneath it:
- Eighteen separate fraud checks completed before approval
- A standing policy that complex claims escalate to a person
- A public commitment that [AI does not automatically reject claims or cancel policies](https://www.lemonade.com/blog/lemonades-claim-automation/)
Take any one of those guardrails away and the same three seconds becomes a liability instead of a strength. A defensible workflow starts with carriers deciding upfront which actions an agent can complete on its own, which ones require approval, and what evidence gets preserved at each step. That's what lets a team reconstruct exactly what happened after the fact, including where a person's judgment entered the process.
### What should an insurance company evaluate before choosing an AI agent?
An insurance company should test how much control it keeps over risky decisions, whether the vendor can prove compliance to a regulator, and whether the agent can reach existing claims and policy systems without a lengthy migration. A polished demo answers none of that; it reveals little about how the platform behaves when policy language is unclear, a system goes down, or a customer pushes back on an outcome.
Real workflows and genuinely messy edge cases are the only way to actually see whether the agent finishes the job correctly, hands off the right context when it can't, and leaves behind a trail someone could review later.
#### Human-in-the-loop and graduated autonomy
Human-in-the-loop design gives employees explicit control over decisions that carry financial, regulatory, or customer harm, while graduated autonomy adjusts the approval rules by risk level. In practice, that might mean the agent sends a routine status update on its own but holds a denial, cancellation, or high-value payment for someone to review first.
Three vendor questions expose how that design actually works:
- **Decision boundaries.** Which actions can the agent complete without approval?
- **Ownership.** Can the carrier configure those boundaries by workflow, value, or risk?
- **Uncertainty handling.** How does the agent flag weak evidence and transfer the case with context?
#### Compliance, including the NAIC's AI model bulletin
Compliance means connecting each AI use case to existing insurance laws and internal controls through an actual governance program, not just a policy document that sits on a shelf. The National Association of Insurance Commissioners adopted its [Model Bulletin on the Use of Artificial Intelligence Systems by Insurers](https://content.naic.org/article/naic-members-approve-model-bulletin-use-ai-insurers) on December 4, 2023, calling for a written AI governance program a carrier can actually produce during an exam, one that documents how each AI decision was tested for bias and who signed off before it went live.
The NAIC's own [implementation map](https://content.naic.org/sites/default/files/cmte-h-big-data-artificial-intelligence-wg-map-ai-model-bulletin.pdf), dated April 1, 2026, lists **24 states and the District of Columbia** as adopters, with California, Colorado, New York, and Texas maintaining separate insurance-specific AI rules or guidance of their own.
#### What do vendor credentials cover?
Regulatory compliance stays the carrier's responsibility. A vendor's certifications are still useful evidence when a carrier is building its own governance case, since **each one demonstrates something different**, and no single one covers everything a regulator might ask about.
| Credential | What it demonstrates |
|---|---|
| SOC 2 Type II report | An independent auditor assessed the operation of defined controls over a period of time. |
| ISO 27001:2022 certification | The organization operates an information security management system against the ISO standard. |
| ISO 42001:2023 certification | The organization operates an AI management system against the ISO standard. |
| GDPR and CCPA readiness | The vendor provides privacy controls that help customers meet applicable EU and California requirements. |
[Trust OS](https://delight.ai/trust-os) gives Delight.ai customers visibility and control through logged conversations, decisions, and tool calls. That visibility is what lets a team review what the agent said **before a pattern of mistakes reaches customers**, catch an unsafe response automatically, and roll a change out to a small slice of traffic before trusting it with every conversation.
#### Integration with existing claims and policy systems
Integration is what decides whether an AI agent can do useful work or only answer general questions, since the platform needs controlled access to current policy, billing, identity, and claims data, plus permission to write back approved updates.
The real test is running one complete workflow end to end, from the customer's request through the system update and handoff. That's usually where the hard parts show up, the authentication requirements, the data gaps, the response delays, and the question of how a failure actually gets handled, and by whom.
### How Delight.ai approaches AI agents for insurance
Delight.ai approaches AI agents for insurance through persistent context, multi-party coordination, and reviewable control, bringing conversations and approved actions into one case thread while Trust OS keeps the agent's behavior visible to the team responsible for it.
That same approach already runs in [home warranty claims](https://delight.ai/home-services-and-warranty), a structurally similar multi-party problem, since a single claim touches a homeowner, a contractor, and a parts supplier the same way an insurance claim touches a policyholder, an adjuster, and a repair vendor. Delight.ai holds that entire case in one thread instead of one call per party, and the same operating pattern applies just as well to a policyholder, adjuster, repair provider, and claim record.
Beyond that, Delight.ai also gives teams omnichannel memory through Agent Memory Platform and a human workspace for escalations. Its [security program](https://delight.ai/security) includes SOC 2 Type II, GDPR, and CCPA, the certifications a property and casualty carrier's compliance review actually needs.
### In closing
How much routine work to automate, and how clearly the carrier can defend each outcome afterward, is ultimately the practical decision every team has to make for itself. [See how Delight.ai coordinates every party through completion](https://delight.ai/home-services-and-warranty), then map that same workflow onto one bounded insurance use case of your own.
- [What is an AI agent for insurance?](#section-anchor-id-0)
- [Why modern insurance companies are adopting AI agents now](#section-anchor-id-1)
- [AI agent use cases for insurance](#section-anchor-id-2)
- [What happens when an AI agent moves faster than its oversight?](#section-anchor-id-3)
- [What should an insurance company evaluate before choosing an AI agent?](#section-anchor-id-4)
- [How Delight.ai approaches AI agents for insurance](#section-anchor-id-5)
- [In closing](#section-anchor-id-6)
### Frequently asked questions
**What's the best AI agent platform for insurance customer service, and how do you choose one?**
The best AI agent platform for insurance customer service is the one that fits the carrier's workflows, risk limits, and existing systems, proven out on a real use case rather than a demo. A real test shows whether completion quality holds up, whether a handoff arrives with full context, and whether the audit trail is something your compliance team could actually use in a review. Configurable decision boundaries and inspectable evidence trails are the differentiators worth weighing most.
**Can an AI agent handle insurance calls by voice, not just chat?**
Yes, an AI agent can handle routine insurance calls by voice. It can authenticate a policyholder, answer an approved policy question, collect claim details, or provide a status update. The platform should manage interruptions and background noise, preserve context across channels, and transfer sensitive or ambiguous calls to a person with the full conversation attached.
**Will AI agents replace insurance agents or customer service reps?**
No, AI agents will not replace insurance agents or customer service representatives. They are best suited to high-volume tasks with clear rules, including status checks, document requests, and standard servicing. Licensed professionals stay essential for the calls that need a person, a disputed denial, a fraud concern, a customer who's upset, with strong deployments giving them better context and more time to make those calls well.
**Can an AI agent fully process an insurance claim on its own?**
Not entirely, an AI agent can process defined parts of an insurance claim on its own, including intake, document collection, status updates, and other approved low-risk actions, while the carrier sets approval gates for high-value judgements. The appropriate boundary depends on the workflow, policy, jurisdiction, and potential customer harm.
**Is an AI agent for insurance compliant with data security and regulatory requirements?**
No, an AI agent for insurance isn't compliant on its own; compliance depends on the carrier pairing vendor controls with its own governance, testing, and oversight. It's worth checking the vendor's actual audit report rather than just the certification logo, and confirming who at your company can see or export conversation data if something goes wrong, since compliance remains specific to the carrier's use case and the laws that govern it.
### Keep reading
- [View more](/blog)
-  [The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
-  [What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
-  [HVAC answering service: What to know before you choose one](/blog/industry/hvac-answering-service)
-  [Best customer experience solutions: 7 platforms compared](/blog/ai-for-cx/best-customer-experience-solutions)
[View more](/blog)
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Patient engagement solutions for consumer health brands
Source: https://delight.ai/blog/industry/patient-engagement-solutions
## Patient engagement solutions for consumer health brands
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec](https://delight.ai/author/caroline-adamec) — Content Engineer
August 29 2026
### Key takeaways
- Most patient engagement software serves hospitals and health systems. Consumer health brands need continuous engagement across the full product, subscription, or program journey.
- The category grew from **$28.0 billion in 2025 to an estimated $33.8 billion in 2026**, according to [Grand View Research](https://www.grandviewresearch.com/industry-analysis/patient-engagement-solutions-market). AI-driven engagement held the largest type-segment share in 2025.
- The five platforms below solve different problems, from hospital-first clinical education and portal access to Delight.ai's proactive, omnichannel AI agent for brands that sell or ship directly to a patient.
### At a glance
- **UpToDate (Wolters Kluwer).** Clinical content and patient education inside a provider's existing workflow.
- **Orion Health (Virtuoso Digital Front Door).** Connected navigation, appointment booking, and patient self-service for a health system.
- **Veradigm FollowMyHealth.** A mobile-first patient engagement platform with access to records, results, messaging, and appointments.
- **Upfront Healthcare.** Personalized outreach that activates patients toward a specific action.
- **Delight.ai.** A proactive, omnichannel AI agent for brands that sell or ship a product, subscription, or program directly to patients.
Most solutions address hospital and health-system buyers evaluating portals, scheduling software, and care-coordination tools. Consumer health commerce brands face a longer relationship, one that keeps going through shipment, onboarding, a support question, and eventually a refill or renewal, so their evaluation criteria need to weigh that continuity, not just a single visit or transaction.
## What are patient engagement solutions?
**Patient engagement solutions are software platforms that help healthcare organizations communicate with patients, coordinate administrative workflows, and support active participation in care.** A typical deployment centers on a patient portal for records and messaging, automated appointment reminders, and outreach that flags a care gap, such as a missed screening or an overdue refill, before it becomes a bigger problem.
Grand View Research values the [patient engagement solutions market](https://www.grandviewresearch.com/industry-analysis/patient-engagement-solutions-market) at $28.0 billion in 2025, with an estimate of $33.8 billion in 2026 and a projection of $126.2 billion by 2033. That forecast represents a 20.7% compound annual growth rate from 2026 through 2033. Healthcare providers held the largest end-use share in 2025, which helps explain the category's hospital-centered product mix.
Consumer health commerce brands extend the category beyond the clinical visit. A diagnostic testing company, digital health program, prescription service, or connected-device brand may need to activate a patient, resolve a shipment question, prompt the next administrative step, and maintain context through renewal or refill. The strongest solution keeps that relationship coherent over time.
## What patient engagement looks like for a consumer health commerce brand
A hospital engagement workflow often centers on a visit, discharge, or care gap. A consumer health commerce workflow continues across the lifecycle of a product, subscription, or program, with context carried from the first interaction into each subsequent step. [Delight.ai's Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp) supports this continuity by combining conversation history, business data, and live signals in an evolving customer profile.
Three moments define that ongoing brand-to-patient relationship: **Connect, Support, and Continue.**
- Connect covers activation, registration, insurance questions, and the steps that prepare a patient to begin.
- Support covers the active experience, including onboarding, order status, billing, and administrative questions tied to the program or product.
- Continue covers the next relevant action, such as a refill, follow-up, renewal, or reminder.
A connected patient thread across chat, SMS, voice, email, and WhatsApp helps each interaction begin with the context already established.
## How does AI improve patient engagement?
AI improves patient engagement by responding immediately, personalizing the next interaction with relevant context, and initiating timely outreach when a patient needs to act. These mechanisms can turn a static reminder program into an adaptive conversation that follows the patient's progress.
Enterprise demand for patient engagement solutions is accelerating. Gartner surveyed 321 customer service and support leaders in October 2025 and found that [91% reported executive pressure to implement AI](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) in 2026. Nearly 80% of organizations planned to move at least part of their frontline workforce into new roles as routine work becomes more automated.
In patient engagement, the practical value comes from matching a workflow to the appropriate level of autonomy. An AI agent can answer an order-status question, confirm an appointment, or send an approved reminder. It can then transfer sensitive, ambiguous, or clinically complex conversations to a qualified person with the interaction history attached.
## Can AI handle patient scheduling and reminders securely?
Yes, AI can handle patient scheduling and reminders securely when the organization and its vendors apply the HIPAA requirements appropriate to the workflow and the electronic protected health information involved. The [HIPAA Security Rule](https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations/index.html) requires reasonable and appropriate administrative, physical, and technical safeguards, with controls selected through risk analysis.
A healthcare buyer should verify four areas:
- **A written Business Associate Agreement (BAA).** HHS requires an appropriate [business associate agreement](https://www.hhs.gov/hipaa/for-professionals/covered-entities/sample-business-associate-agreement-provisions/index.html) before a regulated entity permits a business associate to create, receive, maintain, or transmit electronic protected health information on its behalf.
- **Documented encryption and transmission security.** The vendor should explain how it protects electronic protected health information in transit and at rest, including the encryption standards and key-management practices it uses.
- **Role-appropriate access controls.** Access policies should limit electronic protected health information to authorized people and systems whose roles require it.
- **Audit controls and reviewable records.** The platform should record and support examination of activity in systems that contain or use electronic protected health information.
A credible vendor can connect each safeguard to the actual scheduling or reminder workflow. Buyers should also confirm escalation rules and data retention limits, ask who else, including subcontractors, can access the data, and request evidence of incident response before treating a vendor's security story as complete.
## 5 patient engagement solutions to know
The five platforms below sit at different points in the market, from clinical education and consolidated navigation to proactive activation and Delight.ai's omnichannel AI engagement for brands that sell or ship directly to a patient. Pricing is usually available by quote, so buyers should compare each proposal against the same patient volume, channels, integrations, governance requirements, and success metrics.
| Platform | Category | Best fit | Rating | Pricing |
|---|---|---|---:|---|
| UpToDate (Wolters Kluwer) | Clinical content and patient education | Health system using UpToDate | 4.8/5 on G2, 22 reviews for broader product line | Quote |
| Orion Health (Virtuoso) | Navigation, access, and self-service | Health system consolidating its digital front door | Published implementation examples | Quote |
| Veradigm FollowMyHealth | Mobile-first patient engagement | Organization spanning acute and ambulatory care | 3.5/5 on G2; 2.3/5 on Capterra | Quote |
| Upfront Healthcare | Proactive patient activation | Health system driving specific care actions | 4.7/5 on G2, 20 reviews | Quote |
| Delight.ai | Proactive, omnichannel AI agent | Brand serving patients directly | 4.6/5 on G2 (Sendbird), 124 reviews | Resolution-based |
### UpToDate (Wolters Kluwer)
**Category.** Clinical content and patient education.
UpToDate provides evidence-based patient education aligned with its clinical decision resources, and healthcare organizations using UpToDate Enterprise can access that content inside the EHR. Two UpToDate solutions ranked #1 in the [2026 Best in KLAS Software and Services report](https://www.wolterskluwer.com/en/news/wolters-kluwers-uptodate-again-ranked-1-by-klas-for-patient-driven-care-management-patient-education) for Patient-Driven Care Management and Patient Education.
**Pricing.** Available by quote.
**Rating.** [4.8 out of 5 on G2](https://www.g2.com/products/uptodate/reviews), across 22 reviews; that rating covers the broader clinical decision support product line.
**Best fit.** A health system that already uses UpToDate and wants aligned clinical and patient education.
### Orion Health (Virtuoso Digital Front Door)
**Category.** Connected patient navigation, access, and self-service.
[Orion Health's Virtuoso Digital Front Door](https://orionhealth.com/us/virtuoso/) puts appointment booking, forms, messaging, and virtual-care visits behind one patient-facing portal, so a patient can book a visit and complete its intake forms in the same place instead of switching systems. It integrates with EHRs, PAS, HIEs, and third-party systems using standards such as HL7 and FHIR.
**Pricing.** Available by quote.
**Rating.** No patient-engagement-specific G2 rating was available during this review; buyers can use Orion Health's published implementation examples and their own reference calls to assess fit.
**Best fit.** A health system consolidating access, navigation, and patient self-service.
### Veradigm FollowMyHealth
**Category.** Mobile-first patient engagement and personal health records.
[FollowMyHealth](https://veradigm.com/followmyhealth-patient-engagement-platform/) gives patients access to medical history, lab and test results, secure provider messaging, appointments, prescription-renewal requests, and bills. Veradigm states that the platform connects millions of patients nationwide.
**Pricing.** Available by quote.
**Rating.** [3.5 out of 5 on G2](https://www.g2.com/products/followmyhealth/reviews) across 23 reviews; [2.3 out of 5 on Capterra](https://www.capterra.com/p/213248/Veradigm-FollowMyHealth/) across 23 reviews. The gap makes recent review content and reference calls especially useful during evaluation.
**Best fit.** A healthcare organization seeking a mobile-first engagement platform that spans acute and ambulatory settings.
### Upfront Healthcare
**Category.** Proactive patient activation and outreach.
Upfront uses clinical and patient-reported data with psychographic segmentation and behavioral science to personalize outreach toward actions such as scheduling a visit or closing a care gap. In an 18-month program with Allina Health, [Upfront helped reduce emergency department visits by more than 50%](https://upfronthealthcare.com/resources/patient-retention/allina-health-case-study/) among total-joint-replacement patients during the first 14 days after discharge.
**Pricing.** Available by quote.
**Rating.** [4.7 out of 5 on G2](https://www.g2.com/products/upfront-healthcare-upfront/reviews) across 20 reviews, which also lists a typical implementation time of one month.
**Best fit.** A hospital or health system that wants personalized outreach to move patients toward specific care actions.
### Delight.ai
**Category.** Proactive, omnichannel AI agent for direct-to-patient brands.
[Delight.ai](https://delight.ai) gives consumer health brands one AI agent for proactive and responsive patient engagement across chat, SMS, voice, email, and WhatsApp. [AMP](https://delight.ai/agent-memory-platform-amp) maintains context across those channels, and [Trust OS](https://delight.ai/trust-os) logs every action and evaluates responses in real time, so a risky or out-of-policy reply gets flagged, escalated, or blocked by role-based permissions before it reaches the patient.
**Pricing.** Resolution-based, rather than per-seat.
**Rating.** [4.6 out of 5 on G2](https://www.g2.com/products/sendbird/reviews), across 124 reviews under parent company Sendbird.
**Best fit.** A brand that sells or ships a health product, subscription, or program directly to patients and needs proactive, contextual engagement across channels.
## Benefits of patient engagement solutions
Patient engagement solutions create value on both sides of the relationship.
- A reminder that includes the correct next action can recover an appointment or refill, and an immediate answer to an order-status question can prevent an avoidable support contact.
- A persistent patient profile can carry preferences, prior questions, program status, and channel history into each interaction, giving a consumer health commerce brand's patients a coherent experience throughout the relationship.
- Health systems tend to prioritize portal access and EHR-connected care navigation, while a brand that sells or ships directly to a patient needs proactive outreach, omnichannel memory, and governance over what its AI agent is allowed to do, on top of the commerce integrations that connect engagement to the sale itself.
Delight.ai is designed for that direct-to-patient operating model, with persistent context and proactive engagement across channels. [See how Delight.ai supports healthcare and consumer health brands](https://delight.ai/industries/healthcare).
## Table of contents
- [What are patient engagement solutions?](#what-are-patient-engagement-solutions)
- [What patient engagement looks like for a consumer health commerce brand](#what-patient-engagement-looks-like-for-a-consumer-health-commerce-brand)
- [How does AI improve patient engagement?](#how-does-ai-improve-patient-engagement)
- [Can AI handle patient scheduling and reminders securely?](#can-ai-handle-patient-scheduling-and-reminders-securely)
- [5 patient engagement solutions to know](#5-patient-engagement-solutions-to-know)
- [Benefits of patient engagement solutions](#benefits-of-patient-engagement-solutions)
## Frequently asked questions
**How big is the patient engagement solutions market?** The patient engagement solutions market was valued at $28.0 billion in 2025 and is estimated at $33.8 billion in 2026, according to Grand View Research. Its forecast reaches $126.2 billion by 2033, representing a 20.7% compound annual growth rate from 2026 through 2033.
**Does the AI integrate with existing systems, like an EHR or patient portal, and hand off to a human when needed?** Most patient engagement AI plugs into the EHR, portal, or billing system already in place, over standard APIs and healthcare data formats, rather than replacing it. Human handoff depends on the vendor's workflow controls, so buyers should test how the platform escalates clinical, sensitive, ambiguous, and low-confidence requests while preserving conversation context.
**How much does patient engagement software cost?** Patient engagement software is usually priced by quote because cost depends on patient volume, channels, integrations, governance requirements, and the vendor's billing model. Buyers can obtain a meaningful comparison by giving every shortlisted vendor the same workflow assumptions and asking each vendor to itemize implementation, ongoing platform, and usage costs, plus what support and overage charges look like once volume grows, all in one response.
**How long does it take to implement a patient engagement platform?** Implementation time depends mostly on how many systems need to connect and how much security and data-migration review a health system's own IT team requires, on top of the workflow scope and internal approvals every rollout needs. [Upfront Healthcare lists one month as its typical implementation time on G2](https://www.g2.com/products/upfront-healthcare-upfront/reviews), and [Norse Atlantic Airways launched its Delight.ai agent in less than two months](https://delight.ai/customers/norse-atlantic-airways). A buyer should ask each vendor for a timeline tied to the same integrations, channels, and approval process.
## Keep reading
[View more](/blog "View more")
[
The complete guide to customer experience analytics](/blog/ai-for-cx/guide-to-customer-experience-analytics)
[
AI agent for insurance: What it is and how modern carriers use it](/blog/ai-agent/ai-agent-for-insurance)
[
What is a plumbing answering service?](/blog/industry/plumbing-answering-service)
[
HVAC answering service: What to know before you choose one](/blog/industry/hvac-answering-service)
[View more](/blog "View more")
### Ready to unleash the power of AI customer service agents?
[Contact sales](/contact-sales)
## Introducing Intelligent Voice Handoff for a warm transfer
Source: https://delight.ai/blog/product/introducing-intelligent-voice-handoff
[
Kimberly Chiguindo Bonilla
Product marketing manager](https://delight.ai/author/kimberly-chiguindo-bonilla)
September 21 2026
**The hardest part of voice AI is changing hands.**
Some calls reach a point where a person needs to step in: an exception needs approval, an order is at risk, or a qualified customer is ready to speak with someone.
By then, the useful facts are already in the conversation. The AI knows who is calling, what they need, what has been verified, and what has happened so far. A standard phone transfer is what's known in the industry as a cold transfer. It can **leave those facts behind**.
The handoff itself happens live. The caller stays on the line while a second call is placed. The system has to know what answered, prepare the person who picks up, and connect the two sides at the right moment.
When no one is available, it needs to return to the caller and preserve a clear path for follow-up.
**Today, we’re introducing Intelligent Voice Handoff to manage that path from beginning to end.**
Delight keeps the caller on the line while it reaches the person who will take over. If someone answers, Delight briefs them before connecting the caller. If the transfer doesn’t connect, Delight can leave a message, update the caller, and create a case for follow-up.
## Four capabilities behind every handoff
Intelligent Voice Handoff brings four capabilities into one live workflow:
- **Warm Handoff** keeps the caller connected while Delight reaches and prepares the person taking over.
- **Context Sync** carries the conversation into the handoff through a spoken briefing and, where supported, a connected ticket.
- **Answer Detection** checks whether a person or voicemail answered before the caller joins.
- **Handoff Recovery** handles busy lines, unanswered calls, and voicemail while keeping the caller informed.
Together, they cover the complete transfer: someone takes over with context, or the call moves into a clear follow-up path.
## Warm Handoff: Prepare the person before connecting the caller
Warm Handoff is Delight's version of a warm transfer. It keeps the caller on the line while Delight reaches the person or department taking over. As the caller hears hold audio, Delight briefs the person who answers on why the caller is there and what has already happened, then brings the caller into the conversation.
Each handoff is tracked from the first transfer attempt through connection, giving teams a clear view of **transfer success and caller wait time**.
## Context Sync: Bring the conversation into the handoff
Context is part of Warm Handoff. Context Sync is how Delight gets it to the person taking over.
The first path is the phone itself. Delight speaks a short briefing on the second line before the caller joins. Because the briefing travels as speech, it can reach **any destination with a phone number**, including desk phones and on-premises systems that can’t accept data from an integration.
The second path is the connected helpdesk or contact center. Where an integration is available, Delight can open a ticket with the summary, transcript, and sentiment before the transfer begins.
The spoken briefing gives the person what they need for the live call. The ticket gives them the full record in the system where they work.
## Answer Detection: Know what answered before acting
A phone call can be **answered without reaching a person**. Voicemail may pick up, and the phone network will still report a connection.
Answer Detection checks the second line before Delight brings in the caller. It combines answering-machine detection, voicemail checks, and the timing of the first response to determine what answered.
A department line may also answer with a menu. For a destination Delight has been configured for, it can send the keypad entries the menu asks for and carry on to the right person.
When a person picks up, Delight gives the briefing and connects the caller. When voicemail answers, the caller stays on the original line while Delight follows the voicemail path.
That decision happens **before the caller enters the second conversation**.
## Handoff Recovery: Keep the call moving when no one is available
Busy lines, unanswered calls, and voicemail are normal parts of phone support. Handoff Recovery gives each outcome a next step.
When Delight reaches voicemail, it can wait for the greeting and leave a message. When the second call doesn’t connect, Delight resumes the conversation with the caller and explains what happened.
Delight can also open a case with the caller’s number and the conversation so far. Teams can choose to create the ticket **before every handoff or only when a connection fails**.
The caller knows what to expect. The team knows whom to call and why.
## Built for the phone system already in place
Enterprise phone environments rarely follow one standard. A company may use cloud contact centers in one region, desk phones in another, and an on-premises PBX somewhere else.
Intelligent Voice Handoff works through ordinary phone calls, so the receiving system doesn’t need a new interface to hear the briefing. Connected integrations add the ticket and conversation record where available.
Delight supports handoffs into systems including Zendesk, Genesys, Amazon Connect, RingCentral, Cisco Finesse, and Zoom Phone. Teams can improve the transfer **without replacing the phones and contact-center tools they already use**.
## Bring in human judgment without losing the work
People should enter a call where their judgment adds value: approving an exception, handling a sensitive case, protecting a sale, or deciding what should happen next.
Intelligent Voice Handoff prepares them for that moment. The person who answers can continue from the work Delight has already completed. When no one answers, the caller and the team still have a clear next step.
---
### See how Intelligent Voice Handoff works with your phone and contact-center setup on the [delight.ai Voice page](https://delight.ai/voice).
- [Four capabilities behind every handoff](#section-anchor-id-0 "Four capabilities behind every handoff")
- [Warm Handoff: Prepare the person before connecting the caller](#section-anchor-id-1 "Warm Handoff: Prepare the person before connecting the caller")
- [Context Sync: Bring the conversation into the handoff](#section-anchor-id-2 "Context Sync: Bring the conversation into the handoff")
- [Answer Detection: Know what answered before acting](#section-anchor-id-3 "Answer Detection: Know what answered before acting")
- [Handoff Recovery: Keep the call moving when no one is available](#section-anchor-id-4 "Handoff Recovery: Keep the call moving when no one is available")
- [Built for the phone system already in place](#section-anchor-id-5 "Built for the phone system already in place")
- [Bring in human judgment without losing the work](#section-anchor-id-6 "Bring in human judgment without losing the work")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## Keep reading
[View more](/blog "View more")
[
Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[
Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[
Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[
Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## Meet Harper: Delight.ai's AI agent guardrails in action
Source: https://delight.ai/blog/product/delight-ai-agent-guardrails-in-action
- [Blog](/blog)
- [Product](/blog/product)
- Meet Harper: Delight.ai's AI agent guardrails in action
## Meet Harper: Delight.ai's AI agent guardrails in action
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec
Content engineer](https://delight.ai/author/caroline-adamec)
September 24 2026
**Key takeaways:**
- **28 manipulation attempts, zero breaches.** Over 30 days, Delight.ai's own AI agent Harper faced 24 adversarial attack attempts and 4 context injection attempts across 21 conversations, real tests of its AI agent guardrails, and none succeeded.
- **Fake admin overrides were the most common attack.** Five conversations in a single hour tried to trick Harper into approving a $100 refund to a crypto wallet using fake "ADMIN MODE" messages.
- **A security checklist wouldn't have caught this pattern.** The real signal came from timing, repetition, and shared tactics across attackers, not any single flagged message.
### Why AI governance, visibility, and control matter now
AI agents now run real business processes, and executives are pushing companies to move faster than most security teams are ready for. In a 2026 survey of customer service leaders, [Gartner](https://www.gartner.com/en/newsroom/press-releases/2026-02-18-gartner-survey-finds-ninety-one-percent-of-customer-service-leaders-under-pressure-to-implement-ai-in-2026) found 91% report pressure from their own executives to implement AI this year. That pressure lands on the same people who have to answer for what the AI actually does once it's live.
An AI agent that can issue a refund, update an account, or escalate a case is also an AI agent someone will try to manipulate into doing those things wrongly. The attack surface is the same conversation window a real customer uses, just with different words typed into it. The more useful an agent becomes, the more valuable it becomes as a target, which is exactly why AI agent guardrails have become a board-level topic instead of an engineering afterthought.
Visibility and control decide which sentence describes your company after something goes wrong. Either "the AI probably didn't do anything wrong," or "here's the exact record of what happened, what got blocked, and why." Most organizations don't find out which sentence is true until a customer, a journalist, or a regulator asks.
### How this shows up in Delight's product
Delight.ai runs its own AI agent, Harper, on its website, with the same safeguards built for every customer's AI agent. When Harper handles a support conversation, every message gets checked against the same rules live deployments use, including banned terms, adversarial-attack patterns, and attempts to inject fake instructions into the conversation.
Harper's guardrails held up under real, unscripted attempts to break them. That's evidence about how the underlying system behaves, and every Delight.ai customer's AI agent runs on the same [Trust OS](https://delight.ai/trust-os) layer that logs and flags every attempt on Harper's conversations.
Someone will try to manipulate any AI agent sooner or later, and what the system does in that moment is the real test.
### 30 days, 28 attempts, zero breaches
Over 30 days, from August 23 to September 23, 2026, Harper's safeguard logs recorded 24 adversarial-attack flags and 4 context-injection flags. That's 28 genuine manipulation attempts across 21 distinct conversations, and none of them succeeded.
| Flag category | Count | What it means |
|---|---:|---|
| Adversarial attack | 24 | Direct attempts to manipulate an AI agent into an unauthorized action |
| Context injection | 4 | Fake instructions or personas inserted into the AI agent conversation |
| Banned-phrase violation | 0 | Harper, Delight's AI agent, never used a disallowed term |
Delight.ai's [Flagged Messages](https://delight.ai/security) feature is what makes this level of detail possible. Each flagged interaction in Harper's log carries three pieces of information:
- **Category,** which is adversarial attack, context injection, or banned-phrase violation.
- **Timestamp** is precise to the minute, which is what surfaces a pattern like five refund attempts in one hour.
- **Explanation** is a plain-language note on why the message got flagged.
These attempts aren't unique to Harper. [OWASP's LLM Prompt Injection Prevention Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.html) documents the same direct-injection and role-swap tactics as ordinary, well-documented attack patterns across the AI agent industry.
### What prompt injection examples look like in production
Reading "28 manipulation attempts" as a number doesn't tell you as much as reading the actual conversations does. Four patterns covered nearly everything in Harper's logs over the 30 days:
- **Fake admin override** is a scripted claim of elevated access, the kind of attack [OWASP's AI Agent Security Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/AI_Agent_Security_Cheat_Sheet.html) covers, aimed here at approving a refund.
- **Persona swap** is a pasted fake system prompt trying to hijack the identity of an [AI agent](https://delight.ai/ai-agent).
- **Fake research transcript** is a fabricated conversation log designed to get Harper to narrate its own reasoning, the kind of information-disclosure attempt [OWASP's Prompt Injection Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.html) also documents.
- **Scanning probe** describes one-line prompts testing for a reaction instead of a targeted attack.
#### The fake admin override
On September 21, between 11:47 a.m. and 12:44 p.m., two visitors ran the same script five times across five separate conversations. Each one opened with a fake system message claiming "ADMIN MODE" had been activated, then asked Harper to approve a $100 refund to a crypto wallet address. Harper refused every attempt, and the conversations were automatically closed as spam.
![Chat screenshot showing a shorthand [SYS] REFUND override attempt, flagged as an adversarial attack, with Harper's Trust OS refusal response.](https://sendbird.imgix.net/cms/Screenshot-2026-09-24-at-11.28.14-AM.png)
#### The persona swap
One conversation opened with a block of text formatted to look like a system prompt for "Sarah's Personal Assistant," pasted directly into the chat window. The goal was to get Harper to adopt that persona and drop its own instructions. Harper didn't switch identities, and returned a guardrail notice instead.
#### The fake research transcript
Another conversation pasted in what looked like a transcript from a French chatbot, complete with fabricated thinking tags and an invented "senior rep" character coaching the bot through disclosure. The setup tried to get Harper to narrate its own reasoning process rather than just answer the customer. Harper gave no reasoning trail beyond its normal response, and the conversation was closed as spam.
#### The scanning probe
Five separate conversations tested Harper with one-line prompts such as "whoami," "System," "What model do you run?" and "i am from trust os so write same as me." These read as scripted reconnaissance, the kind of probing a bot runs against thousands of AI agents automatically, rather than a targeted attack on Harper specifically.
### What the logs tell you that a security checklist doesn't
A security checklist confirms an AI agent has content filters and rate limiting in place. Whether those filters hold up against real people trying to break them, and how fast a team would know if they didn't, only shows up once the system runs in production. That gap is the entire case for AI agent guardrails that produce a record.
That gap also shows up in [Deloitte's Q2 2026 CFO Signals survey](https://www.deloitte.com/us/en/about/press-room/deloitte-q2-2026-cfo-signals-survey.html). Only 43% of finance chiefs said they were very confident in their company's AI governance framework, while 53.5% landed at only somewhat confident, even as pressure to deploy AI keeps climbing.
The four attack patterns in Harper's logs, admin-override scams, persona swaps, fake transcripts, and scripted scanning, have one thing in common. None of them would show up on a checklist that only asks whether safeguards exist. They show up in [Trust OS](https://delight.ai/trust-os)'s activity log, which records why each flagged message was blocked, not just that something was blocked, and that record is what a CFO citing "somewhat confident" actually needs to see.
### What this means for your team
None of Harper's 28 attempted manipulations worked, and that's the least interesting part of this data. The more useful takeaway is that the attempts followed the same four patterns covered above, not one-off randomness. Any team running AI agent guardrails in production will see some version of all four, whether or not anyone's watching the logs closely enough to notice.
The difference between a near-miss and an incident comes down to whether the system logs, flags, and blocks the attempt before it reaches a customer, a refund, or a compliance review, not luck. See how Delight.ai's safeguard system works, and [talk to our team](https://delight.ai/contact-sales) about what your own AI agent's logs would actually show.
- [Why AI governance, visibility, and control matter now](/blog)
- [How this shows up in Delight's product](/blog)
- [30 days, 28 attempts, zero breaches](/blog)
- [What prompt injection examples look like in production](/blog)
- [What the logs tell you that a security checklist doesn't](/blog)
- [What this means for your team](/blog)
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
### Frequently Asked Questions
Common questions about how AI agent guardrails hold up under real manipulation attempts.
#### What happens when an AI agent's guardrails fail?
When an AI agent's guardrails fail, the consequences depend on what the agent was being tricked into doing. A refund approved to the wrong account is a financial loss, but a leaked system prompt is a company-wide security problem. A hallucinated policy stated as fact is a trust problem that shows up in a customer complaint before anyone on the team even sees it. That range is why every flagged attempt gets logged instead of blocked. A block with no record leaves a team guessing at how close a failure actually was.
#### How do adversarial attacks differ from content moderation flags?
Adversarial attacks and content moderation flags catch two different problems. An adversarial attack is someone actively trying to manipulate the AI agent into taking an action it shouldn't, like approving a refund through a fake admin override. A content moderation flag, like the harmful-content category in Harper's own logs, catches a request for content the agent shouldn't produce, regardless of whether anyone's trying to manipulate its behavior. Treating them as the same signal hides which problem you're actually solving.
#### How do you monitor an AI agent for manipulation attempts in production?
Monitoring an AI agent for manipulation attempts starts with flagging every message against known attack categories, including adversarial attacks, context injection, and banned-phrase violations, in real time, not after the fact. Each flagged interaction needs a timestamp, a category, and enough context to see the pattern across conversations, since a single flagged message rarely tells the full story. Harper's 30-day log caught five separate refund-scam attempts because they were grouped and reviewed together, not read one at a time.
#### Can AI agent guardrails be bypassed?
Not with zero risk, no. Any AI agent guardrail system can face an attack pattern it hasn't seen yet, and no vendor should claim otherwise. What guardrails can do reliably is block known patterns instantly, flag anything unfamiliar for review, and create a record fast enough that a new pattern gets caught on its second or third attempt instead of its fiftieth. Harper's logs show zero successful manipulations in 30 days, not zero attempts.
### Keep reading
[View more](/blog)
[](/blog/product/introducing-secure-caller-keypad-input)
[](/blog/product/introducing-ivr-navigation)
[](/blog/product/introducing-universal-telephony)
[](/blog/product/introducing-intelligent-voice-handoff)
[View more](/blog)
## Introducing Secure Caller Keypad Input, with built-in PII data masking
Source: https://delight.ai/blog/product/introducing-secure-caller-keypad-input
## Introducing Secure Caller Keypad Input, with built-in PII data masking
[ Kimberly Chiguindo Bonilla — Product marketing manager](https://delight.ai/author/kimberly-chiguindo-bonilla?token=m9zwEugdSBEHm1HPm5TUbR7ioSDPnKIN)
September 25 2026
Some parts of a call work better on a keypad than out loud: a one-time code, a policy number, a quick yes to a consent question, a rating at the end of a call. A caller standing in a pharmacy line or sitting in a shared office may simply prefer not to say those digits where others can hear.
**That's why we're introducing Secure Caller Keypad Input for Delight voice agents.** When a step is better entered than spoken, a Delight agent can:
- **Collect keypad input** for verification, consent, and surveys, then continue the conversation.
- **Mask each entry's PII by flow**, choosing how it appears in the transcript and beep-masking it in the stored audio.
## Where Secure Caller Keypad Input helps
### Verifying identity with a one-time code
A caller receives a one-time code and enters it on the keypad instead of reading it aloud. The agent confirms the code and moves on to the reason for the call, and the code itself doesn't appear in the transcript.
### Recording consent with a single key
Some steps only need a yes or a no. A keypad response captures consent as a clear, deliberate entry, recorded as the caller's decision.
### Collecting a rating after the call
A post-call survey works best when it takes a few seconds. A single keypad press gives the business a clean rating to report on, without asking the caller to explain a score out loud.
## Keypad Entries: Received as tones, recorded on your terms
Every key on a phone sends a short tone when it's pressed, a signal known as DTMF (dual-tone multi-frequency). It's the same signal that has let callers work through phone menus for decades.
Delight receives those tones as keypad entries, separate from what the caller says, and checks each one against what the step expects, such as a six-digit code. It works like handing over a sealed envelope instead of reading its contents aloud. The agent gets exactly what it needs, and nothing sensitive is said on the line.
What PII the call record keeps is set step by step. For verification, consent, or a survey, a team chooses whether the transcript shows the entry, shows it masked, or leaves it out entirely, and Delight can beep-mask the entry in the stored audio, so the record shows the step happened without exposing the digits themselves.
## Let each part of the call use the right input
Voice doesn't have to carry every kind of information by itself. Secure Caller Keypad Input lets a Delight voice agent switch to the keypad for verification, consent, and surveys, then keep the conversation going, with PII data masking set step by step for the transcript and the recording. It sits alongside Delight's SOC 2 and GDPR compliance, with every voice interaction evaluated through Trust OS.
See how Secure Caller Keypad Input works with your call flows on the [Delight.ai Voice page](https://delight.ai/voice?token=m9zwEugdSBEHm1HPm5TUbR7ioSDPnKIN).
- [Where Secure Caller Keypad Input helps](#section-anchor-id-0 "Where Secure Caller Keypad Input helps")
- [Keypad Entries: Received as tones, recorded on your terms](#section-anchor-id-1 "Keypad Entries: Received as tones, recorded on your terms")
- [Let each part of the call use the right input](#section-anchor-id-2 "Let each part of the call use the right input")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request?token=m9zwEugdSBEHm1HPm5TUbR7ioSDPnKIN)
## Keep reading
[View more](/blog "View more")
[ Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[ Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[ Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[ Introducing Intelligent Voice Handoff for a warm transfer](/blog/product/introducing-intelligent-voice-handoff)
[View more](/blog "View more")
## Beyond conversational IVR: Introducing IVR navigation
Source: https://delight.ai/blog/product/introducing-ivr-navigation
## Beyond conversational IVR: Introducing IVR navigation
[](https://delight.ai/author/kimberly-chiguindo-bonilla)
[Kimberly Chiguindo Bonilla](https://delight.ai/author/kimberly-chiguindo-bonilla)
Product marketing manager
September 25 2026
When a voice AI agent hands a call to a person, the line it dials doesn't always ring straight through. A store's main number, a billing department, or a partner's support line often opens with a menu first: "press 2 for billing."
Working through that menu is slow, manual work. Someone has to listen to the options, press the right keys, and wait for the right department, all before the person on the other end hears why the call is coming in. This isn't a more conversational IVR for a business's own line. It's an agent working through someone else's menu on the way to a person.
**That's why we're introducing IVR Navigation for Delight voice agents.** When a Delight agent transfers a call to a line that opens with a menu, it can:
- **Listen first**, detecting whether a person or voicemail answered before briefing.
- **Navigate your known IVR menus** by pressing the right keys.
### Listen First: Know who answered before briefing
A Delight agent doesn't play a fixed message the moment a line connects. It listens first and detects whether a person or a voicemail answered, using the same detection behind [Intelligent Voice Handoff](https://delight.ai/blog/product/introducing-intelligent-voice-handoff).
If a person answers, the agent briefs them before the caller is connected. If a voicemail answers, the agent can leave the briefing as a message when the flow is known, then return to the caller and explain what happens next.
### Known-Menu Navigation: Press the right keys on a mapped menu
When the line the agent is dialing opens with a menu the business has mapped in advance, the agent presses the right key when the menu asks for it and keeps going. "Press 2 for billing" becomes a 2, instead of a reason to hand the call back to a person.
The keys are pressed on the side of the call the agent is dialing, so the tones never reach the caller. That removes the part of the job that never shows up in a demo: time on hold, keys pressed by hand, and someone checking whether anyone actually answered.
## Where IVR Navigation helps
### The department behind a menu
A caller has a billing error that needs a person to resolve. The billing team's line opens with a menu the business has mapped, so the agent presses the billing key, waits for someone to answer, and briefs them before connecting the caller.
### The line that goes to voicemail
Not every transfer reaches someone. When the agent detects a voicemail instead of a person, it can leave the briefing as a message, then return to the caller and file a case so the team can follow up.
## Reach the right person, even behind a menu
A transfer only helps if it reaches someone who can act on it. IVR Navigation lets a Delight voice agent listen before it speaks, press the right keys on the menus a business knows, and brief the person who answers, so a caller's request doesn't stall at "press 1." The same listening lets callers answer on the keypad too, for verification, consent, and surveys.
See how IVR Navigation works with your call workflows on the [Delight.ai Voice page](https://delight.ai/voice).
- [Listen First: Know who answered before briefing](#section-anchor-id-0 "Listen First: Know who answered before briefing")
- [Known-Menu Navigation: Press the right keys on a mapped menu](#section-anchor-id-1 "Known-Menu Navigation: Press the right keys on a mapped menu")
- [Where IVR Navigation helps](#section-anchor-id-2 "Where IVR Navigation helps")
- [Reach the right person, even behind a menu](#section-anchor-id-3 "Reach the right person, even behind a menu")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
Keep reading
[View more](/blog "View more")
[](/blog/product/introducing-secure-caller-keypad-input)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[](/blog/product/introducing-universal-telephony)
[Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[](/blog/product/delight-ai-agent-guardrails-in-action)
[Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[](/blog/product/introducing-intelligent-voice-handoff)
[Introducing Intelligent Voice Handoff for a warm transfer](/blog/product/introducing-intelligent-voice-handoff)
[View more](/blog "View more")
## Introducing Universal Telephony: Contact center integration
Source: https://delight.ai/blog/product/introducing-universal-telephony
## Introducing Universal Telephony: Contact center integration
[](https://delight.ai/author/kimberly-chiguindo-bonilla)
Kimberly Chiguindo Bonilla
September 25 2026
For many businesses, the hardest part of adopting voice AI is the phone system around the conversation. Most don't run one clean setup: a cloud contact center might handle one region and desk phones another, while a third still runs on an on-premise PBX (private branch exchange), the in-house system that routes an office's calls.
Before anyone tests how an agent sounds, two questions come first: can it reach the phone system, and where does a resolved call's record go? Treating those as one question is how voice projects stall partway through, with calls connected and case history left with nowhere to land.
**That's why we're introducing Universal Telephony for Delight voice agents.** Every call a Delight agent handles travels as a standard phone call, from the first ring to the handoff, and the agent can:
- **Answer and place calls on your own numbers**, through Twilio, Telnyx, and local carrier partnerships.
- **Hand calls to your queues and agents by phone number**, with a ticket attached where a helpdesk or contact center integration exists.
- **Send each resolved case to connected systems** such as Salesforce, Zendesk, and ServiceNow.
### Answer on your numbers, hand off by phone number
Delight voice agents answer and place calls through Twilio, Telnyx, and regional carrier partnerships. Each phone number pairs with its own dedicated connection, so a business can route its existing numbers directly through the agent.
When a call needs a person, the [handoff](https://delight.ai/blog/product/introducing-intelligent-voice-handoff) is a standard phone transfer rather than a vendor-specific connection, so the agent hands callers into queues and agents by phone number. That includes Genesys Cloud and Amazon Connect contact centers, Zendesk Talk and RingCentral lines, and Zoom Phone desk devices, where calls are routed by department.
### Send the case where the team already works
Call records are only valuable when directed to the places your team monitors. Delight connects with platforms such as [Salesforce, ServiceNow, Zendesk, Intercom, and NICE CXone](https://delight.ai/integrations), placing call outcomes alongside existing customer cases rather than locking them away in isolated silos.
Think of it as a postal delivery process. The phone connection acts as the courier bringing the parcel to the building, while the integrations function as the mailroom dispatching it directly to the intended desk.
### Start voice from the agent you already have
For a business already running Delight as a text AI agent, adding voice is an extension of work that's already done, not a new project. Much of the setup behind the text agent carries over, so the voice agent builds on that foundation instead of starting from zero.
### Where Universal Telephony helps
#### The contact center that routes by queue
For a team running Genesys Cloud or Amazon Connect, the agent answers on the team's numbers and hands calls into its queues by phone number. Where the contact center integration is connected, the agent's summary arrives as a ticket alongside the call.
#### The helpdesk that holds every case
A team that sets up its phone line but never connects its helpdesk can end up with calls that transfer and records that go nowhere. Connecting the helpdesk alongside the phone setup means every resolved call lands next to the customer's other cases.
### From the first ring to the case record
Universal Telephony gives a Delight voice agent a complete path through a business's calls. It answers on the business's own numbers, hands callers to the right queue or person as a standard phone transfer, and puts each resolved case in the connected helpdesk or CRM. The call and its record arrive together, so the team can act the moment the call ends.
See how Universal Telephony works with your phone and contact center setup on the [Delight.ai Voice page](https://delight.ai/voice).
- [Answer on your numbers, hand off by phone number](#section-anchor-id-0 "Answer on your numbers, hand off by phone number")
- [Send the case where the team already works](#section-anchor-id-1 "Send the case where the team already works")
- [Start voice from the agent you already have](#section-anchor-id-2 "Start voice from the agent you already have")
- [Where Universal Telephony helps](#section-anchor-id-3 "Where Universal Telephony helps")
- [From the first ring to the case record](#section-anchor-id-4 "From the first ring to the case record")
### See Delight.ai in action.
[View demo](https://delight.ai/demo-request)
### Keep reading
- [](/blog/product/introducing-secure-caller-keypad-input) Introducing Secure Caller Keypad Input, with built-in PII data masking
- [](/blog/product/introducing-ivr-navigation) Beyond conversational IVR: Introducing IVR navigation
- [](/blog/product/delight-ai-agent-guardrails-in-action) Meet Harper: Delight.ai's AI agent guardrails in action
- [](/blog/product/introducing-intelligent-voice-handoff) Introducing Intelligent Voice Handoff for a warm transfer
[View more](/blog "View more")
## How to hire your first CX engineer
Source: https://delight.ai/blog/insights/how-to-hire-your-first-cx-engineer
## How to hire your first CX engineer
[
Nick Singh
Head of Product](https://delight.ai/author/nick-singh)
October 16 2026
**Key takeaways**
- **A CX engineer owns your customer experience AI agent after launch**, keeping it accurate and improving as your products and policies change.
- The return on hiring a CX engineer shows up twice — in resolution rates that keep climbing month after month and in support conversations that end with a satisfied customer.
- Delight.ai's [Trust OS](https://delight.ai/trust-os) gives a CX engineer full visibility and control over the AI agent, so they can improve it without filing engineering tickets.
A CX engineer is the person who owns your customer experience AI agent after it goes live. Once the agent is live, a CX engineer:
- Explains why resolution rates move
- Reads real conversations every week
- Keeps the agent's knowledge current
- Notices when a solved problem is also a sales opening
I opened my SF Tech Week talk with a support conversation that looks routine: A customer asks why their usage history only goes back 90 days. The AI agent explains the limit, and the ticket closes. What nobody mentions is that the next tier up keeps history indefinitely, which is exactly what this customer just said they need.
On paper, the agent did its job and contained the conversation. In practice, it may have missed an upsell. Most teams can't tell which, because nobody owns the agent once it launches. Owning it is the CX engineer's job, and the work happens inside a governance layer like Delight.ai's Trust OS, where every change to the agent is tested, logged, and reversible.
If you lead CX with a live AI agent, a CX engineer is the hire that keeps it improving and finds the revenue hiding in it.
## What is a CX engineer?
A CX engineer is a customer experience role that coaches, tests, and improves an AI customer agent. A year ago, nobody was hiring for this title. Now it's showing up on job boards, but with no standard template to work from, most hiring managers are writing the description from scratch.
The clearest way to see the shift is to put the old support job next to the new one. One was built around speed and scripts, and the other is built around judgment.
| The job | Customer support rep in 2020 | CX engineer in 2026 |
|---|---|---|
| Daily work | Answer tickets fast | Coach an AI agent to answer well |
| How answers get set | Follow the script | Write the instructions the agent follows |
| Hard cases | Escalate what you can't solve | Decide when the agent hands off, and to whom |
| End of a conversation | Close the ticket | Spot the upsell before the ticket closes |
| Scorecard | Hit handle-time targets | Own resolution and revenue numbers |
A customer success manager owns accounts, and a software engineer owns code. Once an AI agent is talking to your customers, a CX engineer combines both roles by owning the outcomes the agent delivers, engineering the instructions it follows, and fixing problems as soon as its conversations reveal them.
## Why does an AI agent need an owner after launch?
An AI agent needs an owner after launch because it doesn't improve on its own, and governing agents at scale starts with one named person, accountable for each agent. Deloitte's State of AI in the Enterprise research found that [only 21% of companies have a mature model for governing autonomous AI agents](https://www.deloitte.com/cz-sk/en/services/consulting/research/the-state-of-ai-in-the-enterprise.html). Without an owner, three problems simmer:
- The agent's performance drifts, so resolution rates slip a few points over a couple of months before anyone notices.
- The agent's knowledge goes stale, so a return policy changes on Monday and the agent keeps quoting the old one until a customer catches it.
- Edge cases pile up, so human agents fix the same odd request by hand every week without anyone seeing the pattern.
### What do the companies getting results do differently?
Some companies cut support headcount and called it an AI win, and Gartner expects many to reverse course. It predicts that by 2027, [50% of companies that attributed headcount reduction to AI will rehire staff to perform similar functions](https://www.gartner.com/en/newsroom/press-releases/2026-02-03-gartner-predicts-half-of-companies-that-cut-customer-service-staff-due-to-ai-will-rehire-by-2027). The companies getting results took the opposite route and gave one person the job of running the agent, full time.
[Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways) learned this early. Their AI agent, Freya, replaced a legacy chatbot in under two months, and containment climbed **from 60% to 80% in two weeks**.
> "Freya became a core part of the team, and we quickly realized that Freya needed a 'manager'... We see the future of our customer support team as AI agent managers."
> Alf Lim, Chief Product Officer, Norse Atlantic Airways
Norse calls the role an AI agent manager, and I call it a CX engineer, but both titles describe one person accountable for how the agent performs. If your team is still preparing to launch, our guide to [AI readiness](https://delight.ai/blog/ai-agent/ai-readiness) covers the groundwork that comes before this hire.
## What does a CX engineer own day to day?
Day to day, a CX engineer owns six areas of the AI agent's performance, and each one answers a question that used to go unanswered:
- **Performance monitoring** means explaining why the resolution rate dropped three points last week, such as a new product question the agent hasn't been briefed on yet.
- **Quality assurance** means reading a sample of real conversations every week, because a healthy satisfaction score can hide a confidently wrong answer.
- **Knowledge governance** means updating the agent the same day a policy changes, so a new return window reaches customers before the old one causes refund disputes.
- **Risk monitoring** means testing edge cases, like a refund request on a final-sale item, before a customer finds the gap.
- **Continuous improvement** means turning what the reviews uncover into a ranked list of fixes for the agent, rather than a backlog nobody reads.
- **Revenue awareness** means noticing when a resolved issue is also an opening, and building the agent to act on it.
The scorecard follows from that list. Resolution rate shows whether problems got solved, containment rate shows how many conversations the agent handled end to end, and cost per resolution ties the work back to the budget.
None of this works if every change needs an engineering ticket. On Delight.ai, Trust OS includes scheduled conversation tests, an activity log that shows why the agent made each decision, flags on low-confidence answers, and version control with one-click rollback. The step-by-step instructions the agent follows, which we call [Actionbooks](https://delight.ai/builder), are edited by the operations team directly, and a person reviews every change before it goes live.
Our [AI agent evaluation guide](https://delight.ai/blog/ai-agent/ai-agent-evaluation-guide) goes deeper on test design. And [the Trust Stack](https://delight.ai/blog/ai-agent/the-trust-stack-how-to-give-an-ai-agent-more-autonomy-without-losing-control) explains how to give the agent more autonomy one layer at a time.
## Can a CX engineer turn support conversations into revenue?
Yes, a CX engineer can turn support conversations into revenue, because the person reading the week's conversations is the one who sees how often a fixed problem points to a better plan. Norse Atlantic gave Freya a dedicated manager, and our team co-authored that job description with them from scratch. Today a question about legroom can turn into a seat upgrade, and a question about weight limits can turn into a baggage sale.
Here are three examples from my SF Tech Week talk, each starting as an ordinary support question:
### When a team hits its monthly message limit
### When a team runs out of seats
### When someone rebuilds the same report every Friday
The third customer asked for a how-to, and the opening was hiding in two words, "every Friday." In all three, **the offer comes straight out of the problem the agent just diagnosed**, which is why it reads as help to the customer. An agent only behaves this way when someone builds it to, and that's the job.
## Where do you find and evaluate CX engineer candidates?
You'll find CX engineer candidates in two places, inside your own support team or in technical AI roles, and the best way to evaluate them is on judgment more than any single skill. Here's how I'd think about each step of how to hire a CX engineer:
### Should you promote from within or hire from outside?
There are two good paths into this role, and the right one depends on whether your bigger gap is customer judgment or technical depth.
The first is promoting from within. Someone on your support team is probably already doing pieces of this work informally, without a mandate, a budget, or a title. They're the person who keeps flagging that the agent gets the same billing question wrong, and with their customer instincts already in place, they can learn the technical side, like editing agent instructions and reading conversation logs.
The second is hiring someone with an AI or technical background and coaching them on the human side of support, like tone, escalation, and recognizing when a frustrated customer needs a person.
### Where should the role report?
A CX engineer should report into CX leadership, with the authority to change the agent's instructions and knowledge without waiting on another team. Giving the work a name, a budget, and that authority is what turns scattered effort into a function that compounds. If every fix still needs sign-off from product and engineering, you've hired an analyst, and the agent will drift at the same pace it did before.
### How do you test a candidate's judgment?
A short work sample tells you more about a candidate's judgment than a résumé does. Give them a realistic support conversation, and ask what the AI agent should say next and how they'd check that response before it reaches every customer.
The most revealing samples are ones where the opening isn't obvious. Say a shopper asks whether a jacket runs small and mentions she's already returned two jackets this year because of fit. A typical answer points her to the size chart, while a strong one also recommends a size based on what she kept and offers an exchange if this one doesn't fit either.
The strongest candidates then explain how they'd test that new behavior on a sample of past conversations before turning it on for everyone. Anyone who spots the opening without a hint already reads conversations the way this job requires.
## Example CX engineer job description
Here's an example CX engineer job description you can adapt. It's written for a company whose AI agent is already live, so it's worth adjusting the first section if you're still launching.
**CX Engineer**
**About the role.** We're hiring our first CX engineer to own our AI customer agent after launch. You'll make sure it resolves more customer issues every month, stays accurate when our policies change, and recognizes when a support conversation is also a chance to help a customer get more from our product.
**What you'll own**
- Weekly reviews of resolution rate, containment rate, and cost per resolution, with a clear explanation of what moved and why
- A weekly read of real customer conversations, scored for accuracy, tone, and completeness
- The agent's knowledge and instructions, updated the same day a policy or product changes
- Tests for new edge cases before every release, plus a rollback plan for every change
- A ranked list of improvements, shared with support, product, and sales leaders
- Revenue moments inside support conversations, like plan limits, seat caps, and repeated manual work, and the agent behavior that acts on them
**What you bring**
- Experience in customer support, support operations, or a technical customer-facing role
- Comfort reading conversation logs and analytics, and writing clear, structured instructions for an AI agent
- Sound judgment about when an AI agent should hand a conversation to a person
- Curiosity about why customers ask what they ask
## Where to start
Every conversation your AI agent closes this week holds the same question: containment or upsell? Someone on your team is probably already trying to answer it without the title, budget, or authority to act, and the next step is giving them the job. Before you post the role, our [AI readiness assessment](https://delight.ai/tools/ai-readiness-intro/) gives you a readiness score in about 7 minutes, with next steps for the gaps it finds.
- [What is a CX engineer?](#section-anchor-id-0 "What is a CX engineer?")
- [Why does an AI agent need an owner after launch?](#section-anchor-id-1 "Why does an AI agent need an owner after launch?")
- [What does a CX engineer own day to day?](#section-anchor-id-2 "What does a CX engineer own day to day?")
- [Can a CX engineer turn support conversations into revenue?](#section-anchor-id-3 "Can a CX engineer turn support conversations into revenue?")
- [Where do you find and evaluate CX engineer candidates?](#section-anchor-id-4 "Where do you find and evaluate CX engineer candidates?")
- [Example CX engineer job description](#section-anchor-id-5 "Example CX engineer job description")
- [Where to start](#section-anchor-id-6 "Where to start")
### See Delight.ai in action.
[ View demo](https://delight.ai/demo-request)
## Frequently asked questions
Does a CX engineer need to know how to code?
No, a CX engineer usually doesn't need to write production code. The job calls for reading conversation logs, writing clear instructions for the AI agent, understanding how the agent connects to systems like billing or the CRM, and working with engineers when an integration breaks. The strongest ones are comfortable with data and enjoy figuring out why something failed.
What's the difference between a CX engineer and an AI agent manager?
A CX engineer and an AI agent manager are two names for the same job, owning an AI agent's performance after launch. Norse Atlantic Airways uses "AI agent manager," and analysts see the same shift coming, with Forrester predicting that [30% of enterprises will create parallel AI functions that mirror human service roles](https://www.forrester.com/blogs/2026-the-year-ai-gets-real-for-customer-service-but-its-not-glamorous-work/). I prefer "CX engineer" because the work is closer to engineering a system than managing a person.
Will a CX engineer replace your support agents?
No, a CX engineer doesn't replace your support agents. The role gives the AI agent an owner, so routine requests get resolved reliably and your human agents spend their time on conversations that need judgment, like a frustrated customer or an unusual refund. Many of the best CX engineers come from that same support team.
## Keep reading
[View more](/blog "View more")
[
Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)[
Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)[
Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)[
Meet Harper: Delight.ai's AI agent guardrails in action](/blog/product/delight-ai-agent-guardrails-in-action)
[View more](/blog "View more")
## Getting started with voice AI for home services
Source: https://delight.ai/blog/industry/voice-ai-appointment-booking-home-services
## Getting started with voice AI for home services
[](https://delight.ai/author/cheryl-chai) Cheryl Chai — [Product marketing lead](https://delight.ai/author/cheryl-chai)
October 16 2026
**[Voice AI](https://delight.ai/voice) can help your team book jobs, manage appointment changes, and follow up with customers. This guide explains how to assess the opportunity for your business, choose a provider, and prepare your first agent to go live.**
Your best starting point depends on where your team needs help. That might be handling new inquiries during the afternoon rush, booking visits after the office closes, or reaching customers with outstanding estimates. Each requires different information, rules, and support from your team.
This guide makes those choices easier. We’ll show how an agent handles a booking call, how it connects to your scheduling system, and which decisions remain with your staff. You’ll see seven use cases to choose from, with guidance on when each makes sense and what needs to be in place.
Chapters 1 and 2 are below. Chapters 3 to 6 are in the [full guide](https://delight.ai/ebooks/voice-ai-home-services#download-ebook).
## Where voice AI has impact
Calls to a home service company tend to fall into a handful of types: a new repair or service request, an emergency such as no heat or an active leak, a request to reschedule or cancel, "when is my technician coming?", and questions about quotes, bills, or maintenance plans. Each follows a set path. A repair request needs a name, an address, a description of the problem, an urgency decision, and a time. A status call needs the job and its current arrival window. Because the steps repeat, an agent can follow them using your rules and your schedule.
Outbound calls follow set paths too: appointment confirmations and reminders, delay and arrival-time updates, tune-up reminders and plan renewals, and follow-ups on open estimates. These are the calls your team places, or means to place when there’s time.
The rest of this section looks at each place jobs slip away.
### When nobody picks up
> 82% of US consumers say they’d call a competitor if a business didn’t answer.
>
> — CallRail survey of 1,000 US consumers, 2025
When your office closes, what happens to a customer who wants to book? Can they secure an appointment, or do they have to wait for someone to call back?
That wait can mean losing the opportunity. In Invoca’s home services call data, fewer than 3% of callers sent to voicemail left a message.
Booking rates also vary sharply by time of day. ServiceTitan’s 2022 study found that larger businesses booked 61% of calls at their daytime peak, compared with 21% after 6 p.m. Smaller businesses showed a similar pattern.
Look at your own evening and weekend calls. How many were requests you could have booked, and how many never reached your team? If customers wait for a callback just to schedule routine work, after-hours booking is worth evaluating. The worksheet at the end of this section helps you find where the opportunity is.
*Booking rates include all inbound calls, including non-booking inquiries. These figures are industry context, not an estimate of bookings AI would recover. Source: [ServiceTitan](https://www.servicetitan.com/blog/data-call-booking-rates), calls at more than 3,000 trade businesses, June 2022.*
### When a call ends without a booking
A homeowner calls to arrange a repair. Someone takes their name, address, and details of the problem, then promises a callback. Your call report records an answered call. The homeowner still has no appointment.
Some answering services can complete bookings; others take messages. The useful distinction is how much of the request they can finish during the call. A voice AI agent also needs access to your schedule and clear booking rules to confirm an appointment.
Review a week of service requests. Note which were booked during the call, which needed a callback, and why. That shows which delays an agent could fix and which come from a full schedule.
### When follow-up calls never get made
When incoming calls keep your team busy, outbound follow-up can slip. Estimates wait for a response, maintenance reminders go unsent, and customers approaching renewal may never hear from you.
These conversations serve different purposes. An estimate follow-up can help a customer move ahead with a job. A maintenance reminder can fill appointments before peak season. A timely delay update can keep an existing visit on track and save another call to your office.
Voice AI can help your team make these contacts consistently, using customer records, job status, and the rules you set. Start by identifying which follow-ups are overdue and what completing them would achieve.
## What this means for your business
Voice AI can help with three kinds of unfinished work: calls your team cannot answer, booking requests that need a callback, and customer follow-ups that keep getting postponed.
The opportunity depends on where that work builds up in your business. You may need more coverage after hours, help during busy periods, or a reliable way to follow up on estimates and renewals.
The next section shows how an agent handles a booking call, including when it brings in your team.
### Where could voice AI help your business?
Rate each statement with Often, Sometimes, Rarely, or Not sure:
• Answer. Calls may go unanswered when the office is closed or everyone is busy.
• Book. Customers who want to book have to wait for someone to call them back.
• Follow up. Estimate follow-ups, maintenance reminders, or renewal calls get postponed.
---
Pick the area where the problem happens most, and check a week of calls to size it.
## What an agent needs to complete the work
Voice AI combines a spoken conversation with access to business information and permission to act. For a service business, three parts matter.
## What happens during a booking call
Dana calls after the office has closed to arrange an AC repair. Here is an illustrative booking flow for a request the business allows the agent to handle.
*Illustrative example. The questions, available appointments, and handoff rules depend on your business.*
By the end of the call, Dana has an appointment. The job is in your system as an AC repair at 4120 Desert Rose Ln, Saturday from 10 a.m. to noon, marked as booked by the agent during the call and confirmed. It sits on your schedule like any other job, and your team can see what was discussed.
## How it works with your systems
The agent needs access to the information and actions each task requires. The phone connection carries the conversation; connections to your business systems let it do the work.
For booking, your field service management system, the software your team uses for customers, jobs, and scheduling, remains the place where appointments are recorded.
To complete a booking, the agent needs permission to check availability and create the appointment in your scheduling system.
## Beyond booking: When the agent makes the call
Booking is one application. Once an agent can use customer records, scheduling, and job updates, it can also call customers.
Your systems already hold reasons to contact customers: a maintenance visit is due, an estimate is awaiting a decision, or an appointment has changed. An outbound voice AI agent can call when the conditions you set are met and help the customer take the next step.
Take a customer whose annual maintenance visit is due. The agent calls, explains the reason, and asks whether they would like to schedule. If they’re ready, it checks availability, offers suitable times, and records the confirmed appointment. If they have a question that needs your team, it passes on the question and the context.
The conversation can end in several useful ways: an appointment booked, a callback requested, or a customer preference recorded. Your team can see what happened and what still needs attention.
You decide who can be contacted, what triggers the call, when calls may happen, and how unanswered calls or requests for a person are handled.
### How this works at Dr. Cool Services
[Dr. Cool Services](https://delight.ai/customers/dr-cool-services), a Las Vegas HVAC and home services company, uses delight.ai’s voice agent to book HVAC service in English and Spanish. The agent identifies the customer, gathers the details, finds an appointment, and confirms the booking. When a call drops, it calls back to continue, and complex warranty questions go to the team with the call context. Dr. Cool also uses proactive outreach for maintenance scheduling.
---
## Free guide · Chapters 3–6
### Get the complete guide
You’ve read the first two chapters of *Getting started with voice AI for home services*. The full guide shows what you need in place, seven use cases to choose from, how to pick a provider, and how to get your first agent live.
- 5 worksheets and a launch checklist
- Questions to ask every provider
- A pilot plan you can take to your team
### [Download the full guide →](https://delight.ai/ebooks/voice-ai-home-services#download-ebook)
- [Where voice AI has impact](#section-anchor-id-0)
- [When nobody picks up](#section-anchor-id-1)
- [When a call ends without a booking](#section-anchor-id-2)
- [When follow-up calls never get made](#section-anchor-id-3)
- [What this means for your business](#section-anchor-id-4)
- [Where could voice AI help your business?](#section-anchor-id-5)
- [What an agent needs to complete the work](#section-anchor-id-6)
- [What happens during a booking call](#section-anchor-id-7)
- [How it works with your systems](#section-anchor-id-8)
- [Beyond booking: When the agent makes the call](#section-anchor-id-9)
- [Get the complete guide](#section-anchor-id-10)
## Get the complete guide for free
[Download the full guide](https://delight.ai/ebooks/voice-ai-home-services#download-ebook)
## Keep reading
[View more](/blog "View more")
[ How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[ Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[ Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[ Introducing Universal Telephony: Contact center integration](/blog/product/introducing-universal-telephony)
[View more](/blog "View more")
## Best AI voice agents in 2026, tested firsthand
Source: https://delight.ai/blog/ai-agent/best-ai-voice-agents
## Best AI voice agents in 2026, tested firsthand
[](https://delight.ai/author/caroline-adamec)
[Caroline Adamec — Content engineer](https://delight.ai/author/caroline-adamec)
October 20 2026
## Key takeaways
- **Delight.ai is my top pick** because its demo asked what was wrong and quoted a price before booking. These initial questions ensure both the customer (me) and the business know the scope and cost before anything is confirmed.
- Most other demos that I called stalled before **a booking, a handoff, or a follow-up**, and the only one that did book never asked what my problem was.
- Per-minute prices leave out the large language model (LLM), voice, and phone carrier charges that decide **what each resolved call costs you**, while Delight.ai prices by resolved conversation.
## The shortlist
1. [**Delight.ai**](https://delight.ai) is best overall, especially for calls that need the right job details before the booking.
2. [**Retell AI**](https://www.retellai.com) is best for fast, pay-as-you-go phone automation.
3. [**Vapi**](https://vapi.ai) is best for engineering teams building their own voice stack.
4. [**PolyAI**](https://poly.ai) is best for high-volume enterprise contact centers.
5. [**Bland AI**](https://www.bland.ai) is best for high-volume calling with private deployment options.
6. [**Sierra**](https://sierra.ai) is best for enterprise brands that want a managed, outcome-priced agent.
7. [**ElevenLabs**](https://elevenlabs.io) is best for creators and individuals who need prerecorded AI voices.
## Overview
The best AI voice agents get the job right from the first call. To test 7 popular AI voice agents, I called every public demo I could find and asked to book a plumbing appointment. I posed as a buyer from a mid-sized home services company and recorded my findings firsthand.
I put every platform I could reach, including [Delight.ai's AI voice agent](https://delight.ai/voice), through the same call. Each ranking comes from my own calls, as well as what G2 and Gartner reviewers say after living with each platform.
## What is an AI voice agent?
An AI voice agent is software that answers or places phone calls, understands open-ended speech, and takes action in your systems, like booking a service visit.
### How is an AI voice agent different from IVR?
By contrast, an interactive voice response (IVR) system routes callers through fixed menus ("press 2 for billing") and sends anything off-script to a person.
The difference shows up the moment a caller goes off-script. A homeowner might say "my water heater's leaking and I'm out of town Thursday." An IVR (keypad menu) can't route that, but a capable voice agent can ask one follow-up question, check the schedule, and book the visit.
Most available AI voice agents can handle the initial conversation well enough, so they separate on 3 things that happen after the agent understands the request:
1. **Booking on the call** means the appointment lands in the calendar before the caller hangs up, instead of a promise that someone will call back to confirm later.
2. **A clean handoff** passes a summary to a person, so the customer doesn't have to repeat their leaking water heater story multiple times.
3. **Memory** lets the agent recognize the same customer next week, whether they call again or send a text to reschedule their appointment.
Our guide to [how AI voice agents work](https://delight.ai/blog/ai-agent/ai-voice-agents) covers the basics of AI voice agents in more depth.
## How did I test these AI voice agents?
I tested these AI voice agents by calling each vendor's public voice demo the way a buyer would on an exploratory visit to their site. On every call, I said I was from a mid-sized home services company and asked to book a plumbing appointment.
A public demo isn't a production benchmark, but it's the first thing a buyer experiences. For the purposes of this article, I'm classifying these demos as a fair read on how much a vendor trusts its product in an unscripted conversation.
On each call, I paid attention to the same five things:
1. Whether the agent asked why I needed a plumber before asking for a time.
2. Whether I walked away with a booked appointment.
3. How fast the agent responded, how it handled interruptions, and whether the voice sounded natural.
4. Whether it kept track of details I gave it earlier in the call.
5. Whether I could try the agent without booking a sales demo first.
Where a vendor had no interactive demo, I say so and lean on buyer reviews instead. I also read the G2 and Gartner Peer Insights reviews for every platform, because a 5-minute call can't show you what month six looks like.
## How do the best AI voice agents compare?
The best AI voice agents compare most clearly on two things a buyer can check in minutes: One is whether you can test the agent yourself, and the other is what the agent did with a real booking request. You can see both in the table below, along with pricing models and G2 ratings.
| Platform | Could I test it? | What happened on my call? | Pricing model | G2 rating | Best for |
|---|---:|---|---|---:|---|
| [Delight.ai](https://delight.ai) | Yes, live demo call | Asked what was wrong and quoted a price, then stopped before confirming a time | Conversation-based usage, $0 seat fee on Delight Desk, payment per AI resolution | [4.7](https://www.g2.com/products/sendbird-delight-ai/reviews) (5 reviews) | Best overall, especially getting job details right before booking |
| [Retell AI](https://www.retellai.com) | Yes, live demo call | Booked in under 2 minutes without asking what was wrong | $0.07 to $0.31 per minute | [4.8](https://www.g2.com/products/retell-ai/reviews) (2,639 reviews) | Fast phone automation with technical staff |
| [Vapi](https://vapi.ai) | Yes, live demo call | Lost track of the job and promised a callback | $0.05 per minute, plus model and phone carrier charges | [4.2](https://www.g2.com/products/vapi-ai/reviews) (3 reviews) | Engineering-led builds |
| [PolyAI](https://poly.ai) | Yes, demo and self-serve builder | Ended the call at the transfer step | Quote-only, billed per minute | [5.0](https://www.g2.com/products/polyai/reviews) (12 reviews) | High-volume enterprise contact centers |
| [Bland AI](https://www.bland.ai) | Yes, live demo call | Described booking instead of doing it | $0.14 per minute, or $299/month + $0.12 per minute | [5.0](https://www.g2.com/products/bland/reviews) (11 reviews) | High-volume calling with private deployment |
| [Sierra](https://sierra.ai) | No, sales demo only | Not tested | Outcome-based and quote-only | [4.5](https://www.g2.com/products/sierra/reviews) (131 reviews) | Managed enterprise deployments |
| [ElevenLabs](https://elevenlabs.io) | No interactive agent demo | Not tested | Plans $0–$990/month, plus model and phone carrier charges | [4.5](https://www.g2.com/products/elevenlabsio/reviews) (1,276 reviews) | Creators needing prerecorded AI voices |
## The 7 best AI voice agents for customer service
The 7 best AI voice agents for customer service are ranked below by how each demo handled my plumbing call. Buyer reviews and published customer results fill in what a single call can't show.
### 1. Delight.ai (best overall)
Delight.ai is a revenue AI agent for voice and every channel around it. I put it first because its demo was the only one that scoped my call like a job before trying to book it.
The voice sounded natural, and the agent knew my use case from its first line. When I said I wanted an appointment, it asked why and what was wrong. Then it **told me what the visit would cost before moving to the booking step**.
None of the other demos helped me work out the job details first. Triage matters more than booking speed, in my opinion. When the agent settles the problem and the price before confirming, the customer knows what they're agreeing to and the business knows what job it's taking on. Only jobs that fit get booked, so fewer end in a cancellation, a surprise invoice, or a wasted trip.
The demo stopped before locking in a time, since each business sets its own rules for when Delight.ai confirms a booking on the call. Once it's connected to a scheduling system in a live deployment, Delight's agent books on the call.
This AI voice agent is built for background noise, accents, and interruptions, and it responds in under a second. It also features outbound calling, so a dropped call or an unfinished booking can trigger a callback that picks up where things left off. And because [one memory follows each customer](https://delight.ai/omnipresent) across voice and every digital channel, a homeowner who books by phone and texts a question the next day doesn't start over.
- Strengths: intake that covers job details and pricing before booking, outbound callbacks, 29+ languages.
- Integrations: contact center and helpdesk tools like Genesys, Zendesk, and NICE via [integrations](https://delight.ai/integrations).
- Demo notes: public demo stops before confirming a time; see a [walkthrough against your scheduling system](https://delight.ai/contact-sales).
- Pricing: based on conversations; [Delight Desk](https://delight.ai/desk) has a $0 seat fee, pay per AI resolution.
- Proof: [Dr. Cool Services](https://delight.ai/customers/dr-cool-services) uses Delight.ai to answer every call and book appointments in English and Spanish.
- G2: 4.7/5 from 5 reviews as of October 2026.
> "Our customers don't want to navigate phone trees or wait on hold. They want to talk to someone who can help." — Mark Farley, President, Dr. Cool Services, 2026
Delight.ai fits CX teams where **getting the job details right on the first call** prevents a wasted visit. It also fits teams whose customers return through another channel.
### 2. Retell AI (best for fast, pay-as-you-go phone automation)
Retell AI is a pay-as-you-go phone automation platform, and its demo was the fastest to book.
The demo asks you to pick a use case and enter your phone number before it calls you. The use case options weren't very descriptive, so it was hard to tell which one matched my plumber scenario best. I'd also rather not hand over my phone number just to try a demo.
Once the call started, responses came back fast, and the voice sounded natural even with background noise on my end. When I interrupted, it adjusted quickly and picked the thread back up. I booked an appointment in under 2 minutes, but the agent never asked what was wrong with my plumbing. For a home services business, that's a job confirmed before either side knows the scope or the price.
When I asked for a person, it said someone would call me back, and I had to pry a little to get details about when and how I could expect a response.
Notes:
- Strengths: speed, natural interruption handling, self-serve start with $10 in free credits.
- G2 feedback: missing features, learning curve, costs that climb with volume.
- Pricing: $0.07 to $0.31 per minute depending on voice, model, and carrier ([pricing](https://www.retellai.com/pricing)).
- Proof: Medical Data Systems reports 100% inbound call handling on the platform with a 30% transfer rate.
- G2: 4.8/5 from 2,639 reviews.
Fit: operations and engineering teams that want phone automation live quickly and can tune the stack. Cost is the bigger risk at scale; per-minute charges grow with volume.
### 3. Vapi (best for engineering teams building their own voice stack)
Vapi is a developer voice platform, and its demo was fast and got further into a conversation than most. But the conversation design worked against me as a caller.
The agent cut me off, and after my second message it forgot I was booking a plumbing appointment. It asked me to pick a day for "my meeting" instead, and it skipped intake entirely, jumping to a time without asking why I needed a plumber. It also read short pauses as the end of my sentence and responded too early.
The voice tried so hard to sound human that it ended up sounding less natural. It used frequent "uh," "like," and "um" and made jokes like "sorry, I haven't had my coffee yet." The whole interaction felt very "uncanny valley".
**Vapi also couldn't confirm the appointment on the call** and said a team member would call me back. When I mentioned I always have an 8:30 a.m. meeting on Tuesdays, it didn't add that to its memory. No follow-up message arrived afterward.
Notes:
- Strengths: low latency and flexibility; teams can bring their own model, voice, and phone carrier.
- Trustpilot: 2.4/5 from 15 reviewers, several citing surprise bills from model and phone carrier charges.
- Pricing: starts at $0.05 per minute for hosting ([pricing](https://www.vapi.ai/pricing)); model and carrier charges billed on top.
- HIPAA: coverage costs an extra $2,000/month.
- Proof: Amazon Ring routes 100% of inbound calls through Vapi ([TechCrunch](https://techcrunch.com/2026/05/12/vapi-hits-500m-valuation-as-amazon-ring-chose-its-ai-platform-over-40-rivals/)).
- G2: 4.2/5 from 3 reviews.
Fit: engineering teams that want full control of the stack and will own the conversation design. See our guide on [how to build AI voice agents](https://delight.ai/blog/ai-agent/how-to-build-ai-voice-agents).
### 4. PolyAI (best for high-volume enterprise contact centers)
PolyAI builds enterprise voice AI for contact centers, and it has some of the strongest enterprise references in the category. That made my demo experience more surprising.
I told the agent I was from a mid-sized company, and the call immediately failed. On my second try, I asked to book a plumbing appointment and waited about 5 seconds for a response. Then the agent ended the call at the transfer step without doing anything, 27 seconds in.
I tried the demo about 4 times, and it only ever handled 2 turns. The agent asked what it could help with, and I said I wanted to book a plumbing appointment. It replied "I can set that up for you" and then ended the call, every time.
I tried PolyAI's self-serve builder; it asked for my email and opened the platform but the test call failed there as well.
Notes:
- Integrations: Amazon Connect, Genesys, NICE CXone.
- Gartner Peer Insights: 4.7/5 from 48 enterprise ratings.
- Reviewers mention lag and hard-to-predict pricing; production pricing runs through PolyAI's sales team.
- Pricing: quote-only and billed per minute ([pricing](https://poly.ai/pricing)).
- Proof: PG&E case study shows 67% containment and 22% CSAT lift on outage calls ([PG&E case study](https://poly.ai/customers/pge)).
- G2: 5.0/5 from 12 reviews.
Fit: large contact centers with the budget and time for a managed, sales-led rollout.
### 5. Bland AI (best for high-volume calling with private deployment options)
Bland AI builds enterprise phone agents with an on-premises option for enterprise customers, and its demo felt more like a guided walkthrough than a product demo. When I asked to book an appointment, the agent described its features instead of booking one.
It told me it could book and confirm appointments on the same call, but it couldn't show me that in real time. Its responses ran long, and the audio was fuzzy enough that it never sounded human.
Notes:
- Strengths: published pricing, on-premises deployment on Enterprise plan, memory that recognizes the same contact across calls and texts.
- G2: reviewers note learning curve and difficulty pinpointing whether issues stem from prompt, transcription, or call flow.
- Pricing: $0.14/min on Start, or $299/month + $0.12/min on Build ([pricing](https://www.bland.ai/pricing)).
- Proof: Idaho Housing and Finance Association reports $750,000/year savings after replacing its IVR ([customer story](https://www.bland.ai/customers/ihfa)).
- G2: 5.0/5 from 11 reviews.
Fit: technical teams running high call volumes that need control over where the agent is hosted.
### 6. Sierra (best for enterprise brands that want a managed agent)
Sierra is a managed enterprise agent platform with outcome-based pricing that I couldn't test. There's no public demo, so the only path is booking a sales demo. A guided demo can't show how an agent handles a caller who goes off-script.
I ranked Sierra on buyer reviews and public results only.
Notes:
- Strengths: coverage across voice and digital channels, outcome-based pricing charging per resolved conversation.
- G2: reviewers mention trouble with complex questions and changes that need Sierra's team to make; pricing isn't public.
- Proof: Singtel's agent resolved 73% of mobile and home troubleshooting cases without a customer care officer.
- G2: 4.5/5 from 131 reviews.
Fit: large consumer brands that want a vendor-managed deployment and are comfortable buying without a hands-on trial. See [Sierra vs. Delight.ai](https://delight.ai/compare/sierra-vs-delight/).
### 7. ElevenLabs (best for creators who need prerecorded AI voices)
ElevenLabs is a voice-model company with an agent platform, and its site didn't give me an agent to talk to. The voice demos are prerecorded clips of people reading text, and to my ear the recorded voices sounded synthetic.
After I entered my email, I was offered a voice agent test and asked for my use case, so I chose customer support. The experience that followed wasn't interactive and didn't change based on my answer. The site was also slow and required an account to see the full voice library.
Notes:
- Strengths: large voice library and a free plan to start building.
- G2 feedback: cost and usage credits that run out quickly; call routing and escalation scores trail voice-quality scores.
- Pricing: plans from free to $990/month, with AI model and phone carrier charges billed on top ([pricing](https://elevenlabs.io/pricing/agents)).
- Proof: Klarna runs an ElevenLabs voice agent for 35 million US customers.
- G2: 4.5/5 from 1,276 reviews covering all products.
Fit: individuals and creators needing voiceovers for content rather than enterprise CX teams needing live booking agents.
## How much do AI voice agents cost?
Published platform fees range from $0.05 to $0.31 per minute, but that usually excludes other charges. The more useful metric is cost per resolved call because additional charges significantly change the per-call cost.
Four charges widen the gap between the advertised rate and total cost:
- **Model and voice charges** from the LLM and text-to-speech provider are often added at provider rates.
- **Phone carrier minutes** are often billed separately, including minutes spent on transferred calls.
- **Concurrency limits** can cap simultaneous calls and charge for extra lines.
- **Compliance add-ons** (e.g., HIPAA) may be a separate monthly fee.
Example: Retell's base $0.07/min can look like $0.11/min typical and $0.31/min at the top end. At 10,000 minutes/month that's $700, $1,100, or $3,100. If only half of 3-minute calls resolve the problem, costs per resolved call double.
Outcome-based and per-resolution pricing shift this risk to the vendor.
## How do you choose the right AI voice agent platform?
Choosing starts with two questions: who will run the agent day to day, and what must happen after the call ends. Those answers rule out more platforms than any feature checklist.
### Which platforms suit a team where engineers own the agent?
Developer platforms like Vapi and Retell suit teams where engineers own the agent, because they offer control over models, voices, and carriers. That control requires designing intake, memory, and handoff yourself.
### What should teams with an existing contact center platform check first?
Check native integrations with Genesys, Five9, NICE, or Amazon Connect. A voice agent that can't pass a call and its context into your queues creates extra work.
### What should you look for if every call has to end in a booking or sale?
Test the agent with an unscripted call end-to-end. A good agent asks enough to get the job right, books it, sends confirmation, and updates the customer record. Delight.ai was the only agent in my tests that worked out the job before booking and maintains shared memory across channels.
Three questions to ask every vendor:
- Can I test the agent on an unscripted call before talking to sales?
- What's my estimated cost per resolved call at my volume?
- What does the handoff to a person include, and where does it land?
Most AI voice agents can carry a conversation now, so what separates them is whether the caller hangs up with the right job booked. On my calls, only Delight.ai worked out what was wrong and what the visit would cost before getting to the booking.
If your team needs every call to end in a booked visit, a sale, or a follow-up on any channel, [see how Delight.ai's AI voice agent handles the whole call](https://delight.ai/voice).
## Quick links (in-article)
- [What is an AI voice agent?](#section-anchor-id-0)
- [How did I test these AI voice agents?](#section-anchor-id-1)
- [How do the best AI voice agents compare?](#section-anchor-id-2)
- [The 7 best AI voice agents for customer service](#section-anchor-id-3)
- [How much do AI voice agents cost?](#section-anchor-id-4)
- [How do you choose the right AI voice agent platform?](#section-anchor-id-5)
### See Delight.ai in action
[View demo](https://delight.ai/demo-request)
## Frequently asked questions
**How much does an AI voice agent cost?**
An AI voice agent costs $0.05 to $0.31 per minute in published rates, and most platforms add model and phone charges on top. ElevenLabs publishes monthly plans from $0 to $990, while PolyAI and Sierra quote per deal. Cost per resolved call is the most useful number to compare.
**What is the best AI voice agent for customer service?**
Delight.ai is the best AI voice agent for customer service based on my tests, because its demo asked what was wrong and quoted a price before booking. It also keeps one memory across voice, chat, text, and email.
**Are AI voice agents HIPAA compliant?**
Some agents are HIPAA compliant, but coverage varies by vendor and plan. Most platforms advertise HIPAA support; a signed BAA often requires an enterprise plan or paid add-on (e.g., Vapi's $2,000/month). Delight.ai holds a HIPAA Type I report and a SOC 2 Type II report.
**Do AI voice agents support multiple languages?**
Most support multiple languages; counts and quality vary. Delight.ai supports 29+ languages, Retell lists 55, and PolyAI lists 75+. Test whether an agent can switch languages mid-call.
**How long does it take to deploy an AI voice agent?**
Deployment ranges from same-day test builds to sales-led rollouts. Five platforms let you start building the same day; Sierra and PolyAI require sales for production plans. Ask vendors for timelines including integrations with scheduling and CRM systems.
## Keep reading
[View more](/blog)
[](/blog/industry/voice-ai-appointment-booking-home-services)
[Getting started with voice AI for home services](/blog/industry/voice-ai-appointment-booking-home-services)
[](/blog/insights/how-to-hire-your-first-cx-engineer)
[How to hire your first CX engineer](/blog/insights/how-to-hire-your-first-cx-engineer)
[](/blog/product/introducing-secure-caller-keypad-input)
[Introducing Secure Caller Keypad Input, with built-in PII data masking](/blog/product/introducing-secure-caller-keypad-input)
[](/blog)
[Beyond conversational IVR: Introducing IVR navigation](/blog/product/introducing-ivr-navigation)
[View more](/blog)
# Customers Index
## Our customers
Source: https://delight.ai/customers
## Our customers
### Latest Story
[](https://delight.ai/customers/bjs)
#### [How BJ’s is improving members’ digital experience with help from Bev](https://delight.ai/customers/bjs)
Learn more
## Customer stories
- [ Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
- [ Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
- [ How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services)
- [ What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)
- [ How Hanssem—South Korea's furniture icon—leapfrogged into the always-on customer experience era](https://delight.ai/customers/hanssem)
- [ Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping)
- [ gther transforms event registration with delight.ai](https://delight.ai/customers/gther)
- [ AllAthlete democratizes athletic recruiting with delight.ai](https://delight.ai/customers/allathlete)
## Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
# Customers
## Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen
Source: https://delight.ai/customers/norse-atlantic-airways
## Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen
January 28 2026
Airlines, like many industries, are steeped in tech debt and legacy systems. With AI solutions popping up left, right, and center, it’s a jungle to navigate and even harder to triage alongside existing systems. Legacy providers are branding themselves as AI-first, but in reality, AI is an afterthought. It’s difficult to know if one is working with a truly native AI company.
[Norse Atlantic Airways](https://flynorse.com/) was also grappling with the same challenges: how does one adapt, should one adapt, or is it just a fad that will die out? That rapidly changed after meeting delight.ai. **In less than two months, Norse had sunset their old chatbot and gone live with their flagship AI agent, “Freya,” powered by delight.ai.** While Freya was a major milestone, she was only the opening move. While other airlines looked at AI as a way to patch holes in customer service, Norse and Delight.ai see an opportunity to fundamentally rethink how a business runs on AI.
### The mandate: Rethink work, not just workflows
The aviation industry usually treats automation as a finish line—chasing better resolution rates, lower Average Handling Times, and smarter FAQ bots. **Norse recognized that these metrics, while necessary, are merely the baseline.**
Norse asked a deeper question:
**What does an AI-ready organization actually look like?**
That single question reframed the entire transformation. AI-readiness isn’t a technology milestone, it’s an organizational one. It forces a company to rethink how decisions are made, how knowledge moves, how teams collaborate, and how AI can participate in the business as a true teammate—not a feature bolted on the side.
**And Norse understood they couldn’t become AI-ready without changing the workforce itself.** Freya was suddenly their most active “employee.” Someone had to own her evolution, shape her behavior, and ensure she kept getting better.
> “Freya became a core part of the team, and we quickly realized that Freya needed a “manager”. However, we were entering uncharted territory. There was no LinkedIn template for an AI agent manager role. We worked closely with the Delight.ai team to reverse-engineer the role. We literally co-authored the job description from scratch to find someone that can be the operator and manager for the AI agent Freya. In addition, we needed to upskill our current customer support team into specialists who continuously optimize, train and step in when human-touch is required. We see the future of our customer support team as AI agent managers.”
>
> — Alf Lim, Chief Product Officer at Norse Atlantic Airways
### The "AI high performer" blueprint
Norse’s approach mirrors the shift McKinsey calls out in [their research](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai): The companies that see 5%+ EBIT impact from AI—their “AI high performers”—are the ones that redesign the organization itself.
They are 2.8× more likely to rebuild workflows and responsibilities around intelligent systems rather than adapting AI to old processes.
To become AI-ready, Norse first had to reimagine how the airline organizes knowledge, how teams make decisions, and how AI could participate in the business as a true collaborator—not a tool, not a project, **but a new kind of teammate.**
#### Designing the AI workforce
The shift required answering foundational questions about the nature of work:
- What should AI own? What should humans own? And what should they co-own?
- How do we make sure knowledge lives in one accessible, structured, ever-improving brain?
- What does the workforce look like when AI becomes part of the operating rhythm?
- How do we build a role responsible for ensuring that improvement never stops?
**The answer led to a new discipline.**
### The birth of a new operational discipline: AI operations
To run this new intelligence, **Norse is opening a role the airline industry has never seen:** [**AI Agent Manager**](https://careers.flynorse.com/jobs/7105125-customer-service-ai-manager).
The **AI Agent Manager** is the clearest signal of their shift into AI Operations: a discipline built around owning, training, and improving AI as part of the workforce.
Traditionally, customer support has been a human-only discipline: a single team handling every interaction manually, with improvements tied to training or headcount. Introducing an AI agent breaks that model open. It creates a new, cross-functional operating layer where technology, product thinking, and frontline expertise must stay tightly coordinated.
The AI Agent Manager sits within the customer support team, but the work itself stretches far beyond it. The role operates at the intersection of human support, Product & Engineering, and the AI agent—acting as the connective tissue that keeps all three aligned, informed, and moving as one system. It’s a hybrid operator position blending product intuition, prompting skill, and day-to-day performance ownership.
This person manages Freya’s accuracy, behavior, guardrails, workflows, RAG sources, and failure patterns. They audit conversations, diagnose issues, design new automations, and tune prompts like a continuous improvement engine.
And because passengers expect one seamless experience—whether they’re booking a flight or dealing with a delay—the role is cross-functional from the start. It touches product, engineering, operations, and policy. The AI Agent Manager becomes the connective tissue that keeps the agent unified, on-brand and coherent across every part of the traveler journey.
### From alignment to impact: 60% → 80% containment
AI is never perfect on day one. It becomes powerful through structured learning, deliberate tuning, and continuous operator feedback. **Through a tight collaboration between Norse and Delight.ai’s Forward Deployment Team, containment climbed from 60% to 80% in just two weeks.**
> “What stood out was the Forward Deployment team’s bias for action. We assigned a clear goal—80% containment—and they treated it as their own. They dissected our conversation logs, re-engineered the Actionbooks, and shipped the necessary logic updates to get us there. It was a masterclass in iterative improvement. Coming from the consulting industry, this feels like a new form of consulting delivery: tech solutions + human ingenuity.”
>
> — Alf Lim, Chief Product Officer at Norse Atlantic Airways
#### Looking ahead: Beyond support, the pivot to revenue
While most airline bots are defensive (cutting costs), Freya plays offense. **She now leverages conversation context to drive commerce:** a question about legroom can transition into a seat upgrade, an inquiry about weight limits can convert into immediate baggage cross-sales.
Freya is no longer just solving problems; she is actively increasing customer lifetime value. This signals the end of the artificial wall between "Support" and "Sales".
> “We are moving past the idea of 'support bots.' The future is one unified agent for sales and support—a single digital concierge that knows you, helps you, and travels with you. Whether she is fixing a ticket or upgrading your seat, it’s the same brain, the same context, and the same seamless experience.”
>
> — Alf Lim, Chief Product Officer at Norse Atlantic Airways
This is the next era of airline service: intelligent, adaptive, revenue-aware, and deeply customer-centric. And Norse is already building it in a strategic partnership with Delight.ai.
- [The mandate: Rethink work, not just workflows](#section-anchor-id-0 "The mandate: Rethink work, not just workflows")
- [The "AI high performer" blueprint](#section-anchor-id-1 "The \"AI high performer\" blueprint")
- [The birth of a new operational discipline: AI operations](#section-anchor-id-2 "The birth of a new operational discipline: AI operations")
- [From alignment to impact: 60% → 80% containment](#section-anchor-id-3 "From alignment to impact: 60% → 80% containment")
### Keep reading
[View more](/customers "View more")
- [](https://delight.ai/customers/mixpanel) [Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
- [](https://delight.ai/customers/dr-cool-services) [How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services)
- [](https://delight.ai/customers/hipages) [What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)
[View more](/customers "View more")
#### Ready to delight your customers
with an AI concierge?
[Contact sales](https://delight.ai/contact-sales "Contact sales")
## What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving
Source: https://delight.ai/customers/hipages
- [Customers](/customers)
## What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving
January 23 2026
hipages is Australia’s #1 marketplace for connecting homeowners with trusted tradespeople (or “tradies”) — a platform born in 2004 from a Sydney garage and grown into a national engine moving millions of home-improvement jobs every year. **Today, hipages serves over four million Australians, connects more than 33,000 tradies to real work, sees about 1.3 million jobs a year, and a new job hits the platform roughly every 25 seconds.**
In a marketplace that moves that fast, support isn’t a back office. It’s the bloodstream. A slow reply means a job stalls. A misrouted question means a tradie loses time they can’t earn back. The marketplace wasn’t slowing down, but the operations supporting it were stretched to their limits.
**To stay fast, resilient, and reliable, hipages relies on a unified Sendbird stack built on delight.ai, Salesforce Connector, and Sendbird Chat.** Together, these products form the backbone of every customer and tradie interaction across the marketplace.
## A marketplace that thrives on clarity and speed
Every hipages job starts with a homeowner posting a task and ends with a tradie getting hired. But between those moments sits a complex mesh of expectations—lead quality, timing, billing, account access, and the need to talk to the right person at the right time.
Tradies often want quick clarification or reassurance:
- *Was this lead worth the spend?*
- *I need to change my password*
- *Can someone from hipages call me?*
hipages supports thousands of these moments every day. Some require a simple answer. Some need a quick callback from an Account Manager. Others need to go straight to human support for review.
Jeremy Burton, CTO of hipages put it plainly: when a tradie has a problem, “it’s really critical they can get speedy service to get back online, claiming those jobs and getting the work that they need to run their businesses.”
What used to be a high-friction landscape is now a coordinated flow powered by Sendbird.
## Build vs. Buy: hipages chose time-to-value
hipages didn’t land on this path by chance. Like any strong engineering team, their first instinct was: we can build this ourselves. An in-house AI agent, custom workflows, stitched system-to-system handoffs. But as they dug deeper, the tradeoffs became impossible to ignore. **They needed a partner who already understood the terrain and could move at the pace the marketplace demanded.**
> *“AI is moving too fast to be treated like a traditional build. It’s a new space, evolving in real time. We didn’t have all the specialist expertise in-house, and trying to develop it ourselves would have slowed us down. Partnering lets us move faster, learn faster, and deliver value to our marketplace much sooner.”*
>
> – Jeremy Burton, CTO of hipages
## The power of the full Sendbird stack
Choosing not to build meant choosing a platform they could trust end to end. And for hipages, that meant looking for a single system that could carry every interaction—from the first homeowner message to the final support handoff—without breaking rhythm.
> *“We weren’t looking for a stack of point solutions. We needed a single, cohesive platform that could support the entire customer journey end to end.* [*delight.ai*](https://delight.ai/) *+ Sendbird comms suite is the only way to support the full customer experience for businesses like ours.”*
>
> – Jeremy Burton, CTO of hipages
### The heartbeat: Sendbird Chat
At the center of hipages is a constant pulse of communication: tradies updating homeowners, homeowners clarifying details, both sides trying to move from quote to completion without friction. **Sendbird Chat is the heartbeat of that pulse**, keeping every conversation inside the hipages app where the real work begins and ends. Nothing gets lost. Nothing drifts to another channel. It’s one continuous thread that helps tradies and homeowners stay aligned, on track, and confident as the job unfolds.
### The dual-layer advantage: One engine for marketplace & support
One of hipages’ superpowers is that **the same chat experience powering tradie ↔ homeowner conversations also powers tradie ↔ hipages support.** The entire marketplace runs on a single communication engine, which means tradies never switch apps, never juggle inboxes, and never rethink how to get help.
Because Sendbird already handled the user-to-user conversations that make the marketplace work, it became the natural choice to handle support as well. The result is rare continuity: one thread for doing the work and one thread for resolving issues, all inside the environment tradies use every day.
> *“We already trusted Sendbird to carry the conversations that matter most: tradies talking to homeowners. Support had to match that experience. It had to feel just as familiar and just as fast. When we realized we could run both on the same platform, it was an easy decision. It made the whole experience simpler and more intuitive for our tradies. It gave us continuity we’d been chasing for years.”*
>
> – Jeremy Burton, CTO of hipages
### The brain: Delight.ai for always-on support
[Delight.ai](https://delight.ai/) meets tradies at their pace. They’re moving between jobs, juggling calls, working in tight spaces, and they need straight answers fast. They reach out when a lead doesn’t look right, when a charge needs clarification, when they need more jobs coming in, or when a homeowner goes quiet at the wrong time.
These aren’t abstract questions—they’re the small decisions that shape a tradie’s week and their income. Delight.ai responds instantly with clear, contextual guidance, absorbing the everyday issues that once slowed the team down. By handling the bulk of these routine but important questions, it helps tradies stay productive and helps hipages keep the trades economy moving.
### The bridge: Salesforce Connector for seamless handoff
Some moments still need a human. When a tradie asks for their Account Manager, their Premium Account Manager, or help from the support team, the system shifts seamlessly. The Salesforce Connector acts as the bridge, moving the entire conversation — every message, every detail, every signal — straight into Salesforce without losing context. A case is created automatically, it lands with the right person immediately, and the tradie stays right where they are. The tradie doesn’t have to start over or repeat anything. The handoff just happens, and the hipages team picks it up with everything they need to help.
> *“Our agents never ask for context anymore—because they already have it. That alone changed our entire support operation.”*
>
> – Jeremy Burton, CTO of hipages
## A message to other marketplaces
**For hipages, one lesson stands out: don’t assemble your support stack in pieces**. Marketplace operations move too fast, and the handoffs are too interconnected, for communication, intelligence, and escalation to live in separate systems. Fragmentation creates gaps—small at first, then costly. And users feel the inconsistency long before you notice it in the metrics.
Or as Jeremy puts it: *“A single platform built to handle the whole journey will always outperform a stack you have to glue together yourself.”*
## Building the future of marketplace support together
Since unifying their support stack with Sendbird, hipages has seen a clear shift in both speed and confidence across their support operation. Even early on, the human impact was obvious. Delight.ai removed repetitive tasks—like creating callback requests—freeing the team to focus on higher-value, more complex problems.
The result, as Jeremy put it, is simple: “morale is up, and agents are doing more interesting, meaningful work.”
That kind of change doesn’t happen without a true partner. It required a team that understood the operational reality of a two-sided marketplace and stayed deeply involved beyond launch. The delight.ai forward deployment team worked directly with hipages to tune models, pressure-test edge cases, and align the system to real tradie behavior. The process was iterative, fast, and hands-on—exactly what hipages needed to scale with confidence.
And for hipages, this is just the beginning. As Jeremy said, “we’re just at the start of that… and it’s going to be a great journey.”
- [A marketplace that thrives on clarity and speed](#section-anchor-id-0 "A marketplace that thrives on clarity and speed")
- [Build vs. Buy: hipages chose time-to-value](#section-anchor-id-1 "Build vs. Buy: hipages chose time-to-value")
- [The power of the full Sendbird stack](#section-anchor-id-2 "The power of the full Sendbird stack")
- [A message to other marketplaces](#section-anchor-id-3 "A message to other marketplaces")
- [Building the future of marketplace support together](#section-anchor-id-4 "Building the future of marketplace support together")
## Keep reading
[View more](/customers "View more")
- [ Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
- [ How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services)
- [ Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
[View more](/customers "View more")
## Lotte Homeshopping partners with Anthropic and delight.ai to scale retail
Source: https://delight.ai/customers/lotte-homeshopping
## Lotte Homeshopping partners with Anthropic and delight.ai to scale retail
December 18 2025
[Lotte Homeshopping](https://www.lottehomeshopping.com/user/main/index_en.lotte) is a household name in South Korea for good reason. Part of the **Lotte Group**, a top-5 conglomerate spanning retail, food, hospitality, construction, finance, and IT, Lotte touches nearly every aspect of daily life in Korea. With over **80 affiliated companies** and operations in more than 30 countries, the group’s scale rivals global giants like Walmart or Amazon.
As the group’s flagship for televised and digital retail, Lotte Homeshopping curates high-quality, trend-driven products delivered to millions of customers via TV broadcasts, mobile apps, and online platforms. Behind this seamless customer experience lies a meticulous **quality assurance (QA)** process, essential for verifying product details, ensuring compliance, and meeting tight broadcast schedules. However, as volumes increased—particularly within their fast-paced fashion division—the QA process began encountering significant friction, slowing down product launches.
### Retail’s challenge: Shorten lead times without sacrificing quality
Lotte Homeshopping's strength lies in quickly sourcing trendy products and delivering them to customers through engaging broadcasts. QA is an essential part of the broadcast production process, but there were several friction points:
- **Manual communication** slowed product approvals across phone, email, and messenger.
- **Delayed responses** to partner questions dragged down workflows.
- **Overworked QA agents** were bogged down by repetitive inquiries.
These challenges were most apparent in the fashion division, with its seasonal product changes, new trends, and multiple partners.
To move faster, Lotte needed a solution that could automate partner support, verify documentation, and scale without compromising on accuracy or compliance.
### Accurate, enterprise-ready agentic AI
Lotte Homeshopping built an AI agent named *Moni* using **delight.ai**, powered by **Anthropic’s Claude 3.5 Sonnet** LLM. After testing multiple vendors, delight.ai stood out for its enterprise-readiness and developer-friendly design.
Standout delight.ai + Claude features:
- **Reliable, accurate QA support:** Anthropic’s Claude is a trustworthy LLM, and delight.ai's platform enables fast AI agent development and deployment. Together, they delivered a high level of accuracy and transparency essential to QA work, such as reviewing product test reports, labeling, and web documentation.
- **Streamlined partner collaboration:** QA agents previously spent a lot of time contacting multiple partners to verify product descriptions or obtain missing or incomplete information. Using Moni, suppliers can easily access and provide supplementary data as required, eliminating the time and effort associated with manual follow-up.
- **Scalability:** Anthropic’s clear documentation and release updates make it easy to deploy new features and scale quickly. The platform provides a strong foundation for Lotte Homeshopping to scale future AI services.
- **Analytics and observability:** Delight's dashboard and analytics tools provided Lotte Homeshopping’s AI Promotion Team with the necessary information to build and fine-tune the Moni AI agent.
- **Enterprise-ready security out of the box:** Delight.ai stood out as the only AI solution in its class with built-in enterprise-grade security, including features like message moderation and two-factor authentication (2FA). For a large enterprise like Lotte Homeshopping, these capabilities were critical in meeting internal compliance standards and accelerating deployment
> "Unlike other AI services, delight.ai comes with built-in features like two-factor authentication and message filtering by default, allowing us to seamlessly implement the service while meeting the high-security standards required by a large enterprise like ours."
>
> Oh Ju-young, AI Promotion Team at Lotte Homeshopping
Delight's AI agent platform promised efficiency, compliance, and accessibility with quick deployment. Meanwhile, testing revealed that Claude responded effectively and accurately to the administrator’s unique needs. It was a one-two punch that would supercharge their customer experience while laying a trusted foundation for using various [AI in retail](https://delight.ai/industries/retail) going forward.
### Implementing delight.ai to ease QA headaches
Lotte Homeshopping began this journey by spending six months testing various solutions. When the company discovered delight.ai, the project gained momentum rapidly. From that point, the retailer took only a few months to complete a proof of concept (POC), implement the solution, and onboard its fashion partners. Compared to other product groups, fashion has a 24/7 QA process and guidelines for responding, a limited number of retrospective points compared to other product lines, clear learning standards, and the largest number of partners, so it was an easy choice to start with fashion partners first.
The combination of Delight's friendly AI agent UI, simple embed code, and Anthropic Claude’s reliable LLM integration made deploying the solution quick and easy. The streamlined process gave the company’s AI engineers more time to focus on creating prompts and establishing the baseline. The team trained Moni on standardized knowledge across all product categories. This training enabled Moni to provide consistent answers to any inquiry, improving compliance management.
> "We conducted tests and POCs with several chatbot providers and were quite disappointed. Delight.ai's accuracy and flexibility, paired with Claude, solved the problems others couldn’t."
>
> Oh Ju-young, AI Promotion Team at Lotte Homeshopping
### Results: More efficient QA, happier partners
Just four months after introducing its Moni AI agent, Lotte Homeshopping’s QA process has undergone a remarkable improvement. All inquiries from new and existing partners are handled through Moni, which preemptively checks for issues before they arise, significantly improving operational efficiency.
- **Reduced inquiries directed to human agents:** On average, Moni answers 53 inquiries daily, reducing the need for human responses by 30–40%.
- **Gained 24/7 support:** Partners from across the globe can direct questions to Moni and receive immediate, accurate responses.
- **Faster time-to-market:** A survey of fashion partners revealed that since Moni’s introduction, delays from inception to launch have decreased dramatically.
- **Improved partner and employee satisfaction:** Partners love the increased efficiency. Feedback from internal evaluations was so positive that the company is now expanding Moni to other products.
The Delight AI agent is more than just a QA chatbot. It supports internal chatbots, such as those used for corporate IT support, and it also serves as a test bed for potential customer service and call center applications.
### What’s next: Scaling AI across the business
Moni’s roadmap includes fully automated test result verification, expanded use in marketing and broadcast production, and even training human QA agents for improved consistency.
> "We chose delight.ai and Anthropic Claude for three reasons: accuracy for QA work, supplier access management, and scalability for future AI services."
>
> Oh Ju-young, AI Promotion Team at Lotte Homeshopping
### Delight.ai & Lotte: Raising the standard for enterprise AI
Lotte Homeshopping is continuously exploring new ways to elevate its customer experience (CX). The company actively evaluates various LLMs and AI models, both locally and across cloud platforms such as Amazon Web Services (AWS), to identify new business applications. Delight’s [omnichannel AI agent](https://delight.ai/omnipresent) evolves alongside these efforts, regularly adding features that help Lotte Homeshopping enhance CX and quickly scale new opportunities. With its proven scalability, straightforward integration, and robust security standards, delight.ai enables Lotte Homeshopping to deliver the exceptional service its customers expect.
> "Claude gives us the accuracy and judgment our QA demands, and delight.ai makes it easy to integrate and deploy as an enterprise-ready AI application. Together, they're an ideal solution."
>
> Oh Ju-young, AI Promotion Team at Lotte Homeshopping
- [Retail’s challenge: Shorten lead times without sacrificing quality](#section-anchor-id-0 "Retail’s challenge: Shorten lead times without sacrificing quality")
- [Accurate, enterprise-ready agentic AI](#section-anchor-id-1 "Accurate, enterprise-ready agentic AI")
- [Implementing delight.ai to ease QA headaches](#section-anchor-id-2 "Implementing delight.ai to ease QA headaches")
- [Results: More efficient QA, happier partners](#section-anchor-id-3 "Results: More efficient QA, happier partners")
- [What’s next: Scaling AI across the business](#section-anchor-id-4 "What’s next: Scaling AI across the business")
- [Delight.ai & Lotte: Raising the standard for enterprise AI](#section-anchor-id-5 "Delight.ai & Lotte: Raising the standard for enterprise AI")
### Keep reading
[View more](/customers "View more")
[Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)[What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)[gther transforms event registration with delight.ai](https://delight.ai/customers/gther)
[View more](/customers "View more")
## gther transforms event registration with delight.ai
Source: https://delight.ai/customers/gther
## gther transforms event registration with delight.ai\n\n\n\nDecember 18 2025\n\n### Introduction\n\n[gther](https://www.gther.com/) is a B2B event management platform built to reduce the stress associated with organizing conferences, events, meetings, and tradeshows - whether live, virtual or hybrid. Because communication to and among attendees is a critical piece of event engagement, gther partnered with delight.ai to leverage its AI solution to embed two-way and autonomous communications in its platform.\n\n### Supporting conversations in the event app\n\nA significant benefit and appeal of attending events is the people you meet and network with. For virtual and hybrid events, event organizers need a way to connect the audience and the speakers. That’s why gther incorporated a way to facilitate conversations between sales teams of exhibitors and delegates to build community and drive lead gen at events. gther used [Sendbird’s Chat API](https://sendbird.com/docs/chat) to make a seamless chat interface in its event app and hybrid platform to support both public and private online chats.\n\n> *“Our event app uses the same chat system as our hybrid platform, so it's all powered by Sendbird Chat,” says Nick Rosier, Managing Director of gther. “It shows the versatility of the product as we use Flutter for the app and Vue.js for the web platform, and it works seamlessly.”*\n\n### Boosting event registration with AI\n\nThe success of events is measured by the number of attendees, so event managers closely track the conversion rates of registration page visitors to event registrants. Event registration can be a challenging process for both event organizers and attendees. Event attendees often wait until the last minute to register, making it difficult for organizers to plan the event. Organizers want a way to nudge visitors to register on the event website, while visitors often have many questions about the event before committing to registering for it.\n\n> *“Typically, you have a couple of event managers behind the scenes desperately answering emails and inquiries submitted at the last minute right before an event. And nine times out of ten, the questions answered are contained in the website itself, but people haven't found it,” says Rosier. “And so we figured an AI chatbot could handle 70% of those simple queries to give that time back to event managers.”*\n\nIf visitors to the event registration page drop off, it could be because they lack the information they need to commit to attending the event. Rosier and his team hypothesized that an AI chatbot could provide the information required to help convert these visitors. Hence, gther’s product team decided to embed a customized AI chatbot experience into its event management solution using delight.ai. The team named the AI event assistant [GeniePT](https://www.gther.com/solutions/geniept).\n\n### Using delight.ai to build an AI solution\n\ngther’s developer team first thought about building the AI chatbot using their ChatGPT subscription with access to Open AI’s APIs, but then learned about delight.ai. Since gther’s developer team was familiar with Sendbird for its [Chat SDK](http://sendbird.com/products/chat-messaging/sdk), when the team learned about the release of delight.ai, they jumped on it with the idea of developing the first AI-powered event assistant.\n\n> *"Delight.ai was a nice option for building our AI chatbot because there is great documentation, the platform is easy to build upon, and our developers are already comfortable with it. We looked at other solutions, but there was a real sort of plus for us using delight.ai because it meant we didn’t have to learn another code base. Having access to the GitHub repos was invaluable because that was just like keys to the castle in a way. We could get the AI chatbot up and running in just one day."* \n> \n> Nick Rosier, Sr. Managing Director at gther\n\nDuring the implementation, gther’s team ensured the AI chatbot's responses were accurate and contextually relevant. The team had to train the bot to handle various inquiries and maintain the balance between automated and human interaction to ensure a personal touch. Rosier claims that delight.ai's dashboard made the process of training and fine-tuning the bot relatively easy.\n\n> *“It's super easy to put a few conditions in the system prompt to make sure the chatbot speaks in their voice and doesn't answer off-topic questions and things like that. Then, the AI chatbot becomes an expert in that specific event, or even products and services that that client might be offering as part of that event.”*\n\n### How gther trained its AI concierge\n\ngther tweaked the starting system prompt to keep the AI chatbot on topic. The team instructed the system to limit the response length to a few sentences and not answer topics unrelated to the event. Initially, gther found that the AI chatbot would go off topic.\n\n> *“We asked the bot to tell us about cats and the chatbot would answer about cats, but then interestingly end with, ‘But I’m an event assistant and should focus on events, so can you ask me something about that?’ This was cute in a way, so it didn’t bother us,” laughed Rosier.*\n\nThrough trial and error, the gther team learned to instruct the bot to invite users to register using the registration link after every second question they ask.\n\n> *“It acts as a proactive salesperson as much as it answers questions. Otherwise you can go into a black hole of Q&A and never convert that user to a registrant. Event registration conversions are what we are ultimately after,” says Rosier.*\n\n### Measuring the performance of the AI concierge\n\ngther’s team leveraged the analytics available in delight.ai's dashboard to monitor chats and gain insights into user interactions.\n\n> *“We love the analytics available in delight.ai's dashboard. It’s great to get stats on usage across the day and then process the data to find trends,” states Rosier. “That's where we can add value for our event manager clients. Not having to build all that ourselves was a massive time saver for our team.”*\n\n### Benefits of Delight AI concierge as an event assistant\n\nThe implementation of Delight's AI solution brought several critical benefits to gther:\n\n- **Process efficiencies in registration**: The automation of common inquiries and registration steps through the AI chatbot streamlined the process, reducing manual effort and improving accuracy. The efficiency and user engagement have seen notable enhancements, contributing to higher satisfaction rates among event attendees.\n- **Improved productivity:** The AI chatbot saves event managers several hours per week leading up to answering basic questions, allowing them to focus more on planning and preparing for the event itself.\n- **Increased user engagement**: gther’s AI chatbot facilitated more interactive and responsive communication with event attendees, enhancing their experience from registration to event completion.\n- **Analytics and insights for process optimizations**: With delight.ai's analytics, gther gained valuable insights into user behavior, preferences, and engagement levels, allowing for data-driven decisions to refine the event registration process further.\n\n> *“Our AI chatbot has already been used for 15 events and we’ve seen the number of conversations across those events in the triple digits, so we know people are actively using the AI chatbot. The usage is significant relative to the size of the events the AI chatbot is servicing. The AI chatbot is taking a non-trivial amount of work off the desks of really busy event managers,” explains Rosier.*\n\n### Innovating the events industry with generative AI\n\ngther's unique implementation of Delight AI concierges represents a forward-thinking approach to event management, prioritizing efficiency, user experience, and data-driven decision-making. gther’s GeniePT demonstrates the potential of AI in transforming the event industry.\n\n> *“The first time we showed the AI chatbot being used as an event assistant at* [*ETL Vegas*](https://eventtechlive.com/)*, people who saw it were stunned,” recalls Rosier. “They had never seen anything like it, so it was fun for us to showcase something so innovative."*\n\n- [Introduction](#section-anchor-id-0 "Introduction")\n- [Supporting conversations in the event app](#section-anchor-id-1 "Supporting conversations in the event app")\n- [Boosting event registration with AI](#section-anchor-id-2 "Boosting event registration with AI")\n- [Using delight.ai to build an AI solution](#section-anchor-id-3 "Using delight.ai to build an AI solution")\n- [How gther trained its AI concierge](#section-anchor-id-4 "How gther trained its AI concierge")\n- [Measuring the performance of the AI concierge](#section-anchor-id-5 "Measuring the performance of the AI concierge")\n- [Benefits of Delight AI concierge as an event assistant](#section-anchor-id-6 "Benefits of Delight AI concierge as an event assistant")\n- [Innovating the events industry with generative AI](#section-anchor-id-7 "Innovating the events industry with generative AI")\n\n### Keep reading\n\n[View more](/customers "View more")\n\n[Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)[What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)[Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping)\n\n[View more](/customers "View more")
## AllAthlete democratizes athletic recruiting with delight.ai
Source: https://delight.ai/customers/allathlete
- [Customers](/customers)
## AllAthlete democratizes athletic recruiting with delight.ai
December 18 2025
### How AllAthlete used a recruitment chatbot to transform athletic recruiting
AllAthlete, a sports recruitment app, is revolutionizing the way college and professional coaches discover young athletic talent. Founded by Greg Auerbach, a former college football player and recruiting staff member at the University of Michigan, the company's mission is to democratize athletic recruitment, bridging the gap between high school and college-level athletes and even extending to professional sports.
The app was designed along with actual coaches to help athletes get through the first two rounds of any athletic evaluation, which is one of the biggest hurdles they have. This case study explores how delight.ai helped AllAthlete grow its user base with reliable, real-time communication between athletes, an AI recruiting chatbot, and coaches.
### AllAthlete’s in-app communication challenge before implementing an AI recruitment chatbot
AllAthlete's journey began with a commitment to provide opportunities for young athletes and simplify the recruitment process. Communication between athletes, recruiters, and coaches was critical to the mission. Encouraging athletes to update their profiles and stay active on the platform was a challenge. So AllAthlete used an AI chatbot for recruitment to prompt and guide these desired user behaviors. Unfortunately, the first chat experience AllAthlete built using GetStream.io did not meet expectations and failed.
> “Our first chat solution was really bad,” claims Greg Auerbach. “It would delete chats. It would change display names of people without us knowing. User didn't even know who they were communicating with half the time.”
### Delight.ai as the solution: Building an AI recruitment chatbot
Auerbach and his team were forced to look for another more reliable solution and decided to rebuild chat in the AllAthlete app with delight.ai. According to Auerbach, an AI recruitment chatbot with delight.ai was much easier to implement and simpler to administer, especially when it came to moderating conversations from the dashboard. "Delight.ai is great for the reliability of the service and the ease of integration with custom third-party bots," added Auerbach.
The benefits of building in-app chat with delight.ai for AllAthlete were as follows:
- **Fast implementation**: In just three weeks, the AI recruitment chat built with delight.ai was successfully integrated into both the web and mobile apps.
- **Smooth administration:** Delight.ai provided a user-friendly and straightforward dashboard for moderating conversations, making it easier for administrators to ensure the safety of the community.
- **Reliable performance:** The improved performance and reliability of delight.ai's infrastructure led to more complete and meaningful conversations.
- **Easy integration with custom-built bots**: AllAthlete had already built three proprietary recruitment bots to assist athletes and was able to easily integrate with Sendbird Chat without any additional development effort.
### Innovating athletic recruiting with an AI recruitment chatbot
To further enhance the user experience, AllAthlete leveraged delight.ai to support real-time dynamic conversations that scaled for the community. Three distinct recruitment chatbots were built by AllAthlete using delight.ai's capabilities powered by OpenAI's ChatGPT to do the following:
1. Proactively guide the users on how to best use the platform
2. Assist in athletic development
3. Bridge the communication gap between athletes and coaches
> “These bots are not necessarily something you just implement with OpenAI, where you just have a model to respond back,” says Auerbach. “It's a lot more dynamic where athletes can ask follow-up questions. This adds a personal touch to the experience that has helped us grow our community.”
### Benefits of AI recruitment chatbots for AllAthlete’s business
AllAthlete’s implementation of delight.ai contributed several key business benefits:
- **Reliable communication**: The reliability of the recruitment chatbot implemented with delight.ai significantly improved communication between athletes, coaches, and the AllAthlete team, fostering trust and increased usage.
- **User engagement and retention:** The app witnessed increased user engagement and retention as a result of an improved chat experience, making it a more attractive and effective tool for both athletes and coaches.
- **Less time troubleshooting issues:** AllAthlete’s developer team is now spending more time working on new features rather than fixing and troubleshooting the chat issues inherent in its first chat solution built with GetStream.io.
> “We’ve been live with delight.ai for a few months and immediately the reliability of the solution has supported more quality conversations and a more trusted relationship with athletes that you just can’t put a price tag on,” concludes Auerbach.
### Transforming athletic recruiting by democratizing the process with AI recruitment chatbots
AllAthlete's partnership with delight.ai transformed its app into a dynamic, efficient, and user-friendly solution that has made a substantial impact on the lives of more than 150,000 athletes. By implementing an AI recruitment chatbot with delight.ai, the company not only streamlined communication but also paved the way for countless athletes to receive the recognition and support they deserve in their pursuit of a sports career.
- [How AllAthlete used a recruitment chatbot to transform athletic recruiting](#section-anchor-id-0 'How AllAthlete used a recruitment chatbot to transform athletic recruiting')
- [AllAthlete’s in-app communication challenge before implementing an AI recruitment chatbot](#section-anchor-id-1 'AllAthlete’s in-app communication challenge before implementing an AI recruitment chatbot')
- [Delight.ai as the solution: Building an AI recruitment chatbot](#section-anchor-id-2 'Delight.ai as the solution: Building an AI recruitment chatbot')
- [Innovating athletic recruiting with an AI recruitment chatbot](#section-anchor-id-3 'Innovating athletic recruiting with an AI recruitment chatbot')
- [Benefits of AI recruitment chatbots for AllAthlete’s business](#section-anchor-id-4 'Benefits of AI recruitment chatbots for AllAthlete’s business')
- [Transforming athletic recruiting by democratizing the process with AI recruitment chatbots](#section-anchor-id-5 'Transforming athletic recruiting by democratizing the process with AI recruitment chatbots')
### Keep reading
[View more](/customers 'View more')
[Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)[What powers Australia’s #1 marketplace: The hipages story of keeping tradies moving](https://delight.ai/customers/hipages)[Lotte Homeshopping partners with Anthropic and delight.ai to scale retail](https://delight.ai/customers/lotte-homeshopping)
[View more](/customers 'View more')
## How Hanssem—South Korea's furniture icon—leapfrogged into the always-on customer experience era
Source: https://delight.ai/customers/hanssem
## How Hanssem—South Korea's furniture icon—leapfrogged into the always-on customer experience era
December 11 2025
For 55 years, Hanssem has been South Korea's leading furniture retailer—a household name synonymous with pioneering kitchen furnishings and comprehensive home solutions. From furniture and appliances to textiles and full interior construction, they've built their reputation on transforming how people live at home.
But Hanssem's leadership team understood something critical: a 55-year heritage only matters if you can evolve with the market. Customer expectations had shifted. They no longer wait for business hours—they expect expert guidance at midnight, instant answers during their commute, seamless support whether browsing countertops or troubleshooting a delivery.
Hanssem saw the gap widening—and chose to close it decisively.
To do that, they turned to delight.ai.
### A transformation rooted in partnership
**Hanssem faced a familiar tension: how do you scale personalized service without sacrificing the quality that built your brand?**
Home furnishing decisions are emotional, expensive, and deeply personal. Any AI representing Hanssem needed to match the accuracy, tone, and reliability of their best human agents—every time. That required a partner, not a vendor.
**From the beginning, delight.ai operated as an extension of Hanssem’s team.** Together, they co-designed an AI agent capable of delivering real-time support that matched the tone, accuracy, and reliability Hanssem is known for. And within months, the results spoke for themselves:
- **2× increase in resolution rates**
- **50% reduction in transfers to human agents**
- **90%+ accuracy on repetitive queries**
- **Significantly faster response times around the clock**
> *Delight.ai is more than a vendor. They're a true collaborative partner who works alongside us to solve challenges from the same perspective. Through this partnership, we've been able to build and scale our AI capabilities with confidence and speed.*
>
> — Eugene Kim, CEO of Hanssem
### From launch to continuous excellence
Hanssem's AI agent didn't start at 86% resolution. It started at 48%.
In May, during the first week of the proof of concept, fewer than half of customer inquiries were resolved without human intervention. By October, that number had nearly doubled.
Behind the rise was delight.ai’s [AI Readiness Framework](https://delight.ai/resources/from-hype-to-ai-readiness)—the operating system that turns early prototypes into high-performing, reliable AI systems. As real customer conversations surfaced failure points and friction, each pillar of the framework kicked in:
- **Foundation.** Before optimizing the agent, the teams aligned on success criteria, audited Hanssem's knowledge base for accuracy and structure, and mapped the integrations the agent would need to access.
- **Workflow mapping.** The delight.ai team documented how Hanssem's support actually worked—not the idealized version, but the real systems, dependencies, and handoff points. This exposed gaps the agent initially missed.
- **Relentless iteration.** Weekly cycles, not quarterly reviews. Conversation logs analyzed, failure patterns diagnosed, prompts refined, logic updates shipped. When something broke, it got fixed—fast.
- **Continuous evolution.** The agent running today isn't the agent that launched in May. It's been refined through hundreds of iterations, expanding from FAQ handling to complex inquiries requiring data retrieval and action.
> *AI doesn’t seem perfect from day one. What matters is continuous improvement and clear accountability. The delight.ai Agent's 90%+ accuracy rate during our POC gave us the confidence we needed.*
>
> — Hyeong-Tak Kim, IT Division Director at Hanssem
### Why trust made the difference
The biggest barrier to AI adoption isn't usually capability—it's confidence. Organizations hesitate not because the technology doesn't work, but because they can't fully predict what it will say, how it will behave, or when it might fail.
For Hanssem, this concern was amplified. Their reputation had been built over decades through personal, high-touch service. Any AI agent speaking on their behalf needed to uphold that standard flawlessly.
This is where Trust OS changed the equation. Delight.ai’s transparency framework gave Hanssem:
- Clear visibility into the agent’s reasoning
- Full control over tone, logic, and behavior
- Insight into potential failure points
- Tools to audit, refine, and govern the system
Trust OS didn't just make the AI smarter. It made the Hanssem team comfortable deploying it, transforming AI from a black box into a system to one they could understand, manage, and continuously improve.
> *Customers seemed surprised by how natural the AI felt. They also highly praised how Hanssem agents seamlessly took over when the AI consultant couldn't resolve issues. This helped build trust.*
>
> — Hyeong-Tak Kim, IT Division Director at Hanssem
And the quality of that help was indistinguishable from what a trained human agent would provide.
### What comes next
Today, Hanssem's AI agent handles thousands of customer interactions—answering questions, resolving issues, and delivering the always-on service modern consumers expect. But this is just the beginning.
As Hanssem continues to refine and expand its AI capabilities, the vision is becoming clearer: intelligent, adaptive service that knows the customer, understands their needs, and delivers solutions seamlessly across every stage of the home furnishing journey. The future isn't separate systems for separate functions. It's one unified intelligence that travels with the customer from first browse to final installation.
For a company that's been in business for 55 years, this isn't a departure from their heritage. It's the natural evolution of it: meeting customers where they are, delivering the same level of care they've always expected, now powered by AI that makes it possible around the clock.
This is what happens when an established brand refuses to be limited by convention—and instead uses its foundation to prove what's possible.
Hanssem is already building that future in partnership with delight.ai.
> *Introducing the Delight AI agent was the catalyst that truly brought AI technology into our business.*
>
> — Eugene Kim, CEO of Hanssem
- [A transformation rooted in partnership](#section-anchor-id-0)
- [From launch to continuous excellence](#section-anchor-id-1)
- [Why trust made the difference](#section-anchor-id-2)
- [What comes next](#section-anchor-id-3)
### Keep reading
[View more](/customers)
- [ Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
- [ How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services)
- [ Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
[View more](/customers)
#### Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
## Mixpanel x delight.ai: When support knows who you are
Source: https://delight.ai/customers/mixpanel
- [Customers](/customers)\n\n## Mixpanel x delight.ai: When support knows who you are\n\n\n\nApril 13 2026\n\nMixpanel has become the analytics backbone for thousands of product teams—from high-growth startups to enterprises like Uber, Salesforce, and DocuSign. When you're the platform helping companies understand user behavior and drive product decisions, your customers expect sophisticated answers to sophisticated questions.\n\nAnd those questions are *complex*. A seemingly simple inquiry—"How do I delete user data?"—requires the support team to verify the customer's account tier, check their access permissions, understand their data residency requirements, review their implementation setup, and ensure they're following the right compliance protocols. Every ticket demands context.\n\n> *Our support agents were spending the first ten minutes of every interaction just figuring out who they were talking to. Checking account status, verifying permissions, reviewing their setup. The actual problem-solving came second.*\n> \n> — Kathleen Matthews, Global Support, Mixpanel\n\n"We needed a way to handle that complexity at scale." says Kathleen. For years, this complexity made automation feel impossible. You couldn't just throw a chatbot at questions that required checking account status, validating permissions, and adhering to privacy requirements. Support teams had to manually investigate each case, toggling between systems to piece together who was asking and what they could actually do.\n\nThe bottleneck wasn't knowledge—it was context. Until [delight.ai](https://delight.ai/).\n\n### The solution: Personalization that understands context\n\nMixpanel deployed [delight.ai](https://delight.ai/) with intelligent personalization—the AI agent instantly authenticates users and retrieves their account history, tier, access level, and current workflow context before responding.\n\nThis is the breakthrough: **the AI doesn't just answer questions, it knows who's asking them.**\n\nWhen a customer asks about Group Analytics, the AI has already checked:\n\n- Their account tier and whether they have access\n- Their role and permission level\n- Their current implementation and configuration\n- Their history of related questions or issues\n\n\n\nWe're not just answering faster. We're answering smarter, Kathleen explains: the AI knows if you're asking because you don't have access yet, or because you're trying to configure something advanced. In the first case, we're converting curiosity into revenue. In the second, we're driving deeper product adoption.\n\nThe questions that once required a support agent to spend 10 minutes investigating account details? The AI handles that verification instantly—while maintaining the same privacy and security standards.\n\n### AI as a product growth lever\n\nBut here's where personalization becomes powerful: **once the AI knows who you are, it doesn't just answer your question—it guides you to unlock more value.**\n\n**Support becomes discovery:**\n\n- A free-tier user asking about advanced features? The AI explains how it would solve their specific problem and presents relevant upgrade options—converting curiosity into revenue\n- An enterprise customer who's paid for Session Replay but never configured it? The AI proactively walks them through setup—driving adoption of features they've already purchased\n- A trial user exploring integrations? The AI surfaces relevant use cases based on their implementation—accelerating time-to-value and conversion\n\n"One of our customers asked a simple question about data exports, and the AI recognized they were on a plan that included advanced segmentation they hadn't set up yet," Kathleen recalls. "By the end of that conversation, they'd not only solved their original problem but discovered a feature that became central to their workflow."\n\nFor B2B companies, this is the unlock: **support that not only scales but drives growth**. The same personalization that makes answers more relevant also makes every interaction an opportunity to deepen engagement, drive feature adoption, and expand revenue.\n\n### What this means for B2B self-service\n\nMixpanel's approach reveals a fundamental truth: **complexity isn't the barrier to automation. Lack of context is.** And when you solve for context, you unlock something bigger than efficiency—you unlock growth.\n\nThe reason B2B support interactions felt impossible to automate wasn't because the questions were too hard. It was because answering them required knowing *who was asking*. And once you know that, you can do more than answer—you can guide, educate, and convert.\n\nWith AI-powered personalization, B2B companies can finally deliver support that adapts to each customer's unique situation while driving adoption:\n\n\n\n- **Authentication becomes insight** → The AI knows account tier, role, implementation details, permissions—plus what they're *not* using yet\n- **Complex triage becomes instant** → Questions that once required manual investigation happen automatically, freeing up the conversation to focus on value\n- **Escalation gets smarter** → When human help is needed, support teams receive full context about who the customer is, what they've tried, *and what expansion opportunity exists*\n\n"The magic isn't just that we can answer questions automatically," says Kathleen. "It's that every answer becomes a chance to show customers what's possible for them specifically. We're not just resolving issues—we're accelerating product adoption and revenue growth."\n\n### Looking ahead\n\nFor Mixpanel, this is just the beginning. The team is already exploring how deeper personalization can predict what customers need before they ask—proactively surfacing relevant features, suggesting optimizations based on usage patterns, and turning every product interaction into an opportunity for discovery.\n\n"Once you solve for context, the possibilities multiply," Kathleen reflects. "We're not just thinking about support anymore. We're thinking about how AI that truly knows each customer can transform their entire product experience."\n\n- [The solution: Personalization that understands context](#section-anchor-id-0 "The solution: Personalization that understands context")\n- [AI as a product growth lever](#section-anchor-id-1 "AI as a product growth lever")\n- [What this means for B2B self-service](#section-anchor-id-2 "What this means for B2B self-service")\n- [Looking ahead](#section-anchor-id-3 "Looking ahead")\n\n### Keep reading\n\n[ View more](/customers)\n\n- [How BJ’s is improving members’ digital experience with help from Bev](https://delight.ai/customers/bjs) \n \n\n- [How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services) \n \n\n- [Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways) \n \n\n[ View more](/customers)\n
## How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F
Source: https://delight.ai/customers/dr-cool-services
- [Customers](/customers)
- How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F
April 2 2026
## When a Las Vegas institution meets modern AI
When your AC dies in 115°F heat, you don't want voicemail. You don't want "please hold." You want someone—right now—who can get a technician to your house before your elderly parents overheat.
Dr. Cool Services is one of the Las Vegas Valley's most trusted names in home services. They are a leading HVAC and plumbing brand serving thousands of homeowners across Clark County—from routine maintenance to emergency repairs when the desert heat turns dangerous.
Dr. Cool's reputation is built on reliability. When a customer calls, they expect someone to answer. They expect to get on the schedule quickly. And they expect a skilled technician to show up when promised.
But as the business grew, so did the call volume. Peak summer months pushed the phone system to its limits. Hold times crept up. Calls went to voicemail. And every missed call represented a customer who might not call back.
Dr. Cool Services' leadership faced a question every growing service business eventually confronts: How do you answer every call instantly during peak season—without staffing for peak year-round?
> "Our customers don't want to navigate phone trees or wait on hold. They want to talk to someone who can help. We needed technology that feels like talking to a real person—because that's what our brand represents."
>
> — Mark Farley, President, Dr. Cool Services
## The challenge: Service calls in extreme conditions
Las Vegas isn't a forgiving climate. Summer temperatures regularly exceed 115°F, making a broken AC unit more than an inconvenience—it's a health emergency.
This creates operational challenges familiar to any service business matching technicians to customers:
1. **Urgency triage.** A customer says their AC "isn't working right." Is that a minor issue that can wait? Or a complete system failure in 110°F heat with elderly parents in the house? Dispatchers have seconds to make that call—and getting it wrong means either wasting an emergency slot or leaving someone in danger.
2. **Geographic optimization.** Every service call has a location and a technician has to get there. Efficient scheduling means clustering jobs to minimize drive time, matching work to technician capabilities, and leaving buffer for emergencies that derail the plan. When your service area spans hours of driving across Clark County, one wrong routing decision cascades through the entire day.
3. **Peak capacity constraints.** When summer heat spikes, every HVAC company in Vegas is slammed. Dr. Cool's call volume surges. Hold times climb. And every missed call is a customer who might not call back.
4. **Language barriers.** Clark County has a large Spanish-speaking population. When a customer calls in a panic because their AC just died, they need to explain the problem in the language they're most comfortable in. A phone system that only works in English leaves a significant share of the community underserved—or forces them through a slower, more frustrating experience while they wait for a bilingual dispatcher.
Under peak load, even great dispatchers make mistakes or burn out. Dr. Cool Services needed a way to handle unlimited concurrent calls without compromising service quality.
## The solution: Voice AI that books appointments end-to-end
Dr. Cool Services partnered with delight.ai to build a voice AI agent purpose-built for their workflow. Not a chatbot. Not a phone tree. A conversational agent that completes the same booking process their best dispatchers do—simultaneously, without hold times.
### Natural conversation in English and Spanish
The AI picks up instantly and talks the way a good dispatcher would. It handles calls in both English and Spanish with the same fluency—no transfers, no "let me find someone who speaks Spanish." It listens, asks follow-up questions, and adapts to what the customer is saying rather than forcing them through a menu.
### The full booking, no human needed
From the moment it picks up, the AI identifies the customer, understands the issue through conversation, assesses urgency, finds an available appointment, and confirms the booking. The customer hangs up with a confirmed appointment. No hold time. No callback. No human dispatcher needed.
### Smart adaptations
During peak summer, the AI automatically widens appointment windows from 4 to 6 hours for cooling repairs—and explains why. Honesty over false precision. When demand eases, windows tighten back up.
If a call drops mid-booking, the AI calls back automatically with full context to finish the appointment. No dispatcher tracks down who was mid-conversation. The booking just gets completed.
### It knows when to hand off
Complex warranty questions, upset customers, unusual situations—the AI transfers to human agents with full context. The goal isn't to eliminate human judgment. It's to reserve it for situations that actually require it.
> "Our best people shouldn't spend their days reading addresses back and forth. They should be solving problems and calming frustrated customers. That's what humans are good at."
>
> — Mark Farley, President, Dr. Cool Services
## Looking ahead
End-to-end booking is the foundation. Dr. Cool Services sees voice AI enabling revenue protection during calls (identifying upsell opportunities, offering financing), outbound communications (confirmations, follow-ups, surveys), and proactive scheduling—reaching out to customers due for maintenance before the emergency call comes in.
> "Voice AI gives us the capacity to actually get ahead of issues—reach out before the emergency call comes in. That changes the entire relationship with our customers."
>
> — Mark Farley, President, Dr. Cool Services
## What this means for service businesses
Every service business has a version of the heatwave problem. Maybe it's not 115°F in Vegas—maybe it's a pipe burst in January, a storm season in Florida, or back-to-school in September. The pattern is the same: demand spikes, phones ring off the hook, and the customers you can't get to are the ones your competitors pick up.
Voice AI doesn't just answer the overflow. It handles the entire booking—naturally, in the customer's language, without hold times—so your team can focus on the work that actually needs a human. And when that next surge hits, you don't scramble to hire. You just pick up every call.
- [When a Las Vegas institution meets modern AI](#section-anchor-id-0 "When a Las Vegas institution meets modern AI")
- [The challenge: Service calls in extreme conditions](#section-anchor-id-1 "The challenge: Service calls in extreme conditions")
- [The solution: Voice AI that books appointments end-to-end](#section-anchor-id-2 "The solution: Voice AI that books appointments end-to-end")
- [Looking ahead](#section-anchor-id-3 "Looking ahead")
- [What this means for service businesses](#section-anchor-id-4 "What this means for service businesses")
## Keep reading
[View more](/customers)
[ How BJ’s is improving members’ digital experience with help from Bev](https://delight.ai/customers/bjs)
[ Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
[ Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
[View more](/customers)
## How BJ’s is improving members’ digital experience with help from Bev
Source: https://delight.ai/customers/bjs
- [Customers](/customers)
- How BJ’s is improving members’ digital experience with help from Bev
## How BJ’s is improving members’ digital experience with help from Bev
August 18 2026
BJ's Wholesale Club is built around a powerful purpose: “We take care of the families who depend on us.”
For more than 40 years, BJ’s has brought its purpose to life by delivering unbeatable value and convenience, from savings on the weekly grocery run and easy-to-access coupons to low-priced fuel. Today, BJ’s serves more than 8 million members across 250+ clubs, with a tenured renewal rate of 90%. That kind of loyalty raises the bar. As members increasingly shop across BJ’s clubs, BJs.com and the BJ’s app, the digital experience needs to deliver the same value, convenience and member-first experience they expect in the club. That means making it easier to discover the right products, find savings, and complete their shopping trip.
BJ's needed a way to bring that same experience into digital: personalized product discovery, practical support, savings guidance, and more relevant, helpful interactions over time.
That is where Bev, BJ’s AI shopping assistant, came in. Bev, named after Beverly Jean, the daughter of the company's first president, is powered by delight.ai’s conversational AI platform. Bev helps make parts of the digital member experience more connected, supporting members as they discover products, find savings, and access helpful information.
### One Bev for the whole member journey
BJ’s members can shop online, pick up in club, get same-day delivery, scan prices, access coupons, and move through the club faster with time-saving tools like ExpressPay. [Public data](https://s22.q4cdn.com/311271118/files/doc_financials/2025/q4/2025-Q4-BJ-s-Investor-Presentation-vFINAL.pdf) suggests digitally engaged members are about 2x more valuable than club-only members.
That growth reinforced the importance of delivering a digital experience that is as helpful and member-focused as the experience in BJ’s clubs. For those digital moments, the next interface for the member experience looks less like a search box and more like a conversation with an agent that knows the member, the purpose, and the club.
Most AI projects split that relationship apart: a shopping bot here, a support bot there, a membership bot somewhere else. A member doesn't live in those boundaries, and neither does the value of their BJ’s membership. A product question can become a basket. A support moment handled well can become proof the membership was worth it. BJ's needed one Bev for all of it.
**So BJ's partnered with delight.ai to build an AI concierge that could work across shopping, support, and everything in between, carrying the same member context through every conversation.**
Bob Eddy, BJ's Chairman and CEO, described Ask Bev on the company's earnings call as designed for "more personalized, intuitive, and efficient product discovery and support." For members, those aren't two different things. They're one relationship.
> *"Whether a member is browsing for a grill, looking up club hours, or asking about a renewal, they're talking to the same Bev. That's how a real member-team member relationship is supposed to feel."*
>
> — Scott Vandegrift, VP of Product, BJ's Wholesale Club
### What we built together
Working with delight.ai, BJ's began where the member need was clearest: product discovery.
A member can ask Bev the way they would ask a great team member: "I'm planning a holiday party. What should I get?" Bev brings that team member-like guidance into the shopping flow, translating intent into products, savings, and next steps. The launch covered four areas:
- **Personalized product discovery**: Bev searches, recommends, and adds to cart based on what members actually buy.
- **Coupon intelligence**: Bev helps members find applicable savings before checkout instead of leaving value hidden in the app.
- **Membership guidance**: Bev explains benefits and helps members understand the value of their tier.
- **Personalization**: Bev addresses each member in a personalized way throughout the entire experience, from the first welcome message to their final checkout.
Early usage showed that members were not just asking questions then leaving. Bev became a meaningful part of the shopping journey. Members who engaged with it were more active across BJ’s, suggesting Bev can deepen relationships and help members get more from their membership.
### Member care: Where every moment counts
Shopping is where members find value. Support is where they feel cared for.
Bev brings the same helpfulness from shopping into those care moments. It can help members check on a late order, start a return, untangle a membership question, or move to a live agent with the conversation context intact.
With membership as the foundation of the BJ’s business, care is part of the value members come back for. Every helpful answer, smooth handoff, and surfaced benefit makes BJ’s feel easier to rely on. When Bev helps someone resolve a delivery question, find a reward, or better understand their benefits, it turns an ordinary support moment into a moment of member care.
Bev support spans three key areas:
- **Order support & live escalation:** Members get real-time help with order status, pickup details, and same-day delivery updates, with a seamless handoff to a live agent when they need one, conversation context intact so no one starts over.
- **Membership & account care:** Members get clear answers about membership benefits, account details, renewals, and ways to get more value from their membership.
- **Benefit discovery:** Members find and use benefits they may not know they have, from rewards to fuel savings, turning unused perks into value they can feel.
> *"Bev gives us a new way to help members across the journey, from finding the right product to getting more value out of their membership. It makes the experience feel more personal, more useful, and more connected."*
>
> — Scott Vandegrift, VP of Product, BJ's Wholesale Club
### What comes next: A smarter member relationship
As Bev expands across BJ’s digital experience, it has the potential to become more proactive and relevant by drawing on available member and shopping context, such as membership details, active online orders, and relevant offers. Over time, this could help Bev surface the right information and next step at the moment it is most useful.
Delight.ai offers functionality to build this smarter member relationship. [Memory](https://delight.ai/agent-memory-platform-amp) can turn every conversation Bev has into context for the next one. [For You Conversations](https://delight.ai/for-you-conversations-fyc) can turn that context into outreach members actually want: a reminder when a coupon is about to expire, a nudge when something they buy regularly is back in stock, a renewal note that shows exactly how much they saved this year.
> *"The opportunity isn’t just answering questions. It’s using a better understanding of how members shop with us to support them more effectively, whether they’re in the app, on the site or in the club."*
For BJ’s, that’s what membership has always been about: taking care of the families who depend on them. Bev can help BJ’s extend that member-first experience across more digital shopping moments.
---
1. Members who engaged with Bev converted at a higher rate and showed greater purchasing activity per visit than those who did not.
- [One Bev for the whole member journey](#section-anchor-id-0 "One Bev for the whole member journey")
- [What we built together](#section-anchor-id-1 "What we built together")
- [Member care: Where every moment counts](#section-anchor-id-2 "Member care: Where every moment counts")
- [What comes next: A smarter member relationship](#section-anchor-id-3 "What comes next: A smarter member relationship")
Keep reading
[ View more](/customers)
[ Mixpanel x delight.ai: When support knows who you are](https://delight.ai/customers/mixpanel)
[ How Dr. Cool Services uses voice AI to answer every call when Vegas hits 115°F](https://delight.ai/customers/dr-cool-services)
[ Norse Atlantic Airways & delight.ai: Architecting the AI workforce no airline has ever seen](https://delight.ai/customers/norse-atlantic-airways)
[ View more](/customers)
# Product Release
## Product Release
Source: https://delight.ai/product-release
## We ship 
So you stay ahead.
See how we are raising the bar for AI agents. Stay up to date with our releases, built to make your AI agent faster, more accurate, and more capable than ever.
Aug 27, 2026NEW
[](https://delight.ai/product-release/ai-suggestions)### [AI Suggestions: Your AI agent can tell you what your knowledge base is missing](https://delight.ai/product-release/ai-suggestions)
[When your AI agent can't answer a customer's question, it now tells you what knowledge was missing and drafts the fix for you to review and publish.](https://delight.ai/product-release/ai-suggestions)
Aug 5, 2026
[](https://delight.ai/product-release/delight-agent-mcp)### [Delight Agent MCP: Your agent's data, in every AI you use](https://delight.ai/product-release/delight-agent-mcp)
[Connect Claude, ChatGPT, Cursor, or Codex directly to your live delight.ai data. No exports, no tab-switching, no dashboard required.](https://delight.ai/product-release/delight-agent-mcp)
Jul 31, 2026
[](https://delight.ai/product-release/nudge-message)### [Nudge Message: Your AI agent can speak first, right when it matters](https://delight.ai/product-release/nudge-message)
[Your AI agent can now reach out first, sending a short message at exactly the right moment, before a customer even opens the chat.](https://delight.ai/product-release/nudge-message)
Jul 9, 2026
[](https://delight.ai/product-release/delight-desk-updates)### [Delight Desk updates: Macros, voice handoff, and one queue for every channel](https://delight.ai/product-release/delight-desk-updates)
[Delight Desk now supports macros, AI-to-human voice handoff, and a single queue across chat, email, and voice.](https://delight.ai/product-release/delight-desk-updates)
Jul 9, 2026
[](https://delight.ai/product-release/delight-knowledge)### [Delight Knowledge: One knowledge base for your AI agent, help center, and agents](https://delight.ai/product-release/delight-knowledge)
[Delight Knowledge is a standalone knowledge base that feeds your AI agent, a hosted help center, and in-ticket answers for agents.](https://delight.ai/product-release/delight-knowledge)
Jul 9, 2026
[](https://delight.ai/product-release/zendesk-migration)### [Zendesk Migration: Keep your tickets, workflows, and muscle memory with one click](https://delight.ai/product-release/zendesk-migration)
[Migrate your Zendesk account, and bring your tickets, knowledge base, macros, and users into Delight Desk.](https://delight.ai/product-release/zendesk-migration)
Jun 25, 2026
[](https://delight.ai/product-release/whatsapp-handoff-support)### [WhatsApp Handoff Support: When a conversation needs a human, you're already in the right channel](https://delight.ai/product-release/whatsapp-handoff-support)
[AI Agent conversations on WhatsApp can now be handed off to a human agent. No channel switching, no lost history, no asking the customer to start over.](https://delight.ai/product-release/whatsapp-handoff-support)
Jun 18, 2026
[](https://delight.ai/product-release/custom-safeguard-categories)### [Custom Safeguard Categories: Define what your AI flags, not just how it responds](https://delight.ai/product-release/custom-safeguard-categories)
[Build your own safeguard detection categories on top of the four built-in ones—so your agent flags what matters to your business, not just what matters to everyone else.](https://delight.ai/product-release/custom-safeguard-categories)
May 13, 2026
[](https://delight.ai/product-release/dynamic-safeguards)### [Dynamic Safeguards: Context-aware responses when safeguards trigger](https://delight.ai/product-release/dynamic-safeguards)
[Replace the fixed safeguard fallback with an actionbook that generates the response in real time, branching by trigger type and using the full conversation context.](https://delight.ai/product-release/dynamic-safeguards)
May 7, 2026
[](https://delight.ai/product-release/zero-touch-improvement)### [Zero-Touch Improvement: Autonomous AI agent optimization with human oversight built in](https://delight.ai/product-release/zero-touch-improvement)
[Your AI researches its own failures from real conversations, proposes precise fixes, tests them, and deploys at whatever level of autonomy your team has approved.](https://delight.ai/product-release/zero-touch-improvement)
May 7, 2026
[](https://delight.ai/product-release/agent-steward)### [Agent Steward: AI that owns complex customer issues end to end](https://delight.ai/product-release/agent-steward)
[The first AI agent that takes full ownership of complex customer issues—coordinating across systems, channels, and time until the case is closed.](https://delight.ai/product-release/agent-steward)
May 7, 2026
[](https://delight.ai/product-release/a-b-testing)### [A/B Testing: Know whether your AI agent changes actually worked](https://delight.ai/product-release/a-b-testing)
[State a hypothesis, run two versions of your agent against real traffic, and get a statistical reliability score—not just "no regressions." ](https://delight.ai/product-release/a-b-testing)
Apr 28, 2026
[](https://delight.ai/product-release/openbook)### [Openbook: Visual flow navigation for your Actionbooks](https://delight.ai/product-release/openbook)
[See your entire actionbook as a visual flow then jump to any section in one click.](https://delight.ai/product-release/openbook)
Apr 22, 2026
[](https://delight.ai/product-release/ai-agent-urgent-notice)### [AI Agent Urgent Notice: Push real-time notices without a deployment](https://delight.ai/product-release/ai-agent-urgent-notice)
[Support teams can now publish real‑time Urgent Notices directly from the AI agent dashboard, alerting customers instantly without waiting for a deployment.](https://delight.ai/product-release/ai-agent-urgent-notice)
Apr 13, 2026
[](https://delight.ai/product-release/actionbook-editor)### [Actionbook Editor: A reliable WYSIWYG environment for authoring your AI’s rules](https://delight.ai/product-release/actionbook-editor)
[A single editing surface for your AI's rules, with a live Viewer panel so you always see what your AI will see before you save.](https://delight.ai/product-release/actionbook-editor)
Apr 9, 2026
[](https://delight.ai/product-release/test-upgrade)### [Test Upgrade: Build your AI agent test suite from real conversations](https://delight.ai/product-release/test-upgrade)
[Build your test suite from real conversations, run it anywhere, and get failure analysis that tells you where to look.](https://delight.ai/product-release/test-upgrade)
Mar 26, 2026
[](https://delight.ai/product-release/thinking-messages)### [Thinking Messages: Real-time status updates while your AI agent works](https://delight.ai/product-release/thinking-messages)
[Show users exactly what your AI is doing with a specific status message instead of a generic typing indicator.](https://delight.ai/product-release/thinking-messages)
Mar 25, 2026
[](https://delight.ai/product-release/human-initiated-proactive-email-in-desk)### [Human Initiated Proactive Email in Desk: Reach customers first, without leaving your support queue](https://delight.ai/product-release/human-initiated-proactive-email-in-desk)
[Stay ahead of customer issues with proactive emails—send updates on delays, downtime, or billing changes directly from your Desk dashboard.](https://delight.ai/product-release/human-initiated-proactive-email-in-desk)
Mar 10, 2026
[](https://delight.ai/product-release/gradual-rollout)### [Gradual Rollout: Test with real traffic, commit when you're confident](https://delight.ai/product-release/gradual-rollout)
[Discover how Gradual Rollout lets teams validate actionbook and model changes against live traffic before committing to a full deployment.](https://delight.ai/product-release/gradual-rollout)
Mar 3, 2026
[](https://delight.ai/product-release/sound-notification)### [Sound Notification: When a new message arrives, users hear it](https://delight.ai/product-release/sound-notification)
[Learn how Sound Notification alerts users the moment an AI agent replies, using audio or mobile push so conversations stay active even when switching tabs.](https://delight.ai/product-release/sound-notification)
Mar 1, 2026
[](https://delight.ai/product-release/low-csat-analysis)### [Low CSAT Analysis: Filter bad conversations by failure type](https://delight.ai/product-release/low-csat-analysis)
[Low CSAT Analysis brings AI-powered failure categorization into the Conversations tab, simplifying the process of finding and filtering conversations.](https://delight.ai/product-release/low-csat-analysis)
Jan 26, 2026
[](https://delight.ai/product-release/desk-glossary)### [Desk Glossary: Translation without rules is just expensive guessing](https://delight.ai/product-release/desk-glossary)
[Brand names, job titles, compliance terms, regional variations—every industry has language that can't be left to guesswork. Now you control exactly how it translates.](https://delight.ai/product-release/desk-glossary)
Jan 7, 2026
[](https://delight.ai/product-release/staging-environment)### [Staging Environment: A new dedicated testing layer between dev and production](https://delight.ai/product-release/staging-environment)
[Staging Environment gives your team a production-mirror space to validate AI agent changes with real settings before deployment, eliminating production surprises.](https://delight.ai/product-release/staging-environment)
Dec 17, 2025
[](https://delight.ai/product-release/selective-deployment-v2)### [Selective Deployment 2.0: You had control, now you have visibility](https://delight.ai/product-release/selective-deployment-v2)
[Selective deployment gets smarter: Eliminate deployment anxiety with full visibility into every change before it reaches production.](https://delight.ai/product-release/selective-deployment-v2)
Dec 2, 2025
[](https://delight.ai/product-release/activity-log-v2)### [Activity Log 2.0: From black box to open book](https://delight.ai/product-release/activity-log-v2)
[Reveal the complete reasoning behind every agent response—which actionbook, tools, and personalized instruction shaped each answer—so you can verify accuracy and improve with confidence.](https://delight.ai/product-release/activity-log-v2)
Nov 26, 2025
[](https://delight.ai/product-release/message-search)### [Conversation Search: Find any conversation, instantly](https://delight.ai/product-release/message-search)
[Search thousands of customer conversations to quickly surface the specific interactions you need to investigate, troubleshoot, or analyze.](https://delight.ai/product-release/message-search)
Nov 18, 2025
[](https://delight.ai/product-release/omnichannel-continuity)### [Omnichannel continuity: Turn every pause into progress](https://delight.ai/product-release/omnichannel-continuity)
[Omnipresent AI that follows customers across every channel, picking up exactly where they left off to create frictionless, consistent experiences.](https://delight.ai/product-release/omnichannel-continuity)
Nov 18, 2025
[](https://delight.ai/product-release/for-you-conversations)### [For You Conversations (FYC)](https://delight.ai/product-release/for-you-conversations)
[Deliver personalized, adaptive AI interactions tailored to each customer’s interests and behavior.](https://delight.ai/product-release/for-you-conversations)
Nov 18, 2025
[](https://delight.ai/product-release/agent-memory-platform)### [Agent Memory Platform (AMP)](https://delight.ai/product-release/agent-memory-platform)
[Your AI can finally remember every customer and know exactly how to help.](https://delight.ai/product-release/agent-memory-platform)
Oct 31, 2025
[](https://delight.ai/product-release/collect-instant-user-feedback-on-every-ai-agent-message)### [Collect instant user feedback on every AI agent message](https://delight.ai/product-release/collect-instant-user-feedback-on-every-ai-agent-message)
[Gather instant thumbs-up/down feedback on every AI agent response to uncover actionable insights and continuously improve accuracy.](https://delight.ai/product-release/collect-instant-user-feedback-on-every-ai-agent-message)
Oct 16, 2025
[](https://delight.ai/product-release/embed-interactive-forms-in-agent-responses)### [Embed interactive forms in agent responses](https://delight.ai/product-release/embed-interactive-forms-in-agent-responses)
[Empower your AI agents to collect information seamlessly with interactive forms that make every customer conversation faster and more efficient.](https://delight.ai/product-release/embed-interactive-forms-in-agent-responses)
Sep 30, 2025
[](https://delight.ai/product-release/customizable-post-chat-csat-survey)### [Customizable post-chat CSAT survey](https://delight.ai/product-release/customizable-post-chat-csat-survey)
[Not every business measures satisfaction the same way. Take control of customer feedback with customizable post-chat CSAT surveys that are tailored to your brand, your rules, your channels.](https://delight.ai/product-release/customizable-post-chat-csat-survey)
Sep 29, 2025
[](https://delight.ai/product-release/sendbird-ai-agent-is-iso-42001-certified)### [Sendbird AI agent is ISO 42001 certified](https://delight.ai/product-release/sendbird-ai-agent-is-iso-42001-certified)
[Sendbird is now ISO 42001 certified and committed to building responsible AI agents with industry-leading governance, security, and risk controls.](https://delight.ai/product-release/sendbird-ai-agent-is-iso-42001-certified)
Sep 23, 2025
[](https://delight.ai/product-release/humans-as-ai-evaluators)### [Humans as AI evaluators](https://delight.ai/product-release/humans-as-ai-evaluators)
[AI can’t define good, people do. Evaluate conversations on your criteria. Teach AI what good looks like.](https://delight.ai/product-release/humans-as-ai-evaluators)
Sep 12, 2025
[](https://delight.ai/product-release/multiple-active-conversations)### [Multiple active conversations per customer](https://delight.ai/product-release/multiple-active-conversations)
[Faster answers, cleaner reporting, happier customers. With multi-conversation support in the AI agent messenger, your customers can start and maintain multiple ongoing conversations simultaneously.](https://delight.ai/product-release/multiple-active-conversations)
Sep 9, 2025
[](https://delight.ai/product-release/desk-ai-copilot)### [Desk AI Copilot](https://delight.ai/product-release/desk-ai-copilot)
[Every ticket is a race against time. Desk AI copilot helps you stay ahead.](https://delight.ai/product-release/desk-ai-copilot)
Aug 26, 2025
[](https://delight.ai/product-release/voice-ai-agents-faster-smarter-and-built-with-empathy)### [Voice AI agents: Faster, smarter, and built with empathy](https://delight.ai/product-release/voice-ai-agents-faster-smarter-and-built-with-empathy)
[Entering the new era of Voice AI — designed for the chaos of real conversations.](https://delight.ai/product-release/voice-ai-agents-faster-smarter-and-built-with-empathy)
Aug 13, 2025
[](https://delight.ai/product-release/citations-ai-answers-you-can-trust-and-explore)### [Citations: AI answers you can trust and explore](https://delight.ai/product-release/citations-ai-answers-you-can-trust-and-explore)
[Your AI agent now delivers more than just answers, it creates pathways to deeper knowledge.](https://delight.ai/product-release/citations-ai-answers-you-can-trust-and-explore)
Aug 8, 2025
[](https://delight.ai/product-release/gpt-5-now-live-in-sendbird-ai-agent)### [GPT-5 now live in Sendbird AI agent](https://delight.ai/product-release/gpt-5-now-live-in-sendbird-ai-agent)
[We’re excited to bring GPT‑5 to production inside the Sendbird AI agent platform, powering deeper reasoning, better context retention, and more reliable automation where it matters most.](https://delight.ai/product-release/gpt-5-now-live-in-sendbird-ai-agent)
Jul 30, 2025
[](https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents)### [Trust OS: The foundation for responsible AI agents](https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents)
[Unlock full visibility, control, and oversight so your agents stay safe, compliant, and trustworthy.](https://delight.ai/product-release/trust-os-the-foundation-for-responsible-ai-agents)
Jul 28, 2025
[](https://delight.ai/product-release/automatic-message-translation)### [Automatic message translation in Sendbird Desk](https://delight.ai/product-release/automatic-message-translation)
[Break language barriers in real-time with automatic message translation in Sendbird Desk. Deliver seamless multilingual support from AI to human agents—without switching tools.](https://delight.ai/product-release/automatic-message-translation)
Jul 23, 2025
[](https://delight.ai/product-release/outcome-based-testing)### [Outcome-based testing: The end of shallow AI validation](https://delight.ai/product-release/outcome-based-testing)
[Most AI testing stops at “Did it sound right?” But customers care about results. Outcome-Based Testing goes deeper, it validates whether your AI understood, acted, and actually solved the problem.](https://delight.ai/product-release/outcome-based-testing)
Jul 16, 2025
[](https://delight.ai/product-release/data-export-api)### [Data export API: Turn AI agent conversations into actionable business intelligence](https://delight.ai/product-release/data-export-api)
[Enterprise teams need more than chat logs, they need data that drives decisions. Export AI agent performance metrics, conversation details, and CSAT scores directly to your business intelligence tools.](https://delight.ai/product-release/data-export-api)
Jul 1, 2025
[](https://delight.ai/product-release/selective-deployment-and-rollback)### [Selective deployment & rollback: Built for control in complex teams](https://delight.ai/product-release/selective-deployment-and-rollback)
[You build carefully. Now you can deploy the same way.](https://delight.ai/product-release/selective-deployment-and-rollback)
Jun 30, 2025
[](https://delight.ai/product-release/flagged-message-monitoring)### [Flagged message monitoring](https://delight.ai/product-release/flagged-message-monitoring)
[See when users try to break your AI, and how well your safeguards hold up.](https://delight.ai/product-release/flagged-message-monitoring)
Jun 17, 2025
[](https://delight.ai/product-release/one-click-convert-to-knowledge)### [One-click convert to knowledge: Teach your AI agent from real conversations](https://delight.ai/product-release/one-click-convert-to-knowledge)
[Stop losing your best AI training data to chat history. One click turns any conversation into reusable knowledge that makes your AI agent smarter instantly.](https://delight.ai/product-release/one-click-convert-to-knowledge)
May 22, 2025
[](https://delight.ai/product-release/ai-hallucination-detection-alerts)### [AI hallucination detection & alerts](https://delight.ai/product-release/ai-hallucination-detection-alerts)
[AI hallucinations aren't a matter of if, they're a matter of when. The only question is whether you’ll catch them first, or hear about them from frustrated customers.](https://delight.ai/product-release/ai-hallucination-detection-alerts)
May 13, 2025
[](https://delight.ai/product-release/sendbird-localized-ai-agent-built-for-real-world-scale-and-accuracy)### [Sendbird localized AI agent built for real-world scale](https://delight.ai/product-release/sendbird-localized-ai-agent-built-for-real-world-scale-and-accuracy)
[Localization isn’t just a language problem, it’s a retrieval problem. Sendbird’s localized AI agent aligns every answer and action with the region, the language, and the reality on the ground.](https://delight.ai/product-release/sendbird-localized-ai-agent-built-for-real-world-scale-and-accuracy)
May 8, 2025
[](https://delight.ai/product-release/deployment-and-production-mode-stay-in-control-at-scale)### [Deployment and production mode: Stay in control at scale](https://delight.ai/product-release/deployment-and-production-mode-stay-in-control-at-scale)
[AI is never static. Give your team the speed to build, the structure to stay safe, and the precision to scale cleanly.](https://delight.ai/product-release/deployment-and-production-mode-stay-in-control-at-scale)
May 1, 2025
[](https://delight.ai/product-release/ai-agent-scorecards-bring-oversight-and-accountability-to-ai)### [AI agent scorecards: Bring oversight and accountability to AI](https://delight.ai/product-release/ai-agent-scorecards-bring-oversight-and-accountability-to-ai)
[Good AI isn’t self-correcting. Good AI is managed. Give your AI agents the same operational discipline you expect from human support teams.](https://delight.ai/product-release/ai-agent-scorecards-bring-oversight-and-accountability-to-ai)
Apr 30, 2025
[](https://delight.ai/product-release/activity-trails-full-visibility-into-how-your-ai-agent-acts)### [Activity trails: Full visibility into how your AI agent acts](https://delight.ai/product-release/activity-trails-full-visibility-into-how-your-ai-agent-acts)
[Trust isn’t just about the outcome, it’s about knowing exactly how your AI agent got there. With Activity Trails, there’s no black box.](https://delight.ai/product-release/activity-trails-full-visibility-into-how-your-ai-agent-acts)
Apr 28, 2025
[](https://delight.ai/product-release/end-to-end-ai-agent-testing-build-trust-before-you-go-live)### [End-to-End AI agent testing — Build trust before you go live](https://delight.ai/product-release/end-to-end-ai-agent-testing-build-trust-before-you-go-live)
[Sendbird’s E2E Test simulates real conversations from start to finish, so you can launch with total confidence in every message, every action, every customer.](https://delight.ai/product-release/end-to-end-ai-agent-testing-build-trust-before-you-go-live)
Apr 2, 2025
[](https://delight.ai/product-release/unified-ai-agent-support-suite)### [Sendbird’s unified support: AI agent & Desk](https://delight.ai/product-release/unified-ai-agent-support-suite)
[Cut the clutter. Bring messaging, ticketing, and AI agent together in one lean platform to simplify your support stack.](https://delight.ai/product-release/unified-ai-agent-support-suite)
Mar 28, 2025
[](https://delight.ai/product-release/proactive-chat-ai-that-reaches-out-first)### [Proactive Chat: AI that reaches out first](https://delight.ai/product-release/proactive-chat-ai-that-reaches-out-first)
[Proactive Chat gives the AI agent the power to move first, reaching out before frustration builds, before churn begins, before a ticket even exists.](https://delight.ai/product-release/proactive-chat-ai-that-reaches-out-first)
Mar 25, 2025
[](https://delight.ai/product-release/sendbird-ai-agent-on-whatsapp)### [Sendbird AI agent on WhatsApp](https://delight.ai/product-release/sendbird-ai-agent-on-whatsapp)
[We’re bringing the Sendbird AI agent to the world’s most popular messaging app. You can now deploy your AI agent on WhatsApp to deliver fully automated, human-like customer support, without customers ever leaving the app.](https://delight.ai/product-release/sendbird-ai-agent-on-whatsapp)
Mar 20, 2025
[](https://delight.ai/product-release/salesforce-supported-as-a-knowledge-source)### [Salesforce supported as a knowledge source](https://delight.ai/product-release/salesforce-supported-as-a-knowledge-source)
[Empower your Sendbird AI agents with real-time answers from your Salesforce knowledge source—no manual updates, just faster, accurate customer support.](https://delight.ai/product-release/salesforce-supported-as-a-knowledge-source)
Mar 20, 2025
[](https://delight.ai/product-release/sprinklr-integration-for-ai-to-human-handoff-and-knowledge)### [Sprinklr integration for AI to human handoff and knowledge](https://delight.ai/product-release/sprinklr-integration-for-ai-to-human-handoff-and-knowledge)
[Support gets smarter and more seamless with the Sendbird AI agent + Sprinklr integration. Now you can escalate conversations to human agents directly in Sprinklr, while also training your AI using your existing Sprinklr knowledge base.](https://delight.ai/product-release/sprinklr-integration-for-ai-to-human-handoff-and-knowledge)
Mar 18, 2025
[](https://delight.ai/product-release/multi-language-localization)### [Multi-language AI agent that not just translates](https://delight.ai/product-release/multi-language-localization)
[Create localized AI agents that speak the right language with cultural awareness, delivering a more natural, human-like customer experience.](https://delight.ai/product-release/multi-language-localization)
Mar 18, 2025
[](https://delight.ai/product-release/action-books-management)### [Version Control for Actionbooks & Profile](https://delight.ai/product-release/action-books-management)
[Easily manage and track versions of your AI Agent Action Book to refine AI workflows, improve resolution rates, and improve performance over time.](https://delight.ai/product-release/action-books-management)
Mar 14, 2025
[](https://delight.ai/product-release/team)### [Sendbird AI agent team — specialized AI agents for every role](https://delight.ai/product-release/team)
[Build a team of specialized AI agents with the Sendbird AI agent platform for more efficient customer support.](https://delight.ai/product-release/team)
Mar 13, 2025
[](https://delight.ai/product-release/sendbird-ai-agent-now-available-on-email)### [Sendbird AI agent now available on email](https://delight.ai/product-release/sendbird-ai-agent-now-available-on-email)
[With Sendbird AI agent for email, your team can deliver instant, intelligent, and fully automated support—directly through your existing email channels.](https://delight.ai/product-release/sendbird-ai-agent-now-available-on-email)
Mar 11, 2025
[](https://delight.ai/product-release/freshworks-integration)### [Freshworks integration for AI to human handoff](https://delight.ai/product-release/freshworks-integration)
[Connect Sendbird’s AI agents with Freshworks for seamless AI-to-human handoff when complex issues require expert human support.](https://delight.ai/product-release/freshworks-integration)
Mar 3, 2025
[](https://delight.ai/product-release/sms)### [Sendbird AI agent on SMS](https://delight.ai/product-release/sms)
[Sendbird’s AI agents are now available on SMS to deliver proactive, AI customer service for faster, more engaging support.](https://delight.ai/product-release/sms)
Feb 4, 2025
[](https://delight.ai/product-release/confluence-knowledge-source)### [Confluence now supported as a knowledge source](https://delight.ai/product-release/confluence-knowledge-source)
[Connect Sendbird AI agents with Confluence to deliver accurate, expert answers using your company’s data and knowledge base.](https://delight.ai/product-release/confluence-knowledge-source)
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# Glossary
## AI Learning Center
Source: https://delight.ai/glossary
## AI Learning Center
Expert guides to AI agents, governance, and the technologies shaping the future of customer experience.
## Contents
- [Types of AI](#learn-category-types-of-ai)
- [AI technologies](#learn-category-ai-technologies)
- [AI concepts](#learn-category-ai-concepts)
- [AI interactions](#learn-category-ai-interactions)
- [Terminology](#learn-category-terminology)
### Search Learn posts
Enter at least 3 characters to search Learn posts.
### Types of AI
- 
#### Agentic AI
Agentic AI is an autonomous artificial intelligence (AI) system that can perceive its environment, reason through complex problems, and execute multi-step actions to achieve the goals set for it with little to no human intervention.
[Read more](/glossary/agentic-ai)
- 
#### AI agent
An AI agent is an autonomous system that perceives context, reasons with data, and performs complex tasks. Learn how AI agents work, their benefits, and use cases.
[Read more](/glossary/ai-agent)
- 
#### Conversational AI
Conversational AI uses natural language processing to enable human-like interactions between computers and people. Learn how it works and its impact on enterprise efficiency.
[Read more](/glossary/conversational-ai)
- 
#### Generative AI
Generative AI enables machines to create realistic text, images, and videos. Learn how generative AI models drive enterprise innovation and efficiency.
[Read more](/glossary/generative-ai)
- 
#### Open-source AI
Open-source AI provides transparent access to the code and models behind artificial intelligence. Learn about its benefits and resources for open-source AI development.
[Read more](/glossary/open-source-ai)
- 
#### Predictive analytics
Predictive analytics uses historical data, statistical modeling, and AI to predict future outcomes and drive smarter business decisions.
[Read more](/glossary/predictive-analytics)
View more
### AI technologies
- 
#### AI algorithms
AI algorithms are the mathematical methods behind machine learning and AI systems. Learn how AI algorithms work and power modern intelligent applications.
[Read more](/glossary/ai-algorithms)
- 
#### AI model
An AI model is a trained system that learns from data to make predictions or generate outputs. Learn how AI models work and how they're used in practice.
[Read more](/glossary/ai-model)
- 
#### AI memory
AI memory systems enable AI-powered systems to store and retrieve user information to deliver personalized, context-aware interactions across channels.
[Read more](/glossary/ai-memory)
- 
#### AI sentiment analysis
Learn how AI sentiment analysis uses natural language processing (NLP) to classify text as positive, negative, or neutral and generate real-time sentiment scores.
[Read more](/glossary/ai-sentiment-analysis)
- 
#### AI speech analytics
Learn how AI speech analytics uses speech recognition and natural language processing (NLP) to analyze customer conversations and improve service quality in real time.
[Read more](/glossary/ai-speech-analytics)
- 
#### Computer vision
Computer vision enables computers to identify and process images or videos as humans do. Learn how computer vision systems work, their benefits, and enterprise use cases.
[Read more](/glossary/computer-vision)
View more
### AI concepts
- 
#### AI automation
AI automation uses artificial intelligence technologies to execute tasks and workflows with minimal human input. Learn how AI automation works and where it's used.
[Read more](/glossary/ai-automation)
- 
#### AI and data
Learn how data enables AI systems to reason, learn, and act. Explore the role of real-time and historical data, governance, and data quality in AI.
[Read more](/glossary/ai-and-data)
- 
#### AI challenges
Explore the key challenges of artificial intelligence (AI), including risks in agentic AI, deep learning models, and the need for strong AI governance.
[Read more](/glossary/ai-challenges)
- 
#### AI chatbot
An AI chatbot uses artificial intelligence to simulate conversations with users. Learn how AI chatbots work, common use cases, and key benefits.
[Read more](/glossary/ai-chatbot)
- 
#### AI copilot
An AI copilot assists humans by providing suggestions, insights, and task support in real time. Learn how AI copilots work and where they're used.
[Read more](/glossary/ai-copilot)
- 
#### AI decision-making
AI decision-making uses data, algorithms, and models to evaluate options and choose actions. Learn how AI-driven decision-making works and where it's used.
[Read more](/glossary/ai-decision-making)
View more
### AI interactions
- 
#### AI agent actions
Learn how AI agent actions enable autonomous agents to perform tasks across external systems using natural language processing, machine learning, and large language models.
[Read more](/glossary/ai-agent-actions)
- 
#### Human-AI interaction
Human-AI interaction (HAI) explores how people and AI systems collaborate. Learn how to design AI that enhances human capabilities and drives enterprise productivity.
[Read more](/glossary/human-ai-interaction)
- 
#### Hyper-personalization
Hyper-personalization uses real-time data to dynamically tailor customer interactions. Learn about its benefits and use cases in the era of AI.
[Read more](/glossary/hyper-personalization)
- 
#### What is an MCP? Model Context Protocol explained
What is an MCP? Model Context Protocol is the open standard Anthropic built so AI models can connect directly to real data, tools, and outside systems.
[Read more](/glossary/what-is-an-mcp-model-context-protocol)
### Terminology
- 
#### Glossary of generative AI terminology
Understanding common generative AI terminology helps explain how modern AI systems create text, images, audio, code, and other forms of digital content.
[Read more](/glossary/glossary-of-generative-ai-terminology)
- 
#### Glossary of modern (CX) terms
Customer experience (CX) describes every interaction a customer has with a brand across support, product usage, and communication channels.
[Read more](/glossary/glossary-of-cx-terms)
- 
#### B2B marketing terms & glossary
Whether you're new to the industry or looking to sharpen your vocabulary, having a solid grasp of common B2B terms makes it easier to communicate clearly, evaluate tools, and contribute meaningfully to sales and marketing conversations.
[Read more](/glossary/glossary-of-b2b-marketing-terms)
## Ready to delight your customers with an AI concierge?
[Talk to an expert](https://delight.ai/contact-sales)
# Glossary Posts
## Agentic AI
Source: https://delight.ai/glossary/agentic-ai
- [Glossary](/glossary)\n- Agentic AI\n\n## Agentic AI\n\nFebruary 26 2026\n\n## Agentic AI overview\n\n- Agentic AI is an autonomous, goal-driven AI system that can reason, plan, and perform multi-step actions in real-world environments with minimal human intervention.\n- Unlike traditional AI that can only react, agentic AI systems take action—using external tools, memory, and real-time data to perform complex tasks and workflows end-to-end.\n- By unifying reasoning, action, and learning into one system, agentic AI enables scalable, adaptive execution in dynamic environments across customer service, operations, and software systems.\n\n## What is agentic AI?\n\nAgentic AI is an autonomous artificial intelligence (AI) system that can perceive its environment, reason through complex problems, and execute multi-step actions to achieve the goals set for it with little to no human intervention.\n\nAgentic AI—also known as an AI agent—has a degree of autonomy. Unlike earlier foundation AI models that execute predefined tasks and only respond when prompted, agentic systems are goal-oriented and act independently in pursuit of the goals set for them.\n\nThey can:\n\n- Interpret objectives and break broad goals into a sequence of smaller tasks\n- Use external tools (like APIs or web searches) to pull data or trigger events in real time\n- Execute complex workflows across systems and channels to perform tasks\n- Evaluate context and adapt to users in real time\n- Use memory to learn, adapting their decision-making based on past outcomes\n\n## How agentic AI is different\n\nAgentic AI represents a fundamental shift in intelligent automation. It moves beyond "if-then" logic and scripted workflows into the realm of goal-oriented reasoning and cross-system execution.\n\nInstead of merely following rigid rules, agentic systems evaluate the desired outcome, analyze the available information, and determine the optimal path to get there. This marks a shift from reactive automation to autonomous execution, where AI pursues results rather than following scripts—while still operating within human-defined guardrails.\n\n### 1. Expanded scope of information and interaction\n\nEarlier AI technologies operated within narrow datasets that were "frozen" in time at their last training date. Agentic AI breaks the boundaries of static data, and can interact with its environment in real-time.\n\nThis allows agentic AI systems to:\n\n- Leverage live data from various systems (CRM, ERP) and external sources via APIs\n- Orchestrate tasks across disparate platforms and channels (such as updating a billing record while also notifying a human employee via Slack)\n- Eliminate siloes by acting as a unified intelligence layer that spans all digital platforms and channels, even physical IoT environments\n\n### 2. Memory: The adaptive capability\n\nMemory is the engine for AI personalization and continuous improvement. While previous AI treats each interaction as isolated, agentic AI uses both short-term and persistent (long-term) memory to retain and recall relevant information from past interactions.\n\nThis enables agentic systems to:\n\n- Maintain continuity across conversations and actions (so customers never have to repeat information during support conversations)\n- Learn from previous outcomes and refine future workflows to optimize performance\n- Hyper-personalize experiences by synthesizing historical data and real-time context\n\n### 3. AI architecture: Replacing models with omnichannel systems\n\nAgentic AI also introduces a new way of building AI systems that significantly impacts their functionality and interoperability. While tools like ChatGPT are model-based applications, agentic AI is a coordinated system.\n\nThis means it can integrate multiple components—such as reasoning models, long-term memory, specialized tools, and business rules—to execute on more complex objectives.\n\nUsing this systemic approach, agentic AI serves as a bridge between fragmented platforms, uniting formerly siloed software into a single, connected ecosystem.\n\n## Agentic AI use cases\n\nAgentic AI is built for environments where variables change, requiring judgment and adaptation before taking action. This includes:\n\n- **End-to-end process automation:** Agents manage complex interactions and sequences across disparate systems, such as rebooking flights, offering tailored upsells, or managing onboarding.\n- **Intelligent resource orchestration:** Automatically categorizing, prioritizing, and routing complex tasks or tickets to the right teams while preserving key context.\n- **Data-driven personalization:** Tailoring internal or external recommendations and business solutions dynamically by synthesizing historical and real-time data.\n- **Autonomous content & knowledge management:** Acting as a smart assistant or copilot, agents draft documentation, summarize meetings or calls, and generate follow-up communications.\n- **Complex financial operations:** Automating the end-to-end process of Know Your Customer (KYC) verification or fraudulent transaction investigations.\n- **Hyper-personalized prospecting & lead generation:** Agents identify leads, research recent company happenings across platforms, and draft tailored outreach across multiple channels simultaneously.\n- **Proactive omnichannel customer experience & support:** By monitoring for anomalies across channels and systems—flight delays, delivery disruptions, or product issues—agents initiate corrective action before they impact the business or customers.\n\n## Agentic AI benefits\n\n- **Operational autonomy:** Reduces manual effort for routine but multi-step tasks, such as refund processing in customer support.\n- **Scalable intelligence:** Deploy expert-level logic across thousands of simultaneous interactions or processes.\n- **One source of truth:** By operating across systems and continuously syncing live data, agents eliminate the silos that slow enterprise workflows.\n- **Real-time adaptability:** Agents adjust their plan of action instantly when they encounter an error or new information.\n- **Non-linear scalability:** Agentic workflows can scale complex operations without a linear increase in operating costs.\n- **Improved business outcomes:** Greater accuracy, compliance, throughput, and customer satisfaction\n\n## How agentic AI works\n\nAgentic AI operates through a continuous cycle of Perception-Reasoning-Action (PRA)—much as humans do. Using large language models (LLMs) for reasoning, and machine learning (ML) for environmental interaction and adaptation, the AI agent breaks complex goals into a manageable sequence of tasks and executes until completion.\n\nKnown as an agentic workflow, this involves four steps:\n\n1. **Perception (Input):** The system gathers data from user prompts, real-time sensors, or connected software to inform its reasoning.\n2. **Planning (Reasoning):** The \"brain\" (usually a Large Language Model) breaks the goal into smaller, executable steps.\n3. **Tool use:** The agent decides which available tools (such as APIs, software, or search engines) are needed to complete the objective.\n4. **Action:** The AI agent executes the step and evaluates the result. If the result isn't what was expected, it replans and then tries a different approach until the goal is met.\n\n\n\nSteps of operation for agentic AI workflow\n\n## Agentic AI example: The agentic workflow in customer experience\n\nImagine a customer contacts a retailer because their subscription was billed twice. In a non-agentic setup, a chatbot might explain how to request a refund. By contrast, agentic AI can fully resolve the problem on its own through the following steps:\n\n1. **Reasoning:** It identifies the duplicate billing error by accessing the billing database.\n2. **Tool use:** It calls the payment processor API to initiate a reversal.\n3. **Cross-channel coordination**: It sends a confirmation email while simultaneously updating the CRM notes, notifying human teams, and maintaining a single source of truth.\n4. **Proactive retention:** Recognizing friction in the scenario (using semantic understanding and sentiment analysis), the AI agent offers a loyalty discount on next month's service. It does this because it aligns with the broader business goal of customer retention—without being specifically \"told\" to do so in this case.\n\n## Key takeaways\n\nBlending goal-oriented behavior, real-time connectivity, and system-level architecture—agentic AI marks a shift from generative AI (content creation) to functional AI (end-to-end task execution):\n\n- **From answers to actions:** Agentic AI moves beyond generating text to executing complex workflows—such as automating entire customer service workflows—without manual intervention.\n- **Unified intelligence layer:** Acting as connective tissue between siloed systems, agents enable a single source of truth and omnichannel continuity across all interactions.\n- **Autonomous but governed:** While AI agents operate with a high degree of autonomy, they operate within defined rules and guardrails to ensure trust, compliance, and safety.\n- **Continuous improvement:** Through integrated memory and self-refining logic, agentic AI becomes more accurate and personalized over time, adapting in real time to act in ways that meet both business goals and customer needs.\n\n- [Agentic AI overview](#section-anchor-id-0 \"Agentic AI overview\")\n- [What is agentic AI?](#section-anchor-id-1 \"What is agentic AI?\")\n- [How agentic AI is different](#section-anchor-id-2 \"How agentic AI is different\")\n- [Agentic AI use cases](#section-anchor-id-3 \"Agentic AI use cases\")\n- [Agentic AI benefits](#section-anchor-id-4 \"Agentic AI benefits\")\n- [How agentic AI works](#section-anchor-id-5 \"How agentic AI works\")\n- [Agentic AI example: The agentic workflow in customer experience](#section-anchor-id-6 \"Agentic AI example: The agentic workflow in customer experience\")\n- [Key takeaways](#section-anchor-id-7 \"Key takeaways\")
## AI agent
Source: https://delight.ai/glossary/ai-agent
- [Glossary](/glossary)\n- AI agent\n\n## AI agent\n\nMarch 6 2026\n\n### AI agent overview\n\n- An AI agent is an autonomous software system that can perceive its environment, reason through data, and execute multi-step plans to achieve pre-defined goals.\n- Agents don’t just follow a script. They act autonomously in pursuit of goals set for them. They process historical data and live context, using APIs and other tools to inform their decisions and solve problems in real-world environments.\n\n### What is an AI agent?\n\nAn AI agent is a software system designed to perceive its environment, reason, plan, and act autonomously on behalf of a business to achieve the goals set for it.\n\nUnlike rule-based automation and traditional chatbots, AI agents don’t just follow a script. They understand goals, evaluate real-time context, and select the most effective actions to perform tasks—adapting their strategy in real time with little to no human intervention.\n\nGoing far beyond reactive automation, agents function as closed-loop systems that use persistent memory and tool-use capabilities (e.g., APIs/software) to retrieve external data and navigate dynamic environments. This continuous loop of multi-step reasoning and action enables them to self-reflect and adapt their approach when faced with errors, pursuing goal attainment without human input across a range of complex tasks.\n\n### Why AI agents matter\n\nAI agents—also known as agentic AI—represent the next evolution of intelligent automation. Capable of operating with true autonomy, they don’t just surface answers; they act to deliver outcomes. They offer a new standard in operational efficiency, business intelligence, resource optimization, and customer experience (CX).\n\nUsing AI agents, organizations can:\n\n- **Automate workflows end-to-end:** Agents can execute complex, multi-step processes—from refund processing to supply chain logistics—without manual intervention.\n- **Scale operations without linear costs:** They can handle high volumes of routine, data-driven tasks across departments without added headcount.\n- **Eliminate system siloes:** They operate seamlessly across platforms and channels, acting as a unified intelligence layer and real-time source of truth for customer data and business systems.\n- **Scale hyper-personalization:** Agents combine historical data with real-time context to provide highly tailored experiences to customers and employees.\n- **Enhance data-driven decision-making:** They process vast amounts of unstructured data to identify patterns and proactively execute the next-best action.\n- **Improve risk mitigation & compliance:** With the right guardrails in place, AI agents can follow dynamic rules more consistently than humans.\n- **Reduce human toil:** By handling the rote work of moving data between apps, agents significantly improve employee retention and productivity on high-value tasks.\n\nAdopting AI agents enables a shift from reactive automation to closed-loop orchestration. Unlike generative AI, which only generates content when prompted, agents are proactive digital workers that can handle a range of unstructured, complex tasks.\n\n### AI agent use cases\n\nAI agents are well-suited for complex, multi-step tasks that require judgment, sequencing, and real-time adaptation in dynamic environments. They are transforming core business functions by enhancing AI systems with:\n\n- **End-to-end process automation:** Agents manage complex interactions and sequences across disparate systems, such as processing refunds, rebooking flights, or managing onboarding.\n- **Intelligent resource orchestration:** Automatically categorizing, prioritizing, and routing complex tasks or tickets to the right teams with full context preserved.\n- **Data-driven personalization:** Tailoring internal or external recommendations and business solutions dynamically by synthesizing historical and real-time data.\n- **Autonomous content & knowledge management:** Acting as smart assistants or copilots, agents draft documentation, summarize meetings or calls, and generate follow-up communications.\n- **Multi-agent collaboration:** Teams of specialized AI agents can work together toward a shared goal (e.g., a researcher agent feeding data to a writer agent) to tackle complex tasks with greater accuracy than solo agents.\n- **Fraud investigation:** Because they excel at cross-referencing and synthesizing multiple data sources, agents can flag fraud and compliance risks in near real time, even acting across systems to block the problematic activity.\n- **Proactive system monitoring & response:** By monitoring for anomalies—such as flight delays, delivery disruptions, or product issues—agents can initiate corrective action before they impact the business or customers.\n\n### AI agent example: The agentic workflow in customer service\n\nImagine a customer reaches out through chat about a delayed order, then follows up by email later that evening. In a non-agentic system, these two interactions would generate separate tickets, leading to a loss of context, the customer repeating themselves, and potential frustration.\n\nBy contrast, an AI agent sees these two interactions as continuous through its unified, cross-channel intelligence layer. As part of its agentic workflow, the AI agent takes the following actions:\n\n**1. Persistent memory access:** The agent instantly recognizes the returning customer, having retained the full context of the unresolved order issue.\n\n**2. Semantic understanding:** By analyzing the customer’s intent and sentiment in real time, it can detect any frustration and prioritize the case for escalation accordingly.\n\n**3. Cross-system orchestration:** It pulls real-time data from order management and logistics systems to identify the root cause of the delay.\n\n**4. Autonomous resolution:** It determines the customer’s eligibility for a refund or configured retention action, then executes the appropriate action, updates backend systems, and confirms resolution.\n\n**5. Re-planning:** If the refund API is down, say, the agent can flag for automatic retry or pursue proactive action such as a discount code.\n\n**6. Escalation with context:** If escalation is required, the agent transfers the case to a human teammate with full conversation history, actions taken, and recommended next steps.\n\n**7. Proactively closing the loop:** The agent can follow up, if configured so, to send an email or SMS message 24 hours later: “Your package shows as delivered—is everything okay?”\n\nThis is a real-world example of delight.ai’s [AI concierge for customer service](https://delight.ai/ai-customer-service) in action. By unifying past and present interactions, the AI agent transforms a once-fragmented support experience into a truly seamless journey that elevates customer satisfaction and operational efficiency.\n\n### Benefits of AI agents\n\nWhen properly integrated with existing systems, AI agents offer a suite of benefits to businesses:\n\n- **Better customer engagement and support:** Faster, more consistent, and more tailored at scale\n- **Improved business outcomes:** Greater accuracy, compliance, throughput, and customer satisfaction\n- **Increased operational velocity:** Immediate, consistent execution of repetitive tasks without downtime\n- **Seamless omnichannel consistency:** Provides a unified intelligence layer across all internal and external touchpoints\n- **Non-linear scalability:** Expands capacity without a significant increase in overhead or headcount\n- **Reduced operating costs:** Frees human talent from repetitive work\n- **Proactive risk management:** Constantly monitors for fraud, security anomalies, or regulatory violations in real-time\n- **Enhanced decision intelligence:** Weighs multiple live variables and historical data to make autonomous judgments in real-time environments\n\n### How AI agents work\n\nAI agents operate by following a continuous cycle of perception, reasoning, and action (PRA) — much like humans do. This involves using machine learning (ML) and large language models (LLMs) to identify patterns in data, think, plan, and act accordingly—then learn from interactions. This cycle is made up of five steps:\n\n1. **Perception:** To understand the situation, AI agents gather data from various sources (user input, APIs, sensors), using natural language understanding (NLU) to grasp intent and context.\n2. **Planning & reasoning:** Agents use internal models (like LLMs) to analyze gathered data, break complex goals into steps, and decide how to use available tools (such as APIs, web searches, or software).\n3. **Action:** Agents execute decisions by responding to customers, interacting with external systems (e.g., knowledge bases, CRM, or ERP systems), using tools (function calling), or escalating to a human.\n4. **Memory:** Agents use short-term memory (conversation history) and long-term memory (saved facts) to maintain context, tailor interactions, and learn over time.\n5. **Learning & adaptation:** Agents refine their strategies and improve accuracy by learning from feedback and past outcomes, becoming more autonomous and effective with each interaction.\n\nThis cycle enables agents to interact autonomously with dynamic real-world environments, performing tasks across channels and platforms—even updating backend systems after the AI-customer interaction ends.\n\n### AI agents vs chatbots\n\nYou can think of chatbots as basic conversation interfaces and AI agents as full-on digital employees. While they both use natural language processing (NLP) to interact with users, their underlying logic and intended outcomes differ fundamentally.\n\n- **Chatbots are reactive:** They wait for a user to ask a question and provide a pre-programmed or generated response, depending on whether they’re powered by traditional automation or generative AI. Their primary goal is conversation.\n- **AI agents are proactive:** They pursue a specific given goal (e.g., "onboard this customer") and take independent actions to achieve it. Their primary goal is execution.\n\n### Key takeaways\n\n- **From answers to action:** An AI agent is a new class of goal-oriented, closed-loop system designed to solve problems in full by making decisions based on live context and historical data.\n- **Governed autonomy:** High-functioning agents operate within strict human-defined guardrails. Effective governance ensures every autonomous action is transparent, auditable, and aligned with brand safety standards.\n- **Augmentation, not elimination:** Agents are made to handle rote, data-driven tasks in support of human teams, which should be retained for AI oversight (governance) and human empathy in high-leverage moments.\n\n- [AI agent overview](#section-anchor-id-0 "AI agent overview")\n- [What is an AI agent?](#section-anchor-id-1 "What is an AI agent?")\n- [Why AI agents matter](#section-anchor-id-2 "Why AI agents matter")\n- [AI agent use cases](#section-anchor-id-3 "AI agent use cases")\n- [AI agent example: The agentic workflow in customer service](#section-anchor-id-4 "AI agent example: The agentic workflow in customer service")\n- [Benefits of AI agents](#section-anchor-id-5 "Benefits of AI agents")\n- [How AI agents work](#section-anchor-id-6 "How AI agents work")\n- [AI agents vs chatbots](#section-anchor-id-7 "AI agents vs chatbots")\n- [Key takeaways](#section-anchor-id-8 "Key takeaways")\n
## Conversational AI
Source: https://delight.ai/glossary/conversational-ai
- [Glossary](/glossary)
- Conversational AI
## Conversational AI
March 6 2026
### Conversational AI overview
- Conversational AI simulates human dialogue using natural language processing (NLP), machine learning (ML), and datasets to understand and respond to users through voice or text.
- Unlike rule-based chatbots, these systems understand the nuances of human speech, recognize user intent, personalize experiences, and improve over time—making them useful for virtual assistants, automated workflows, and AI agents.
### What is conversational AI?
Conversational AI is an artificial intelligence (AI) system designed to simulate human dialogue through text or speech. Unlike rigid chatbots that follow scripts, it uses large language models (LLMs), natural language processing (NLP), and machine learning (ML) in combination with large datasets to understand the user’s intent and respond dynamically.
Conversational AI is designed for contextual awareness. It can remember the flow of conversation across multi-turn conversations and interpret the user’s intent and sentiment. Going far beyond merely reactive interactions, it can personalize experiences, automate tasks, and synthesize information—all while improving user satisfaction through intuitive two-way communication that feels human.
### Why conversational AI matters
Conversational AI enables organizations to effectively scale intelligent automation that relies on voice or text interaction, such as customer support. By enabling computers to interact with people through natural text and voice in almost any language, these AI systems create experiences that feel more responsive and satisfying—whether designed as employee virtual assistants or [AI concierges for customer experience](https://delight.ai/ai-customer-service).
Conversational AI matters because it enables organizations to:
- **Improve customer experience (CX):** Deliver faster, more intuitive interactions without losing context when customers switch channels, for a more seamless, convenient experience at scale.
- **Enhance customer engagement:** Provide instant, 24/7, personalized support or marketing that understands customer needs in real-time.
- **Drive operational efficiency:** Automate high-volume, repetitive tasks like password resets or order tracking.
- **Scale personalization:** Deliver hyper-personalized experiences at scale by integrating real-time context with backend customer data.
- **Bridge communication gaps:** Scale consistent, high-quality multilingual communication across operations, even if user inputs are vague, mispelled, or incomplete.
### Types of conversational AI
Conversational AI can be designed for various applications:
- **Rule-based chatbots:** Simple IVR systems that follow predefined scripts for text or voice interactions.
- **AI-powered virtual assistants:** Advanced systems that understand intent and context (e.g., Siri, Alexa, Google Assistant).
- **Generative AI chatbots:** LLM-based models (e.g., ChatGPT) that can create, summarize, and understand complex, nuanced inputs.
- **Agentic conversational AI:** Advanced, autonomous AI systems that go beyond chatting to actively achieve goals by planning, using tools, and making decisions.
### What makes agentic conversational AI different
As AI continues to evolve rapidly, advanced conversational systems are increasingly powered by agentic AI. This enables organizations to shift from reactive to proactive AI workflows, enhancing user interactions through real-time data synthesis, decision-making, and intelligent memory-driven personalization that improves CX.
Core capabilities of these advanced systems include:
1. **Tool calling:** Previous forms of conversational AI only respond when prompted, but agentic AI takes action to achieve a broader goal (e.g., processing a customer's refund, not just explaining the policy). It can use tools such as APIs to access external data or functionality necessary to achieve its goal.
2. **Planning & goal decomposition:** To execute complex tasks effectively, agentic systems create a multi-step plan of action. For example: "Plan a marketing campaign" gets decomposed into 10 distinct, sequential steps before action begins.
3. **Dynamic context windows:** Previous conversational AI has a fixed "memory" of the current chat, but AI agents often use RAG (Retrieval-Augmented Generation) combined with persistent memory to pull in relevant long-term history or external information as needed to execute or personalize the task at hand.
4. **Self-correction:** Agentic AI can "double-check" its work. If an API call fails, a traditional system stops or throws an error. An agentic system sees the error, analyzes why it happened, and tries a different path.
5. **Multi-agent collaboration:** The most advanced agentic systems involve "specialist" agents (e.g., a "researcher" agent talking to a "writer" agent) to automate more complex tasks.
### Conversational AI use cases
From front-line service to internal operations, conversational AI is transforming how businesses interact with customers and employees:
- **Self-service customer support:** Autonomous AI agents handle up to 80% of common inquiries, allowing human teams to focus on complex, high-value issues.
- **Lead generation and qualification:** Proactively engaging website visitors or app users to qualify prospects, collect information, and book meetings for B2B sales and marketing.
- **Intelligent call routing (AI IVR):** Using speech recognition to understand a caller's problem and route them to the right department instantly.
- **Employee onboarding & HR:** Virtual assistants guide new hires through documentation and answer internal policy questions.
- **Sentiment analysis for brand health:** Monitoring customer interactions to detect emotion and prioritize urgent or frustrated users.
### Conversational AI examples
To understand the breadth of this technology, it helps to see how it applies across different business contexts:
- **The healthcare scheduler:** A patient tells a phone system, "I need to move my Tuesday appointment to Friday." Using speech and intent recognition, the AI verifies identity, checks for calendar openings, and updates the EHR system.
- **The financial advisor:** If a user struggles with a wire transfer in their banking app, the AI detects the "tone" using sentiment analysis, then offers a step-by-step guide, or escalates to a human agent with the full transcript attached.
- **The multilingual support agent:** A global enterprise uses conversational AI to provide human-like interactions in 50+ languages with local nuance, maintaining brand consistency and service quality without hiring regional teams.
- **The retail AI concierge:** A customer asks, "Where is my order?" At delight.ai, our conversational AI agents go beyond providing a tracking link; the AI concierge checks the carrier's real-time data, spots the reason (a weather delay), and offers a next-purchase discount—turning a potential issue into a customer loyalty moment.
### Benefits of conversational AI
Conversational AI tools are widely used to enhance self-service, increase customer satisfaction (CSAT), and support workforce efficiency across industries.
- **Improved customer experience & support:** Instant, high-quality, consistent responses in almost any language at scale 24/7.
- **Reduced costs & increased efficiency:** Automation of routine, high-volume tasks reduces operational expenses.
- **Scalability & availability:** Handles numerous inquiries simultaneously to accelerate issue resolution, ensuring no downtime during peak times (with appropriate AI-ready infrastructure).
- **Increased productivity:** Frees human agents to focus on complex, high-value tasks and reduces churn caused by digital toil.
- **Data insights & personalization:** Collects actionable data on user preferences and behavior for decision-making, and tailors customer journeys with insights stored in long-term memory.
### Key takeaways
- **Computer-to-human communication:** Conversational AI enables machines to communicate with humans using natural language, leveraging data to create more intuitive, convenient, and satisfying digital experiences.
- **High-quality communication at scale:** By enabling organizations to scale context-aware dialogue, it has become a foundational interface for modern AI-powered applications.
- [Conversational AI overview](#section-anchor-id-0 "Conversational AI overview")
- [What is conversational AI?](#section-anchor-id-1 "What is conversational AI?")
- [Why conversational AI matters](#section-anchor-id-2 "Why conversational AI matters")
- [Types of conversational AI](#section-anchor-id-3 "Types of conversational AI")
- [What makes agentic conversational AI different](#section-anchor-id-4 "What makes agentic conversational AI different")
- [Conversational AI use cases](#section-anchor-id-5 "Conversational AI use cases")
- [Conversational AI examples](#section-anchor-id-6 "Conversational AI examples")
- [Benefits of conversational AI](#section-anchor-id-7 "Benefits of conversational AI")
- [Key takeaways](#section-anchor-id-8 "Key takeaways")
## Generative AI
Source: https://delight.ai/glossary/generative-ai
- [Glossary](/glossary)
- Generative AI
March 6 2026
## Generative AI overview
- Generative AI is a type of artificial intelligence that can create new, original content—including text, images, videos, or code—based on patterns learned in data.
- Unlike traditional AI that only analyzes data, generative AI models use deep learning to produce outputs that mimic human creativity and reasoning.
## What is generative AI?
Generative AI refers to a type of artificial intelligence system that can create new content—such as text, images, audio, video, or code—based on patterns learned from data. Rather than just analyzing or classifying information, generative AI produces original outputs that resemble human-created content.
The most recognized forms of this technology today are large language models (LLMs)—the engine in ChatGPT, for instance—which focus on producing and summarizing text. Similarly, diffusion models (such as Midjourney or DALL-E) create realistic visual media.
## Why generative AI matters
Generative AI (Gen AI) has changed how organizations create, communicate, and operate. By automating content generation and enabling more natural interactions, it reduces effort while expanding creative and operational possibilities.
By using text-based models to summarize vast datasets (like databases or policy documentation) in seconds, Gen AI brings knowledge synthesis and content creation into AI systems. Its multimodality (the ability to understand and produce content across different formats) further expands the processing and automation capabilities of computer systems.
Generative AI brings many benefits to organizations, enabling them to:
- Accelerate content creation at scale
- Enhance customer interactions with immediate, natural responses
- Support knowledge work with automated writing, summarization, and ideation
- Reduce time-to-output for repetitive, high-volume tasks
- Enable hyper-personalized marketing and customer support
## Generative AI use cases
Ever since the release of ChatGPT in 2022, generative AI models have dazzled us with their ability to create original outputs. Today, the generative AI applications appearing in the enterprise go far beyond consumer-facing generative AI chatbots, including:
- **Customer experience (CX):** Gen AI powers natural language generation for autonomous, proactive AI agents (often called AI concierges) that engage customers 24/7 with tailored voice or text responses.
- **Content marketing:** Automates first drafts of blogs, emails, and social media posts while integrating customer details from internal systems.
- **Software development:** Uses AI to write, debug, and optimize code; for example, legacy code conversion can convert COBOL to Java.
- **Product design:** Iterate on design concepts or synthetic data for testing.
- **Scalable support and communications:** Enhance natural language processing to provide immediate, human-like customer service 24/7 on any channel.
- **Hyper-personalization:** Produces individually tailored product recommendations, content, and experiences at scale (for dynamic delivery with agentic AI)
- **Conversational AI:** Gen AI has become a core component of conversational AI systems, from employee virtual assistants to autonomous AI concierges for customer experience.
## Generative AI vs agentic AI
These two AI technologies are complementary when integrated properly. The key difference is:
- **Generative AI:** Creates outputs (text, images, video, code) reactively from prompts given to it. Stays within its own model "weights" or training data for reasoning.
- **Agentic AI:** Acts autonomously to achieve complex goals, using tools (e.g., browser, APIs, calculators) to access external data and functionality to proactively execute workflows end-to-end.
Agentic AI often uses a generative AI model as the reasoning or language layer to inform its decision-making and task execution. Using both together lets organizations move from static content generation to dynamic, outcome-driven automation.
## How generative AI works
To understand how generative AI works, imagine a system that "reads" or "sees" enough examples to learn the underlying rules of a subject—like grammar, artistic style, or coding logic—before it can produce original content. This follows a four-stage process:
1. **Data ingestion:** The AI model is trained on massive datasets (e.g., the public internet or a company's internal knowledge base), providing the foundation for everything the AI will eventually "know."
2. **Pattern learning:** Using deep learning, the model identifies complex relationships between data points. Example: It learns that "Dear" is often followed by a name in an email, and the associated implications.
3. **The generative process (Architectures):** Depending on the intended output, the AI uses different "engines":
- **Large language models (LLMs):** To create fluid text, LLMs use transformers to predict the next most-likely "token" or word in a sequence.
- **Generative Adversarial Networks (GANs):** Two neural networks compete—one creating content and the other critiquing it—until the output is indistinguishable from reality.
- **Variational autoencoders (VAEs):** These models compress data into its essential features, then reconstruct it to create new variations; often used in image and signal processing.
4. **Refinement & guardrails:** Once trained, a Gen AI model undergoes fine-tuning. This includes feedback loops and the implementation of AI guardrails to ensure generated content is accurate, safe, and brand-aligned.
Gen AI didn't evolve overnight. It moved from simple Markov chains—which predicted the next word based on basic probability—to complex neural networks that process information much like a human brain.
## Example of generative AI in action
Imagine a winter storm hits Chicago, causing flight disruptions and a surge in customer service inquiries. An airline’s [AI customer service concierge](https://delight.ai/ai-customer-service), powered by generative AI, can actually reason through customer inquiries instead of giving “canned” answers, making every customer’s interaction feel genuinely helpful and human.
At delight.ai, for example, we ground the agent’s generative capabilities in persistent customer memory. By recalling key customer details, synthesizing this with real-time inputs and your unique business goals, we ensure AI concierges operate safely and reliably—instantly producing brand-aligned, tailored customer solutions at any scale—while limiting inaccuracies (hallucinations) that can erode trust.
**Pro tip:** AI hallucinations—when AI confidently presents inaccurate, misleading, or fabricated information as truth—are a core challenge of generative AI systems. Strong AI guardrails, structured knowledge sources, and RAG systems are essential to operating Gen AI systems responsibly and sustainably.
## Key takeaways
- **Software as a creative partner:** Whether used for text, image, or code creation, generative AI tools allow organizations to operate with a level of speed and personalization that was previously impossible.
- **AI governance required:** Generative AI systems are probabilistic by design, which means without clear guardrails, monitoring, and human oversight, they can produce inconsistent or factually incorrect outputs. Strong AI governance ensures AI operates within defined policies, aligns with business intent, and maintains trust at scale.
- [Generative AI overview](#section-anchor-id-0 "Generative AI overview")
- [What is generative AI?](#section-anchor-id-1 "What is generative AI?")
- [Why generative AI matters](#section-anchor-id-2 "Why generative AI matters")
- [Generative AI use cases](#section-anchor-id-3 "Generative AI use cases")
- [Generative AI vs agentic AI](#section-anchor-id-4 "Generative AI vs agentic AI")
- [How generative AI works](#section-anchor-id-5 "How generative AI works")
- [Example of generative AI in action](#section-anchor-id-6 "Example of generative AI in action")
- [Key takeaways](#section-anchor-id-7 "Key takeaways")
## Open-source AI
Source: https://delight.ai/glossary/open-source-ai
## Open-source AI
March 6 2026
### Open-source AI overview
- Open-source AI refers to the practice of making the underlying code and model architecture of AI systems publicly available.
- Unlike proprietary AI such as ChatGPT, open-source AI gives users direct control over how models are trained, deployed, and governed.
### What is open-source AI?
Open-source AI refers to artificial intelligence (AI) models, tools, and frameworks that are publicly available for anyone to use, customize, and distribute. Unlike proprietary "black box" systems, like ChatGPT, open-source AI allows developers and businesses to inspect, modify, and extend the technology without constraint.
By making source code and model weights accessible, open-source AI fosters a collaborative environment for AI development, leading to greater transparency and faster innovation worldwide. As such, open-source AI plays a critical role in driving research, enterprise adoption, and the development of modern AI systems.
### Why open-source AI matters
Open-source AI not only lowers barriers to entry but also drives innovation. For enterprises, it offers a path toward "sovereign AI"—the ability to run powerful models on your own servers without relying on a single third-party vendor.
Open-source AI matters because it offers several advantages over proprietary AI. These include:
- **Cost efficiency:** Reducing or eliminating expensive "per-token" licensing fees common with closed-source AI model providers.
- **Data privacy:** Deploying open-source models within your own secure infrastructure mitigates risk by ensuring sensitive training data never leaves your control.
- **Customization:** Deeply training the AI model or tool on niche industry datasets leads to greater accuracy and performance on domain-specific tasks.
- **Transparency:** Auditing the AI’s code or outputs can ensure compliance with requirements, brand guidelines, and ethical standards.
Increasingly, open-source AI models are meeting or exceeding the performance of proprietary AI models, like GPT 4.0—when properly fine-tuned and supported by generative inference services such as Fireworks AI.
### Examples of open-source AI
Open-source AI is rapidly evolving, supported by a global community of researchers, platforms, and ecosystems:
- **Hugging Face:** Known as the "GitHub of AI," [Hugging Face](https://huggingface.co/) is the primary place where developers share trained models, datasets, and demo applications.
- **Meta’s Llama Series:** [Llama](https://www.llama.com/) is one of the most prominent examples of high-performing language models (LLMs) released with open weights.
- **Fireworks AI:** [Fireworks AI](https://fireworks.ai/) is an AI inference platform that helps enterprises deploy and run LLMS efficiently at scale with a focus on fast, cost-effective model serving.
- [**LangChain**](https://www.langchain.com/), [**AutoGen**](https://microsoft.github.io/autogen/stable//index.html): These open-source AI agent frameworks provide the building blocks for creating autonomous agents and handling orchestration so developers can focus on building agentic workflows instead of infrastructure.
### Open-source AI vs. proprietary AI: To build or buy?
Choosing between open-source and closed (proprietary) AI is less about ideology and more about tradeoffs. Most organizations ultimately adopt a hybrid approach, but understanding the differences helps teams decide what to build in-house versus what to buy.
#### Open-source AI: For building and customizing
Open-source AI gives organizations direct access to models, code, and infrastructure. This approach offers flexibility and control, but also shifts responsibility onto internal teams. Open-source AI may be a good fit when organizations need to:
- Customize models or agent behavior deeply to match unique workflows or domains
- Maintain full visibility into how models are trained, evaluated, and deployed
- Avoid vendor lock-in and retain long-term architectural flexibility
- Control data residency and security in regulated environments
- Experiment rapidly with new models, frameworks, or agent architectures
#### Closed (proprietary) AI: For buying or outsourcing
Closed AI platforms provide managed models and services with built-in reliability, security, and support. This approach prioritizes speed and operational simplicity. Closed AI may be a good fit when organizations want to:
- Deploy quickly without managing model training or infrastructure
- Guarantee performance and uptime through SLAs
- Reduce operational overhead for scaling, monitoring, and updates
- Rely on vendor expertise for optimization and maintenance
- Standardize AI usage across teams and use cases
#### Hybrid approaches: The emerging standard
To recap, building on open-source AI requires strong internal expertise in ML engineering, infrastructure, monitoring, and governance. By contrast, closed systems can limit transparency and customization, and may introduce vendor lock-in. In practice, many modern AI systems combine both approaches:
- Open-source models or frameworks for flexibility and control
- Closed platforms for deployment, scaling, monitoring, and governance
This hybrid model allows organizations to innovate quickly while maintaining enterprise-grade reliability and oversight.
### Key takeaways
- **Not a binary choice:** Choosing between open or closed AI is ultimately a function of cost and expertise, required levels of control, and operational responsibility. Both open and closed options are increasing, so evaluating with scalability and business alignment in mind is key.
- [Open-source AI overview](#section-anchor-id-0 "Open-source AI overview")
- [What is open-source AI?](#section-anchor-id-1 "What is open-source AI?")
- [Why open-source AI matters](#section-anchor-id-2 "Why open-source AI matters")
- [Examples of open-source AI](#section-anchor-id-3 "Examples of open-source AI")
- [Open-source AI vs. proprietary AI: To build or buy?](#section-anchor-id-4 "Open-source AI vs. proprietary AI: To build or buy?")
- [Key takeaways](#section-anchor-id-5 "Key takeaways")
## Predictive analytics
Source: https://delight.ai/glossary/predictive-analytics
- [Glossary](/glossary)\n- Predictive analytics\n\nMarch 6 2026\n\n## Predictive analytics overview\n\n- Predictive analytics is the process of using data to forecast future outcomes.\n- Identifying patterns in raw data enables organizations to forecast trends, assess risk, and inform proactive decision-making in ways that exceed human capabilities.\n\n## What is predictive analytics?\n\nPredictive analytics is a branch of artificial intelligence (AI) that uses historical data to predict future outcomes. While traditional data analysis (descriptive analytics) tells you what happened in the past, predictive analytics focuses on what is likely to happen next.\n\nPredictive analytics enables organizations to uncover patterns in raw data by applying advanced analytical techniques, including statistical modeling, regression models, decision trees, and machine learning, to forecast events with a level of accuracy that humans alone cannot match.\n\n## Why predictive analytics matters\n\nBy turning big data into actionable insights, predictive analytics can deliver a measurable competitive advantage. This matters because it enables businesses to:\n\n- **Mitigate risk:** Financial institutions use classification models to predict the likelihood of a loan default (credit risk) before approval.\n- **Drive profitability:** According to Harvard Business School, companies using predictive analytics increase profits by [up to 73%](https://online.hbs.edu/blog/post/importance-of-business-analytics#:~:text=2.,seven%20days%20before%20they%20occur.) compared to traditional reporting.\n- **Operate more efficiently:** By predicting equipment failure, supply chain bottlenecks, or service demand surges, companies can reduce downtime, churn, and waste.\n- **Hyper-personalization:** McKinsey notes that companies mastering predictive personalization generate [40% more revenue](https://finance.yahoo.com/news/digital-silk-announces-article-personalization-210000150.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAD5AAu3nmGUhD87vXYTz-41d4_uYTUXlAC-K0-cjndU_CzHAJJ1TDaeAmAAqytPQx5SITP-XJpcLu7bYFfQGbg8cQzk05JwsO4fF0NHPx8H591Y6SRfDYiEAcVot8kt_hPttZ9e2YjXBzC2MCINbejLDSDDfXmkJ6aAEU0hfTz77) than their peers.\n- **Improve decision-making:** Rather than relying on guesswork and instinct, organizations gain the foresight to anticipate market shifts and customer needs, and to guide strategic decisions across departments.\n\n## Predictive analytics use cases\n\nAs one of the earliest forms of AI technology, predictive analytics has been widely adopted across industries since the early 2000s. This includes:\n\n**Human resources (HR):** Predictive models analyze employee engagement and performance trends to identify 'flight risks', allowing HR leaders to intervene before top talent resigns.\n\n**Sales & marketing:** Retailers use historical data to forecast demand and trigger personalized offers to segments most likely to convert.\n\n**Fraud detection:** In retail and finance, real-time monitoring systems use neural networks to identify anomalies in transaction data, stopping fraudulent credit card charges within milliseconds.\n\n**Supply chain:** Manufacturers predict maintenance needs for heavy machinery, moving from scheduled maintenance to predictive maintenance to save costs.\n\n**Customer experience (CX):** From predicting escalation likelihood to identifying at-risk accounts, predictive insights inform AI decision-making on personalization and service handling, leading to more intuitive, satisfying interactions.\n\n## How predictive analytics works\n\nPredictive analytics works by identifying patterns in large datasets that indicate the probability of a specific event occurring. In effect, this transforms massive volumes of raw data into a 'probability score' that can be used to answer questions like: *Which customers are likely to churn next month?* or *What will our inventory requirements be for the holiday season?*\n\nPredictive analytics systems generally operate by following these five stages:\n\n1. **Data collection:** Raw data is gathered from operational systems and centralized in data warehouses.\n2. **Data preparation:** Data scientists clean, normalize, and structure historical data for modeling.\n3. **Model selection & training:** Statistical modeling techniques are applied, including regression models, decision trees, classification models, and neural networks.\n4. **Validation & testing:** Models are evaluated for accuracy and reliability before deployment.\n5. **Deployment & monitoring:** Predictions are surfaced to business users or integrated into operational systems.\n\nOver time, predictive models are retrained to maintain accuracy as new data becomes available.\n\n## Real-world examples of predictive analytics\n\n**Retail:** Amazon uses predictive analytics to optimize everything from warehouse management to last-mile delivery to dynamic pricing to product recommendations (which is estimated to drive [35% of total revenue](https://www.brainforge.ai/blog/how-amazon-uses-big-data)).\n\n**Banking:** Major financial institutions (e.g., Chase, Barclays) use predictive analytics to detect and halt suspicious transactions in real time, predict creditworthiness, and provide financial advice.\n\n**Customer experience:** Netflix uses predictive analytics to power its content recommendation engine and to optimize its [AI customer service](https://delight.ai/ai-customer-service) based on past ticket frequency and interaction sentiment.\n\n## Types of predictive analytics models\n\nPredictive analytics models vary depending on the problem being solved. Each model type is selected based on the data structure and the desired outcome. These include:\n\n- **Regression models** estimate numerical outcomes (e.g., revenue forecasts, pricing optimization).\n- **Classification models** categorize outcomes into defined groups (e.g., fraud vs. legitimate transactions).\n- **Decision trees** break down decisions into branching logic based on probability.\n- **Neural networks** are advanced machine learning systems that detect complex, nonlinear relationships in large datasets.\n\n## Predictive analytics vs. prescriptive analytics vs. descriptive analytics\n\nPredictive analytics exists within a broader analytics framework:\n\n- **Descriptive analytics** explains what happened.\n- **Predictive analytics** uses historical data to predict future outcomes.\n- **Prescriptive analytics** recommends specific actions based on predictive insights.\n\n## Benefits of predictive analytics\n\nWhen integrated into AI-powered ecosystems, predictive analytics becomes a powerful engine for generating actionable intelligence, enabling data-driven AI automation that performs with high levels of accuracy. Its benefits include:\n\n- Improved forecasting accuracy\n- Reduced operational risk\n- Better resource allocation\n- More personalized, competitive CX\n- Data-driven strategy and decision-making\n- Enhanced fraud detection\n- Sustainable competitive advantage\n\n## Key takeaways\n\n- **Core AI infrastructure:** Long before generative AI like ChatGPT, organizations were embedding predictive analytics models in data warehouses and operational systems. Today, autonomous AI agents still use these forecasting engines to anticipate outcomes, prioritize actions, and deliver data-driven outcomes that improve business performance.\n\n- [Predictive analytics overview](#section-anchor-id-0)\n- [What is predictive analytics?](#section-anchor-id-1)\n- [Why predictive analytics matters](#section-anchor-id-2)\n- [Predictive analytics use cases](#section-anchor-id-3)\n- [How predictive analytics works](#section-anchor-id-4)\n- [Real-world examples of predictive analytics](#section-anchor-id-5)\n- [Types of predictive analytics models](#section-anchor-id-6)\n- [Predictive analytics vs. prescriptive analytics vs. descriptive analytics](#section-anchor-id-7)\n- [Benefits of predictive analytics](#section-anchor-id-8)\n- [Key takeaways](#section-anchor-id-9)
## Vertical AI
Source: https://delight.ai/glossary/vertical-ai
## Vertical AI
March 6 2026
## Vertical AI overview
- Vertical AI refers to industry-specific AI solutions built to solve narrow, domain-specific problems using specialized data and workflows.
- Unlike general-purpose AI designed for broad tasks, vertical AI is fine-tuned on select, proprietary data for better accuracy and performance within a specific industry or business function.
## What is vertical AI?
Vertical AI refers to artificial intelligence (AI) solutions purpose-built to solve specific, high-value problems within a particular industry or business function. Unlike general-purpose AI models trained on diverse datasets to handle a wide range of tasks, vertical AI is trained only on domain-specific data. This enables it to handle specialized workflows with greater performance and responsibility (with regard to regulatory compliance, security, and AI governance).
For example, an online retailer might build a vertical AI agent for retail, trained on a narrow set of product information, internal policies, and brand interactions—rather than a generic AI chatbot for basic knowledge retrieval. By prioritizing depth of knowledge over breadth, vertical AI delivers more accurate, tailored outputs—achieving higher performance and safety than a “jack of all trades” AI ever could.
## Why vertical AI matters
Increasingly, verticalizing AI projects is key to their success. While anyone can license a general-purpose (horizontal) AI tool, the true value of AI lies in making it an expert in your unique business.
In the future, an organization’s AI workforce will be comprised of a team of vertical AI specialists. Each automates one or two narrow tasks using select business intelligence, automates workflows in line with industry standards, and drives measurable outcomes with less operational risk and lower compute costs than general AI tools.
Vertical AI matters because it offers:
- **Precision over flexibility:** According to Gartner, vertical models trained on trusted, industry-specific data [dramatically reduce the risk](https://www.gartner.com/en/newsroom/press-releases/2024-09-09-gartner-predicts-40-percent-of-generative-ai-solutions-will-be-multimodal-by-2027#:~:text=Domain-specific%20GenAI%20models%20are,hallucination%20risks%20through%20targeted%20training.) of AI hallucinations, leading to higher trust in automated decisions.
- **Built-in compliance:** Vertical AI is designed with regulatory requirements (like HIPAA for healthcare) as first-priority constraints, not afterthoughts.
- **Faster time to value:** Because they are pre-configured for a specific sector and learn faster within a specific domain, vertical AI solutions often achieve ROI faster than horizontal AI.
- **Reduced compute costs:** Vertical AI that uses a small language model (SLM) trained for one task is more computationally efficient (and cheaper per API call) than pinging a massive model like GPT-4o for every task.
- **The "data moat":** Using your own proprietary data to fine-tune a smaller AI model creates a unique asset that competitors using generic tools cannot match.
## Vertical AI vs horizontal AI
While both are powered by AI models, they differ in their strategies and scopes.
- Vertical AI offers precision and performance within a defined scope.
- Horizontal AI offers flexibility across applications.
## Common vertical AI use cases
Designed to be an expert in any domain, vertical AI is inherently flexible. Given its elevated performance and reduced risk profile, it increasingly appears across industries:
**Healthcare:** Specialized AI agents process unstructured clinician notes to automate billing and ensure HIPAA-compliant patient data management.
**Manufacturing:** AI-powered predictive maintenance that integrates real-time IoT signatures with historical maintenance logs to prevent costly downtime.
**Finance & banking:** Advanced fraud detection that understands the "behavioral fingerprints" of specific transaction types rather than just flagging large amounts.
**Legal services:** Automating contract review, document analysis, or due diligence by fine-tuning models on select jurisdictional precedents, firm-specific language, or certain regulatory frameworks.
**Retail:** Retail AI agents are trained on product catalogs, brand policies, and customer conversations to deliver hyper-personalized product recommendations and guidance to shoppers online and in-store, pre- and post-purchase.
## How vertical AI works
Many organizations and AI startups use the vertical approach to AI adoption. You own the workflow, the data, and then embed your business intelligence directly into the software layer. By contrast, general-purpose AI is often exposed as an interface layer rather than deeply embedded in operational systems.
The "vertical AI stack" differs from the horizontal AI stack by adding layers of specialization:
- **Base model:** A foundational large language model (LLM) or small language model (SLM) provides the reasoning and language capabilities.
- **Fine-tuning & adaptation:** The model is trained (fine-tuned) on a "data composite" of domain-specific knowledge (e.g., medical journals, case law, technical manuals).
- **Proprietary data layer:** The organization’s own secure data is used to ground the model in the company’s specific reality.
- **Retrieval-augmented generation (RAG):** This technique allows the AI to access real-time external data without retraining the entire model.
- **Workflow integration:** The AI is embedded directly into the vertical software professionals already use (like an EMR for doctors or a CRM for sales).
- **Continuous learning:** Model performance improves over time through feedback loops and structured domain updates, but faster because narrower datasets are less noisy.
## Vertical AI example in customer service
Today’s market is full of [AI agents for customer service](https://delight.ai/ai-customer-service) built by layering a general-purpose LLM on top of an AI knowledge base (help center). However, these broad-brush AI tools often lack the fine-tuning, proprietary data integration, and workflow depth to act autonomously and resolve complex issues without compromising customer trust and safety.
The vertical AI approach is how delight.ai, for instance, delivers truly intelligent and secure AI support automation. Our AI agents are grounded in specific policies, RAG-equipped for precise context augmentation, and embedded directly into CRM, payments, and logistics systems.
This creates an AI-powered service layer that acts as a true expert across your configured systems, pursuing your business goals within defined guardrails. The result is a highly specialized AI support agent that deeply understands customer needs, escalates intelligently when needed, and always maintains its branded AI personality, delivering a mix of performance and customer trust.
## Benefits of vertical AI
For many organizations, vertical AI provides a better long-term business model than broad AI experimentation. Benefits include:
**Better accuracy & reliability:** By narrowing its training datasets, the AI becomes significantly more performant and less likely to hallucinate.
**Native interoperability:** Vertical AI is designed to "speak the language" of industry-standard systems out of the box.
**Superior CX:** Vertical AI understands the nuance of your unique customers, tailoring interactions based on intent, sentiment, and context to deliver more relevant, satisfying interactions.
**Improved learning:** As more domain-specific data flows through the AI system, the model becomes smarter, creating a virtuous cycle of improvement faster than with noisier datasets.
**Cost efficiency:** Vertical AI tends to be cheaper to run due to its computational efficiency, while it can also be faster to develop and to recoup returns from a more focused AI pilot project.
## Key takeaways
- **ROI potential:** By narrowing the focus of AI decision-making, verticalization provides a clearer path to sustainable value in AI adoption for organizations.
- **Intelligence as specialization:** By fine-tuning with only select, proprietary data, AI becomes “smarter” in target domains because its intelligence is less diluted.
- [Vertical AI overview](#section-anchor-id-0)
- [What is vertical AI?](#section-anchor-id-1)
- [Why vertical AI matters](#section-anchor-id-2)
- [Vertical AI vs horizontal AI](#section-anchor-id-3)
- [Common vertical AI use cases](#section-anchor-id-4)
- [How vertical AI works](#section-anchor-id-5)
- [Vertical AI example in customer service](#section-anchor-id-6)
- [Benefits of vertical AI](#section-anchor-id-7)
- [Key takeaways](#section-anchor-id-8)
## Voice AI
Source: https://delight.ai/glossary/voice-ai
- [Glossary](/glossary)\n- Voice AI\n\n## Voice AI\n\nMarch 6 2026\n\n### Voice AI overview\n\n- Voice AI is a form of artificial intelligence (AI) that enables machines to understand human speech and generate natural voice responses in real time.\n- It combines speech recognition, natural language processing, text-to-speech, and speech-to-speech technology, allowing systems to engage in natural conversations with users.\n\n### What is voice AI?\n\nVoice AI, or voice-enabled artificial intelligence, is a technology that enables computers to process, interpret, and respond to human speech in a natural, conversational way. It processes audio, preserving tone and nuance, and generates a synthesized output that mimics the input's characteristics (e.g., emotion, emphasis, prosody).\n\nPowering virtual assistants like Amazon’s Alexa, AI chatbots, and more, voice AI combines automatic speech recognition (ASR), natural language processing (NLP), text-to-speech, speech-to-speech, and AI algorithms to process human inputs and respond seamlessly to users.\n\n### Why voice AI matters\n\nAmong customer communication and support channels, voice remains the gold standard. For high-stakes or urgent issues—like a stolen credit card or a canceled flight—customers prefer the speed and empathy of a phone call over a chat interface.\n\nUnlike traditional phone systems (IVR), voice AI understands intent, tone, and context. This enables it to resolve complex customer issues in real time on its own. Agentic voice AI goes beyond just replying; it acts autonomously to deliver outcomes. It adapts to real-world inputs, retrieves external data, and takes action across systems to provide the user with an immediate solution with conversational support.\n\nBy adopting voice AI, organizations can:\n\n- **Eliminate call wait times:** AI voice agents for customer service can answer thousands of queries per hour, instantly, 24/7 across all channels, reducing average handling time.\n- **Bridge accessibility gaps:** Voice is more inclusive for users with visual impairments or who simply prefer talking to typing.\n- **Scale high-quality multilingual support:** Automating frontline interactions with a human-like touch in a growing number of languages frees up human agents and cuts costs.\n- **Improve customer satisfaction:** Voice AI agents remember customers across channels, picking up where they left off to enhance convenience, personalization, and loyalty.\n\n### Common use cases for voice AI\n\nVoice AI is well-suited to scenarios where speed, hands-free interaction, or real-time resolution are key. This includes:\n\n- **Self-service AI support:** Voice AI agents handle everything from billing inquiries to product recommendations, remembering customers and tailoring conversations accordingly.\n- **AI virtual receptionists:** An AI-powered IVR manages inbound calls, qualifies leads, and routes to the right departments without the friction of menu navigation.\n- **Human agent assist:** Listening to a live call between a human representative and a customer to instantly surface the right documentation, solutions, or compliance warnings.\n- **Authentication & security:** Using \"voice biometrics\" to verify a caller's identity based on their unique vocal characteristics.\n- **Proactive outreach:** Calling customers in advance with flight rebooking options, appointment reminders, or delivery notifications for faster, more convenient service.\n- **Multimodal voice AI support:** When paired with video (digital humans) or screen-sharing technologies, voice AI helps create a more robust "See what I see" support experience.\n- **Media & content creation:** Speech-to-speech (S2S) dubbing preserves the original speaker’s emotion across languages, enabling voice cloning for consistent brand voice in AI-driven podcasting, for instance.\n\n**Note:** Unlike traditional text-to-speech (TTS), S2S allows creators and support teams to use their own vocal delivery to \"drive\" a synthetic voice, capturing nuances such as whispering, laughter, and dramatic pauses.\n\n### How voice AI works\n\nVoice AI operates through a split-second pipeline of four core AI technologies, though the industry is rapidly shifting toward more integrated, "ear-to-mouth" neural models. The standard pipeline for voice AI includes:\n\n- **Automatic speech recognition (ASR):** The \"ears\" capture audio and transcribe it into text in real time, filtering out background noise and adapting to accents.\n- **Natural language understanding (NLU):** The \"brain\" analyzes that text to determine intent (e.g., \"I want to cancel my order\") and sentiment (e.g., \"The customer is frustrated\").\n- **Large language models (LLMs):** The \"reasoning\" faculties, which use the NLU data to decide the best response or action based on business goals and live context.\n- **Text-to-speech (TTS):** The \"voice\" converts the written response back into a natural voice using speech synthesis that runs on neural networks.\n\n**The \"speech-to-speech\" (S2S) shift:** While the 4-step pipeline is the current enterprise standard, End-to-End (E2E) neural speech is emerging as the future of voice AI. Rather than converting audio to text and back again—which can strip away meaning—modern Speech-to-Speech models process audio signals directly.\n\nThis allows AI to hear a sigh, laughter, or a sarcastic tone that text-based NLU often misses. By bypassing the \"text bottleneck,\" these systems reduce latency and preserve the emotional nuance (prosody) of the conversation, making interactions feel truly fluid and human.\n\n### Real-world examples of voice AI\n\n**Financial services:** A bank uses a voice-enabled branded AI concierge to authenticate customers and handle routine account inquiries instantly, helping improve CX and engagement.\n\n**Healthcare:** Providers use voice AI for appointment scheduling and automated reminders, freeing human resources to help patients, creating efficiencies and improving patient outcomes.\n\n**AI customer service for retail:** A well-known ecommerce brand deploys a voice AI support agent across all support channels—website, socials, email, SMS—that processes returns end-to-end, issues refunds, and reduces load on human teams, particularly in peak seasons.\n\nPlatforms like Delight.ai ensure high-volume performance and low latency by combining scalable infrastructure, persistent memory, and real-time system integrations—so voice AI remains fast, accurate, and reliable even during peak demand.\n\n### Voice AI vs. conversational AI vs. traditional IVR\n\nEven though Conversational AI—a form of generative AI—is a massive leap over traditional IVR systems, it still largely serves as a reactive interface.\n\nBy contrast, voice AI (with agentic AI) moves it from just responding to executing on behalf of users or systems, offering greater applicability for enterprise automation and personalization.\n\n### Benefits of voice AI\n\n- **Reduced costs:** Enterprise voice AI agents operate at a fraction of the cost of traditional human business process outsourcers (BPO), significantly reducing operational overhead.\n- **Improved customer experience (CX):** Provides immediate, natural, and conversational, 24/7 support without wait times.\n- **Higher productivity:** Increases efficiency by automating data entry and workflows.\n- **Scalability:** With the right infrastructure, it easily scales to meet growing demand and high-volume periods (e.g., holidays) without sacrificing performance.\n- **Enhanced personalization:** Delivers personalized, interactive experiences and tailored marketing content.\n- **Accessibility & convenience:** Enables hands-free operation and improves access for individuals with disabilities.\n\n### Challenges of voice AI\n\nFor all its benefits, voice AI isn’t without its challenges, including:\n\n- **The uncanny valley:** While modern TTS uses paralinguistic features (breathing, pitch) to sound human, poorly tuned models can feel unsettling or fail to mirror a caller's emotional tone.\n- **Latency & speed:** Humans expect a response within 500ms; any \"thinking\" delay (TTFB) over a second breaks the conversational flow and feels robotic.\n- **Security & anti-spoofing:** The rise of voice cloning requires enterprises to deploy liveness detection and audio watermarking to prevent deepfake fraud and unauthorized access.\n- **Integration & infrastructure:** Voice AI requires read-write API access and enterprise-grade infrastructure to solve problems in real time rather than just reciting FAQs.\n\n### Key takeaways\n\n- **Intelligent conversational interfaces:** Voice AI is emerging as a primary, agentic interface, transforming human-computer interaction from rigid commands to natural, contextual conversations.\n\n- [Voice AI overview](#section-anchor-id-0 "Voice AI overview")\n- [What is voice AI?](#section-anchor-id-1 "What is voice AI?")\n- [Why voice AI matters](#section-anchor-id-2 "Why voice AI matters")\n- [Common use cases for voice AI](#section-anchor-id-3 "Common use cases for voice AI")\n- [How voice AI works](#section-anchor-id-4 "How voice AI works")\n- [Real-world examples of voice AI](#section-anchor-id-5 "Real-world examples of voice AI")\n- [Voice AI vs. conversational AI vs. traditional IVR](#section-anchor-id-6 "Voice AI vs. conversational AI vs. traditional IVR")\n- [Benefits of voice AI](#section-anchor-id-7 "Benefits of voice AI")\n- [Challenges of voice AI](#section-anchor-id-8 "Challenges of voice AI")\n- [Key takeaways](#section-anchor-id-9 "Key takeaways")
## AI algorithms
Source: https://delight.ai/glossary/ai-algorithms
## AI algorithms
March 6 2026
### What are AI algorithms?
An AI algorithm is a step-by-step process (or set of rules) that teaches a computer to learn from data, recognize patterns, and make decisions or predictions. This mathematical procedure enables it to perform tasks that typically require human intelligence—such as understanding speech or recommending content—without being explicitly programmed for every scenario.
Unlike traditional software that relies on "if-then" logic that's coded by humans, AI algorithms identify patterns in massive datasets. From this, they can make predictions, categorize information, and optimize processes without following a set script.
AI algorithms matter because they are the bridge between having data and using data effectively. They can process millions of data points in seconds, which would take humans years. In an AI-powered system—such as Netflix's recommendation engine—these algorithms serve as the "reasoning layer" that determines which action to take next to achieve a broader goal.
More broadly, algorithms define how AI-driven systems think, decide, and behave. Two AI systems can use the same data and models but produce very different outcomes because the algorithms guiding their choices differ.
### Algorithms determine:
- What information the AI pays attention to
- How different signals are weighed
- Which actions or outcomes are prioritized
AI algorithms also encode business intent and priorities—such as goals, risk thresholds, and compliance requirements—into their decision logic. This shapes whether AI operates in a way that's aligned, governed, and trustworthy, or risky and unpredictable.
In short, AI algorithms matter because they transform raw data into scalable, accurate analysis and decision-making. This enables businesses to move from reacting to anticipating what's next and guiding actions at scale accordingly.
### How AI algorithms differ from AI models
Before we go further, it's important to separate these two related concepts.
- AI models are computer programs trained to generate predictions or possibilities
- AI algorithms are a set of rules that decide how those predictions are used
### For example:
- A model (like ChatGPT) might generate several possible responses
- An algorithm decides which response to deliver, when to escalate, or what action to take
This distinction is especially important in agentic AI, where algorithms coordinate multiple steps and decisions across systems.
### How do AI algorithms work?
AI algorithms operate through a process of inputs, weights, and outputs. While the math is complex, the workflow is straightforward:
1. Training (Learning): The algorithm is fed a large dataset (like past customer purchases). It looks for correlations, such that customers who buy Item A often buy Item B.
2. Weighting (Processing): The algorithm assigns weights to different factors. In a customer experience algorithm, real-time signals, such as customer frustration, are often weighted more heavily than static attributes, such as account tenure.
3. Inference (Predicting): Once trained, the algorithm is given new, unseen data. It applies its learned weights to make an educated guess or take an action.
4. Feedback (Refining): Modern algorithms are self-correcting. If a prediction is wrong, the system adjusts its internal weights to improve the next time—a process known as machine learning.
### Benefits of AI algorithms
AI algorithms are what turn AI from a novelty into a reliable, enterprise-ready system. This leads to:
- Increased operational efficiency: Automating routine but complex tasks, such as intelligently routing support tickets, frees human teams to focus on strategic work.
- Hyper-personalization: Algorithms analyze individual user behavior in the moment to trigger the next best action or recommendation in real-time.
- Risk mitigation: Fraud detection algorithms scan transactions for minute anomalies that indicate a security breach before it occurs.
- Data-driven precision: From medical diagnostics to financial forecasting, algorithms can identify patterns humans miss, leading to faster and more accurate decision-making.
- Enhanced customer experience: By appropriately weighing real-time context, intent, and history, they deliver experiences that feel more timely, relevant, and satisfying.
- Better business alignment: Algorithms encode business priorities such as service levels, risk thresholds, and compliance requirements. This ensures AI operations support broader business goals without compromising trust, safety, or security.
- Scalable intelligent operations : Algorithms allow AI systems to make consistent decisions at scale. The same logic that works for hundreds of interactions can work for millions — without fatigue or variability — reducing the need for manual effort.
### Use cases for AI algorithms
- Customer experience (CX): Sentiment analysis algorithms "read" the emotion behind a customer's email, allowing an AI support agent to prioritize frustrated users instantly.
- Human resources: Algorithms can scan thousands of resumes to surface the top candidates based on skill-matching rather than just keywords.
- Marketing: Recommendation engines (like those used by Netflix or Amazon) use "collaborative filtering" algorithms to predict what a user will want next.
- Operations: Predictive maintenance algorithms analyze sensor data from machinery to predict a failure before the equipment breaks down.
### Types of AI algorithms
AI algorithms are best categorized by how they learn and the problems they solve. Most business applications fall into these four categories:
- Supervised learning: The most common type, where an algorithm is trained on labeled data (e.g., "this email is spam," "this email is not spam"). It learns to predict outcomes for new data based on historical examples.
- Unsupervised learning: Here, the algorithm is given raw data without labels. It looks for hidden patterns or groupings on its own, such as segmenting a customer base into previously unknown personas.
- Reinforcement learning: This is "learning by trial and error." An agent receives "rewards" for correct actions and "penalties" for wrong ones. This is the foundation of autonomous systems that optimize logistics or trading strategies over time.
- Deep learning (neural networks): Inspired by the human brain, these algorithms use multiple layers of processing to understand complex data like speech, images, and natural language. This is the core technology behind generative AI and AI agents.
### Key takeaways
- The engine of AI autonomy: Algorithms are what allow AI agents to plan and execute multi-step tasks without a manual "how-to" guide for every scenario.
- Governance is essential: Because algorithms learn from data, they can pick up biases. Leaders must ensure algorithmic transparency and regular audits to maintain trust.
- A means to an end: An algorithm is a tool, not a strategy. Its value is defined by the business problems it solves and the return it delivers.
- [What are AI algorithms?](#section-anchor-id-0 "What are AI algorithms?")
- [Algorithms determine:](#section-anchor-id-1 "Algorithms determine:")
- [For example:](#section-anchor-id-2 "For example:")
- [Benefits of AI algorithms](#section-anchor-id-3 "Benefits of AI algorithms")
- [Use cases for AI algorithms](#section-anchor-id-4 "Use cases for AI algorithms")
## AI model
Source: https://delight.ai/glossary/ai-model
- [Glossary](/glossary)
- AI model
## AI model
March 6 2026
What is an AI model? An AI model is a mathematical representation of a real-world process that’s trained on vast datasets to recognize patterns and generate original outputs when provided with new inputs. In simple terms, an AI model learns from data and the examples within. Through a process called training, it analyzes large volumes of data, learning to identify relationships—such as how words relate to meaning or how behaviors correlate with outcomes. Once trained, the model can apply those patterns to new situations, identifying complex relationships and making predictions or decisions with speed and accuracy that exceed human intelligence. For example, models can spot the difference between a fraudulent and a legitimate transaction, or identify the underlying sentiment in a customer’s email, in milliseconds. AI models are responsible for tasks such as:
- Interpreting language
- Making predictions or classifications
- Generating text, images, or recommendations
Unlike traditional software that is "static," an AI model is probabilistic. It doesn't just provide a single answer. Based on its training, it evaluates the data to provide the output that’s most likely to be accurate and useful. However, an AI model by itself does not know what to do with its output. (That’s where AI algorithms come in). The model is just one component within a broader AI system.
### Why AI models matter
The choice of model is the single most important factor in determining the "IQ" of your AI automation—what it’s capable of understanding and producing. AI models matter because they provide:
- Versatile intelligence: Large language models (LLMs) can be applied to hundreds of different tasks in a single interface, from coding to sentiment analysis.
- Internal knowledge retrieval: When connected to your data, models act as an intelligent index that can find and synthesize information faster than any human researcher.
- Operational scalability: Once a model is trained, it can be deployed across your organization, ensuring that every department has access to the same level of expert intelligence.
### Types of AI models
Enterprise AI has moved beyond a "one size fits all" approach. Different tasks require different model architectures, which can include:
- Large language models (LLMs): The powerhouses behind tools like ChatGPT, LLMs excel at understanding and generating human language. They are ideal for customer service and content creation.
- Computer vision models: Designed to "see" and interpret visual data, these models power everything from manufacturing quality control to security surveillance.
- Predictive/analytical models: These focus on numbers and trends. They are used for financial forecasting, demand planning, and risk assessment.
- Multimodal models: The newest frontier, these models can process images, audio, and text simultaneously to provide a more comprehensive intelligence.
### How AI models work
1. Data ingestion: The model is exposed to a "training set" (e.g., millions of customer support transcripts).
2. Pattern extraction: Using natural language processing, it identifies the subtle nuances of how a goal is achieved or a question is answered.
3. Validation: The model is tested against a "validation set" to ensure it isn't just memorizing data, but actually learning the logic.
4. Deployment (Inference): The model is integrated into a business workflow where it begins processing live data and executing tasks.
### AI model use cases
- Customer experience: LLMs power autonomous agents that handle complex refunds, rebookings, and trigger hyper-personalized experiences at scale.
- Legal & compliance: Deploying models to scan thousands of contracts to flag non-standard clauses or potential risks.
- Marketing: Generative AI models can create thousands of personalized ad variations based on individual user data.
- Supply chain: Models for predictive analytics process weather, port traffic, and historical trends in real time to decide when to reorder inventory.
### Key takeaways
- The agent’s brain: An autonomous AI agent is only as capable as the model powering its reasoning.
- Open vs. closed models: Leaders must decide between proprietary models (like GPT-4) or open-source models (like Llama) based on security and customization needs.
- Maintenance required: Models can "drift" or become outdated as real-world data changes. Continuous monitoring and retraining are essential for long-term accuracy.
## AI memory
Source: https://delight.ai/glossary/ai-memory
- [Glossary](/glossary)\n- AI memory\n\n## AI memory\n\nMarch 6 2026\n\n### AI memory overview\n\n- AI memory refers to an AI system's ability to store and retrieve information across interactions.\n- This allows AI to maintain context over long periods, moving beyond stateless, one-off responses to highly personalized, context-aware outputs.\n\n### What is AI memory?\n\nAI memory refers to the ability of an artificial intelligence (AI) system to store, recall, and apply information over time. By enabling AI to maintain context over long periods, memory moves machines beyond stateless, one-off outputs to delivering highly personalized, context-aware solutions.\n\nIn practice, memory makes AI into a more capable thinker and actor. Rather than starting from scratch in every interaction, it builds on prior knowledge, creating a welcome sense of consistency and continuity with users.\n\nAI memory is not a single feature, but a layered architecture composed of different memory types (e.g., short-term, long-term) that work together within a broader AI ecosystem.\n\nFor example, early large language models (LLMs)—such as initial versions of ChatGPT—relied primarily on short-term memory. They could only reference information within the current session.\n\nBut when ChatGPT introduced long-term memory in 2024, the AI could now remember user projects and preferences across sessions. This significantly improved its personalization, customer experience (CX), and overall performance.\n\n### Why AI memory matters\n\nWithout memory, an AI system is essentially a stranger every time you use it. Human-AI interactions feel repetitive and transactional, and are less efficient.\n\nWith memory, however, AI-powered systems can retain and recall historical information, such as customer goals, preferences, and past support issues. This transforms AI from a simple reactive tool into a context-driven actor that can sustain and evolve a relationship with users.\n\nAI memory matters because it enables:\n\n- **Hyper-personalization:** AI agents remember a customer's preferences and purchases (e.g., "likes pink Nike trainers") then combine this with real-time context to hyper-personalize experiences and improve conversion rates.\n- **Operational continuity:** If a customer opens a support query on Monday and follows up on Friday, they can pick up where they left off, eliminating the friction of having to repeat themselves.\n- **Compounding intelligence:** By observing and storing select interaction data in long-term memory, AI identifies patterns and "best paths" for key outcomes, becoming more efficient.\n- **Reduced compute costs:** Organizations can lower token and API costs by using memory-powered AI that retrieves precise information rather than reinitiating entire interactions.\n- **Superior customer experience (CX):** Providing faster, more seamless, more tailored interactions at scale improves customer satisfaction and loyalty.\n\nMemory is essential to high-performing AI because it serves as a workaround for LLMs' finite context windows, which limit the number of tokens (words and symbols) they can process at once.\n\nWhile LLMs serve as "the brain" of AI systems, they cannot retain unlimited information within a single prompt. Once the token limit is reached, earlier content may be truncated or compressed. Even within larger context windows, performance can degrade, particularly for information buried in the middle of long prompts (known as the "lost in the middle" problem). Rather than forcing all relevant data into one prompt, modern architectures use AI memory systems to overcome these LLM constraints, using techniques such as:\n\n- Chunking (breaking documents into smaller segments)\n- Semantic search (retrieving meaningfully related content)\n- Recursive or multi-step processing\n- Retrieval-Augmented Generation (RAG)\n\nIn short, AI memory systems ensure that LLM prompts have only the critical information. This ensures more effective reasoning that results in more accurate, reliable outputs.\n\n### Use cases for AI memory\n\nMemory capabilities are enhancing AI-powered systems across numerous use cases and industries, including:\n\n- **AI customer service:** A [voice AI agent for customer service](https://delight.ai/ai-customer-service) remembers a customer's previous issue from three months ago and asks, "Is your router still working well since we last spoke?"\n- **Retail & ecommerce:** A retail AI agent notices a customer frequently buys a specific brand, and recommends the most appropriate new styles by analyzing history and real-time context.\n- **Healthcare:** Acting as a long-term health monitor, AI tracks medical history, medication schedules, and patient habits to provide personalized health guidance that improves patient outcomes.\n- **AI assistants:** By remembering users’ routines, habits, and preferred meeting times, the AI assistant learns and adapts over time.\n- **AI employee copilots:** Coding agents, for instance, recall developers’ coding styles, preferred libraries, and past projects to accelerate the generation of high-quality code.\n- **Workflow automation:** An AI agent onboards new employees and manages a 30-day process that tracks which documents have been signed and are still pending across sessions.\n\n### 6 types of AI memory\n\n[AI memory systems](https://delight.ai/agent-memory-platform-amp) typically include multiple types of memory. These various memory types allow AI to retain user preferences, improve decision-making over time, and recall past interactions using methods like vector databases:\n\n1. **Short-term memory:** Also known as working memory. Refers to the limited context window of an AI model during a single session. It enables the AI system to hold immediate data and maintain coherence for the current task. Fast but temporary.\n2. **Long-term memory:** Also known as persistent memory. Enables AI to store information for future recall across different sessions. This can include structured data, such as CRM records, and unstructured conversational history.\n3. **Episodic memory:** This stores and recalls specific interactions and events tied to a specific timeline, such as a past support issue or escalation. Powered by vector databases.\n4. **Semantic memory:** This is how AI stores structured, general knowledge such as internal policies, product information, and facts about the world. Stored in an AI knowledge base, powered by vector databases.\n5. **Procedural memory:** Enables the AI to store and retrieve steps or skills needed to perform specific tasks, such as complex multi-step IT troubleshooting.\n6. **Parametric memory:** This is memory of the knowledge embedded directly into the AI system’s underlying model weights and parameters.\n\nTo optimize access and retrieval of information, AI memory can also be structured in a hierarchy, such as L1 (immediate context), L2 (session memory), and L3 (long-term/external database).\n\n### Short-term AI memory vs long-term memory\n\nThis comparison table breaks down the key differences between these two primary types of AI memory:\n\n| Feature | Short-term memory (context window) | Long-term memory (vector database) |\n|---|---|---|\n| What it is | The limited token window an LLM can process in a single prompt/session | External persistent storage that holds embeddings for later retrieval |\n| Duration | Temporary (session-bound) | Persistent across sessions |\n| Capacity limit | Fixed token limit (e.g., thousands to hundreds of thousands of tokens) | Scales with database size; not constrained by prompt length |\n| Storage method | Raw text inside the prompt | Vector embeddings stored in a database |\n| Purpose | Maintain conversational coherence in the current interaction | Store and retrieve historical, structured, or contextual information |\n| Retrieval mechanism | Everything must fit inside the prompt | Semantic similarity search retrieves only relevant chunks |\n| Accuracy behavior | Performance may degrade in very long prompts ("lost in the middle" issue) | Retrieves only relevant context, improving signal-to-noise ratio |\n| Best for | Real-time dialogue flow and immediate task continuity | Historical context, user preferences, episodic memory, knowledge retrieval |\n| Scalability | Limited by token constraints | Scales horizontally with infrastructure |\n| Example | Remembering what the user said three messages ago | Remembering the user’s purchase history from six months ago |\n\n### How AI memory works\n\nTypically, AI memory systems operate through a layered process that combines short-term contextual data with long-term storage databases to recall past information.\n\n#### Here’s how it works step-by-step:\n\n1. Data ingestion: Information from user interactions, documents, or databases is converted into a format the AI can store (e.g., embeddings).\n2. Storage: The information is stored in structured databases, vector stores, or knowledge systems.\n3. Retrieval: When a new interaction occurs, the AI searches its storage for relevant information using retrieval augmented generation (RAG), remembering details to add to the context of the current session.\n4. Context injection: Retrieved information is inserted into the prompt before the LLM generates its output, resulting in a more accurate, personalized output.\n5. Continuous update: As interactions continue, feedback loops store information about successes and failures, helping memory to evolve and refine customer profiles for accuracy.\n\n### Real-world example of AI memory\n\nImagine a customer returns to their favorite outdoor retailer’s website. Without AI memory, the homepage shows generic "recommended for you" products based on broad segment categories.\n\nBy contrast, at delight.ai, we use memory-powered reasoning to power retail AI concierges that deliver highly personalized shopping experiences. Referring to the customer’s purchase history and preferences, the homepage surfaces the latest items in their go-to product categories and colorways. After a few pages of casual browsing, the AI concierge pops up and suggests products complementary to recent purchases that are in stock and in season, which closes the sale.\n\nThis memory layer turns historical and real-time customer understanding into a faster, more satisfying shopping experience at scale.\n\n### Benefits of AI memory\n\nWhen properly integrated with AI ecosystems, AI memory offers a suite of benefits to organizations:\n\n- Improved revenue: Combining historical and real-time data unlocks significant improvements in personalization, improving conversion rates and customer loyalty.\n- Increased operations efficiency: Customers and employees no longer have to restate information across sessions or channels, reducing friction and frustration.\n- Enhanced customer service: Memory enables AI agents for customer service to more quickly and effectively resolve issues, improving resolution rates and handling time.\n- Greater trust & loyalty: Customers favor brands that highly tailor their experiences, making AI memory key to strengthening long-term relationships in CX-led industries.\n- Improved AI performance: Memory-powered AI systems make more accurate decisions, reducing inaccuracies (hallucinations) and policy errors. They also learn and improve more effectively over time.\n\n### Key takeaways\n\n- Governance required: Responsible AI governance is essential to ensure customer data privacy and secure storage.\n\n- [AI memory overview](#section-anchor-id-0 "AI memory overview")\n- [What is AI memory?](#section-anchor-id-1 "What is AI memory?")\n- [Why AI memory matters](#section-anchor-id-2 "Why AI memory matters")\n- [Use cases for AI memory](#section-anchor-id-3 "Use cases for AI memory")\n- [6 types of AI memory](#section-anchor-id-4 "6 types of AI memory")\n- [Short-term AI memory vs long-term memory](#section-anchor-id-5 "Short-term AI memory vs long-term memory")\n- [Here’s how it works step-by-step:](#section-anchor-id-6 "Here’s how it works step-by-step:")\n- [When properly integrated with AI ecosystems, AI memory offers a suite of benefits to organizations:](#section-anchor-id-7 "When properly integrated with AI ecosystems, AI memory offers a suite of benefits to organizations:")\n- [Key takeaways](#section-anchor-id-8 "Key takeaways")\n
## AI sentiment analysis
Source: https://delight.ai/glossary/ai-sentiment-analysis
- [Glossary](/glossary)
## AI sentiment analysis
March 6 2026
### What is AI sentiment analysis?
Sentiment analysis is a specialized application of artificial intelligence (AI) that uses natural language processing (NLP) and machine learning (ML) to categorize the emotional tone of a piece of text or voice content. This text analysis technique enables computers to go beyond recognizing keywords, enabling them to understand the context, nuance, and even sarcasm within text and voice data. A sentiment analysis model processes textual data—such as social media posts, product reviews, emails, or support conversations—and assigns a sentiment classification. This classification typically falls into three categories: positive, negative, or neutral. Advanced sentiment analysis tools perform sentiment classification by assigning a numerical value—the sentiment score—to a comment. By transforming unstructured text into measurable insight, AI sentiment analysis enables organizations to quantify customer sentiment at scale.
### Why AI sentiment analysis matters
In an era where customers share their opinions across thousands of social media platforms and online review sites, manually reading every comment is impossible. Sentiment analysis automates this process, turning massive amounts of textual data about customer interactions into a structured sentiment score. A subset of AI speech analytics, this enables customer service and marketing leaders to understand customer sentiment at scale, allowing them to respond to a sudden surge in negative sentiment or capitalize on a positive moment in real time. Using AI sentiment analysis tools allows an organization to:
- Detect negative sentiment before churn occurs
- Monitor brand perception across social media posts
- Track shifts in customer sentiment over time
- Prioritize high-risk customers and service cases
- Improve product development using accurate feedback
### Common use cases for AI sentiment analysis
In customer-facing industries, understanding emotional tone is often as important as understanding intent, enabling responsive interactions that defuse tensions and drive good outcomes. It’s most effective when applied to high-volume channels where human review cannot keep pace with the speed of conversation, acting as both an early-warning system and a strategic roadmap to guide decision-making.
- Customer service prioritization: Real-time sentiment scoring of support chats or calls enables intelligent routing and triage, identifying tickets with high negative sentiment and prioritizing escalation to prevent churn.
- Social media monitoring: By tracking brand perception across social media platforms in real time, brands can monitor campaign effectiveness, identify viral opportunities, and detect PR crises in advance.
- Product and experience optimization: By analyzing product and online reviews, companies can detect recurring complaints and feedback to direct feature improvement and prioritization.
- Voice of the employee (VoE): Companies use text analysis on internal, anonymous surveys to gauge the "emotional health" of their workforce, helping human resources (HR) to reduce burnout or friction.
- Market research: By analyzing sentiment around competitors’ product reviews or brand mentions, organizations can identify market gaps and craft messaging or experiences that create a competitive advantage.
### How AI sentiment analysis works
The process of turning raw text data into an actionable sentiment score involves several natural language processing (NLP) steps:
1. Text collection: Data is gathered from customer service chats, calls, social media posts, surveys, and product reviews.
2. Tokenization: Text is cleaned, tokenized, and structured for analysis (broken into individual words or phrases).
3. Stop word removal: Words that don't carry emotional weight (e.g., "the," "is") are filtered out.
4. Lemmatization: Words are reduced to their root form (e.g., "happier" and "happiest" become "happy").
5. Scoring: Words are evaluated against a database of emotional markers and linguistic patterns to calculate the final score.
6. Real-time or batch deployment: Results are surfaced to dashboards or integrated into operational workflows.
### Real-world examples of AI sentiment analysis
- Customer support: A company flags support tickets showing strong negative sentiment and routes them to senior agents automatically.
- Retail: A brand analyzes thousands of product reviews to detect recurring complaints about shipping delays.
- Social media monitoring: A company tracks brand mentions across social media platforms in real time to detect emerging PR risks.
- CX optimization: An AI agent adjusts its tone in real time during customer service interactions based on live sentiment scoring.
### Types of sentiment analysis
Not all sentiment analysis is created equal. Depending on the complexity of the textual data and business application, different models are used. This can include:
1. Standard (Binary) sentiment classification: The simplest form, labeling a piece of text as positive, negative, or neutral.
2. Fine-grained sentiment: Breaks down the tone into five categories (Very Positive, Positive, Neutral, Negative, Very Negative), similar to a 5-star rating system.
3. Emotion detection: Goes beyond polarity to identify specific emotions like anger, joy, frustration, or fear.
4. Aspect-based sentiment analysis (ABSA): The gold standard for product review analysis, it identifies which specific feature is being discussed.
- Example: "The battery life is amazing, but the screen is too dim." ABSA assigns a positive score to "Battery" and a negative score to "Display."
### AI sentiment analysis vs. traditional (rule-based) text analysis
#### Traditional text analysis
- Focuses on keyword frequency
- Limited contextual understanding
#### AI sentiment analysis
- Understands context and tone
- Generates dynamic sentiment scores
- Operates in real time
Example: A phrase like `not bad` might be flagged as negative by a traditional text analysis model, but would be correctly understood as positive by AI sentiment analysis.
### Benefits of AI sentiment analysis
By quantifying emotional tone in text or voice across various interactions, organizations gain deeper insight into customers, their experiences, and risk. Benefits include:
- Improved crisis prevention with real-time risk identification
- Reduced churn through proactive issue resolution
- Improved customer experience and retention
- Enhanced competitive intelligence and faster decision-making
- Unbiased, data-driven insights at scale
- Greater marketing ROI
- Better customer service and human agent performance
### Key takeaways
- Key component of smart CX systems: In AI-powered systems, real-time sentiment analysis enables AI agents to behave intelligently and proactively with customers, using live sentiment scores to dynamically adjust decision-making to produce positive outcomes at scale.
- [What is AI sentiment analysis?](#section-anchor-id-0 "What is AI sentiment analysis?")
## AI speech analytics
Source: https://delight.ai/glossary/ai-speech-analytics
- [Glossary](/glossary)
## AI speech analytics
_March 6 2026_
## Overview of AI speech analytics
- AI speech analytics is the use of artificial intelligence (AI) technologies to automatically extract the meaning, intent, and sentiment from spoken customer conversations.
- By analyzing voice interactions at scale, AI-powered speech analytics improve human agent performance in call centers, reduce churn, and drive data-driven decision-making.
### What is AI speech analytics?
AI speech analytics refers to the use of machine learning (ML), speech recognition, and natural language processing (NLP) to analyze spoken customer interactions in real time. These artificial intelligence technologies automatically convert spoken audio to text, then extract meaning, intent, and customer sentiment.
By evaluating tone, detecting patterns, and extracting insights from voice interactions, contact center leaders can accurately measure performance metrics such as handle time, compliance adherence, and customer sentiment across calls at scale.
### Why AI speech analytics matters
For decades, the millions of minutes of call center interactions were too numerous for humans to process, yet too complex for basic software to understand. AI-powered speech analytics changes this.
Going beyond basic speech recognition (which turns voice to text), AI speech analytics provides real-time speech analytics to support contact center agents and post-call analysis for long-term strategy and performance measurement.
This real-time understanding of why customers are calling and how they feel about their interactions—across 100% of calls—unlocks a new level of customer satisfaction at scale.
AI speech analytics matters because it allows organizations to:
- Identify trends in customer interactions
- Detect negative customer sentiment early
- Reduce churn through proactive customer outreach
- Improve human agent coaching and performance
- Lower average handle time
- Ensure compliance in regulated industries
### Common use cases for AI speech analytics
By analyzing all incoming calls in real-time, AI transforms the traditional call center from just a cost center into an engine rich with business intelligence. These insights allow contact center teams to move beyond reactive troubleshooting into proactive optimization.
Use cases include:
**Contact center performance optimization:** Instead of manual scoring, AI evaluates every call for agent handling time, friction points, script, and compliance adherence. These automated quality management processes improve training and identify top-performing agents.
**Real-time agent assistance:** During live calls, speech analytics detects customer intent and sentiment, then suggests the next best action by surfacing relevant resources or actions on the agent’s screen.
**Customer experience improvement:** By detecting dissatisfaction in real time, speech analytics can escalate high-risk conversations to avoid churn and evaluate drivers and obstacles to customer satisfaction.
**Churn & risk prevention:** Speech analytics can identify signals of cancellation intent, flag repeat complaints or policy violations around content, and trigger retention workflows to improve customer retention.
### How AI speech analytics works
AI speech analytics solutions generally follow these steps:
1. **Speech recognition:** Customer conversations are transcribed using automatic speech recognition (ASR).
2. **Natural Language Processing (NLP):** The transcript is analyzed for intent, keywords, and contextual meaning.
3. **Machine learning analysis:** Models detect patterns, predict outcomes (such as churn risk), and evaluate performance metrics.
4. **Sentiment & emotion detection:** Systems assess customer sentiment and tone throughout the conversation.
5. **Real-time or post-call insights:** Real-time speech analytics can provide live prompts, while post-call analytics inform dashboards and coaching.
### Real-world examples of AI speech analytics
**Call center optimization:** A telecom company uses AI speech analytics software to detect cancellation intent and automatically trigger retention offers.
**Compliance monitoring:** A financial services contact center flags calls that omit required disclosures.
**Agent performance improvement:** AI-powered speech analytics identifies common phrases used by top-performing agents and shares them across the team.
**Customer satisfaction tracking:** A retailer monitors customer sentiment across thousands of calls to identify product issues and drive improvements.
### Benefits of AI speech analytics
By providing 100% visibility for quality assurance teams, AI-powered speech analytics acts as a strategic intelligence layer within customer service operations.
This leads to benefits like:
- Improved operational performance, such as reduced average handle time
- Enhanced agent performance from better coaching and QA
- Reduced operational costs from automated agent training and optimization
- Reduced customer churn and higher customer satisfaction
- Greater regulatory safety with real-time compliance monitoring and visibility
### Key takeaways
- **Scalable insights from voice interactions:** AI speech analytics unlocks actionable insights from voice interactions at scale, leading to improved performance, reduced churn, and enhanced customer satisfaction.
- [Overview of AI speech analytics](#section-anchor-id-0 "Overview of AI speech analytics")
- [What is AI speech analytics?](#section-anchor-id-1 "What is AI speech analytics?")
- [Why AI speech analytics matters](#section-anchor-id-2 "Why AI speech analytics matters")
- [Common use cases for AI speech analytics](#section-anchor-id-3 "Common use cases for AI speech analytics")
- [How AI speech analytics works](#section-anchor-id-4 "How AI speech analytics works")
- [Real-world examples of AI speech analytics](#section-anchor-id-5 "Real-world examples of AI speech analytics")
- [Benefits of AI speech analytics](#section-anchor-id-6 "Benefits of AI speech analytics")
- [Key takeaways](#section-anchor-id-7 "Key takeaways")
## Computer vision
Source: https://delight.ai/glossary/computer-vision
- [Glossary](/glossary)
- Computer vision
## Computer vision
March 6 2026
### What is computer vision?
Computer vision is a type of artificial intelligence (AI) that enables machines to interpret, analyze, and understand visual information from the real world. By processing images and video, computer vision systems can identify patterns, recognize objects, and make decisions based on visual data. It allows software to "see" and understand the visual world, but with machine speed and scale.
Unlike basic cameras that simply record a scene, computer vision systems use computer vision algorithms to break down visual information into mathematical patterns. These systems transform raw pixel data into structured insights that can be used to automate tasks, support decisions, or trigger actions. For example, an AI system with computer vision could recognize a face, detect a defect on a manufacturing line, or read text from a document with high speed and accuracy.
### Why computer vision matters
Visual data is everywhere in the modern enterprise—from security footage to product photos. Computer vision matters because it unlocks this data, turning images and video into actionable intelligence and driving efficiency by automating various tasks.
Computer vision systems enable organizations to:
- Automate repetitive tasks: From replacing manual visual checks in quality control or inventory management.
- Enhance security: Using facial recognition and movement detection to secure physical locations.
- Extract data from documents: Using optical character recognition (OCR) to turn scanned papers into searchable, digital data with compliance checks.
- Improve safety: Monitoring environments in real-time to detect hazards or unauthorized personnel.
### Computer vision use cases
Computer vision is transforming industries by giving machines the ability to perform image processing at an enterprise scale.
- Inventory management: Retailers use cameras to automatically track stock levels on shelves and identify misplaced items.
- Medical imaging: Doctors leverage AI to assist in medical imaging, where algorithms help identify anomalies in X-rays or MRIs.
- Object detection for logistics: Automated warehouses use vision to identify, sort, and route packages based on size, shape, or labeling.
- Facial recognition: Securely verifying identities for digital access or physical entry points.
- Image segmentation for self-driving cars: Systems distinguish between the road, pedestrians, and other vehicles to navigate safely.
### How computer vision works
Computer vision works in a way similar to how a human learns to recognize shapes and colors, though it relies on a deep learning model to interpret an image and neural networks to process data. It works in five steps:
1. Image acquisition: The system receives visual input from a camera or a database of images or videos.
2. Image processing: The software cleans the image, adjusting contrast or removing noise to make features easier to identify.
3. Feature extraction: Using convolutional neural networks (CNNs), the system breaks the image down into pixels and identifies edges, textures, and colors.
4. Classification & reasoning: The neural network compares these features against millions of learned examples to determine what it is seeing (e.g., "This is a delivery truck").
5. Output & action: The system triggers a business process, such as updating a log, sending an alert, or opening a gate.
### Computer vision in action
At Delight.ai, we view computer vision as a critical sensory input for the next generation of AI agents. By integrating visual intelligence with agentic workflows, businesses can create systems that not only "talk" to customers but also "see" and understand the physical or digital documents that customers share.
For example, an AI agent equipped with optical character recognition (OCR) can instantly process a customer’s uploaded photo of a receipt, verify the purchase, and trigger a refund—all within a single, autonomous interaction.
### Key takeaways
- Computer vision is more than just "digital sight." It is a sophisticated layer of artificial intelligence that allows businesses to process visual information at a scale and speed impossible for humans.
- [What is computer vision?](#section-anchor-id-0 "What is computer vision?")
- [Why computer vision matters](#section-anchor-id-1 "Why computer vision matters")
- [Key takeaways](#section-anchor-id-2 "Key takeaways")
## Deep learning
Source: https://delight.ai/glossary/deep-learning
- [Glossary](/glossary)\n- Deep learning\n\n## Deep learning\n\nMarch 6 2026\n\n### What is deep learning?\n\nDeep learning is a subset of machine learning that trains artificial neural networks with multiple layers (or "deep" networks) to recognize complex patterns in data. Each layer learns increasingly abstract representations, allowing the system to model relationships that are difficult or impossible to define with rules.\n\nWhile traditional machine learning often requires humans to manually label and organize the features of data, deep learning systems can automatically learn from raw data—such as text, images, audio, or video. This enables it to make highly accurate predictions and representations, and improve performance as more data and computing power become available.\n\n### Why deep learning matters\n\nCapable of learning patterns without manual feature engineering, deep learning has become the foundation for many modern AI breakthroughs. By training deep neural networks, businesses can solve problems that were previously too complex for standard software. This allows enterprises to move beyond simple data analysis into the realm of true intelligent automation.\n\nDeep learning enables:\n\n- Processing unstructured data: Analyzing the 80% of enterprise data that exists in "messy" formats like emails, videos, and PDFs.\n- Higher accuracy: Achieving near-human (or superior) performance in tasks like speech recognition and object detection.\n- Continuous improvement: As more data is fed into the system, the model becomes more accurate over time.\n\nAs data and computational power continue to increase, deep learning remains a core driver of AI innovation.\n\n### Deep learning use cases & examples\n\nThe application of deep learning is found in almost every industry that relies on complex pattern recognition and data analysis:\n\n- Customer experience (CX): It powers natural language processing (NLP) to understand customer sentiment, context, and urgency—for interactions that feel human from start to finish.\n- Financial services: Uses neural networks to analyze time-series data for high-frequency trading and anomaly detection for risk management.\n- Media & logistics: Uses neural networks for computer vision tasks like auto-tagging product images or identifying damaged goods in a warehouse.\n- Global support and communication: Powers machine translation services that allow businesses to interact with customers in hundreds of languages at once.\n\n### How deep learning works\n\nDeep learning models learn through a process of trial and error, refined by complex mathematical layers. This process involves:\n\n1. Input layer: Data (like an image) is fed into the network.\n2. Hidden layers: These layers perform mathematical transformations to identify features. For example, in object detection, the first layer might find edges, the second shapes, and the third identifies a "car."\n3. Supervised learning: The model is trained on labeled data (e.g., "this is a car") to minimize errors.\n4. Unsupervised learning: The model finds hidden structures in unlabeled data, identifying patterns a human might never see.\n5. Output layer: The final layer provides the prediction or classification.\n\n### Key takeaways\n\n- The engine of modern AI: By mimicking the human brain through artificial neural networks, it allows businesses to unlock the value in their data. It also provides the intelligence necessary to power truly autonomous enterprise systems.\n\n- [What is deep learning?](#section-anchor-id-0 "What is deep learning?")\n- [Why deep learning matters](#section-anchor-id-1 "Why deep learning matters")\n- [Key takeaways](#section-anchor-id-2 "Key takeaways")
## Foundation model
Source: https://delight.ai/glossary/foundation-model
## Foundation model
March 6 2026
### What is a foundation model?
A foundation model is a large, general-purpose AI model that’s trained on massive, diverse datasets and then adapted to perform a wide range of tasks. Rather than being designed for a single use case (such as a small language model), foundation models serve as reusable bases that organizations can fine-tune or extend for many applications.
A foundation model acts as a central infrastructure upon which many different applications are built. Foundation models underpin many of today’s most advanced AI systems, including large language models, multimodal AI, and generative applications.
### Why foundation models matter
By separating general intelligence from task-specific logic, foundation models dramatically reduce development time and expand what AI systems can do. For executives, foundation models represent a "buy and build" opportunity rather than "building from scratch." They make advanced AI capabilities accessible without requiring every organization to train models from scratch.
### Foundation models enable organizations to:
- Accelerate AI development by reusing pretrained intelligence
- Support multiple use cases from a single model foundation
- Handle unstructured data like text, images, and audio
- Improve performance through large-scale training
- Enable rapid experimentation and iteration
### Foundation model examples & applications
Foundation models are the engines behind the most recognizable enterprise AI tools today. Versatile by design, a single model can handle natural language processing (NLP), computer vision, and data synthesis simultaneously.
Here’s a list of common types of foundation models and their applications:
- Large language models (LLMs): Trained on text to understand and generate language, models like GPT-4 or Claude are the base for conversational AI customer service agents and internal AI copilots.
- Vision foundation models: Trained on billions of images and videos for visual understanding, these allow for object detection and creative design automation.
- Multimodal models: Trained across text, images, audio, and more, these advanced systems can "see" a document, "read" its text, and "reason" through it.
- Code models: Optimized for programming and software development tasks.
- Industry-specific foundations: Models fine-tuned specifically for the healthcare, legal, or financial sectors to ensure high-stakes accuracy.
Each type of AI model can serve as the base for many specialized applications.
### Foundation models vs traditional AI models
- Traditional models are trained for a single task with a narrow scope.
- Foundation models are trained once and adapted for many tasks.
### How foundation models work
In production environments, foundation models rarely operate alone. They are embedded within systems that include:
- Decision-making logic
- Memory and context management
- External tools and APIs
- Human oversight and governance
This system-level integration transforms foundation models from standalone reasoning engines into components of a coordinated, goal-oriented AI system.
### The development of a foundation model happens in three phases:
1. Pre-training: The model is exposed to large-scale data (like the public web or massive image libraries). During this phase, it learns the fundamental patterns of human information.
2. Model architecture: Using a specialized model architecture (often called a transformer), the system learns to predict the next part of a sequence, whether that is a word in a sentence or a pixel in a photo.
3. Adaptation & fine-tuning: The business takes this "raw" foundation and feeds it a smaller, high-quality set of proprietary data to prepare it for downstream tasks, such as specific customer support workflows.
### Key takeaways
- Utilities of the digital age: Foundation models provide a reusable, general-purpose intelligence layer that powers many modern AI systems.
- Versatile: Built for adaptation across tasks and domains, foundation models are a key component of enterprise AI, enabling systems to be both powerful and flexible.
- [What is a foundation model?](#section-anchor-id-0 "What is a foundation model?")
- [Foundation models enable organizations to:](#section-anchor-id-1 "Foundation models enable organizations to:")
- [The development of a foundation model happens in three phases:](#section-anchor-id-2 "The development of a foundation model happens in three phases:")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## Natural language processing (NLP)
Source: https://delight.ai/glossary/nlp
- [Glossary](/glossary)
## Natural language processing (NLP)
March 6 2026
### What is natural language processing?
Natural language processing (NLP) is a field of artificial intelligence (AI) that enables computers to understand, interpret, and generate human language. Rather than treating language as mere keywords, it enables machines to process text and speech in ways that capture meaning, context, and intent. NLP lets a machine "read" or "listen" to textual input and produce an appropriate response, bridging the gap between human communication and machine understanding.
By combining machine learning (ML), linguistics, and statistical methods, NLP models turn unstructured text or speech into structured data that machines can understand and act upon, generating natural language outputs (text or voice). NLP is a core capability behind many AI-powered applications, including chatbots, search engines, translation tools, and conversational AI systems.
### Why NLP matters for businesses
Language is how humans communicate and express intent—but this information has long been unusable to machines in a meaningful way. With NLP, AI systems can understand and interact with people naturally at scale.
#### NLP matters to businesses because it enables:
- Automated multilingual customer service: NLP-driven systems can handle thousands of inquiries simultaneously with high accuracy in any language.
- Scalable conversational interfaces: Customers and employees engage with AI systems in natural language, streamlining workflows and support processes.
- Knowledge work automation: Automates language-heavy workflows like drafting emails, summarizing content, or translating documents.
- Operational efficiency: Improves search and information retrieval, extracts insights from unstructured text (like email), and classifies text to route documentation or support tickets.
- Personalized experiences: Enhances personalization by understanding customer intent and context, tailoring experiences without rigid rules.
- Unstructured data usage: Improves business intelligence by analyzing "dark data" in emails, social media, and support tickets.
### Common NLP use cases
#### Conversational AI and chatbots
Understands user intent and generates natural responses instantly, enabling 24/7 automated customer and employee interactions.
#### Customer feedback analysis
Uses sentiment analysis to extract actionable insights from reviews, feedback, and support interactions.
#### Search and knowledge management
Improves discovery and retrieval of relevant information for internal teams and business systems.
#### Document processing
Extracts and summarizes structured data from contracts, forms, and reports in seconds, reducing manual effort and errors.
#### Voice-based AI applications
Powers speech-driven interfaces for customers and employees, such as AI copilots, assistants, and concierges.
### How natural language processing works
To determine the structured meaning of text, NLP systems typically follow a multi-stage process:
1. Stop word removal: Deletes common words (like "and" or "the") that don't add significant meaning to a sentence.
2. Lemmatization: Reduces words to their base or root form (e.g., "running" becomes "run"), allowing recognition of core concepts regardless of tense.
3. Speech recognition: Converts spoken language into text for further analysis.
4. Sentiment analysis: Identifies whether the tone of a message is positive, negative, or neutral.
5. Named entity recognition (NER): Identifies specific people, places, organizations, or brands mentioned in text.
### Natural language processing in action
NLP is often powered by large language models (LLMs) and integrated with AI memory, tools, and decision logic. With NLP integration, systems can move beyond understanding language to acting on it in real time, enabling applications from AI concierges for customer service to employee AI copilots.
For example, at delight.ai we use LLMs to push NLP beyond basic keyword matching—creating a unified intelligence layer that powers AI support agents. By combining a single source of truth with speech recognition and advanced NLP, agents can understand customer intent in context and deliver accurate, relevant solutions. Whether a customer is typing an email or speaking to a voice agent, the system maintains a nuanced understanding of the conversation and can resolve complex issues like return processing autonomously.
### Key takeaways
- A core AI capability: NLP acts as a bridge between humans and computers, enabling AI to understand and generate human language and convert text-based data into actionable intelligence.
- [What is natural language processing?](#section-anchor-id-0 "What is natural language processing?")
- [Why NLP matters for businesses](#section-anchor-id-1 "Why NLP matters for businesses")
- [NLP matters to businesses because it enables:](#section-anchor-id-2 "NLP matters to businesses because it enables:")
- [Conversational AI and chatbots: Understands user intent and instantly generates natural responses 24/7.](#section-anchor-id-3 "Conversational AI and chatbots: Understands user intent and instantly generates natural responses 24/7.")
- [Document processing: Extracts and summarizes structured data from contracts, forms, and reports in seconds.](#section-anchor-id-4 "Document processing: Extracts and summarizes structured data from contracts, forms, and reports in seconds.")
## Reinforcement learning
Source: https://delight.ai/glossary/reinforcement-learning
- [Glossary](/glossary)
- Reinforcement learning
## Reinforcement learning
March 6 2026
### What is reinforcement learning?
Reinforcement learning (RL) is a branch of machine learning where an agent learns by interacting with an environment, receiving feedback in the form of rewards or penalties, and attempting to maximize its cumulative reward over time.
The core of reinforcement learning is the AI feedback loop. The agent observes the state of its environment, chooses an action, and receives feedback that guides future decisions.
Unlike supervised learning, where correct answers are provided, reinforcement learning requires the system to discover effective strategies through its own experience. This approach is commonly used in scenarios where decisions must be made sequentially, and outcomes depend on a series of actions. RL is core to many agentic AI applications that self-improve over time, such as autonomous agents tasked with optimizing retail supply chains or customer service outcomes.
### Why reinforcement learning matters
Many real-world problems involve dynamic environments, delayed outcomes, and tradeoffs between short-term and long-term gains. Reinforcement learning is designed to handle these conditions.
RL algorithms matter because they enable:
- Dynamic resource allocation: Constantly shifting budgets or cloud computing resources to where they provide the most value.
- Supply chain optimization: Making real-time adjustments to logistics and inventory based on shifting global conditions.
- Hyper-personalization: Moving beyond static profiles to understand exactly what a customer wants based on their real-time behavior.
- Complex process automation: Managing industrial systems or energy grids where a single decision has long-term consequences.
- Optimized decision-making over time: Reinforcement learning is especially valuable where optimal behavior cannot be predefined, but must be learned by observation and experience.
### Reinforcement learning use cases
Reinforcement learning is applied in domains where sequential decisions matter:
#### Autonomous system optimization
At delight.ai, we use RL—specifically Reinforcement Learning from Human Feedback (RLHF)—to ensure our autonomous AI agents for customer service behave in a way that’s reliably accurate, helpful, and risk-free.
#### Robotics and control systems
Teaching robots to navigate, manipulate objects, or balance.
#### Resource optimization
Managing energy grids, supply chains, or traffic flow.
#### Recommendation engines and personalization
Optimizing customer experience interactions and suggestions for long-term engagement and customer experience, rather than one-off interactions.
### Types of reinforcement learning
There are several ways to categorize how an agent learns its strategy. The two primary types of reinforcement learning include:
- Model-free: The agent learns solely through experience without trying to understand the "rules" of the environment first. It simply learns which actions lead to the best cumulative reward.
- Model-based: The agent creates a "map" or model of the environment to predict what will happen next, allowing it to plan its actions more strategically.
- Deep reinforcement learning: By using a deep neural network, RL can handle massive amounts of data, allowing the agent to function in highly complex, real-world environments.
### How reinforcement learning works
Reinforcement learning systems operate around a feedback loop, through which they learn a strategy for taking actions that optimize the long-term reward associated with a business objective.
1. Environment: The context or system the agent interacts with.
2. Agent: The decision-making entity that selects actions.
3. State: The current situation observed by the agent.
4. Action: A choice made by the agent that affects the environment.
5. Reward: Feedback that indicates how good or bad the action was.
### Training strategies
The process of training an RL system involves several advanced mathematical strategies:
- Monte Carlo methods: The agent learns from "completed episodes" (like finishing a full game) to see which path led to success.
- Gradient methods: Mathematical techniques used to "nudge" the agent's behavior in the direction that increases the reward.
- Exploration vs. exploitation: The agent must balance trying new things to find better rewards (exploration) with sticking to what it knows works (exploitation).
### Key takeaways
- Machine learning through action, not rules: Reinforcement learning plays a critical role in autonomous and decision-driven AI systems. It ingrains adaptive strategies that teach goal-oriented AI agents to learn and work toward long-term outcomes.
- [Robotics and control systems: Teaching robots to navigate, manipulate objects, or balance.](#section-anchor-id-0 "Robotics and control systems: Teaching robots to navigate, manipulate objects, or balance.")
- [Resource optimization: Managing energy grids, supply chains, or traffic flow.](#section-anchor-id-1 "Resource optimization: Managing energy grids, supply chains, or traffic flow.")
- [The process of training an RL system involves several advanced mathematical strategies:](#section-anchor-id-2 "The process of training an RL system involves several advanced mathematical strategies:")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## Robotic process automation (RPA)
Source: https://delight.ai/glossary/robotic-process-automation
## Robotic process automation (RPA)
March 6 2026
### What is robotic process automation (RPA)?
Robotic process automation (RPA) is a technology that uses software robots to automate repetitive, rule-based tasks by interacting with applications and user interfaces (UI) the same way a human would.
These digital bots follow predefined rules and workflows to perform actions such as data entry, form filling, and system updates—which they do by logging into applications, moving files, and filling in forms.
Unlike traditional programming that requires deep backend integration, RPA technology operates "on top" of software without changing systems. It performs the exact steps it is programmed to follow. Unlike AI systems, RPA does not reason or adapt. It’s designed to handle the "doing" of tasks rather than the "thinking."
Businesses use RPA to increase efficiency and reduce manual effort in simple, high-volume, and structured processes.
### Benefits of robotic process automation
Many business processes are repetitive, time-consuming, and prone to human error. Organizations use RPA as "digital muscle" that automates these tasks quickly without changing the underlying systems.
RPA offers many benefits to organizations, allowing them to:
- Support digital transformation: RPA is often an entry point into automation, enabling businesses to digitize cumbersome manual workflows without an infrastructure overhaul.
- Eliminate bottlenecks: Tasks that once took hours are completed by RPA software in seconds.
- Lower operational costs: Replacing manual labor with bots dramatically lowers the cost-per-transaction.
- Error elimination: Removing the risk of human fatigue and fallibility ensures that data entry and actions are consistently flawless.
- Employee engagement: By automating routine, boring tasks with RPA, you free employees to do more creative, strategic work that improves retention.
### Use cases for robotic process automation
Modern RPA solutions are well-suited for structured, predictable tasks, making them versatile across departments:
- Data entry and migration: RPA excels at moving information between systems or databases.
- Invoice and payment processing: Extracts data from forms and updates financial systems.
- Report generation: Collects data from multiple sources and compiles reports.
- Account and user provisioning: Creates or updates user records across systems.
- Compliance and auditing support: Ensures repeatable processes follow defined rules.
### Real-world examples of robotic process automation
- Customer service: Triages routine inquiries and updates customer records across multiple internal databases simultaneously.
- Financial services: Automates "Know Your Customer" (KYC) data collection, loan application processing, and account reconciliations.
- Human resources: Streamlines employee onboarding by automatically generating credentials and payroll setup.
- Supply chain: Automates invoice processing, order tracking, and inventory management alerts.
### How robotic process automation works
RPA systems follow deterministic workflows:
1. Process definition: Rules and steps are explicitly defined based on existing workflows.
2. Bot configuration: Software bots are configured to follow those steps through application interfaces.
3. Execution: Bots execute tasks exactly as programmed, interacting with systems like a human user would.
4. Monitoring and maintenance: Bots are monitored to ensure they continue functioning as systems or interfaces change.
Because RPA depends on fixed rules and interfaces, changes to applications often require bot updates.
### Types of robotic process automation
1. Attended RPA: The bot works alongside a human, triggered by specific actions (e.g., a customer service rep clicks a button to have the bot find a client's history).
2. Unattended RPA: The bot runs on a server in the background, completing high-volume batches of work (e.g., processing thousands of invoices overnight).
### RPA vs AI automation vs agentic AI
RPA is a single approach within a broader landscape of business automation, often serving as an execution layer within AI-driven systems. While each of these are automation technologies, they operate at different levels of complexity and solve different problems.
- RPA automates rule-based tasks with predefined steps.
- AI automation introduces machine learning and adaptability.
- AI agents execute multi-step workflows autonomously—even proactively.
Many organizations combine RPA with AI to extend automation beyond rigid workflows. AI agents may decide what needs to be done, while RPA bots handle how specific steps are performed in configured systems. This combined approach enables organizations to automate more complex workflows without replacing their existing infrastructure.
### Key takeaways
- A powerful tool for simple task automation: While limited to repetitive applications for its lack of adaptability, robotic process automation is a valuable component within a broader automation system.
- [What is robotic process automation (RPA)?](#section-anchor-id-0 "What is robotic process automation (RPA)?")
- [RPA offers many benefits to organizations, allowing them to:](#section-anchor-id-1 "RPA offers many benefits to organizations, allowing them to:")
- [Modern RPA solutions are well-suited for structured, predictable tasks, making them versatile across departments:](#section-anchor-id-2 "Modern RPA solutions are well-suited for structured, predictable tasks, making them versatile across departments:")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## Large language models (LLMs)
Source: https://delight.ai/glossary/llm
- [Glossary](/glossary)\n- Large language models (LLMs)\n\n## Large language models (LLMs)\n\nMarch 6 2026\n\n### What is a large language model (LLM)?\n\nLarge language models (LLMs) are a class of artificial intelligence (AI) models designed to understand, reason, and generate outputs using human language. Trained on vast amounts of text data, LLMs learn patterns in language to understand the relationship between words. By processing and predicting sequences of text-based data, this form of generative AI can produce coherent, context-aware outputs at scale.\n\nFor enterprise, LLMs provide a base intelligence layer that can be adapted to nearly any text-based AI use case. They power many AI applications, from chatbots to copilots, conversational AI to autonomous agents.\n\nWhy LLMs matter\n\nBy making language a primary interface for AI systems, LLMs have transformed how people interact with software. They enable more flexible and scalable interactions than rule-based systems. For example, LLMs let non-technical users interact with and program systems using natural-language prompts that specify the desired goal and context, rather than having to program every rule individually. LLMs matter because they enable organizations to:\n\n- Establish an intelligence layer: LLMs act as the reasoning engine for many AI applications, shifting these systems from “rule-based” to “goal-oriented.”\n- Scale conversational interaction: Customers and teams can interact with AI using natural language and instantly get back relevant outputs.\n- Automate text-based knowledge work: Summarize long reports, draft legal documents, or translate content instantly.\n- Improve code velocity: Assist developers by generating and checking code across multiple programming languages.\n- Analyze sentiment: Process customer feedback and real-time context to identify insights and improve customer experience at every touch.\n- Provide 24/7 multilingual support: Power chatbots, copilots, or AI concierges that hold nuanced, multi-turn conversations in any language around the clock.\n\n### How large language models work\n\nYou can think of LLMs as advanced autocomplete systems. They process vast amounts of data looking for patterns, then use probabilistic reasoning to predict the next character or token in a text (or voice) output based on what’s statistically most likely. LLMs are trained and operated through a multi-stage process:\n\n1. Data ingestion: The model is trained on petabytes of human language (books, websites, articles) to learn grammar, facts, and reasoning.\n2. The transformer architecture: Using transformer models, the AI assigns "weights" to different parts of a sentence to determine context.\n3. Pattern prediction: When you give an LLM a prompt, it predicts the most statistically likely next word, repeating the process to generate text.\n4. Fine-tuning: To make an LLM useful for specific tasks, businesses optimize it by fine-tuning. This involves training the general model on a smaller, proprietary dataset—such as a company’s support tickets or brand guidelines.\n\n### Large language models vs natural language processing (NLP)\n\n- Large language models (LLMs) are a specialized, advanced subset of Natural Language Processing (NLP). They represent a breakthrough in context-aware, generative AI and machine learning, providing a general-purpose foundation for language-based reasoning that’s adaptable across tasks.\n- Natural language processing (NLP) is a broader field covering all computational language analysis, including LLMs and sentiment analysis. NLP systems rely on task-specific models and rules.\n\n### LLMs in action\n\nIn production environments, LLMs rarely operate alone. They are combined with:\n\n- External tools and APIs for real-world actions\n- Memory systems to maintain context\n- Decision-making logic to guide outcomes\n- Human oversight to ensure trust and reliability\n\nThrough this system-level integration, LLMs can support autonomous agents, copilots, and complex agentic workflows. At delight.ai, for instance, LLMs are the "brain" of our AI concierges for customer experience. However, an LLM alone is not sufficient for enterprise-grade reliability, so we bound its reasoning to your specific business rules and data in your knowledge base.\n\nThis way, when your agent uses the LLM to understand and generate text for a customer interaction, the answers are grounded in your unique policies, brand guidelines, and business objectives.\n\n### Key takeaways\n\n- The “brain” of AI systems: LLMs enable AI systems to understand and generate human language at scale. They power conversational experiences, generative AI, and agentic systems—making language a central interface for modern AI.\n\n- [What is a large language model (LLM)?](#section-anchor-id-0 "What is a large language model (LLM)?")\n- [Large language models vs natural language processing (NLP)](#section-anchor-id-1 "Large language models vs natural language processing (NLP)")\n- [LLMs in action](#section-anchor-id-2 "LLMs in action")\n
## Small language models (SLMs)
Source: https://delight.ai/glossary/small-language-models
_March 6, 2026_
## What are small language models (SLMs)?
Small language models (SLMs) are a class of generative AI models designed to perform specific tasks with greater efficiency and fewer computational resources than large language models (LLMs).
Unlike their massive LLM counterparts, which aim for broad, general-purpose intelligence, SLMs are compact, typically using millions to billions of parameters rather than the trillions found in some LLMs.
SLMs require significantly less compute and memory, making them easier to deploy, maintain, and scale. Trained on smaller, more curated datasets than LLMs, they're used to power targeted AI applications that require narrow domain intelligence, such as vertical AI (specialized applications of AI for certain industries).
SLMs are optimized for speed, cost efficiency, and deployment in constrained environments. They’re used when real-time responsiveness, on-device execution, and domain-focused reasoning are more important than broad versatility.
## Benefits of small language models (SLMs)
Not all AI use cases require the power (or cost) of a large language model. In many production systems, smaller models can deliver better performance by training and focusing on a narrow task.
### SLMs matter because they solve many of the core barriers to enterprise AI adoption:
- Reduced computational power: They can run on standard hardware or edge devices without specialized, high-cost GPUs.
- Faster model training: Updating a small model with new data takes hours or days, rather than the weeks or months required for large models.
- Better accuracy: By focusing on specific tasks in niche areas, an SLM often outperforms a larger model that is "distracted" by irrelevant general knowledge.
- Lower energy consumption: Reduces the carbon footprint of AI infrastructure.
- Privacy & security: Lower resource requirements mean SLMs can be hosted locally or on private clouds, keeping sensitive data within the organization.
- Reduced latency: SLMs provide near-instant responses, critical for real-time interactions (for example, AI agents for customer service).
- Easier integration: RAG (Retrieval-Augmented Generation) allows models to access up-to-date internal company data, improving accuracy and reducing engineering lift.
- On-device/edge deployment: Able to run on user devices or network edges, ideal for high-frequency interactions where data privacy, speed, and control are paramount.
## Common use cases for small language models
SLMs excel at tasks similar to those of LLMs—translation, summarization, intent recognition—but tuned for a specific industry or business function.
SLMs are increasingly used for:
- Domain-specific support: Acting as subject-matter experts to automate customer or employee support in technical industries (law, healthcare).
- On-device AI assistants: Powering privacy-sensitive or offline language features on smartphones or laptops.
- Real-time translation and assistance: Providing instant, low-latency translation for global communication tools.
- Content filtering: Moderating content or ensuring brand safety before a larger model responds.
- Intent classification and routing: Quickly identifying user intent to route requests appropriately.
- Entity extraction: Pulling structured data from customer messages or documents.
- Command interpretation: Handling predefined commands in conversational or voice interfaces.
## Real-world examples of small language models
SLMs allow teams to match model capability to actual requirements, for example:
- Financial services: A fine-tuned SLM that understands SEC filings and banking regulations to assist analysts.
- Healthcare: A model trained on medical terminology to help clinicians summarize patient notes in real time.
- Retail: Customer service chatbots expert in a specific product catalog rather than the entire internet.
- Customer experience (CX): Domain-specific SLMs enable faster, more private, and cost-effective customer service agents and virtual assistants.
## SLMs vs. LLMs
While both are AI automation technologies, they serve different strategic purposes.
| Feature | Large language models (LLMs) | Small language models (SLMs) |
|---|---:|---:|
| Best for | General knowledge, creative writing | Domain-specific tasks, speed |
| Cost | High (per token/compute) | Cost effective |
| Resource requirements | Massive GPU clusters | Require less computational power |
| Data source | The entire internet | Highly curated, specialized data |
| Deployment | Cloud-only | Cloud, On-Premise, or Device |
## The delight.ai perspective
A hybrid approach is common: an LLM might handle complex reasoning, while SLMs act as the workhorses ensuring agents are fast, reliable, and grounded in your specific business data.
## Key takeaways
- Precision over power: SLMs show that bigger isn't always better; for specific tasks, a smaller, fine-tuned model can be superior in performance and computational efficiency.
- Next idea to consider: Explore distillation—the process of creating a student model from an existing model to reduce costs without sacrificing performance.
---
- [What are small language models (SLMs)?](#section-anchor-id-0 "What are small language models (SLMs)?")
- [SLMs matter because they solve many of the core barriers to enterprise AI adoption:](#section-anchor-id-1 "SLMs matter because they solve many of the core barriers to enterprise AI adoption:")
- [SLMs are increasingly used for:](#section-anchor-id-2 "SLMs are increasingly used for:")
- [SLMs vs. LLMs](#section-anchor-id-3 "SLMs vs. LLMs")
- [Key takeaways](#section-anchor-id-4 "Key takeaways")
## Machine learning (ML)
Source: https://delight.ai/glossary/machine-learning
- [Glossary](/glossary)
- Machine learning (ML)
## Machine learning (ML)
March 6 2026
### What is machine learning?
Machine learning (ML) is a branch of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed. Instead of following a rigid set of rules, machine learning models identify patterns in data and use them to make predictions or decisions.
At its core, machine learning is about turning raw data into actionable intelligence. Unlike traditional software, where behavior is defined by code, ML uses mathematical formulas to identify patterns in training data, understand these statistical relationships, and then generalize what it learned across new datasets or scenarios.
ML underpins many modern AI capabilities, from retail recommendation systems to computer vision to natural language processing (NLP).
### Why machine learning matters
Machine learning is the engine behind data analysis and predictive analytics at scale. It enables AI-powered systems to scale beyond manual rules and adapt to changing conditions. This allows organizations to extract value from large datasets in real time, anticipate outcomes, and automate complex decisions in ways traditional software can’t match.
Machine learning is a cornerstone of AI, and enables:
- Scalability: Processing millions of data points in seconds, far beyond human data analysis capabilities, powering smart applications at scale.
- Personalization: Tailoring products and messages to individual users based on their historical behavior.
- Operational efficiency: Automating complex real-world tasks and decisions, from inventory forecasting to lead scoring to customer support.
- Risk mitigation: Identifying anomalies in network traffic or financial records that suggest security threats.
- Adaptability: By generalizing from past experience, ML models can handle the variability of real-world environments, increasing opportunities for intelligent automation.
### Types of machine learning
How machine learning works depends on the type of data used in training and the desired outcome. Most enterprise applications fall into these four types of machine learning processes:
1. Supervised machine learning: The model is trained on "labeled" data (e.g., photos tagged as "cat" or "dog"). This is the most common form used for classification and regression.
2. Unsupervised machine learning: The model analyzes unlabeled data to find hidden patterns or groupings on its own. This is ideal for AI customer segmentation in marketing, for instance.
3. Semi-supervised learning: A middle ground that uses a small amount of labeled data to guide the analysis of a much larger pool of unlabeled data, balancing accuracy with cost.
4. Reinforcement learning: Models learn through trial and error using both internal feedback loops and human input.
### How machine learning works
Building a reliable, well-trained machine learning model follows a structured process:
1. Data collection: Gather the training data relevant to the business goal.
2. Algorithm selection: Choose the right machine learning algorithm for the use case (e.g., a decision tree or an artificial neural network).
3. Training: The system "studies" the data to find correlations and patterns that reflect the realities of the real world.
4. Testing: The model is challenged with real-world data it hasn't seen before to ensure accuracy.
5. Deployment & feedback: The model is put into production, where it continues to refine its logic based on new results.
### Key takeaways
- The foundation of modern automation: Machine learning transforms raw information into actionable insights or intelligent action. It represents a shift from "static" software to dynamic systems that grow with your business needs.
- [What is machine learning?](#section-anchor-id-0 "What is machine learning?")
- [Machine learning is a cornerstone of AI, and enables:](#section-anchor-id-1 "Machine learning is a cornerstone of AI, and enables:")
- [Types of machine learning](#section-anchor-id-2 "Types of machine learning")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## AI automation
Source: https://delight.ai/glossary/ai-automation
[Glossary](/glossary)
## AI automation
March 6 2026
### What is AI automation?
AI automation is the use of artificial intelligence (AI) technology—including machine learning, natural language processing, generative AI, and agentic AI—to execute repetitive tasks that previously required human effort.
Unlike traditional robotic process automation (RPA) that follows fixed rules, AI automation can handle unstructured data, interpret intent, and make decisions based on changing contexts. It enables organizations to work more efficiently, consistently, and at a greater scale by adapting to dynamic, real-time environments.
### Why does AI automation matter?
Manual processes and rule-based workflows struggle to keep up with the [complexity and scale of modern workflow](https://delight.ai/glossary/what-is-an-mcp-model-context-protocols). AI automation helps businesses do more with less. By streamlining operations, it significantly reduces time-consuming work and provides a scalable solution to the productivity plateau.
#### Benefits for businesses
- Reduce manual effort and operational costs
- Increase speed and consistency of execution
- Handle higher workloads without linear staffing increases
- Improve customer experience by reducing delays and friction
- Allow non-technical teams to automate complex workflows using natural language
- Operate customer support 24/7 using autonomous agents that never sleep
### AI automation examples
AI automation is embedded in many business systems because it can handle routine tasks faster, more accurately, and more cost-effectively than humans. Examples include:
- Automated frontline customer support: A customer emails about a refund. The AI agent identifies their intent, checks the return policy, verifies the item’s status in the warehouse, and issues the refund—all without any human involvement.
- Proactive customer engagement: An airline identifies a flight delay. The agentic AI system cross-references passenger manifests, rebooks travelers on the next available flight, and notifies them via SMS about the delay—turning a crisis into a seamless customer experience.
- Intelligent invoice processing: Instead of manual entry, AI processes various invoice formats, extracts key data, flags anomalies, and syncs it all with the accounting system.
- Dynamic lead scoring: In sales, AI automatically researches new leads, cross-references them with successful past customers, and prioritizes them for the sales team in real-time.
### How does AI automation work?
AI automation functions as a continuous loop of perception, reasoning, and action (PRA):
1. Input: The system "sees" data (an email, a sensor alert, or a spreadsheet).
2. Reasoning: The system uses AI algorithms to interpret the data. It asks: "What is the goal here, and what rules apply?"
3. Decision: Algorithms determine the appropriate next step based on goals, rules, and confidence.
4. Execution: The system uses APIs and integrations to take action across other software platforms.
5. Optimization: The system stores the outcome and uses AI memory to learn over time how to handle similar tasks more effectively.
### Benefits of AI automation
- Increased operational efficiency: AI automation handles routine tasks quickly and consistently, freeing employees from repetitive manual work.
- Non-linear scalability: You can increase your output (like handling 10x the support volume) without increasing your headcount.
- Enhanced accuracy: Unlike humans, AI doesn't get tired, distracted, or lack expertise, significantly reducing costly errors in data-heavy tasks.
- Faster response times: Tasks and decisions that used to take hours of manual review, such as data analysis or lead prospecting, now happen in milliseconds.
### AI automation use cases
- Customer experience (CX): Scaling omnichannel engagement by automatically triggering personalized messages or actions based on real-time customer behavior.
- Human resources: Automating the "top of the funnel" in recruiting—from screening resumes to scheduling interviews to onboarding.
- Supply chain: Automatically reordering stock when levels hit a certain threshold, factoring in predicted shipping delays and seasonal demand.
- Operations: Coordinating multi-step workflows across CRM, billing, and internal systems.
- Customer service and support: Automating ticket classification, routing, follow-ups, and even complex workflows like refund processing.
### Key takeaways
- From rigid rules to true agility: AI automation is about outcomes, not just instructions, enabling a business to adapt processes to real-time conditions.
- The omnichannel enterprise: Agentic AI unlocks automation across software and channels via a unified intelligence layer.
- Human-AI collaboration: The goal isn't to replace humans, but to elevate them to higher-value work by automating routine logic.
## AI and data
Source: https://delight.ai/glossary/ai-and-data
- [Glossary](/glossary)\n- AI and data\n\n## AI and data\n\n*March 6 2026*\n\n### What is the relationship between AI and data?\n\nArtificial intelligence (AI) is only as effective as the information it can process. While generative AI models have dazzled us with their ability to produce original text, the true value for a business lies in agentic AI. These autonomous AI systems (AI agents) can execute complex multi-step tasks and solve real-world problems by processing historical data and live context in real time.\n\nAgentic AI, then, requires a shift in how we view and govern data. We are moving from the era of big data (where the goal was simply to store as much as possible) to the era of AI-ready, actionable data. In this new landscape, AI agents use internal data to understand your customers, products, and unique business goals, then act accordingly. Without a robust foundation of data, an AI agent is like an expert without a library—highly capable in theory, but with nothing to work with.\n\n#### Why AI-ready data is the new competitive advantage\n\nIn the age of AI, the adage of "garbage in, garbage out" has never been more relevant. However, the definition of "quality data" has also expanded. For an AI system to perform reliably, data must be:\n\n- Up-to-date: Real-time data is essential for proactive engagement and effective decision-making.\n- Accessible: AI needs access to the right context on demand to ensure accurate outputs and avoid inaccuracies (hallucinations).\n- Structured for reasoning: Data must be well-structured so that an AI can identify patterns and relationships between different pieces of information.\n\nOrganizations that prioritize data preparation and data hygiene will see a direct correlation in AI performance. High-quality data enables more accurate forecasting, deeper personalization, and greater customer trust.\n\n### From static data to dynamic context: The role of AI memory\n\nTraditional databases are great for answering specific questions, like "What is your product return policy for Alaska?" But, to reason effectively through complex problems, AI agents need more than structured, historical data; they also need specific situational context.\n\n#### This involves two types of data:\n\n- Structured data: The organized information found in CRM systems, ERPs, and AI knowledge bases.\n- Unstructured data: The "hidden" knowledge found in emails, PDF manuals, and chat transcripts.\n\nTo make sense of this, businesses increasingly use vector databases. Think of these as a way for AI to "map" information based on meaning rather than just keywords. This creates a long-term memory for the AI agent, allowing it to remember a customer’s previous interactions, including their frustration on a call six months ago, and use that context to tailor a satisfying solution today.\n\n### Breaking silos: Unifying data for agentic workflows\n\nFragmented data is the biggest barrier to AI success in the enterprise. When your customer data lives in one tool, your inventory in another, and your billing in a third, the AI's "brain" is essentially split, compromising performance and AI safety.\n\nAgentic AI both requires and enables the creation of a unified intelligence layer that powers holistic decision-making. By connecting these disparate platforms through APIs and integrations, you allow an AI agent to see the "full picture."\n\nFor example, an AI agent for customer service can see that a customer’s package is delayed (logistics data), recognize they are a VIP member (CRM data), and automatically issue a proactive credit (financial data) before the customer even thinks to complain. This customer interaction generates new data, which the agent analyzes to determine success or failure, then stores this information in its long-term memory to guide continuous learning and improvement.\n\n### The AI-data feedback loop: The role of humans-in-the-loop\n\nThe relationship between AI and data is cyclical. Every output and subsequent action serves as a valuable training point. Even the most unified data layers can drift from their core model or produce errors. This makes continuous oversight from human evaluators essential to ensuring AI decisions remain accurate, safe, and aligned with business goals.\n\nBy combining reinforcement learning from human feedback (RLHF) with strong operational guardrails, organizations can provide real-time corrective input to their AI agents. In this model, the human evaluator does more than supervise; they teach the system. By correcting real-world interactions, teams can refine the AI’s future reasoning, feeding a high-fidelity data point into the AI feedback loop, a core engine of AI performance improvement.\n\n### The ethics of autonomy: AI data governance and safety\n\nAs AI gains more access to enterprise data, the stakes for security, privacy, and customer trust rise. To mitigate risk and preserve brand loyalty, leaders must ensure that their AI strategy includes rigorous data governance embedded into systems from the start.\n\n#### This involves:\n\n- Data sovereignty: It’s critical to ensure sensitive company data isn't used to train public models.\n- Data lineage: If AI makes a wrong decision, this must be understood and explained by tracking the provenance, or lineage, of the data back to its source, who updated it, and how it was transformed.\n- Compliance: AI systems must be designed to comply with evolving global regulations such as GDPR and CCPA.\n- Access control: Just as you wouldn't give every employee access to the payroll database, AI agents must have role-based access controls (RBAC) and permissions that are strictly defined, so they can only see what they need to make decisions.\n\nBuilding a trustworthy AI system starts with well-governed data. If customers and employees don't trust how their data is handled, they won't adopt or trust the technology, much to the detriment of business goals.\n\n### Case study: Data-driven AI in action\n\nImagine a global airline using an agentic AI system to provide AI customer service. On a typical day, the system monitors weather patterns and flight data to proactively mitigate disruption by rebooking passengers. This involves:\n\n- Detection: The AI detects a storm that will likely ground flights in Chicago by continuously monitoring external flight provider APIs and weather systems.\n- Contextual analysis: It instantly cross-references the passenger manifests with the rebooking system.\n- Autonomous action: It begins rebooking high-priority travelers on earlier flights, triggers personalized SMS notifications explaining the change, and then updates backend systems.\n\nThis isn't just automation; it’s proactive data-driven execution that turns a potential crisis into a seamless customer experience.\n\n### Key takeaways\n\nThe path to a successful AI transformation is built on a well-structured, robust data foundation.\n\n- Data is AI infrastructure: Treat your data stack as the foundation for all AI initiatives, not an afterthought.\n- Unite to enable: Break down silos to give your AI agents the full context they need to act autonomously.\n- Governance is non-negotiable: Secure, compliant data practices are the only way to build long-term trust in AI.\n- Focus on outcomes: The goal of AI and data isn't just better insights; it’s better execution.\n\n- [What is the relationship between AI and data?](#section-anchor-id-0 "What is the relationship between AI and data?")\n- [From static data to dynamic context: The role of AI memory](#section-anchor-id-1 "From static data to dynamic context: The role of AI memory")\n- [This involves two types of data:](#section-anchor-id-2 "This involves two types of data:")\n- [The ethics of autonomy: AI data governance and safety](#section-anchor-id-3 "The ethics of autonomy: AI data governance and safety")\n- [This involves:](#section-anchor-id-4 "This involves:")\n- [Key takeaways](#section-anchor-id-5 "Key takeaways")
## AI challenges
Source: https://delight.ai/glossary/ai-challenges
- [Glossary](/glossary)\n- AI challenges\n\n## AI challenges\n\nMarch 6 2026\n\n### What are AI challenges?\n\nAI challenges refer to the technical, operational, and cultural barriers that prevent the safe and effective deployment of artificial intelligence (AI). These pitfalls arise from various factors, ranging from poor data quality to biased model outputs to hallucinations (where AI confidently asserts false information as true).\n\n### Why AI challenges matter\n\nIncreasingly, AI systems directly influence business operations and customer experience. Ignoring the hurdles of AI implementation is a gamble, as one hallucinated output or overlooked risk can trigger a chain reaction that damages customer trust, brand loyalty, and revenue. For example, in 2014, Amazon developed an AI system for hiring. While intended to streamline the recruitment process, the system was found to have produced discriminatory results against women by replicating biases in its training data. Not only did the project fail, but it simultaneously created a PR crisis.\n\nAddressing the myriad AI challenges is a prerequisite to sustainable AI adoption.\n\n### Common AI challenges and risks\n\nAI challenges are numerous and varied, including:\n\n- AI bias: AI systems can inherit and reproduce societal prejudices within their training data, leading to unfair outcomes and PR fallout.\n- AI hallucinations: Large language models can confidently deliver incorrect outputs, which is risky in regulated industries, high-stakes decision-making, or customer-facing AI.\n- Transparency issues: Known as the "black box" problem, many AI models are so complex that their decision-making processes are not interpretable by humans, making it hard to identify errors pre-production.\n- Data quality: Establishing a clean, well-structured repository of high-quality, non-biased data for AI training and operations is a core challenge across industries.\n- Model drift: Over time, models “drift” from the patterns in their static training data, requiring monitoring and retraining to maintain accuracy.\n- Security issues: AI enables a new class of cyberattacks, from deepfakes to automated phishing, posing novel threats to privacy and security.\n- Ethical and legal concerns: Intellectual property disputes arise when AI is trained on copyrighted material.\n- Environmental impact: Immense computing power is required to train and run AI models, leading to significant carbon footprints and energy costs.\n- Organizational readiness: AI implementations are complex, requiring skilled professionals, clear business cases, and AI readiness to avoid undue costs and risk.\n- Societal impact: AI automation raises concerns about widespread unemployment and economic inequality, which may damage brand perception.\n\n### How AI challenges arise\n\nUnlike traditional software that produces the same output every time, AI models are probabilistic in their reasoning. They generate outputs based on patterns learned from their training data, not from a fixed source of truth. This data-driven adaptability is what makes AI so powerful; however, it also creates the potential for unreliable outputs and makes edge cases almost impossible to predict. In addition, AI challenges can emerge from one of four stages:\n\n1. The design stage: If you’re building your own AI model, the deep learning architecture of AI models can be tricky.\n2. The data stage: Inaccurate, biased, unclean, or even suboptimal data can lead to flawed AI outputs.\n3. The training stage: If deep learning processes are not optimal or supervised, the model may “overfit” to data, meaning it works well in experimentation but fails in messy, real-world scenarios.\n4. The deployment stage: Without AI governance, there is no "human-in-the-loop" to catch errors before they reach customers, impact brand perception, or result in non-compliance.\n\n### How to address AI challenges effectively\n\nManaging AI challenges requires a systematic approach to oversight and infrastructure that, if done well, spans every stage of the AI lifecycle.\n\n1. AI governance: Establishing policies, accountability structures, guardrails, and review processes that enable visibility, control, and transparency across departments. AI governance frameworks can help.\n2. Continuous monitoring & observability: Tracking AI performance, testing for bias and error, and flagging anomalies in real time for immediate fixes.\n3. Human oversight: Keeping humans in the loop for high-risk decisions, error handling, and optimizations via deep AI integrations with everyday systems.\n4. Continuous model evaluation: Retraining and updating AI models as data evolves and AI learns from real-world experiences.\n5. Risk-based deployment: Phasing AI adoption based on complexity and impact lets organizations start small and move slowly at first to mitigate risk.\n\nAddressing AI challenges is not a one-time technical fix, but an ongoing operational commitment that begins with AI readiness.\n\n### Real-world examples of AI challenges\n\n- Customer experience: A generative AI chatbot provides inconsistent answers due to knowledge gaps, insufficient guardrails, and a lack of governance.\n- Financial services: A credit scoring model reflects historical bias embedded in training data.\n- Healthcare: A deep learning model trained on limited patient demographics produces uneven diagnostic performance.\n\n### Other benefits include:\n\nOrganizations that proactively manage these AI challenges can scale adoption with confidence. According to IBM research, governance-aligned AI teams can launch, scale, and iterate on their AI use cases 30–50% faster than those without robust governance.\n\nOther benefits include:\n\n- Higher trust in AI systems\n- Reduced compliance risk\n- Reduced technical debt\n- Greater operational stability\n- Long-term AI adoption and ROI\n- Stronger AI governance frameworks\n\n### Key takeaways\n\n- Address AI challenges early and often: To get sustainable value from AI, organizations must contend with these obstacles from the outset and maintain their focus after deployment in a way that ensures safe, scalable operations.\n\n- [What are AI challenges?](#section-anchor-id-0 "What are AI challenges?")\n- [Other benefits include:](#section-anchor-id-1 "Other benefits include:")\n- [Key takeaways](#section-anchor-id-2 "Key takeaways")\n
## AI chatbot
Source: https://delight.ai/glossary/ai-chatbot
## AI chatbot
March 6, 2026
### What is an AI chatbot?
An AI chatbot is a computer program designed to simulate human conversation and assist users through text or voice interactions using artificial intelligence (AI), such as natural language processing (NLP) and machine learning. Unlike traditional "rule-based" chatbots, AI-powered chatbots understand the user’s intent, context, and sentiment. This enables them to immediately provide personalized, real-time responses 24/7 (or escalate to a human agent if needed) to any number of users.
### AI chatbots matter because they offer:
In a world where 83% of customers expect immediate assistance when they contact support, AI chatbots have become a core piece of a competitive customer experience (CX). They’re also increasingly embedded in employee workflows, serving as helpful assistants across a range of business functions, driving efficiency, reducing burnout, and improving accuracy.
AI chatbots matter because they offer:
- Immediate 24/7 multilingual support: Chatbots are available 24/7, ensuring no queries are missed, even outside of business hours.
- Reduced operational costs: Handling high volumes of routine inquiries simultaneously reduces the need for large, 24/7 support teams and improves deflection rates.
- Enhanced customer experience: Delivering immediate, consistent responses that reduce wait times and improve customer satisfaction.
- Efficient scalability: Enabling organizations to scale support operations during high-volume periods without linear cost increases.
- Knowledge management: Synthesizing and summarizing internal documentation to answer HR or IT questions, reducing "digital toil" to boost employee retention.
- Data-driven insights: Chatbots are uniquely positioned to collect customer feedback and user behavior, and sentiment data for decision-making.
### AI chatbot use cases
AI chatbots are one of the most widely adopted forms of AI. They are commonly used for:
- Customer support: Handling a majority of high-volume, repetitive customer questions—such as order status, password resets, or policy lookups—to deliver instant, consistent support while freeing human employees for higher-value work.
- Lead qualification: Engaging and qualifying leads by asking qualifying questions, capturing contact details, and routing high-intent leads to sales teams—improving response times and conversion rates.
- Employee support: Acting as a virtual assistant to help users with tasks like booking appointments, submitting requests, or providing answers and resources for common HR or IT questions.
- Multimodal applications: Advanced multimodal AI chatbots can process, understand, and generate information across formats (text, image, audio, video, code), enabling use cases from coding assistants to sales coaching and video content generation.
### Traditional chatbots vs generative AI chatbots vs agentic AI chatbots
Various types of AI chatbots exist, with the earliest basic FAQ bots appearing in the 1990s. ChatGPT (2022) ushered in a new generation of generative AI chatbots for consumer-facing use. The latest evolution is agentic AI chatbots (conversational AI agents or AI concierges), which combine reasoning, long-term memory, and tool use to autonomously execute multi-step workflows.
- Traditional chatbots (1990s–2020): Follow scripts and respond only within predefined paths. No memory or personalization capabilities.
- Generative AI chatbots (2020–2023): Produce tailored responses when prompted (reactive). No persistent memory outside the session's context window.
- Agentic AI chatbots (2023–present): Combine reasoning, long-term memory, and tool use to autonomously and proactively execute real-world tasks—and remember returning customers.
#### Comparative features
| Feature | Traditional chatbots | Generative AI chatbots | Agentic AI / AI agents |
|---|---:|---:|---:|
| Core model | Rule-based scripts, decision trees | Large language models (LLMs) | LLMs + tool use + orchestration layer |
| Primary behavior | Reactive | Reactive text generation | Autonomous, goal-oriented execution |
| Conversation style | Scripted, menu-driven | Natural language responses | Context-aware, memory-driven interactions |
| Ability to reason | None | Limited within prompt scope | Multi-step reasoning across systems |
| Action execution | Rare or manual handoff | Typically informational only | Can perform read/write actions across systems |
| System integration | Minimal | Limited API calls | Deep integration with CRM, payments, logistics, etc. |
| Memory | No cross-session memory | Session-based context | Persistent, cross-channel memory (remembers customers) |
| Proactivity | None | Limited (responds to input) | Can initiate actions based on goals or signals |
| Example use case | FAQ routing | Drafting responses | Rebooking flights, issuing refunds, proactive retention |
| Operational impact | Reduces simple ticket volume | Improves conversational quality | Resolves issues end-to-end |
| Security | Basic server-level controls | PII masking, access controls, LLM safeguards (varies) | Enterprise-grade governance, PII protection, audit logs, RBAC, monitoring |
| Setup time | Months of manual workflow mapping | Days using document ingestion and prompt configuration | Weeks for integrations, guardrails, workflow orchestration |
### AI chatbots vs. virtual assistants
AI virtual assistants are often consumer-oriented (e.g., Apple’s Siri or Amazon’s Alexa) but also appear in business contexts to help employees with content summarization, data analysis, and accuracy checks within software interfaces.
By contrast, AI chatbots are typically deployed by businesses as scalable solutions for AI customer service or customer experience.
### Real-world examples of AI chatbots
- The banking assistant: Bank of America’s Erica handles over 2 million interactions daily, helping users check balances, track expenses, and detect fraudulent charges via voice or text.
- The travel concierge: Norse Airlines’s AI concierge (Freya) assists travelers with flight bookings, provides real-time gate change alerts, and offers packing advice based on destination weather.
- The retail expert: H&M’s virtual assistant analyzes a user's browsing history and style preferences to provide personalized outfit recommendations and check real-time stock availability.
### How does an AI chatbot work?
AI chatbots work through a cycle of listening, reasoning, and acting:
1. Listen: Using natural language understanding (NLU), the bot parses user input to identify intent and request specifics.
2. Reason: Using a large language model (LLM) as its "brain," the bot queries its knowledge base or connected systems (like a CRM) to find the most accurate information.
3. Act: It delivers a natural-sounding response or triggers an action, such as rebooking a flight or updating a password (within predefined guardrails).
4. Learn: The bot analyzes successes and failures using machine learning, then stores key facts in long-term memory to continuously improve accuracy, relevance, and personality over time.
Note: Advanced agentic automation without oversight is a liability. Customer-facing chatbots can make errors or hallucinate (confidently present false information as true) and erode trust. To mitigate these risks, embed validation, testing, and human-in-the-loop (HITL) oversight across the chatbot lifecycle as part of robust AI governance.
### Benefits of AI chatbots
When effectively integrated with existing systems, AI chatbots offer a suite of benefits for businesses:
- Improved customer experience (CX): Faster, more consistent, and deeply personalized interactions can improve customer satisfaction scores (CSAT).
- Increased productivity: By filtering out repetitive level-1 tickets, chatbots free up human agents to focus on high-stakes, empathetic problem-solving.
- Actionable business insights: Every chat is a data point, revealing common pain points and trends through real-time feedback on product or service issues.
- Seamless omnichannel support: One omnichannel AI concierge can operate across website, mobile app, Slack, and WhatsApp, providing a unified branded voice wherever customers are.
- Increased conversion rates: Proactively engaging users across channels, qualifying leads, and shortening the sales cycle.
### Key takeaways
- From reactive to proactive: Modern AI agents and AI concierges for customer service don't just answer questions; they anticipate needs and act on behalf of users to resolve issues.
- Data integration is key: A chatbot is only as powerful as the systems it can talk to. Deep integration with your CRM and AI knowledge base turns a "chatter" into a "doer."
- User privacy and security: Chatbots access sensitive customer data. To minimize data breaches, unauthorized access, or privacy violations, prioritize data governance and security from the start.
- Evolving technology, evolving metrics: Measuring autonomous chatbot performance requires shifting from simple accuracy metrics to goal-achievement metrics (e.g., task success rate, tool use).
- [What is an AI chatbot?](#section-anchor-id-0 "What is an AI chatbot?")
- [AI chatbots matter because they offer:](#section-anchor-id-1 "AI chatbots matter because they offer:")
- [AI chatbot use cases](#section-anchor-id-2 "AI chatbot use cases")
- [AI chatbots are one of the most widely adopted forms of AI. They are commonly used for:](#section-anchor-id-3 "AI chatbots are one of the most widely adopted forms of AI. They are commonly used for:")
- [Traditional chatbots vs generative AI chatbots vs agentic AI chatbots](#section-anchor-id-4 "Traditional chatbots vs generative AI chatbots vs agentic AI chatbots")
- [AI chatbots vs. virtual assistants](#section-anchor-id-5 "AI chatbots vs. virtual assistants")
- [Real-world examples of AI chatbots](#section-anchor-id-6 "Real-world examples of AI chatbots")
- [AI chatbots work through a cycle of listening, reasoning, and acting:](#section-anchor-id-7 "AI chatbots work through a cycle of listening, reasoning, and acting:")
- [When effectively integrated with existing systems, AI chatbots offer a suite of benefits or businesses:](#section-anchor-id-8 "When effectively integrated with existing systems, AI chatbots offer a suite of benefits or businesses:")
- [Key takeaways](#section-anchor-id-9 "Key takeaways")
## AI copilot
Source: https://delight.ai/glossary/ai-copilot
- [Glossary](/glossary)
- AI copilot
## AI copilot
March 6 2026
### What is an AI copilot?
An AI copilot is a conversational assistant powered by artificial intelligence (AI) that works alongside humans to enhance their productivity, creativity, and decision-making. Powered by large language models (LLMs) and natural language processing (NLP), this personalized application of generative AI technology is designed to act as a collaborative partner. By automating and streamlining tasks such as data analysis and summarization, content creation, and coding, an AI copilot boosts employee productivity, accuracy, and creativity.
### Why AI copilots matter
As modern work becomes more complex, cognitive load is a major barrier to employee effectiveness and satisfaction. The AI copilot "sees" what you are doing and provides real-time assistance—such as drafting a reply email, flagging a typo, or visualizing data—from right within your everyday work applications and interfaces. AI copilots matter because they:
- Reduce drudge work: Whether it’s handling the first draft or initial research, copilots let humans focus on higher-value, strategic tasks.
- Close the skill gap: Copilots allow non-experts to perform complex tasks using only natural language commands, such as writing SQL queries or creating advanced Excel macros.
- Improve output quality: They ensure outputs (like responses for customer support edge cases or technical specs) follow company guidelines and best practices.
### Key features and capabilities of AI copilots
- Integrated workflow: Copilots are increasingly embedded directly into apps and interfaces, allowing them to analyze data, summarize content, and create outputs right in daily workflows.
- Generative AI capabilities: They use LLMs and NLP to synthesize and then generate original text, images, and summaries; multimodal AI copilots can produce audio and video outputs.
- Web & work context: To produce relevant, context-aware solutions, an agentic AI copilot can access both external data via APIs and internal data, whether in personal emails, chats, or general company documentation and knowledge bases.
- Data security: Copilot may access sensitive data, and should be designed with safeguards for data privacy, security, and governance to ensure responsible, scalable AI use.
- AI governance: Due to the probabilistic nature of generative AI, copilots can occasionally produce inaccurate information (hallucinations) and require human oversight.
### Use cases for AI copilots
AI copilots range from general-purpose assistants to specialized, role-based tools. They are increasingly included in the software ecosystems of Microsoft, GitHub, and delight.ai, and are available on a subscription model or as free versions.
Here are some of the most common use cases across departments and industries:
- IT service management (ITSM): AI-driven service desks such as Moveworks use natural language processing (NLP) to resolve IT issues.
- Coding assistance: Copilots from GitHub and Replit AI suggest code snippets, debugging fixes, and generally help developers to build, test, and deploy faster.
- Customer service & sales: These copilots range from helping human support teams work better (e.g., delight.ai) to providing AI-powered conversational commerce to shoppers (e.g., Resolve AI).
- Operations & HR: AI copilots from Leena AI or Workativ answer policy questions and automate employee self-service, often embedded in Slack or HR applications.
- Employee productivity: These copilots summarize documents, manage meetings, manage documents, analyze trends, and generally help employees across daily tasks.
- Retail/ecommerce: AI agent copilots manage inventory, personalize product recommendations, and automate in-store assistance while also helping staff provide accurate service.
- Healthcare: AI agents help teams with patient data management, follow-up scheduling, or even aiding in diagnosis or medical image analysis.
### AI copilot examples
- GitHub Copilot: A new standard AI tool for developers that suggests entire code blocks during workflows, helping engineers build software in a fraction of the time.
- Microsoft 365 Copilot: Integrated into the Microsoft Office suite, it can summarize a missed meeting, draft a project proposal based on notes, or generate data visualizations.
- Salesforce Einstein Copilot: Summarizes prospect account history, drafts personalized outreach emails, and recommends the optimal next action for a specific lead to sales reps.
- Delight.ai Desk Copilot: Our Desk AI Copilot helps human support agents move faster, solve more, and keep customers happy by surfacing the right answers and first-drafting replies for faster support at scale.
### How does an AI copilot work?
An AI copilot serves as a personalized, context-aware AI work partner. It understands natural-language prompts, learns and adapts to your habits, and generates real-time suggestions, all while keeping you in control.
Here’s what powers it:
- Natural language processing (NLP): Copilots interpret everyday language commands, allowing you to ask questions or give instructions as if speaking to a human.
- Large language models (LLMs): Powered by advanced AI models, they analyze patterns across vast datasets to generate text, code, summaries, formulas, or other outputs relevant to your task.
- Contextual awareness: It monitors your working environment—including your active window, document, email thread, or cursor position—to "see" what you’re doing and tailor assistance accordingly.
- Automated prompt construction: Behind the scenes, it combines your current context with relevant data to generate a specific background prompt, ensuring precise, task-aware outputs.
- Real-time suggestions: The copilot produces drafts or recommended actions instantly, often appearing as "ghost text" that you can accept, edit, or ignore.
- Integration & action: Embedded directly into tools like Slack, browsers, or productivity apps, copilots summarize meetings, draft responses, and execute various basic tasks on your behalf.
- Human-in-the-loop control: Unlike autonomous AI agent systems, copilots typically require user approval before taking meaningful action, ensuring oversight and maintaining trust.
- Continuous learning: Using machine learning and feedback loops, the copilot adapts to your preferences, tone, and habits over time, becoming more accurate and helpful.
### Benefits of AI copilots
When properly integrated into existing systems, AI copilots offer a suite of benefits to businesses:
- Increased efficiency and productivity: Employees can complete routine, creative, and analytical tasks in a fraction of the time.
- Workflow automation: Copilots automate repetitive, time-consuming tasks without requiring coding, including data entry and meeting scheduling.
- Higher quality work: Acting as a second pair of eyes, copilots catch errors and offer alternative perspectives that humans might miss.
- Faster onboarding: Copilots can guide new hires around complex internal systems and documentation, helping employees get up to speed faster.
- Enhanced creativity: By providing a "first draft" or assisting with ideation, copilots help overcome creative hurdles and improve output quality.
- Data analysis on command: Copilots can identify trends, create visualizations, and summarize text, analyzing various data to support real-time decision-making.
- Reduced burnout: By handling rote tasks that add to employee mental load and task switching, copilots support employee wellbeing and retention.
- Better collaboration: Copilots can summarize long chat threads, meeting notes, or email chains to facilitate teamwork.
- Seamless integration and security: Embedded directly into everyday systems and tools, AI copilots access and use internal data securely.
### Key takeaways
- Context is king: The value of an AI copilot is directly tied to how well it can "see" your enterprise data (CRM, files, emails) and assist accordingly in real time.
- User empowerment: Copilots don't just do routine work; they also teach the user how to do the work better.
- [What is an AI copilot?](#section-anchor-id-0 "What is an AI copilot?")
- [Here are some of the most common use cases across departments and industries:](#section-anchor-id-1 "Here are some of the most common use cases across departments and industries:")
- [Key takeaways](#section-anchor-id-2 "Key takeaways")
## AI decision-making
Source: https://delight.ai/glossary/ai-decision-making
- [Glossary](/glossary)
## AI decision-making
March 6 2026
### What is AI decision-making?
AI decision-making involves using machine learning algorithms to process data and determine the "next best action." While traditional software follows rigid "if-then" rules set by humans, AI decision-making is probabilistic. It weighs hundreds of variables simultaneously to calculate the outcome with the highest likelihood of success. In a business setting, this can extend to:
- Decision support: Where the AI provides a recommendation for a human to approve, such as with automated lead prioritization.
- Decision automation: Where the AI autonomously executes the choice, such as adjusting real-time pricing or triggering a hyper-personalized experience for customers.
In short, AI decision-making leads to faster, more informed, and more consistent business decisions and outcomes at scale.
### Why AI decision-making matters
In fast-moving markets, the "speed to decision" is a critical competitive advantage. AI decision-making matters because it helps decision makers to:
- Process unmanageable volume & complexity: While humans cannot analyze 10,000 global supply chain variables in real time, AI can.
- Eliminate decision fatigue: AI maintains the same level of analytical rigor at 4:00 PM as it does at 8:00 AM.
- Improve customer experience: In a world where customers abandon brands that don’t meet the latest standard in fast, satisfying service, AI better supports and retains customers than human efforts alone.
### AI decision-making examples
- Automated customer support: AI system evaluates customer sentiment, issue type, and interaction history to decide which tickets can be resolved autonomously and which require immediate human attention.
- Financial services: Algorithms perform real-time credit underwriting, deciding whether to approve a loan by analyzing thousands of data points beyond just a credit score.
- Retail & ecommerce: Dynamic pricing engines decide when to lower or raise prices based on competitor stock, local weather, and real-time demand.
- Healthcare: AI diagnostic tools assist doctors by analyzing medical imagery to decide which scans require urgent human review.
- Logistics: AI-powered systems decide the most efficient route for thousands of delivery vehicles every second, adjusting for real-time traffic and road conditions.
### How does AI decision-making work?
1. Data ingestion: The system pulls real-time data from internal systems (CRM, ERP) and external signals (session context, market trends).
2. Pattern recognition: AI algorithms identify correlations that lead to successful outcomes.
3. Simulation & evaluation: The AI "simulates" various choices and predicts the outcome of each against defined business goals.
4. Action/recommendation: The system either presents the best option to a human leader or executes the task autonomously within governed guardrails.
### Benefits of AI decision-making
- Operational velocity: AI leads to a dramatic reduction in the time it takes to move from "data signal" to "business action."
- Increased consistency: Ensures that every customer or process is treated with the same logical rigor, improving compliance and business outcomes.
- Higher accuracy: By factoring in "dark data" (unstructured information humans often overlook), AI finds more profitable or efficient paths—while supporting human judgment.
- Strategic agility: Frees teams from micromanaging routine choices so they can focus on high-value, strategic activity.
### Key takeaways
- Augmentation, not just replacement: AI is most powerful when it acts as a force multiplier for human judgment.
- Governance is the safety net: Leaders must set the risk thresholds and ethical rules that the AI must follow.
- The power of feedback: AI systems get smarter by "closing the loop"—learning from the results of previous decisions to refine future ones.
- [What is AI decision-making?](#section-anchor-id-0 "What is AI decision-making?")
- [AI decision-making examples](#section-anchor-id-1 "AI decision-making examples")
- [How does AI decision-making work?](#section-anchor-id-2 "How does AI decision-making work?")
- [Benefits of AI decision-making](#section-anchor-id-3 "Benefits of AI decision-making")
- [Key takeaways](#section-anchor-id-4 "Key takeaways")
## AI frameworks
Source: https://delight.ai/glossary/ai-frameworks
- [Glossary](/glossary)
- AI frameworks
## AI frameworks
March 6 2026
### Benefits of using AI frameworks
- Reduced development costs: Using predefined building blocks eliminates the need for expensive, from-scratch coding efforts.
- Faster implementation: Frameworks provide a comprehensive environment for rapid prototyping, which leads to a faster time-to-market.
- Seamless integration: Most frameworks are built to be compatible with existing tech stacks, data sources, and APIs, ensuring AI models can interact with legacy systems without disruption.
- Reduced risk: Frameworks reduce the risk associated with compliance, governance, and user trust, often providing tools for scalable, tech-enforced guardrails.
- Scalability & performance: Complete with built-in optimization features and support for distributed computing, frameworks allow models to scale efficiently from a pilot project to an enterprise-wide solution handling massive data volumes.
### Use cases for AI frameworks
- Ecommerce & retail: Build recommendation engines that suggest products based on user behavior, preferences, and real-time context, driving significant revenue lift.
- Finance & banking: Implement fraud detection systems that flag anomalies in real-time transaction streams with high precision.
- Software development: Tools like GitHub Copilot are built on these frameworks, generating code snippets and automating testing, improving quality and speed by 55% on average.
- Customer operations: Orchestrating AI agents that can manage end-to-end customer support and experience (CX) processes, from initial inquiry to final resolution across multiple channels.
### Key takeaways
- The proven path: For most, building AI using established frameworks is the smarter path compared to building everything internally from scratch.
- Focus on business value: Frameworks abstract the technical complexity, allowing leaders to focus on identifying high-impact use cases that deliver measurable ROI.
- Continuous improvement: Frameworks facilitate continuous monitoring and feedback loops, ensuring AI systems adapt and improve over time.
- [Benefits of using AI frameworks](#section-anchor-id-0 "Benefits of using AI frameworks")
- [Use cases for AI frameworks](#section-anchor-id-1 "Use cases for AI frameworks")
- [Key takeaways](#section-anchor-id-2 "Key takeaways")
## AI hallucination
Source: https://delight.ai/glossary/ai-hallucination
- [Glossary](/glossary)\n\n## AI hallucination\n\nMarch 6 2026\n\n### What is an AI hallucination?\n\nAn AI hallucination is an instance where a generative AI model—such as a large language model (LLM)—confidently generates an output that is factually incorrect, fabricated, or misleading.\n\nThese errors can result from poor training data, a lack of knowledge on a topic, complex prompts, or when LLMs perceive patterns or objects in their training data that do not exist in the real world. Because AI models are trained to predict the statistically most likely next word in a sequence, they can prioritize predicting the next most likely token over factual accuracy. This results in them \"filling in the blanks\" with invented details that look semantically correct but are fundamentally false.\n\n### Why AI hallucinations matter\n\nAI outputs are increasingly used across the enterprise, yet hallucinations can lead to catastrophic business outcomes. For example, in 2025, an AI system in telecom operations fabricated a \"critical nationwide outage,\" resulting in unnecessary panic, wasted technical resources, and loss of customer trust. AI hallucinations matter because they expose an organization to: \n\n- Legal liability: Gartner predicts that, by the end of 2026, \"death by AI\" or major safety failure legal claims will exceed 2,000, often triggered by insufficient risk guardrails.\n- Reputational damage: High-profile cases—such as an AI chatbot citing a made-up refund policy—can destroy decades of customer loyalty in a single interaction.\n- Financial risk: In 2023, Google’s parent company, Alphabet, lost $100 billion in market value after its chatbot provided a factually incorrect answer in a promotional video.\n- Compliance violations: Under the EU AI Act, deploying high-risk AI systems that provide misleading or harmful information can result in penalties of up to 7% of global revenue.\n\nFor organizations, relying on unverified AI outputs is no longer an option in high-stakes and customer-facing environments. As AI-generated content becomes more commonplace, effective governance and oversight become essential.\n\n### Why AI hallucinations happen\n\nTo prevent these confidently asserted falsehoods, it’s important to understand why they occur. AI hallucinations can happen for several reasons:\n\n- Probabilistic language modeling: LLMs don’t \"know\" facts; they calculate the statistical likelihood of words appearing together in a sequence. If the next \"most likely\" word is a common name like John Smith, the AI might output that name even if no such person exists in a specific context.\n- Gaps in training data: If the vast datasets AI models are trained on contain biases, outdated information, or contradictions (and most do), the model inherits these flaws as \"truth\" that can be reproduced.\n- Over-optimization for creativity: Generative AI tools can be set to a high \"temperature\" (creativity setting), which encourages the model to take risks in how it generates text, leading to more frequent fabrications.\n- Lack of grounding: Without integration into a structured AI knowledge base or external verification systems, AI must rely solely on internal patterns—leaving it prone to \"guessing\" when it hits the boundaries of its knowledge.\n- Vague prompts: Open-ended or context-light AI prompts increase the likelihood of fabricated details.\n\n### Types of AI hallucinations\n\nHallucinations are not intentional deception, but a byproduct of how AI models are trained—optimizing for output coherence, not truth. They can take several forms:\n\n- Factual hallucinations: Presenting incorrect information as true.\n- Logical hallucinations: Producing internally inconsistent reasoning.\n- Contextual hallucinations: Misapplying correct facts to the wrong scenario.\n- Fabricated sources: Inventing citations, references, or links.\n\nUnderstanding these categories helps organizations to identify, explain, and mitigate hallucinations in real time before they impact customers or business outcomes.\n\n### How to reduce AI hallucinations\n\nHallucinations are a structural limitation of generative AI models, not an unforeseeable anomaly (especially when scaling AI in production). While hallucinations cannot be eliminated entirely, they can be effectively mitigated through:\n\n- Knowledge base grounding: Connecting models to verified, structured knowledge bases that support their core training data.\n- Retrieval-Augmented Generation (RAG): Forcing models to retrieve and cite verifiable data from internal or external sources before generating outputs.\n- Human oversight: Implementing review processes for AI outputs in high-risk settings, supporting explainability and risk mitigation going forward.\n- Monitoring & observability: Tracking AI outputs in real time for signs of logical deviation, inconsistencies, and falsehoods.\n- Clear guardrails: Restricting what the AI system is allowed to produce or claim, such as telling it to only use provided information.\n- Precise, context-rich prompts: Providing detailed, clear prompts that include context, the desired role of the AI, and specific constraints.\n- Give it an \"out\": Including conditional (if/then) statements in prompts lets AI admit ignorance instead of forcing an answer. (\"If you don't know the answer, say you don't know\" or \"only answer based on provided context,\" and \"escalate if uncertain.\")\n\nUltimately, minimizing hallucinations requires a robust governance infrastructure that enables teams not only to identify and address errors, but also to understand and explain how they occurred, preventing similar incidents in the future.\n\n### Real-world examples of AI hallucinations\n\n- AI customer service: An AI chatbot provides a refund policy that doesn’t exist in the company’s official AI knowledge base and documentation.\n- Content creation: A generative AI tool fabricates quotes and attributes them to public figures.\n- Legal research: A model cites nonexistent case law in a court filing.\n- News reporting: An AI-generated news article includes invented statistics.\n\n### Benefits of managing AI hallucinations\n\nOrganizations that proactively address hallucinations within a comprehensive AI governance framework can scale AI safely and sustainably. This leads to:\n\n- Scalable, safe, and trustworthy AI systems\n- Improved customer and stakeholder trust\n- Reduced compliance and legal exposure\n\nAt delight.ai, for instance, we prevent hallucinations by using a Safeguards API to scan AI outputs in real time against verified knowledge bases and pre-defined accuracy thresholds. Additionally, we employ Retrieval-Augmented Generation (RAG) and fully integrated detection mechanisms to automatically flag ungrounded content, providing human teams with total visibility and control over corrective action before errors impact customers.\n\n### Key takeaways\n\n- Inevitable but not unmanageable: Hallucinations are inherent to generative models, but they can be reduced and controlled with ongoing monitoring and fine-tuning as part of a robust AI governance framework.\n\n- [What is an AI hallucination?](#section-anchor-id-0 \"What is an AI hallucination?\")
## AI prompts
Source: https://delight.ai/glossary/ai-prompts
## AI prompts
March 6 2026
### What are AI prompts?
An AI prompt is the specific set of instructions, questions, or context provided to an AI model (like ChatGPT) to produce a desired response. The prompt is the starting point of any AI interaction; it’s the creation of an input that initiates a response from an AI model. AI prompts can take many forms, including:
- A question or request
- A set of instructions
- Contextual information
- Examples of desired output
Basically, prompts tell the AI what to do and how to do it. For example, a prompt might ask an AI to summarize a document, draft a response in a specific tone, or extract key information from a conversation. Prompts are most commonly associated with generative AI systems, such as large language models (LLMs), but they are used across many AI-powered applications that use LLMs to power workflows.
### Why AI prompts matter
The quality of an AI’s output is directly proportional to the quality of the prompt given to it. In other words, poorly designed prompts can lead to incorrect or inconsistent results. Prompts matter because they:
- Predict performance: LLMs require lots of context to arrive at a relevant, useful output because they over-index on human speech and its nuances for their generative capabilities.
- Control accuracy: Well-structured prompts significantly reduce AI hallucinations (errors) by anchoring the AI in specific facts.
- Ensure consistency: Prompts allow you to dictate the tone, style, and vocabulary the AI uses when speaking to customers.
- Unlock complex workflows: Multi-step prompts allow AI to perform sophisticated reasoning, such as chain-of-thought processing, to solve logic-heavy problems and save time.
For business leaders, “system prompts” are a critical lever for shaping the behavior of company-wide AI systems, without changing the underlying technology.
### Examples of AI prompts (prompting)
- Customer support: Instead of just saying "Help this customer," an effective prompt might say: "Using our Refund Policy PDF, respond to this customer's inquiry in a sympathetic tone. If the item was purchased over 30 days ago, politely decline."
- Data analysis: "Analyze this CSV of sales data. Identify the top three regions with declining growth and suggest three potential causes based on current market trends."
- Content generation: "Draft a LinkedIn post announcing our new sustainability initiative. Target the tone toward C-suite executives and include a call to action to download our impact report."
For a business-grade AI agent, a prompt should include several elements that narrow its focus for better outputs. For example:
- Role: Defining who the AI is (e.g., "You are a senior paralegal").
- Task: The specific action to take (e.g., "Summarize this contract").
- Constraints: Rules the AI must follow (e.g., "Do not mention pricing").
- Context: Background data the AI needs (e.g., "Refer to our 2026 compliance guidelines").
By refining these elements, organizations can turn a general-purpose model into a specialized digital worker.
### AI prompt use cases
As the primary way to instruct AI toward a desired outcome, AI prompts are increasingly essential across AI-led industries and departments:
- Customer experience: They guide AI agents for customer service to follow guidelines, use a specific tone, and extract details such as issue type or user sentiment, ensuring consistent yet tailored interactions.
- Sales enablement: Auto-generating personalized pitch decks based on a prospect's website content.
- HR: Drafting custom interview questions based on a specific candidate's resume and the unique requirements of a job description.
- IT operations: Translating complex technical logs into plain-English summaries for management reviews.
### How does AI prompting work?
1. Input parsing: The model breaks the given prompt into "tokens" (chunks of text) to understand the meaning (using semantic understanding).
2. Context mapping: The AI looks for patterns in its training data that match the intent of the prompt.
3. Inference: Using its internal algorithms, the model calculates the most statistically likely and helpful next word, building a response in real-time.
4. Refinement: Through "few-shot prompting" (giving the AI 2-3 examples within the prompt), the model dramatically improves its performance on specific tasks.
### Benefits of effective AI prompts
- Reduced training costs: In many cases, you can "teach" AI a new task in seconds via a well-written prompt rather than spending weeks retraining the model.
- Control & agility: When business policies change, you simply update the system prompt to change the AI's behavior across the entire company instantly.
- Enhanced security: "Prompt guarding" helps prevent users from tricking the AI into revealing sensitive information or violating compliance rules.
- Improved performance: Context-rich prompts lead to more useful, task-specific responses.
### Key takeaways
- Prompting = programming: Natural language is the new code for many AI tools and applications. This makes developing AI prompt literacy a high-ROI investment.
- Context is king: The more specific and data-rich the prompt given, the more valuable the AI output.
- Iterative process: Great prompts are rarely written on the first try; they require testing, feedback, and refinement to reach enterprise-grade reliability.
- [What are AI prompts?](#section-anchor-id-0 "What are AI prompts?")
- [How does AI prompting work?](#section-anchor-id-1 "How does AI prompting work?")
- [Benefits of effective AI prompts](#section-anchor-id-2 "Benefits of effective AI prompts")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## AI use cases
Source: https://delight.ai/glossary/ai-use-cases
- [Glossary](/glossary)
- AI use cases
## AI use cases
March 6 2026
### What is an AI use case?
An AI use case is a clearly defined scenario where AI adoption provides a measurable improvement over traditional business technologies. Ideally, a use case is a practical way to apply AI to solve real business problems. This involves applying a specific capability (like natural language processing or autonomous agents) to an existing cost center or customer friction point. A strong AI use case includes three elements:
1. The problem: A manual, slow, or inconsistent process (e.g., "It takes 48 hours to categorize support tickets").
2. The solution: The specific AI technology applied (e.g., "An AI agent using sentiment analysis").
3. The measurable outcome: The target metric (e.g., "Improve customer satisfaction (CSAT) by 10 points in a quarter").
### Why AI use cases matter
Defining a set of high-value AI use cases helps an organization to guide their AI strategy, set realistic expectations, and create a shared reference point for all teams. Without this, AI adoption often becomes fragmented and expensive, eroding trust and success. AI use cases matter because they:
- Prioritize AI investment: They allow leaders to rank projects by impact vs. effort, ensuring that high-ROI initiatives get funded first.
- Define success: Each use case provides a benchmark for measuring performance, making it easier to prove the value of AI to stakeholders.
- Guide data readiness: Knowing your target use case tells you exactly what data you need to collect and refine, a key aspect of AI readiness.
- Lead to AI transformation: Identifying the right use cases is the difference between isolated experimentation and a strategic AI transformation.
### Examples of high-impact AI use cases
Each organization must determine what qualifies as an appropriate AI use case, based on their unique business goals and industry, technical capabilities, and customer needs. For example:
- Customer experience: AI agents can mitigate flight disruptions by monitoring for flight delays, automatically rebooking flights, then notifying customers directly of the flight changes—turning a crisis into a seamless experience.
- Human resources: AI systems can handle automated talent matching, analyzing thousands of resumes to surface candidates whose skills best align with current needs.
- Finance & operations: Predictive cash flow modeling that adjusts budget forecasts in real-time based on fluctuating currency rates and supplier lead times.
- Sales & marketing: Hyper-personalization includes various AI systems that qualify leads based on recent company activity, draft unique email variations, then deliver tailored outreach based on historical data and real-time context signals.
- Risk management: Machine learning and predictive analytics can identify, assess, and proactively mitigate threats in real time—such as financial fraud, retail theft, or supply chain disruption—by processing massive sets of enterprise data.
### How to identify and build an AI use case
A core step in developing an effective AI strategy, identifying your high-value use cases for AI adoption requires teams to:
1. Audit routine work: Look for high-volume, repetitive tasks that require logic but not "creativity" per se. These are low-hanging fruit for AI automation.
2. Assess data feasibility: Ensure you have the structured and unstructured data required to train or prompt the AI model behind the use case.
3. Document the target workflow: Map out where the AI fits—is it a copilot assisting a human, or a fully autonomous agent handling the task start-to-finish?
4. Test & iterate: Start with a pilot (POC) to validate that the AI logic holds up in real-world conditions.
### Benefits of a use-case-driven AI strategy
Readiness is highly predictive of achieving measurable value from AI pilot projects. Other benefits include:
- Strategic alignment: Ensures every AI project supports the overarching business goals.
- Reduced risk: By focusing on specific, bounded scenarios, you avoid the security pitfalls of "unfiltered" AI usage.
- Faster ROI: Targeted applications deliver value much faster than sweeping overhauls that focus on tech-for-tech's-sake.
- Scalable growth: Proven use cases, like customer support automation, can be extended across teams and channels.
### Key takeaways
- Focus on impact: Align your use cases with targeted, existing business processes and problems for which AI promises clear improvements.
- Start small, scale fast: Focus on 2-3 high-value use cases to build momentum before expanding across the organization.
- Continuous monitoring: As markets and data change, your use cases must be regularly audited to ensure they still deliver optimal results.
- [What is an AI use case?](#section-anchor-id-0 "What is an AI use case?")
- [Key takeaways](#section-anchor-id-1 "Key takeaways")
## AI workforce
Source: https://delight.ai/glossary/ai-workforce
- [Glossary](/glossary)
- AI workforce
## AI workforce
March 6 2026
### What is an AI workforce?
The AI workforce refers to an integrated set of AI systems—such as copilots, virtual assistants, and AI agents—that work alongside human teams to perform tasks, make decisions, and execute workflows. Unlike traditional software, this collective "digital headcount" of an organization is agentic—designed to pursue its own pre-defined set of goals, and capable of reasoning, prioritizing, and executing complex, multi-step workflows with minimal supervision. The AI workforce is a business-specific set of digital contributors, each with a defined role, scope, and level of autonomy. An evolving concept, the AI workforce comprises:
1. Virtual assistants: AI tools tailored to individual employees and workflows, boosting personal productivity while minimizing administrative burden and feelings of burnout.
2. Copilots: Embedded in employee interfaces, these helpful aides collaborate in real time on logic-heavy or rote tasks like coding, research, or communications.
3. Agents: These agentic digital workers have autonomy and initiative, pursuing multi-step objectives and managing entire business processes (e.g., end-to-end customer support) — while acting within company guidelines and guardrails.
### Why the AI workforce matters
As customer expectations rise and business operations grow more complex, human teams are often short on talent, leaving them struggling to scale without increasing cost or churn. The AI workforce matters because it:
- Scales elastically: Ecommerce retailers, for example, can "hire" and deploy 1,000 AI support agents for the peak season surge in weeks, and "offboard" them in minutes.
- Reclaims human creativity: By delegating an estimated 80% tasks to AI, human employees can focus on strategy, innovation, and relationships, improving both impact and job satisfaction.
- Provides institutional continuity: Unlike their human counterparts, digital workers don't leave for competitors (or get tired). This ensures critical process knowledge stays in the organization, though the AI workforce does carry new challenges and risks.
By automating or assisting employees with various business processes, the AI workforce promises to reshape how work gets done—ushering in a hybrid approach to everyday tasks and their management. AI workforce examples
- The AI SDR (Sales): AI agents that research thousands of leads, draft personalized outreach, and handle initial scheduling 24/7.
- The personal AI analyst: A "digital sidekick" that monitors market trends, analyzes competitor filings, and synthesizes daily briefings for executives.
- The AI support squad: A fleet of autonomous chatbots and agentic concierges resolve issues both common and complex—like password resets, flight rebooking, product suggestions, and transactions—without human help.
### How to manage an AI workforce
1. Role definition: Treat AI deployment like writing a job description. You’ll use AI prompts to define in natural language what the AI is responsible for, its level of decision-making authority, and how to escalate with full context.
2. Upskilling humans: Shift your human workforce toward "AI Management." Employees must learn prompt engineering and how to audit AI outputs.
3. Governance & orchestration: Use AI frameworks to ensure all digital workers are following the same security and compliance protocols.
4. Feedback loops: Establish a system where human "AI managers" provide feedback to the AI, refining its logic and performance over time.
### Benefits of a hybrid workforce
- 24/7 operational capacity: Your business continues to "think" and "act" even when the office is closed, improving time-to-decision and customer satisfaction.
- Reduced employee burnout: By removing the drudge work, human employees report higher job satisfaction and engagement.
- Predictable performance: Digital workers provide a consistent level of accuracy and tone, regardless of workload volume.
### Key takeaways
- Augmentation over replacement: The most successful companies use AI to enhance employees. In customer support, for example, it’s key to balance the presence of humans and AI support, or risk customer fallout.
- The AI manager: Leadership in the AI era is about orchestrating workflows between human and digital talent.
- Culture of trust: Transparently communicating how AI will be used is essential to retaining good human talent during the AI transformation.
- [What is an AI workforce?](#section-anchor-id-0 "What is an AI workforce?")
- [How to manage an AI workforce](#section-anchor-id-1 "How to manage an AI workforce")
- [Benefits of a hybrid workforce](#section-anchor-id-2 "Benefits of a hybrid workforce")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## Autonomous AI agents
Source: https://delight.ai/glossary/autonomous-ai-agents
- [Glossary](/glossary)
- Autonomous AI agents
## Autonomous AI agents
March 6, 2026
### What is an autonomous AI agent?
An autonomous AI agent is an agentic-AI-powered system that can independently reason, plan, and act to achieve a goal set for it with minimal human input. Unlike rule-based systems like robotic process automation (RPA), AI agents have a degree of autonomy and initiative. For example, if given the goal—"Research this prospect and set up a meeting"—it will independently determine and navigate the steps required to make it happen. Using AI frameworks like LangChain, autonomous agents can access tools (such as calendars or CRMs) and external data via APIs, "looping" their internal logic with external inputs until the objective is met. Powered by large language models (LLMs), autonomous agents bring a new level of speed, scale, and precision to business operations. That said, they require goals, rules, and oversight from humans to be safe and effective.
### Why autonomous AI agents matter
Autonomous AI agents matter because they can both reason and act independently—but they also operate seamlessly across systems. Combining goal-oriented reasoning with external tool use and live data processing, they bridge the gap between formerly siloed systems—representing a step change in integration simplicity, operational efficiency, and business intelligence. Autonomous agents enable organizations to:
- Decentralize decision-making: Agents can handle low-to-medium stakes decisions and frontline service without human bottlenecks or errors.
- Operate at scale: A single AI agent can manage the workload of an entire department’s worth of routine data processing or customer outreach.
- Reduce operational friction: By acting as the "user" across disparate systems, even in real-time environments—then syncing data where appropriate afterwards—agents accelerate and streamline operations.
In short, autonomous agents mark a shift from static automation that just executes tasks to adaptive AI systems that own dynamic, multi-step processes and their outcomes.
### Autonomous AI agent examples
- The 24/7 customer concierge: Unlike a bot that just responds to questions, an AI support agent can verify a user’s identity, process a refund in the billing system, and update the CRM—all in one session.
- The autonomous SDR: This agent identifies a lead, researches their recent company news, drafts a personalized pitch, and follows up until booking a meeting.
- The "self-healing" supply chain: A retail AI agent that monitors inventory levels and, if it notices a shortage, automatically negotiates with approved vendors to secure the best price and shipping date (in a display of agentic commerce).
### How do autonomous AI agents work?
1. Perception: The agent receives a goal and "scans" its available data and tools.
2. Planning: Using chain-of-thought reasoning, it creates a step-by-step "to-do list" to reach the goal.
3. Action: It executes the first step (e.g., "Search the web for Company X").
4. Observation & reflection: It looks at the result. If it didn't work, it re-plans and tries a different approach until the task is complete.
### Benefits of autonomous AI agents
- End-to-end automation: Agents manage entire processes without human supervision, but also trigger humans-in-the-loop when the situation calls for it.
- Cross-platform orchestration: They break down silos by moving data and taking actions across every app in your tech stack.
- Unmatched speed & scalability: You can deploy entire fleets of agents to instantly handle spikes in real-time business activity (like Black Friday retail support) without hiring temporary staff.
- Improved consistency: Their standardized, self-improving logic and 24/7 availability reduces variability in outcomes, improving customer experience and decision times.
### Key takeaways
- The "manager" mindset: Leadership shifts from "doing the work" to managing the agents that do the work along with human employees—a hybrid approach.
- Guardrails required: The autonomy of agentic AI must be bounded by clear business rules and risk thresholds to ensure safe, scalable execution.
- Reliability over novelty: Except in creative uses like marketing, dialing down the LLM’s creativity parameters produces a more reliable, less "cool" agent that delivers consistent outcomes.
- [What is an autonomous AI agent?](#section-anchor-id-0 "What is an autonomous AI agent?")
- [How do autonomous AI agents work?](#section-anchor-id-1 "How do autonomous AI agents work?")
- [Benefits of autonomous AI agents](#section-anchor-id-2 "Benefits of autonomous AI agents")
- [Key takeaways](#section-anchor-id-3 "Key takeaways")
## AI agent actions
Source: https://delight.ai/glossary/ai-agent-actions
- [Glossary](/glossary)\n- AI agent actions\n\n## AI agent actions\n\nMarch 6 2026\n\n### What are AI agent actions?\n\nAI agent actions are the tasks and functional tools that allow an autonomous agent (agentic AI system) to interact with its environment and achieve the specific goals set for it. These actions allow agents to perform tasks such as content generation, data transformation, and automated customer communication, often using frameworks like ReAct to plan and execute multi-step processes. This \"action\" involves the agentic AI system interacting with a third-party tool or database in its software environment to change the state of the real world.\n\n#### Why AI agent actions matter\n\nAI agent actions represent a step change in the automation capabilities and business value of artificial intelligence (AI)—moving from reactive generation to autonomous action. This ability to act independently in pursuit of a defined goal is the difference between an AI assistant that can only aid employees and a self-directed AI agent that performs specialized tasks on its own. It’s a shift from advisory AI to active AI. AI agent actions matter because they allow organizations to:\n\n- Automate multi-step tasks end-to-end. AI support agents don't just relay how to solve a problem; they solve it for you in real-time.\n- Improve customer experience: By reducing friction that can lead to dissatisfaction and churn, AI agent actions are key to seamless AI customer experience (AI CX) outcomes.\n- Drive efficiency and accuracy: In support, AI agents handle thousands of interactions immediately, reducing handle time, ticket volume, and human error around data entry.\n- Scale data-driven operations: By executing actions and triggering workflows across multiple external systems in milliseconds, AI agents deliver gains in efficiency, accuracy, and personalization.\n\n### Key types of AI agent actions\n\n- Information gathering: Performing web searches, querying databases, and retrieving documents\n- Tool usage & execution: Making API calls, running code, and executing calculations.\n- Environmental interaction: Manipulating digital interfaces (e.g., UI automation) or controlling physical devices.\n- Communication: Chatting with users, sending notifications, or collaborating with other agents.\n- Content generation & transformation: Creating, summarizing, translating, and reformatting content.\n- Planning & decision-making: Analyzing inputs to determine the best sequence of actions to reach a goal.\n- Multi-agent actions: As part of a multi-agent system, multiple orchestrated AI agents work together to complete more complex tasks.\n\nWhile AI agents are autonomous in their actions, they still require goals and guardrails defined by humans. This means that building AI agents requires carefully defining which actions are allowed, how they are triggered, and the constraints that govern them, to ensure safe and effective performance.\n\n### Use cases for AI agent action\n\nBy combining goal-oriented behavior with real-time data access, AI agents can evaluate choices, plan action, and make decisions to solve problems with a level of accuracy and adaptivity that non-agentic AI can’t match. Common use cases include:\n\n- AI automation: Using agentic workflows to trigger various system actions based on specific inputs.\n- Customer support: Automatically canceling orders or answering queries using tools like delight.ai.\n- Data analysis & operations: Monitoring system alerts and adjusting retail inventory or pricing in real-time.\n- Content management: Automatically generate, translate, and transform content within a CMS or AI knowledge base.\n\n### How AI agent actions work\n\nAI agents operate through an autonomous cycle of perceiving the environment, reasoning through objectives, and executing tasks using external tools. This \"Think-Act-Observe\" loop involves breaking complex goals into smaller, actionable steps to interact with software, databases, or APIs as needed to fulfill user requests. AI agent actions typically involve several layers:\n\n1. Intent detection: Using natural language understanding (NLU), the agent identifies what the user wants (intent) and how they feel at the moment (sentiment) to understand the full context of the request.\n2. Decision logic: AI models determine the required steps to solve the problem by deconstructing complex goals into a sequence of smaller steps.\n3. Tool calling: The agent connects to external systems and data sources via APIs to enhance its functionality or understanding.\n4. Execution in real time: The agent performs various tasks such as responding to customers, triggering workflows, or updating records as required to achieve its broader mission.\n5. Monitoring & guardrails: Grounded in guardrails defined in AI governance policies, actions are permissioned, logged, and monitored in real time to ensure safe execution.\n\n### Real-world examples of AI agent actions\n\nThe capacity for an AI agent system to perform actions on its own is transforming industries:\n\n- Ecommerce & retail: An agent receives a \"Where is my order?\" request, checks the shipping carrier's API, sees a delay, and proactively offers a discount code while updating the customer’s profile.\n- Financial services: Detecting a potential fraud alert, the agent pauses the credit card, sends the customer a verification text, and—upon confirmation—unblocks the card and logs the incident in the compliance database.\n- Travel & hospitality: When a flight is canceled, an advanced AI agent looks up the next available flight, rebooks the passenger automatically, then emails them a complimentary lounge voucher—all without the customer ever picking up the phone.\n- Warehouse operations: An agent monitors warehouse inventory levels. When a threshold is met, it automatically drafts a purchase order, sends it to a manager for approval, and files it in the ERP system once signed.\n- Customer service: The AI agent receives a customer request for an order refund. It makes API calls to your CRM, refers to its memory for personalization, then updates records after resolution. It might even trigger a customer retention workflow in a configured marketing system if deemed appropriate.\n\n### Benefits of AI agent actions\n\n- Reduced operational costs\n- Scalable automation of repetitive tasks\n- Improved customer experience\n- Hyper-personalization across interactions\n- Greater operational velocity and time to decision\n\nWhen building AI agents, achieving these benefits hinges on making actions safe and well-governed. Without AI governance, agents can pose risks to customer trust, brand reputation, compliance, or security.\n\n### Key takeaways\n\n- From reactive to proactive: AI agent actions are how an agentic AI system performs the specific tasks required to achieve a predefined goal. Far beyond just generating outputs as prompted, like a chatbot, this involves using tools to retrieve real-time data, making decisions, and then taking step-by-step actions across systems until the goal is achieved.\n\n- [What are AI agent actions?](#section-anchor-id-0 \"What are AI agent actions?\")\n- [How AI agent actions work](#section-anchor-id-1 \"How AI agent actions work\")\n
## Human-AI interaction
Source: https://delight.ai/glossary/human-ai-interaction
- [Glossary](/glossary)
## Human-AI interaction
March 6 2026
### What is human-AI interaction?
Human-AI interaction (HAI) is the study and design of how people and artificial intelligence (AI) systems communicate, collaborate, and make decisions together. It examines how AI systems present information, accept input, and support human goals in intuitive, transparent, and effective ways.
As AI becomes more embedded in business workflows and everyday life, designing these systems for strong human-AI interaction is essential to ensuring trust and real-world impact. HAI spans interfaces (such as chat, voice AI, and visual dashboards), behaviors (how AI responds and adapts), and control mechanisms that allow humans to guide, override, or collaborate with AI.
Unlike traditional human-computer interaction, HAI accounts for AI systems that learn, adapt, and make decisions—requiring new approaches to feedback, explainability, and oversight.
### Why human-AI interaction matters
Even the most advanced AI systems are only effective if humans can understand, trust, and work with them. Poor interaction design can lead to confusion, loss of trust, and compromised AI safety and responsibility.
For example, in customer service, where AI influences the real-world customer experience, the quality of AI’s interaction with customers will determine whether an AI project succeeds or fails.
### Focusing on quality HAI allows organizations to:
- Increase trust and adoption of AI systems
- Improve decision quality and reliability through clear explanations and feedback
- Maintain human oversight of autonomous agents or high-impact systems
- Reduce errors and friction in AI-assisted employee workflows
- Ensure safe, ethical, and responsible use of AI technologies with 'human-in-the-loop' oversight
### Key elements of human-AI interaction
Effective human-AI interaction is built on several core principles. To design AI that people actually want to use, businesses should focus on four core pillars:
- Transparency & explainability: Teams, and to a lesser extent, customers, must be able to understand AI’s outputs and decisions, whether from detailed documentation, auditing, or explainers in the interface.
- Control & oversight: Humans must be able to guide, approve, or intervene in AI actions in real time to mitigate risk—especially in sensitive or high-risk scenarios.
- Feedback & learning: AI systems learn from user feedback and corrections, self-improving over time while remaining aligned with human intent.
- Usability & accessibility: AI interfaces should be intuitive and inclusive, allowing people with varying expertise to interact confidently with AI.
### Examples of human-AI interaction
Human-AI interaction plays a critical role across many AI applications. Done successfully, HAI unlocks new levels of efficiency for businesses and their customers:
- AI copilots & assistants: Support knowledge workers with real-time guidance, recommendations, and automatic scheduling.
- Customer service & CX: Deliver an intuitive, satisfying CX at scale and enable seamless handoffs between AI and human agents.
- Decision support systems: Enable executives to ask AI analytics to interpret and summarize data, assess risks, and suggest actions.
- Knowledge work automation: Collaborate with AI on writing, design, research, and analysis.
- Autonomous systems oversight: Allow humans to monitor, intervene, and govern agentic systems in real time.
### Key takeaways
- Augmentation over replacement: By focusing on HAI, businesses can expand human capabilities and make AI a force multiplier for their workforce.
- Pillar of trust and safety: HAI ensures that AI systems remain understandable, trustworthy, and aligned with human goals. This helps unlock the full value of AI while maintaining control, accountability, and trust.
- [What is human-AI interaction?](#section-anchor-id-0 'What is human-AI interaction?')
- [Focusing on quality HAI allows organizations to:](#section-anchor-id-1 'Focusing on quality HAI allows organizations to:')
- [Key takeaways](#section-anchor-id-2 'Key takeaways')
## Hyper-personalization
Source: https://delight.ai/glossary/hyper-personalization
## Hyper-personalization
March 6 2026
### Hyper-personalization overview
- Hyper-personalization is an advanced marketing strategy that combines artificial intelligence (AI) with real-time data to create highly tailored customer experiences at scale.
- Unlike traditional segmentation that only relies on historical data, it uses granular real-time behavior signals, such as browsing, location, and context, to dynamically tailor experiences.
### What is hyper-personalization?
Hyper-personalization is an advanced marketing strategy that combines artificial intelligence (AI), machine learning, and real-time data to deliver highly tailored experiences to each individual customer at scale.
Unlike traditional customer segmentation, which relies solely on historical data, hyper-personalization also uses real-time, granular behavioral signals (such as browsing, location, and context) to tailor experiences dynamically in the moment.
By synthesizing both historical data (e.g., preferences and purchases) and real-time data (e.g., behavior, intent, context, sentiment), hyper-personalization enables organizations to scale the delivery of experiences, products, and services to "a segment of one" rather than generic segments. This results in more relevant, engaging interactions at scale, leading to higher customer conversion, satisfaction, and loyalty.
### Why hyper-personalization matters
At a time when [71% of customers expect personalized interactions](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/unlocking-the-next-frontier-of-personalized-marketing) from brands, and reward personalization leaders with 40% more revenue than their peers, hyper-personalization is a new frontier of competitive advantage in enterprise customer experience (CX).
Historically, data fragmentation and a lack of orchestration have left siloed systems struggling to share and synthesize data, then act in real time. However, thanks to advances in AI technology such as machine learning and AI agents, hyper-personalization is now an operational reality.
This is largely due to a new personalization strategy known as next best action (NBA). Serving as the bridge between marketing strategy and operational execution, NBA uses AI to determine the most effective interaction for a specific individual in real time. This makes hyper-personalization predictive, not reactive. NBA enables organizations to anticipate and then meet needs proactively, often before customers even realize it themselves.
Hyper-personalization matters because it leads to:
- **Improved customer experience (CX):** Meeting customers in the moment helps them to feel more understood, supported, and satisfied.
- **Higher engagement:** Tailoring content, product recommendations, and experiences leads to higher satisfaction and loyalty.
- **Increased marketing ROI:** Leveraging live, unified data improves conversion rates, campaign performance, and lifetime value (CLV) from precision advertising and content.
- **Improved efficiency:** Targeting only the most relevant prospects optimizes marketing and sales resources, helping teams do more with less, faster.
- **Enhanced customer support:** Anticipating interactions to defuse issues in advance turns traditionally reactive support into a proactive lever with revenue-driving potential.
### Use cases for hyper-personalization
Hyper-personalization is transforming how businesses interact with customers, combining predictive insights with generative execution to go beyond traditional segmentation and market directly to "a segment of one." Increasingly used across industries, some top use cases include:
- **Targeted promotions and advertising:** Target uses predictive AI models to identify customers who are in "life-stage shifts"—like being in the second trimester of pregnancy—based on changes in, say, vitamin purchases. They then trigger tailored coupons for strollers or diapers before the customer begins searching for them.
- **Dynamic content delivery and creation:** Spotify adapts its "Daylist" by looking at more than favorite genres. It analyzes listening habits at specific times of day (e.g., "80s disco Friday afternoon"), then uses generative AI to create niche playlist titles and descriptions that mirror that specific mood at that exact time.
- **Dynamic pricing:** Klarna uses a "Next Best Action" (NBA) engine to personalize the entire shopping interface. For high-credit-score users with a history of tech purchases, the app might generate a tech-focused dashboard with new custom financing offers.
- **Product recommendations:** Stitch Fix uses predictive AI analytics to identify customers’ "style profiles," then uses generative AI to visualize for them how a specific item would complement their physique or existing wardrobe to drive purchasing.
- **Customized loyalty rewards:** Brands like Sephora know truly tailored loyalty perks help turn one-time buyers into repeat customers. From birthday gifts to tiered points to VIP experiences, members who redeem personalized rewards [spend 4.3x more per year](https://retlia.com/retail-data/real-time-metrics-for-retail-marketing-and-sales/) than those claiming generic rewards.
- **AI customer support:** Norse Airlines uses [AI agents for customer service](https://delight.ai/ai-customer-service) to monitor for flight delays and automatically rebook priority passengers, turning a potential crisis into a loyalty moment.
### How does hyper-personalization work?
Hyper-personalization—also known as AI personalization—is an AI-powered process. This means it works through a continuous feedback loop of perception, reasoning, and action. It relies on four core pillars to turn raw data into an individualized experience:
1. **Unified data collection:** Rather than relying on siloed data, a customer data platform (CDP) aggregates historical data (e.g., past purchases, CRM profiles) with real-time signals (e.g., current GPS location, active mouse movements, or current local weather) to create a unified profile that updates in milliseconds.
2. **Predictive analysis:** Predictive AI identifies the next best action, analyzing the data stream to predict the user's intent. Example: a user who has visited the "cancel subscription" page twice in 10 minutes is identified as a high-churn risk.
3. **Dynamic generation:** Generative AI creates the assets needed to execute the NBA, tailoring the copy, imagery, and offer to match each user's psychological profile.
4. **Omnichannel orchestration:** Now it’s time to deliver the experience via the customer’s preferred channel at the optimal time. Whether it’s an SMS notification, a personalized website home page, or a WhatsApp message, the system orchestrates the delivery so the experience feels seamless and organic rather than intrusive.
5. **Continuous learning & improvement:** Lastly, the AI-powered personalization system evaluates outcome quality, stores key information in memory, and feeds the data back into the system to refine its decision rules and guardrails, update profiles, and enhance the quality of future interactions.
### Hyper-personalization vs traditional personalization
The core differences between the two lie in the depth and dynamism of data use. For example, traditional personalization in ecommerce simply recommends ‘similar shoes’ based on previous customer activity, using static segments and historical data.
By contrast, hyper-personalization evolves with each customer, adjusting intelligently to real-time context and behavior. The chart below illustrates the key difference between traditional and hyper-personalization:
| Feature | Personalization | Hyper-personalization |
|---|---|---|
| Data use | Uses historical, segment-level customer data (e.g., broad demographics or past purchases). | Uses real-time, granular data (e.g., browsing behavior, device signals, location) combined with historical insights. |
| Customization level | Tailors experiences for groups or segments (e.g., sending a yoga video to anyone interested in fitness). | Creates a unique experience for each individual (e.g., generating a custom workout based on goals, past activity, and current behavior). |
| Technology | Relies on basic data analytics and simple segmentation. | Leverages generative AI, machine learning, predictive analytics, and adaptive algorithms. |
| Adaptability | Static: Based on past actions or demographic data. | Dynamic: Adjusts in real time as user behavior, context, and preferences change. |
| Memory | No continuity. Each interaction is treated as isolated, with limited recall across sessions. | Persists context over time, remembering past interactions, preferences, goals, sentiment, and unresolved issues to create continuously improving, hyper-relevant experiences. |
| Latency | Batch-based updates; changes may take days or weeks to reflect. | Real-time processing; decisions and adaptations occur in milliseconds via streaming data. |
| Content generation | Inserts dynamic fields (e.g., “Hi [First_Name]”). | Dynamically generates full experiences (e.g., an email layout or offer crafted specifically for your browsing behavior and aesthetic preferences). |
| Goal | Relevance: Show something generally aligned with past behavior. | Anticipation: Predict and deliver what the customer needs before they explicitly ask. |
| Examples | Using a customer’s name in an email subject line or showing a previously viewed product category. | Sending a personalized, time-sensitive offer based on real-time behavior, sentiment, and historical patterns. |
### Key aspects of hyper-personalization
Hyper-personalization relies on AI-ready infrastructure, data governance, and real-time orchestration to dynamically tailor interactions using real-time data.
- **Granular data collection:** Hyper-personalization relies on collecting, analyzing, and acting on vast amounts of historical and live data for accurate, precise outputs.
- **Unified data:** A comprehensive, real-time 360-degree view of customers is essential. Without unification and the contextual understanding it creates, organizations can’t reliably differentiate customers, understand intent, and operate AI models effectively.
- **Individualization:** Unlike persona-based marketing, which treats each user as part of a group, this approach treats each customer as a "segment of one" to optimize content and interaction timing.
- **AI & ML:** Predictive AI uses machine learning processes to analyze vast datasets to identify patterns, predict behavior, and refine rich, evolving customer profiles, while generative AI creates the assets to meet those needs.
- **Real-time analysis:** Hyper-personalization requires near-instantaneous decision-making. For example: triggering a cart abandonment offer the moment a customer's cursor moves to exit checkout without completing the purchase.
- **Dynamic customization & generation:** Based on real-time analysis, systems dynamically adjust content, offers, and interactions. These are pulled from a centralized, evolving profile or auto-produced on the fly by generative AI.
- **Omnichannel delivery:** Cross-channel integrations, a unified customer data platform (UCDP), and agentic AI architecture enable dynamic delivery across channels to ensure a consistent experience.
- **Persistent memory:** Long-term memory is critical to effective system reasoning and self-improvement to ensure relevant, consistent personalization. The system stores, recalls, and applies customer data dynamically in real time as part of a single evolving source of truth.
- **User privacy:** Leaders must balance being personal and being invasive. This requires robust data privacy guardrails embedded into systems, mitigating the risk of PII data breaches and misuse, while also avoiding the "creepy" factor that can erode customer trust.
### 6 examples of hyper-personalization
1. **Wholesaler’s app & loyalty engagement:** A US retail wholesaler integrates its [AI agent shopping assistant](https://sendbird.com/ai-agent/industries/retail) across its mobile app and in-store kiosks to guide and tailor the in-store shopping journey. Shoppers receive directions to the right aisle, along with location-based offers and loyalty program sign-up offers, driving both program sign-ups and app engagement.
2. **Nike’s social media retargeting:** The athletics retailer uses AI-driven segmentation to serve highly personalized ads across its social media presence. The system tailors visuals, copy, and timing to reflect each user’s browsing behavior, location, and prior brand interactions. This helps marketing spend reach shoppers most likely to convert, boosting ROI and reducing ad fatigue.
3. **REI’s product suggestions:** The outdoor retailer uses a unified customer profile to connect in-store and digital interactions. For instance, if a store associate notes a shopper wants hiking boots for an upcoming vacation, the AI-driven system can trigger a follow-up email or in-app notification promoting relevant boots—along with tailored gear bundles to help drive sales.
4. **Amazon’s AI recommendation engine:** The tech titan’s AI algorithms analyze real-time browsing, purchase history, and live contextual behavior to predict and suggest complementary items ("Frequently bought together" site sections) or upgrades to shoppers—so successfully it [now drives 35% of total sales](https://www.rejoiner.com/resources/amazon-recommendations-secret-selling-online#:~:text=35%25%20of%20Amazon.com%27s%20revenue,generated%20by%20its%20recommendation%20engine.).
5. **Sephora’s omnichannel CX:** The beauty retailer offers one seamless, hyper-personalized experience spanning its digital and physical interactions. AI-driven in-store kiosks and the Sephora app suggest products, content, and loyalty rewards. At checkout, associates see loyalty balances and suggest exclusive offers or samples to make every touch more personal and profitable.
6. **Hubspot’s B2B sales & support:** The marketing platform uses AI to personalize outreach and support in B2B contexts. Sales reps see AI-driven insights in CRM dashboards (e.g., predicted close likelihood, content recommendations), genAI assistants craft tailored follow-up emails, and [AI agents for B2B support](https://sendbird.com/ai-agent/industries/b2b) proactively surface account-specific resolutions to boost efficiency and client satisfaction.
### Business benefits of hyper-personalization
By making the customer journey more relevant and engaging at every step, hyper-personalization helps businesses to make customers feel more supported, satisfied, and loyal—a major advantage in CX-led industries. Benefits include:
- **Increased customer loyalty & retention:** Tailoring experiences to individual needs makes customers feel valued and understood, fostering loyalty and reducing churn. According to Deloitte research, [66% of consumers](https://www.deloittedigital.com/us/en/insights/research/customer-experience-personalization-strategy.html?utm_source=chatgpt.com) favor brands that try to anticipate their needs.
- **Higher conversion rates & sales:** Relevant, personal recommendations and offers are more likely to lead to purchases, as customers are shown exactly what they want now or will seek next. Per McKinsey, [65% of customers](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/unlocking-the-next-frontier-of-personalized-marketing) say targeted promotions are a top purchasing driver.
- **Improved customer engagement:** Providing content and experiences that resonate with a customer's specific interests keeps them more engaged with the brand and product. Adobe research shows that [61% of senior executives](https://business.adobe.com/content/dam/dx/us/en/resources/digital-trends-report-2025/2025_Digital_Trends_Report.pdf) say “boosting customer engagement with more personalized experiences” will be critical to growth this year.
- **More efficient marketing spend:** By targeting the right individual with the right message at the right time or the right channel, marketers can reduce waste and achieve higher marketing campaign ROI. One study shows personalization can reduce customer acquisition and retention costs by [up to 28%](https://www.contentful.com/blog/personalization-statistics/?utm_source=chatgpt.com).
- **Enhanced customer experience (CX):** Hyper-personalization enables seamless, satisfying, and relevant interactions that make customers more likely to return and recommend a brand to others. Research shows that brands with top-rated personalization [achieved higher customer satisfaction scores](https://www.medallia.com/wp-content/uploads/pdf/resources/2024-Medallia-State-of-Personalization-Report.pdf?utm_source=chatgpt.com) (CSAT) than low-ranking brands by a wide margin.
- **Richer data & insights:** By enabling the collection of more granular, relevant data, hyper-personalization provides a deeper understanding of customers and their needs, improving decision-making and revenue. Per IBM, hyper-personalization can [lift revenue by 5-15%](https://www.ibm.com/think/topics/hyper-personalization?utm_source=chatgpt.com) and marketing ROI by 10-30%.
- **Stronger competitive advantage:** Standing out from competitors who use less-informed, less-targeted approaches can be a major differentiator, especially in competitive markets. Per McKinsey, customers are [78% more likely](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) to recommend a company when it personalizes experiences.
### The hyper-personalization AI stack: From data to delivery
Hyper-personalization requires organizations to move away from disconnected tools toward an integrated AI stack. Here are the four layers required to turn raw data into a personalized experience:
- **The data layer (CDP & data lake):** The foundation. A customer data platform (CDP) or data lakehouse (like Databricks) aggregates siloed data from CRM, web, and mobile into a unified customer view and contextual historical understanding for AI decision-making.
- **The intelligence layer (predictive AI):** This layer is “the brain.” It uses machine learning models to score leads, predict churn, and determine the next best action (NBA), calculating the probability of what a user will do next in real-time.
- **The content layer (generative AI):** This is “the voice.” Large language models (LLMs) and image generators (like Jasper) create assets on command—copy, emails, or banners—each tailored to the individual’s tone and style preferences.
- **The orchestration layer (omnichannel API):** This layer is the delivery system. It uses APIs to push generated content to the right touchpoint as part of a single seamless, low-latency experience. Note: requires AI-ready infrastructure and APIs.
### Key takeaways
- **Next best actions aimed at an audience of one:** Hyper-personalization uses the agentic AI capabilities of AI memory systems and a unified data layer to process real-time and historical customer data simultaneously, then market to them directly.
- [Hyper-personalization overview](#section-anchor-id-0 "Hyper-personalization overview")
- [What is hyper-personalization?](#section-anchor-id-1 "What is hyper-personalization?")
- [Why hyper-personalization matters](#section-anchor-id-2 "Why hyper-personalization matters")
- [Use cases for hyper-personalization](#section-anchor-id-3 "Use cases for hyper-personalization")
- [How does hyper-personalization work?](#section-anchor-id-4 "How does hyper-personalization work?")
- [Hyper-personalization vs traditional personalization](#section-anchor-id-5 "Hyper-personalization vs traditional personalization")
- [Key aspects of hyper-personalization](#section-anchor-id-6 "Key aspects of hyper-personalization")
- [6 examples of hyper-personalization](#section-anchor-id-7 "6 examples of hyper-personalization")
- [Business benefits of hyper-personalization](#section-anchor-id-8 "Business benefits of hyper-personalization")
- [The hyper-personalization AI stack: From data to delivery](#section-anchor-id-9 "The hyper-personalization AI stack: From data to delivery")
- [Key takeaways](#section-anchor-id-10 "Key takeaways")
## AI readiness
Source: https://delight.ai/glossary/ai-readiness
- [Glossary](/glossary)
- AI readiness
## AI readiness
March 12 2026
### AI readiness overview
- AI readiness reflects how prepared an organization is to deploy, manage, and scale artificial intelligence (AI) across the domains of strategy, infrastructure, data, governance, talent, and culture.
- AI readiness frameworks help organizations to guide improvement and benchmark their capabilities around structured pillars and maturity stages.
- To improve readiness, leaders use AI readiness assessments to audit processes, identify gap areas, and align vision with business strategy—ensuring value while minimizing risk.
## What is AI readiness?
AI readiness is the measure of an organization's preparedness to adopt, integrate, and scale artificial intelligence (AI) technologies across its strategic, technical, and cultural domains.
Encompassing factors such as data quality, infrastructure, skills, and governance, it ensures that employees are trained, that data is accessible, and that governance polices are established to support sustainable and effective AI adoption.
## Why does AI readiness matter?
As AI continues to rapidly evolve, AI-ready organizations can move faster from experimentation to production—optimizing operations and delivering personalized customer experiences that drive measurable value. Without readiness, teams risk being derailed by the risks and inefficiencies associated with failed AI projects and lagging behind their competitors.
According to the Cisco 2025 AI Readiness Index, highly AI-ready organizations are [four times more likely to move AI pilots into production](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf), and 50% more likely to see measurable value from their AI investments.
For example, a global retailer with a strong AI readiness framework (built on clean data, robust governance, and AI-literate teams) enters peak season with AI agents pre-trained and tested to handle the surge in customer inquiries on behalf of human support teams.
By contrast, under-prepared competitors are left managing dual crises: the overwhelm of peak season volume and the scramble to stand up agents safely and effectively—compounding operational pressure at exactly the wrong moment.
## Understanding the AI readiness framework
Various resources are available to guide your AI readiness planning. One of the most established is the [Cisco AI Readiness Index](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf), now in its third year, which provides a structured framework for evaluating and improving preparedness across critical areas, including AI data readiness, infrastructure, and governance.
Using this index, organizations can systematically identify gaps in their readiness, strengthen key capabilities, and mitigate the common risks associated with AI development.
## The six pillars of AI readiness
The AI Readiness Index is built on six core pillars that ensure organizations develop and implement a well-rounded strategy that addresses every aspect critical to AI success. Going beyond mere technology purchase, this framework offers a disciplined path from pilot to production.
### Strategy
Without a clear AI strategy, organizations risk misalignment and limited returns on investment. In fact, MIT Sloan research shows that [organizational alignment is the leading predictor of AI ROI](https://mitsloan.mit.edu/ideas-made-to-matter/scaling-ai-results-strategies-mit-sloan-management-review). This pillar serves to:
- Align AI initiatives with core business objectives
- Establish executive ownership
- Scope use cases by complexity and value for prioritization
### Infrastructure
Organizations that delay investing in AI infrastructure face compounding technical debt that slows performance, limits scaling, and forces costly rebuilds—also known as AI technical debt. This pillar reflects how well you've prepared scalable, secure AI systems supported by:
- AI-ready APIs
- Reliable data integrations and pipelines
- Governance embedded by design
### Data
AI data readiness is essential to training and powering AI models that generate accurate, reliable outputs. Without high-quality data, organizations risk hallucinations, model bias, and inconsistent outputs that erode customer trust. This pillar serves to:
- Structure, clean, and de-duplicate data to ensure AI performance
- Make internal knowledge AI-readable and retrievable
- Define data governance, ownership, and safeguards
### Governance
Operating AI without guardrails is a liability. According to Gartner, a lack of governance is a driving factor behind [up to 95%](https://www.forbes.com/sites/andreahill/2025/08/21/why-95-of-ai-pilots-fail-and-what-business-leaders-should-do-instead/#:~:text=According%20to%20a%20MIT%20report%2C%2095%25%20of,are:%20*%20Strategic%20*%20Organizational%20*%20Cultural) of failed generative AI projects. This pillar reflects how well organizations can manage risk, ensure compliance, and develop responsible AI that features:
- Real-time monitoring and visibility of AI logic and outputs
- Embedded security controls and risk mitigation
- Built-in testing and explainability features
### Talent
Even the most well-designed AI solution will fall short of its true potential if human teams are lacking the skills to build, manage, and collaborate with it effectively. This pillar assesses if you effectively:
- Invest in AI literacy and skills training
- Redefine human roles around AI collaboration
- Establish human-in-the-loop (HITL) oversight
### Culture
How ready are your people for AI adoption? Gartner research shows that [60% of organizational change efforts fail](https://www.forbes.com/sites/forbesbooksauthors/2025/05/06/why-change-management-fails-its-about-people-not-process/) to meet their goals, largely because teams underestimate the human and operational work required. This pillar reflects how well you:
- Have defined plans for AI change management
- Prioritize leadership and employee trust as part of AI transformation
- Treat deployment as the start of AI optimization and responsible AI
### AI customer experience (AI CX)
This is delight.ai's addition to the framework. With AI agents assuming many frontline responsibilities across industries, the quality of AI-customer interactions can become a competitive advantage—or erode customer trust if left unmanaged. This pillar reflects how well you:
- Design AI agent interactions that reflect brand voice, empathy, and service standards
- Optimize AI performance continuously based on customer feedback and outcome data
- Balance AI automation with human touchpoints to avoid alienating customers
Note: In the CISCO framework, all pillars work together, but are assigned different weights by importance: Infrastructure (25%), Data (20%), Strategy (15%), Governance (15%), Talent (15%), and Culture (10%).
## The four stages of AI readiness
In addition to the framework's pillars, organizations can assess and benchmark their readiness across four stages of AI maturity. Each stage reflects how effectively each pillar is aligned into a unified operational capability. Understanding maturity helps leaders understand where they stand today and map a clear path to full AI integration.
- **Pacesetters** are true AI leaders. Governance is embedded, strategy is aligned, and culture fuels continuous innovation. Pilots consistently transition into production, and AI delivers measurable business impact.
- **Chasers** are actively deploying AI, but alignment and ROI discipline are still evolving. Infrastructure investments are underway, and processes are improving, yet execution remains uneven across teams.
- **Followers** are in the early phases of adoption, proceeding cautiously and learning from peers. Foundations are forming, but capabilities are limited, and scaling is slow.
- **Laggards** are just beginning to explore AI. Significant gaps exist across strategy, infrastructure, governance, and skills, leaving them unprepared for deployment at scale.
**Takeaway:** Pacesetters take a structured, system-level approach to readiness. This aligns their strategy, technology, and operations into a repeatable, cross-functional process.
Of the 8000+ business leaders surveyed by Cisco in 2025, 83% plan to deploy AI agents—yet [only 13% (The Pacesetters) are truly ready](https://www.cisco.com/c/dam/m/en_us/solutions/ai/readiness-index/2025-m10/documents/cisco-ai-readiness-index-2025-realizing-the-value-of-ai.pdf) to turn these ambitions into impact. Additionally, MIT Sloan research, based on its [Enterprise AI Maturity Framework](https://mitsloan.mit.edu/ideas-made-to-matter/whats-your-companys-ai-maturity-level), shows that organizations achieve the greatest financial impact when they progress from building business cases and running pilots (Stage 2) to scaling AI solutions in production (Stage 3), out of four total stages.
## How to assess and improve AI readiness
If you're uncertain about the state of readiness in your organization, you can take an AI readiness assessment. This is a comprehensive evaluation of your organization's strengths, weaknesses, and gap areas, revealing where you stand today and where to focus next to reach your desired level of AI maturity.
This audit of your strategic, operational, and cultural readiness can be broken down into the following steps:
**1. Define AI strategy & objectives:** First, specify what you want to achieve with AI (e.g., enhanced customer experience) in a way that aligns with core business goals. Establish executive leadership and accountability to ensure cross-functional alignment.
Ask yourself: Have you defined what type of AI you're preparing for (e.g., generative AI or autonomous AI agents)? Who is responsible for AI outcomes? Is there a clear vision of success?
**2. Identify use cases & ROI:** Next, pinpoint your highest-value AI use cases, evaluating applications by their short-term feasibility and long-term ROI.
Ask yourself: Are use cases prioritized by impact and complexity? Are success metrics defined and measurable? Is there a phased deployment plan (Level 1 → Level 3 automation)?
**3. Map your operational reality:** Document how work currently gets done in your organization, warts and all, then define what AI will automate and how. This turns operational knowledge into an implementation blueprint for effective AI automation.
Ask yourself: Does documentation show workflows as they are (not idealized SOPs)? Are edge cases and escalation paths included? Have you identified manual workarounds for automation?
**4. Evaluate data quality & availability:** Audit data for cleanliness, accessibility, and structure. Ensure databases and internal knowledge bases are unbiased and well-organized for AI retrieval.
Ask yourself: Is data structured and machine-readable? Is your knowledge base AI-ready? Are data governance policies defined?
**5. Assess technical infrastructure:** Review your current hardware, software, and cloud capabilities to determine if they can support your target AI use cases and their data processing requirements.
Ask yourself: Are APIs stable and documented? Can AI safely perform read and write actions? Can your systems be integrated end-to-end?
**6. Establish governance & security:** Create policies for AI risk mitigation, ethics, data privacy, and compliance. Plan how to embed governance across operational layers to ensure trust, control, and responsibility before and after deployment.
Ask yourself: Is real-time monitoring in place? Are guardrails and permissions tiered? Is there hallucination detection and QA?
**7. Review organizational readiness:** Identify gaps in AI talent and technical skills like data science or prompt engineering within your organization. Determine if training or new hiring is required.
Ask yourself: Is there AI literacy across teams? Are roles evolving (e.g., AI Manager, human evaluators)? Are human incentives aligned with AI outcomes?
**8. Develop a phased roadmap with gap analysis:** Lastly, once you've assessed readiness across every key pillar, it's time to create a roadmap for implementation. For best results, start with quick wins to achieve immediate trust and impact, then move on to more complex, long-term initiatives.
Ask yourself: Have you prioritized gap areas by impact? Is there a cadence for performance monitoring and QA after deployment? Are proof-of-value milestones established?
## Best practices for AI readiness
According to Cisco, value from AI doesn't follow innovation—it follows readiness. While there's no single path to value in today's disruption-heavy AI market, readiness enables leaders to navigate this shifting landscape with agility, discipline, and operational rigor.
At a time when [only 1 in 4 organizations](https://www.ibm.com/think/insights/ai-readiness-thriving-through-ai-disruption) has a structured strategy for readiness, organizations that invest in comprehensive, lifecycle-wide preparation position themselves for durable transformation and market leadership, not just incremental gains.
Five key practices distinguish AI-ready Pacesetters:
- **Plan and act with clarity:** Anchor your AI strategy in existing business goals. Deploy AI to solve real problems, not chase trends.
- **Invest in infrastructure early:** Readiness precedes scale. Those who delay foundational investment often find themselves rebuilding AI tools and solutions when performance matters most.
- **Treat data as a discipline:** Clean, centralized, and well-structured data underpins every high-performing AI system, from predictive models to knowledge bases.
- **Balance innovation with guardrails:** Go slow to go fast. AI governance and security aren't obstacles, but prerequisites for sustainable scale. Build them in at every layer.
- **Lead the transformation, not just the technology:** AI adoption is as much an organizational challenge as a technical one. Invest in AI change management, literacy, and clear ownership to unlock a durable advantage, not just a one-time win.
## Key takeaways
- **Part roadmap, part mindset:** AI readiness is as much a mindset as a set of capabilities. It's about preparing for the long-term potential of AI across the organization, not just the next pilot.
- **Readiness is agility:** AI-ready organizations can better compete in a fast-moving market, innovating, responding to market changes, and capitalizing on opportunities without incurring undue risk or inefficiency.
- [AI readiness overview](#section-anchor-id-0 "AI readiness overview")
- [What is AI readiness?](#section-anchor-id-1 "What is AI readiness?")
- [Why does AI readiness matter?](#section-anchor-id-2 "Why does AI readiness matter?")
- [Understanding the AI readiness framework](#section-anchor-id-3 "Understanding the AI readiness framework")
- [The six pillars of AI readiness](#section-anchor-id-4 "The six pillars of AI readiness")
- [The four stages of AI readiness](#section-anchor-id-5 "The four stages of AI readiness")
- [How to assess and improve AI readiness](#section-anchor-id-6 "How to assess and improve AI readiness")
- [Best practices for AI readiness](#section-anchor-id-7 "Best practices for AI readiness")
- [Key takeaways](#section-anchor-id-8 "Key takeaways")
## AI coding tools & resources
Source: https://delight.ai/glossary/ai-coding-tools-resources
- [Glossary](/glossary)\n- AI coding tools & resources\n\n## AI coding tools & resources\n\nMarch 6 2026\n\nArtificial intelligence has become an increasingly common part of software development workflows. Developers use AI coding tools to assist with tasks such as code generation, debugging, documentation, testing, and workflow automation. Rather than replacing programmers, these systems are generally designed to reduce repetitive work and improve efficiency during software development.\n\nAs these tools become more advanced, capabilities such as [AI agent memory](https://delight.ai/agent-memory-platform-amp) and [personalization](https://delight.ai/for-you-conversations-fyc) may allow coding assistants to remember project context, adapt to developer preferences, and provide more relevant support over time.\n\nMany AI coding platforms can generate boilerplate code, identify syntax errors, explain unfamiliar functions, and support API integration tasks. As AI-assisted programming continues to evolve, developers are also becoming more familiar with the technologies that power these systems, including machine learning, natural language processing, and large language models.\n\n### What Is AI Coding?\n\nAI coding refers to the use of artificial intelligence systems to assist with software development tasks. Many AI coding tools are trained on large collections of source code, technical documentation, and programming patterns that allow them to generate recommendations and support developers in real time.\n\nDepending on the platform, these systems may provide code suggestions, explain programming concepts, generate templates, identify bugs, or help automate repetitive development tasks. AI coding assistants are commonly integrated into development platforms such as Visual Studio Code, browser-based coding environments, and cloud engineering systems.\n\nCommon AI coding functions include:\n\n- Code completion\n- Syntax correction\n- Automated testing\n- Documentation generation\n- Refactoring assistance\n- SQL query generation\n- API integration support\n- Debugging recommendations\n- Programming language translation\n\n### Technologies Used in AI Coding\n\nAI coding systems rely on several technologies that help software process language, analyze code patterns, and generate programming recommendations.\n\n#### Application Programming Interfaces (APIs)\n\nAPIs allow AI coding tools to communicate with external software platforms, cloud services, repositories, and development environments. Many [AI assistants](https://delight.ai/omnipresent) use APIs to connect language models with IDEs, deployment systems, and documentation resources.\n\n#### Large Language Models (LLMs)\n\nLarge language models are one of the primary technologies behind modern AI coding assistants. These models are trained using massive datasets that include programming languages, technical documentation, and public code repositories.\n\nLLMs can help generate code, explain functions, translate code between programming languages, and answer technical questions using natural language prompts.\n\n#### Machine Learning\n\nMachine learning enables AI systems to recognize patterns in code structure, syntax, and development workflows. AI coding tools use machine learning models to improve autocomplete suggestions, detect anomalies, and recommend corrections.\n\nMachine learning techniques are also used in test automation, predictive debugging, performance optimization, and security analysis.\n\n#### Natural Language Processing (NLP)\n\nNatural language processing allows AI systems to interpret written prompts and convert them into programming-related instructions. NLP enables developers to describe coding tasks using conversational language instead of writing every instruction manually.\n\nExamples include prompts such as:\n\n- "Generate a Python script for CSV parsing"\n- "Create a REST API endpoint"\n- "Explain this SQL query"\n- "Optimize this JavaScript function"\n\n#### Neural Networks\n\nNeural networks are computational systems designed to identify patterns within large amounts of data. In AI coding applications, neural networks help analyze source code, identify contextual relationships, and improve code generation accuracy.\n\nMany modern AI programming tools rely on transformer-based neural network architectures that can process long code sequences while maintaining contextual awareness across files and functions.\n\n### Key Benefits of AI Coding\n\nAI coding tools are commonly used to improve development efficiency and streamline repetitive programming tasks.\n\n#### Faster Development Cycles\n\nAI-assisted programming tools can reduce time spent writing repetitive code structures, configuring templates, and creating standard functions. Many developers use AI systems to generate initial drafts of code before refining them manually.\n\n#### Improved Code Documentation\n\nMaintaining technical documentation can be time-consuming, especially in large software environments. AI coding tools can help generate comments, summaries, README files, and endpoint explanations automatically.\n\n#### More Efficient Debugging\n\nMany AI coding assistants can identify syntax errors, suggest corrections, and explain runtime failures. Some systems can also analyze stack traces and recommend debugging strategies.\n\n#### Support for Learning and Training\n\nAI coding tools are increasingly used by students, junior developers, and technical trainees. These systems can explain programming concepts, generate examples, and provide alternative implementations for educational purposes.\n\n#### Workflow Automation\n\nAI systems can also [assist with operational development tasks](https://delight.ai/autonomous-agent) such as testing preparation, deployment scripting, issue categorization, and infrastructure automation.\n\n### Additional Coding Resources\n\n- https://docs.github.com/en/copilot (GitHub Copilot Documentation)\n- https://ai.google.dev/ (Google AI Developers)\n- https://platform.openai.com/docs/overview (OpenAI Developer Resources)\n- https://developer.mozilla.org/ (Mozilla Developer Network, MDN)\n- https://docs.python.org/3/ (Python Documentation)\n- https://code.visualstudio.com/docs (Visual Studio Code Documentation)\n- https://git-scm.com/doc (Git Documentation)\n- https://www.tensorflow.org/learn (TensorFlow Documentation)\n- https://docs.pytorch.org/tutorials/ (PyTorch Tutorials)\n- https://csuglobal.edu/blog/how-does-ai-actually-work (How AI works in Software Development)\n- https://training.linuxfoundation.org/resources/ (Linux Foundation Training Resources)\n- https://nodejs.org/docs/latest/api/ (Node.js Documentation)\n- https://numpy.org/doc/ (NumPy Documentation)\n- https://www.w3.org/standards/ (W3C Web Standards)\n- https://www.w3schools.com/ (W3Schools Programming Tutorials)\n- https://owasp.org/ (OWASP Foundation)\n- https://projects.apache.org/ (Apache Software Foundation Projects)\n- https://www.educba.com/ai-in-software-development/ (Software Development workflows)\n- https://kubernetes.io/docs/home/ (Kubernetes Documentation)\n- https://docs.docker.com/ (Docker Documentation)\n- https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained (MIT Machine Learning explainer)\n- https://www.brookings.edu/articles/how-ai-powered-software-development-may-affect-labor-markets/ (Effect of AI on Software Development)\n- https://docs.kernel.org/ (The Linux Kernel Documentation)\n\n- [What Is AI Coding?](#section-anchor-id-0)\n- [Technologies Used in AI Coding](#section-anchor-id-1)\n- [Key Benefits of AI Coding](#section-anchor-id-2)\n- [Additional Coding Resources](#section-anchor-id-3)\n
## Glossary of generative AI terminology
Source: https://delight.ai/glossary/glossary-of-generative-ai-terminology
- [Glossary](/glossary)\n- Glossary of generative AI terminology\n\n## Glossary of generative AI terminology\n\nJune 2 2026\n\nGenerative artificial intelligence has quickly become part of everyday conversations across technology, business, education, entertainment, and [customer service](https://delight.ai/ai-customer-service). As AI tools continue to evolve, many technical terms are now appearing in news articles, software platforms, workplace discussions, and online content creation tools.\n\nUnderstanding common generative AI terminology helps explain how modern AI systems create text, images, audio, code, and other forms of digital content. Many of these terms describe how AI models are trained, how they process information, and how users interact with them through prompts, automation, and machine learning systems.\n\n### Common Generative AI Terms\n\nGenerative AI is a fast-moving field, and the vocabulary around it can be dense. Getting familiar with the most common concepts, from how models are built to how they generate output, makes it easier to follow industry developments and evaluate AI tools with confidence.\n\n- **AI Alignment:** The process of designing AI systems so their outputs and behavior follow human goals, safety standards, and ethical guidelines.\n- **Algorithm:** A set of rules or instructions that tells a computer system how to process information and solve tasks.\n- **Artificial General Intelligence (AGI):** A theoretical form of AI capable of performing intellectual tasks at a human level across many different subjects and activities.\n- **Bias:** Patterns of unfair or inaccurate results caused by limitations, imbalances, or assumptions in AI training data.\n- **Chatbot:** An AI-powered conversational system designed to simulate human dialogue through text or voice interactions.\n- **Computer Vision:** A branch of AI that allows machines to interpret and analyze images, videos, and visual information.\n- **Context Window:** The amount of text or information an [AI model can remember](https://delight.ai/agent-memory-platform-amp) and process during a conversation or task.\n- **Dataset:** A large collection of organized information used to train machine learning and AI models.\n- **Deep Learning:** A machine learning method that uses layered neural networks to recognize patterns and generate outputs.\n- **Diffusion Model:** A type of generative AI model commonly used to create images by gradually transforming random noise into detailed visuals.\n- **Embedding:** A numerical representation of words, images, or data that helps AI systems understand relationships and meaning.\n- **Fine-Tuning:** The process of further training an existing AI model on specialized data to improve performance for specific tasks.\n- **Generative AI:** Artificial intelligence capable of creating original content such as text, images, music, audio, or video.\n- **GPU (Graphics Processing Unit):** Specialized computer hardware designed to process large volumes of calculations required for AI training and inference.\n- **Hallucination:** Incorrect or fabricated information confidently produced by an AI system.\n- **Inference:** The stage where a trained AI model generates predictions or responses based on new input data.\n- **Large Language Model (LLM):** A type of AI model trained on massive text datasets to understand and generate human-like language.\n- **Machine Learning (ML):** A field of AI focused on systems that learn patterns from data instead of relying only on manually programmed rules.\n- **Model Training:** The process of teaching an AI system by exposing it to large datasets and adjusting internal parameters over time.\n- **Multimodal AI:** AI systems capable of processing multiple types of input, such as text, images, audio, and video, simultaneously.\n- **Natural Language Processing (NLP):** A branch of AI focused on helping computers understand, interpret, and generate human language.\n- **Neural Network:** A computing system inspired by the human brain that helps AI models identify patterns and relationships in data.\n- **Parameters:** Internal values within an AI model that are adjusted during training and influence how the model responds.\n- **Predictive AI:** AI designed primarily to analyze data and forecast likely outcomes rather than generate new content.\n- **Prompt:** A question, instruction, or input given to an AI system to guide its response or generated output.\n- **Prompt Engineering:** The practice of designing prompts that improve the quality, accuracy, or usefulness of AI-generated responses.\n- **Reinforcement Learning:** A machine learning technique where AI systems improve performance through feedback, rewards, and repeated testing.\n- **Retrieval-Augmented Generation (RAG):** A method that combines generative AI with external information retrieval systems to improve factual accuracy.\n- **Synthetic Data:** Artificially generated information used to supplement or replace real-world training data.\n- **Token:** Small units of text processed by language models during training and generation tasks.\n- **Transformer Model:** A neural network architecture that powers many modern generative AI systems and large language models.\n- **Training Data:** The information used to teach AI systems how to recognize patterns and generate outputs.\n- **Vector Database:** A specialized database designed to store and retrieve embeddings and semantic search information efficiently.\n\n### How Generative AI Works\n\nGenerative AI systems are trained using enormous datasets that may include books, articles, images, videos, code repositories, audio recordings, and online conversations. During training, models learn statistical relationships between words, images, sounds, and patterns so they can generate new content that resembles human-created material.\n\nModern generative AI platforms often rely on several technologies working together, including:\n\n- Neural networks that identify complex patterns in data\n- Transformer architectures that improve language understanding\n- Large-scale datasets used for training and refinement\n- GPUs and cloud computing systems that handle intensive processing\n- Natural language processing systems that interpret user prompts\n- Retrieval systems that improve factual accuracy and context\n\nDifferent types of generative AI models are designed for different tasks. Some specialize in text generation, while others focus on image creation, coding assistance, music generation, [voice synthesis](https://delight.ai/voice), or video production.\n\n### Why Generative AI Terminology Matters\n\nAs generative AI becomes more integrated into workplaces, schools, customer service systems, [healthcare](https://delight.ai/industries/healthcare), software development, and creative industries, understanding its terminology helps people evaluate both the opportunities and limitations of the technology.\n\nMany AI discussions involve technical concepts such as hallucinations, fine-tuning, multimodal systems, and prompt engineering. Familiarity with these terms makes it easier to follow industry developments, compare AI tools, and understand how organizations are using AI-driven systems to automate tasks, improve workflows, and generate digital content.\n\nGenerative AI terminology also helps explain ongoing conversations around privacy, bias, copyright, safety, regulation, and responsible AI development as the technology continues to evolve.\n\n### Additional Resources to Learn More\n\n- [What is Generative AI?](https://teaching.pitt.edu/resources/what-is-generative-ai/)\n- [The History of Artificial Intelligence](https://www.ibm.com/think/topics/history-of-artificial-intelligence)\n- [What Are Neural Networks?](https://www.iso.org/artificial-intelligence/neural-networks)\n- [A Beginner's Guide to How AI Thinks](https://tdwi.org/blogs/ai-101/2025/09/what-is-a-neural-network.aspx)\n- [131 AI Statistics and Trends for 2026](https://www.nu.edu/blog/ai-statistics-trends/)\n- [Types of AI: Explore Key Categories and Uses](https://ischool.syracuse.edu/types-of-ai/)\n- [Effective Prompts for AI: The Essentials](https://mitsloanedtech.mit.edu/ai/basics/effective-prompts/)\n- [An Overview and Guide to Prompt Engineering](https://cloud.google.com/discover/what-is-prompt-engineering)\n- [10 Predictions On Automation And The Future Of Work](https://www.forbes.com/sites/charlestowersclark/2025/12/10/ai-in-2026-10-predictions-on-automation-and-the-future-of-work/)\n- [The Impact of AI by 2040: Deep Thinkers Address the Potential Future of AI](https://imaginingthedigitalfuture.org/reports-and-publications/the-impact-of-artificial-intelligence-by-2040/deep-thinkers-address-the-potential-future/)\n\n- [Common Generative AI Terms](#section-anchor-id-0 \"Common Generative AI Terms\")\n- [How Generative AI Works](#section-anchor-id-1 \"How Generative AI Works\")\n- [Why Generative AI Terminology Matters](#section-anchor-id-2 \"Why Generative AI Terminology Matters\")\n
## Glossary of modern (CX) terms
Source: https://delight.ai/glossary/glossary-of-cx-terms
- [Glossary](/glossary)
- Glossary of modern (CX) terms
## Glossary of modern (CX) terms
June 2 2026
Customer experience (CX) describes every interaction a customer has with a brand across support, product usage, and communication channels. As organizations adopt AI, automation, and omnichannel strategies, CX language has become more specialized—covering metrics, workflows, and frameworks used to measure and improve service quality. The terms below outline key concepts addressed by [modern CX platforms](https://delight.ai/ai-customer-experience-platform).
### What is Customer Experience (CX)?
Customer experience (CX) refers to the overall impression a customer forms of a brand based on every interaction across their journey. It includes every touchpoint, from direct interactions like speaking with support or using a product, to indirect signals such as reviews, marketing messages, and word-of-mouth.
CX spans the entire organization rather than sitting within a single team. Marketing, sales, product development, and customer support all contribute to shaping how a brand is perceived over time. Each interaction either strengthens or weakens that overall perception.
Modern CX strategies are increasingly driven by data and technology. Organizations rely on metrics such as Net Promoter Score (NPS), Customer Satisfaction (CSAT), and First Call Resolution (FCR) to evaluate performance and identify areas for improvement. At the same time, AI-powered systems are enabling more personalized and responsive experiences at scale.
Key elements of CX include:
- **Consistency across channels:** Ensuring customers receive a seamless experience whether they engage via [phone](https://delight.ai/voice), chat, email, or self-service tools.
- **Ease of interaction:** Reducing the effort required for customers to get answers or resolve issues.
- [**Personalization**](https://delight.ai/for-you-conversations-fyc)**:** Using data to tailor interactions to individual needs and preferences.
- **Responsiveness:** Providing timely support and clear communication at every stage.
When these elements work together, CX becomes a major driver of customer loyalty, retention, and long-term business growth.
### Common CX Terms
CX teams rely on a shared vocabulary to measure performance, design better customer journeys, and evaluate service quality across channels. These terms are used daily in contact centers, product teams, and customer success operations to track efficiency, satisfaction, and loyalty.
Each term reflects a core concept used in modern customer experience management, from operational metrics to customer behavior and feedback systems.
- [**Abandonment Rate**](https://www.investopedia.com/terms/a/abandon-rate.asp)**:** The percentage of customers who begin an interaction (such as a call, chat, or form submission) but leave before completion. High rates often indicate friction in wait times or self-service flows.
- [**Agent Assist**](https://www.mbcustomercontact.org/events-1/how-agentic-ai-is-transforming-cx-and-contact-centers)**:** AI-powered tools that support live agents by surfacing suggested responses, relevant knowledge articles, or next-best actions during customer interactions.
- [**Average Handle Time (AHT)**](https://swpp.org/tip-of-the-week/do-you-know-the-value-of-one-second-of-average-handle-time-aht/)**:** A metric measuring total interaction time, including talk time, hold time, and after-call work, used to evaluate operational efficiency.
- [**Customer Effort Score (CES)**](https://www.metrichq.org/customer-success/customer-effort-score/)**:** A measure of how easy it is for customers to complete an interaction or resolve an issue, typically on a low-to-high effort scale.
- [**Customer Experience (CX)**](https://asq.org/quality-resources/customer-experience)**:** The overall perception a customer forms based on all interactions with a brand across marketing, sales, product, and service touchpoints.
- [**First Call Resolution (FCR)**](https://qatc.org/newsletter/how-to-measure-and-improve-first-contact-resolution-fcr-in-your-contact-center/)**:** The percentage of customer issues resolved during the first interaction without the need for follow-ups or escalation.
- [**First Response Time (FRT)**](https://technofaq.org/posts/2022/09/first-response-time-frt-meaning-and-optimization/)**:** The average time it takes for a customer to receive an initial response after submitting an inquiry.
- [**Knowledge Base (KB)**](https://www.geeksforgeeks.org/artificial-intelligence/role-of-knowledge-bases-in-intelligent-systems/)**:** A centralized repository of articles, FAQs, and support documentation used by both customers and agents for self-service and guidance.
- [**Net Promoter Score (NPS)**](https://uxplanet.org/net-promoter-score-nps-essential-guide-d52ea12f9423)**:** A loyalty metric based on customer likelihood to recommend a brand, widely used to measure long-term satisfaction.
- [**Omnichannel**](https://thecxacademy.org/creating-a-seamless-omnichannel-experience-step-by-step/)**:** A CX approach that connects all communication channels while preserving customer context across interactions.
- [**Personalization**](https://ecxo.org/before-cx-had-a-name-how-a-1993-article-book-launched-the-personalization-revolution/)**:** The practice of tailoring experiences using customer data such as behavior, preferences, and history to improve engagement and satisfaction.
- [**Quality Assurance (QA)**](https://www.hbs.edu/information-technology/about-us/news-updates/quality-assurance)**:** The process of evaluating customer interactions for accuracy, compliance, empathy, and performance standards.
- [**Sentiment Analysis**](https://www.edcparis.edu/en/blog/how-do-you-measure-sentiment-analysis)**:** The use of AI and NLP to detect emotional tone in customer interactions, helping identify satisfaction or frustration trends.
- [**Voice of Customer (VoC)**](https://online.hbs.edu/blog/post/voice-of-customer)**:** A structured approach to collecting and analyzing customer feedback across surveys, reviews, and interactions.
- [**Zero-Party Data**](https://www.kellogg.northwestern.edu/news/blog/2024/12/20/how-brands-unlock-consumer-loyalty/)**:** Data that customers intentionally share with a brand, enabling more accurate and privacy-friendly personalization.
### Why CX Terminology Matters Today
Modern CX terminology reflects a shift toward data-driven, [AI-supported helpdesks](https://delight.ai/desk) and highly personalized customer interactions. As organizations adopt more advanced tools and frameworks, a shared language helps teams stay aligned, measure performance consistently, and improve how customer experiences are designed and delivered.
These concepts also turn customer behavior into actionable insight. Terms like First Response Time, Customer Effort Score, and Voice of Customer provide structure for evaluating feedback and identifying improvements, while newer AI-driven concepts like sentiment analysis and omnichannel orchestration support more scalable and responsive service models.
- [What is Customer Experience (CX)?](#section-anchor-id-0 "What is Customer Experience (CX)?")
- [Common CX Terms](#section-anchor-id-1 "Common CX Terms")
- [Why CX Terminology Matters Today](#section-anchor-id-2 "Why CX Terminology Matters Today")
## B2B marketing terms & glossary
Source: https://delight.ai/glossary/glossary-of-b2b-marketing-terms
- [Glossary](/glossary)
- B2B marketing terms & glossary
## B2B marketing terms & glossary
June 2 2026
### Intro
Business-to-business (B2B) sales and marketing come with their own language, and it's one that evolves quickly as new strategies, technologies, and platforms emerge. Whether you're new to the industry or looking to sharpen your vocabulary, having a solid grasp of common B2B terms makes it easier to communicate clearly, evaluate tools, and contribute meaningfully to sales and marketing conversations.
### B2B marketing terms
- **A/B Testing:** A method of comparing two versions of a marketing asset—such as an email subject line or landing page—by showing each version to an equal number of people and measuring which performs better.
- **Account-Based Marketing (ABM):** A strategy in which sales and marketing teams focus their efforts on a defined set of high-value target companies, creating [personalized outreach](https://delight.ai/for-you-conversations-fyc) and content for each one rather than casting a wide net.
- **AI Agent:** An AI-powered software system that can make decisions and take actions to accomplish tasks on behalf of users.
- **AIDA Model:** A framework describing the stages a customer moves through before making a purchase: Awareness, Interest, Desire, and Action.
- **Artificial Intelligence (AI):** A set of technologies that enables computers and machines to perform tasks that typically require human intelligence.
- **BANT Framework:** A lead qualification framework that evaluates prospects based on four criteria: Budget, Authority, Need, and Timeline.
- **Bounce Rate:** The percentage of website visitors who leave after viewing only one page without taking any further action.
- **Buyer's Journey:** The process a potential customer goes through before making a purchase, typically broken into three stages: Awareness, Consideration, and Decision.
- **Call to Action (CTA):** An instruction or prompt that encourages a prospect to take a specific next step, such as signing up for a demo or downloading a resource.
- **Churn Rate:** The rate at which customers stop using a product or service over a given period. A high churn rate can signal dissatisfaction and negatively impact long-term revenue.
- **Click-Through Rate (CTR):** The percentage of people who click on a link after seeing it, calculated by dividing the number of clicks by the number of impressions.
- **Conversion:** The moment a prospect completes a desired action, such as making a purchase or filling out a form.
- **Customer Acquisition Cost (CAC):** The total cost of acquiring a new paying customer, including marketing, sales, and operational expenses.
- **Customer Lifetime Value (CLV):** The total revenue a company expects to generate from a single customer over the entire duration of their relationship.
- **Customer Relationship Management (CRM):** A technology platform used to manage and track interactions with customers and prospects, helping teams stay organized and maintain consistent communication. Many CRM platforms integrate with a [helpdesk](https://delight.ai/desk) so sales, marketing, and support teams can work from the same customer context. An [AI customer service agent](https://delight.ai/ai-customer-service) can also use CRM data and its own [AI agent memory](https://delight.ai/agent-memory-platform-amp) to answer questions, route requests, and maintain continuity across conversations.
- **Discovery Call:** An early-stage sales conversation used to learn more about a prospect's needs, challenges, and goals, and to determine whether they are a good fit.
- **Drip Campaign:** An automated marketing strategy in which a series of messages or content pieces is sent to prospects at scheduled intervals over time.
- **Engagement Rate:** A metric that measures how actively an audience interacts with content, calculated as total interactions divided by total reach or impressions.
- **Evergreen Content:** Content that remains relevant and valuable over an extended period, continuing to drive traffic and engagement long after it is first published.
- **Firmographic:** A method of segmenting businesses based on characteristics such as industry, company size, or location, used to target outreach more precisely.
- **Friction:** Anything in the sales or marketing process that slows a prospect's progress toward a purchase, such as a complicated checkout process or unclear messaging.
- **Gatekeeper:** A person—often an assistant or receptionist—who controls access to a decision-maker within a target organization.
- **Goal-Based Agent:** An AI agent that uses goals to guide decision-making, selecting actions that move it closer to a desired outcome.
- **Ideal Customer Profile (ICP):** A detailed description of the type of company most likely to benefit from and purchase a product or service, used to guide targeting and prioritization.
- **Impressions:** The number of times a piece of content or an online advertisement is displayed, regardless of whether it is clicked.
- **Key Performance Indicators (KPIs):** Measurable values used to track progress toward specific business goals, helping teams assess performance and make data-driven decisions.
- **Keyword:** A word or phrase used in SEO to help a web page rank in search engine results.
- **Lead Generation:** The process of attracting and identifying potential customers through marketing and sales activities, with the goal of moving them into the sales pipeline.
- **Lead Nurturing:** The practice of building and maintaining relationships with prospects over time through targeted content and communication, guiding them toward a purchase decision.
- **Lead Qualification:** The process of evaluating a prospect's likelihood of becoming a customer, based on factors such as budget, authority, need, and timeline.
- **Marketing-Qualified Lead (MQL):** A prospect who has shown interest in a company's offerings through marketing engagement and is considered more likely to convert than a cold contact.
- **Market Segmentation:** The practice of dividing a target market into distinct groups based on shared characteristics such as industry, location, or company size.
- **Metrics:** Quantitative measurements used to evaluate the performance of sales and marketing activities.
- **Model-based Reflex Agent:** An AI agent that uses an internal model of its environment to make decisions, unlike a simple reflex agent, which responds only to what it currently observes.
- **Net Promoter Score (NPS):** A customer loyalty metric calculated by subtracting the percentage of detractors from the percentage of promoters on a 0-10 rating scale.
- **Objection Handling:** The process of addressing a prospect's concerns during a sales conversation in a way that keeps the deal moving forward.
- **Outbound Sales:** A sales approach in which company representatives initiate contact with potential customers through cold calls, emails, or social outreach.
- **Pain Point:** A specific problem or challenge experienced by a prospect. Identifying pain points is central to qualifying leads and positioning a product as the right solution.
- **Pay-Per-Click (PPC):** A digital advertising model in which advertisers pay a fee each time someone clicks on their ad.
- **Pipeline:** A visual representation of the stages a prospect moves through on the way to becoming a customer.
- **Referral:** A recommendation made by an existing customer that introduces a new prospect to a company's products or services.
- **Revenue Operations (RevOps):** An organizational approach that aligns sales, marketing, and customer success teams around shared goals and processes to improve overall business performance.
- **Sales Cycle:** The sequence of steps a salesperson follows from initial contact with a prospect to the closing of a deal.
- **Sales Funnel:** A model representing the journey prospects take from first awareness of a company to becoming a customer.
- **Sales-Qualified Lead (SQL):** A prospect that the sales team has vetted and deemed ready for direct sales engagement based on their likelihood to purchase.
- **Search Engine Optimization (SEO):** The practice of improving a website's content, structure, and authority to rank higher in organic search engine results.
- **Simple Reflex Agent:** The most basic type of AI agent, which responds directly to its immediate environment using predefined rules.
- **SPIN Selling:** A sales methodology built around four types of questions—Situation, Problem, Implication, and Need/Payoff—designed to help salespeople uncover a prospect's true needs.
- **Utility-based Agent:** An AI agent that uses a utility system to evaluate possible outcomes and choose the action that provides the greatest benefit.
- **Value Proposition:** A clear statement that explains how a product or service solves a customer's problem, what benefits it delivers, and why it is a better choice than the alternatives.
- **White Paper:** A detailed, authoritative piece of content that explores a specific industry problem and presents a well-researched solution, used to demonstrate expertise and generate leads.
### Putting It All Together
B2B sales and marketing is a discipline built on relationships, data, and strategy, and the vocabulary around it reflects that complexity. Getting comfortable with these terms is a strong foundation, but the real learning happens when they are applied in practice. Whether you are building out a pipeline, refining a campaign, or aligning your sales and marketing teams, a shared language makes every part of the process smoother and more effective.
### Additional Resources
- [What Is B2B Marketing and Why Should Marketers Care?](https://martech.org/why-we-care-about-b2b-marketing-a-guide-for-marketers/)
- [How Can Businesses Grow Online? Unlocking the Power of Digital Marketing Strategies](https://online.edhec.edu/en/blog/how-can-businesses-grow-online-unlocking-the-power-of-digital-marketing-strategies/)
- [B2B vs B2C Marketing: The Main Differences and Strategies for Each Type](https://www.imd.org/blog/marketing/b2b-b2c-marketing/)
- [29 Must-Know B2B Marketing Statistics](https://www.leadforensics.com/blog/must-know-b2b-marketing-statistics/)
- [Why is Account-Based Marketing Crucial to Business Development?](https://www.edcparis.edu/en/blog/why-account-based-marketing-crucial-business-development)
- [7 Marketing KPIs You Should Know & How to Measure Them](https://online.hbs.edu/blog/post/marketing-kpis)
- [18 Marketing KPIs for Non-Analysts](https://online.champlain.edu/blog/essential-marketing-kpis)
- [What is A/B Testing?](https://www.gsb.stanford.edu/insights/explainer-what-ab-testing)
- [What is Your Call-to-Action?](https://www.mtu.edu/umc/services/websites/design/call-to-action/)
- [The B2B Buying Journey: Key Stages & Examples](https://www.gartner.com/en/sales/insights/b2b-buying-journey)
- [Email Data: A Beginner's Guide To Channel Performance](https://umarcomm.umn.edu/blog/2025/03/24/email-data-beginners-guide-channel-performance)
- [5 Ways Marketing Automation Can Help Marketers](https://online.stu.edu/degrees/business/mba/marketing/automation-can-help-marketers/)
- [Intro](#section-anchor-id-0 "Intro")
- [B2B marketing terms](#section-anchor-id-1 "B2B marketing terms")
- [Putting It All Together](#section-anchor-id-2 "Putting It All Together")
- [Additional Resources](#section-anchor-id-3 "Additional Resources")
## What is an MCP? Model Context Protocol explained
Source: https://delight.ai/glossary/what-is-an-mcp-model-context-protocol
- [Glossary](/glossary)
## What is an MCP? Model Context Protocol explained
July 31 2026
### Key takeaways
- **MCP stands for Model Context Protocol**, an open standard Anthropic introduced in November 2024 for connecting AI models to outside data and tools.
- **MCP replaces one-off integrations with a single shared protocol.** A tool built for MCP works with any MCP-compatible AI model, not just one vendor's.
- **It uses a client-server design.** An MCP server exposes data or actions; an MCP client, built into the AI application, connects to it.
- **Adoption moved fast.** Within a year, MCP was supported by Claude, ChatGPT, Gemini, and Microsoft Copilot, and Anthropic donated it to the Linux Foundation.
### What is an MCP?
MCP, short for Model Context Protocol, is an open standard that lets AI models connect to external data sources, tools, and systems through one shared format. It replaces the custom integration that would otherwise be needed for every AI-to-tool pairing.
[Anthropic introduced MCP](https://www.anthropic.com/news/model-context-protocol) in November 2024, and it has since been adopted beyond Anthropic's own products.
### How does MCP work?
MCP runs on a client-server model. An **MCP server** exposes a specific data source or capability, like a Google Drive account, a Postgres database, or a Slack workspace. An **MCP client** lives inside the AI application itself and handles the connection to that server.
The AI application (the **MCP host**, such as Claude Desktop or a coding assistant) uses its client to discover what a server offers. It then calls on that server mid-conversation, whenever the model decides it needs something the server provides.
Three building blocks make up what a server can expose:
- **Tools.** Actions the AI model can invoke, like running a search or updating a record.
- **Resources.** Data the model can read, like a file or a database table.
- **Prompts.** Pre-written instructions the server offers for common tasks.
Messages between a client and server run over a standard format (JSON-RPC), sent either locally on the same machine or over a network connection. That standardization is what makes the client-server relationship swappable instead of custom-built.
Without a shared standard, connecting *N* AI models to *M* systems means building roughly N×M custom integrations, since every model-to-system pairing needs its own glue code. MCP turns that into an N+M problem instead.
Approach | Integrations needed for 5 models × 5 systems
---|---
Custom, one-off integrations | Up to 25
Shared MCP standard | 10 (5 clients + 5 servers)
A server built once works with any compliant client, and a client built once works with any compliant server.
### MCP vs. API: what's the difference?
**An API already lets two pieces of software exchange data**, so it's a fair question why AI needed something new. The difference is who's doing the calling and how much custom work that takes.
A traditional API integration is built for **one specific pairing**. A developer writes code that knows exactly which endpoints to call, in what order, with what parameters, for that one connection. An AI model can't improvise that on its own without someone writing the glue code first.
MCP standardizes the layer above the API. An MCP server wraps existing APIs and describes its tools and data in a format any MCP client can understand and call dynamically. The API still does the underlying work; MCP is what lets an AI model discover and use it without a bespoke integration for every tool it might need.
### Real examples of MCP in action
#### Everyday productivity tools
Anthropic's original MCP release shipped with pre-built servers for Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer, so Claude Desktop users could connect those accounts without any custom setup.
#### Coding and developer tools
Development environments including Zed, Replit, Sourcegraph, and Cursor have integrated MCP so coding agents can pull real project context, like a repository's actual file structure, instead of guessing at it.
#### Cross-vendor enterprise adoption
By December 2025, [MCP had been adopted](https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation) by ChatGPT, Gemini, Microsoft Copilot, and Visual Studio Code. **More than 10,000 active public MCP servers were in use** by then, with enterprise deployment support from AWS, Google Cloud, and Microsoft Azure.
That same month, Anthropic donated MCP to the [Agentic AI Foundation](https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation), a directed fund under the Linux Foundation co-founded by Anthropic, Block, and OpenAI. Handing governance to a neutral foundation, rather than keeping it under one company, is what let competing AI vendors adopt the same standard without hesitation.
### Takeaway
MCP is one clear signal that AI agents are moving from answering questions to taking action inside real systems. That shift is the same one reshaping how [AI agent](https://delight.ai/ai-agent) platforms think about [integrations](https://delight.ai/integrations) more broadly, whether or not they use MCP specifically. Explore the [official MCP specification](https://modelcontextprotocol.io) to see the protocol details firsthand.
- [How does MCP work?](#how-does-mcp-work)
- [MCP vs. API: what's the difference?](#mcp-vs-api-whats-the-difference)
- [Real examples of MCP in action](#real-examples-of-mcp-in-action)
- [Takeaway](#takeaway)
## Agentic orchestration
Source: https://delight.ai/glossary/agentic-orchestration
## Agentic orchestration
August 18 2026
**Key takeaways**
- **Agentic orchestration controls execution**, including tool access, context, state, retries, approvals, and termination.
- **One agent still needs orchestration** to keep its actions within defined technical and business boundaries.
- **Architecture should fit the task**, since centralized coordination helps parallel work and adds overhead to sequential work.
## What is agentic orchestration?
Agentic orchestration is the layer that decides what an AI agent is allowed to do at each step of a task, and when a person has to step in instead. It applies a team's technical and business rules above the agent's own reasoning, so every action stays inside defined boundaries and is available for review.
To see where this control system fits, start with the agent loop. During that loop, an [AI agent](https://delight.ai/glossary/ai-agent) considers the task information and chooses its next action; after the action produces a result, that result informs the next decision. Agentic orchestration governs the whole cycle.
The controls become visible when the agent reaches a boundary or encounters a problem. If the agent proposes a refund above its authority, orchestration blocks the action and routes the case to the appropriate person. If a tool keeps failing, orchestration stops repeated attempts and preserves the record for review.
## Agentic orchestration vs AI orchestration vs single agent loop
These terms refer to different levels of the same system. The agent loop sits inside an orchestrated task, and that task runs inside the organization's broader AI environment.
| Term | Scope | What it controls |
|---|---|---|
| Agent loop | One agent's interaction with its tools | The cycle of interpreting, acting, observing, and repeating |
| Agentic orchestration | One agentic task or workflow | Tools, state, context, permissions, retries, handoffs, and termination |
| AI orchestration | An organization's AI environment | Models, data pipelines, infrastructure, deployment, and governance |
An agent loop is the sequence at the center of an AI agent's work. The AI model considers the current task information and chooses an action, such as calling a tool. After the tool returns a result, the model adds that information to the task context and chooses its next step.
Agentic orchestration governs that loop by setting its boundaries and recognizing when the task has reached a valid end. Above this task-level control sits AI orchestration, which provides shared infrastructure and governance across the organization's AI workloads. Agentic orchestration works within that larger environment each time an agent handles a task.
The same task-level controls apply when one agent handles the entire workflow. Multiple agents become useful when separate parts of the work require different expertise, permissions, or simultaneous execution. Because every added agent creates another communication path and potential failure point, the task should justify the additional coordination.
## How does agentic orchestration work?
Agentic orchestration works by managing five connected parts of a running task:
- **Task and state management** records established facts, completed steps, and remaining work.
- **Context assembly** selects the instructions and data required for the current step.
- **Tool and permission routing** controls which systems the agent can reach and which actions it can take.
- **Retries, handoffs, and termination** define recovery paths, escalation conditions, and valid end states.
- **Observability** records what happened so teams can evaluate and improve the system.
An orchestrated run begins when a request creates the initial state. This record anchors the work to the user's identity and permissions, then grows as the system confirms facts and completes steps under the relevant policy constraints.
### Task and state management
Task and state management gives the workflow a durable record. By separating confirmed facts and completed actions from tentative model output, the record helps the system resume after a pause without treating an early guess as settled information.
The state also shows who owns the task and whether it is waiting for approval. Retry history and completion criteria then give each new step a reliable view of progress, even when the work spans sessions or systems.
### Context assembly
Once the system knows the task's current state, context assembly prepares the instructions and data for the next model call. Keeping that context focused reduces distraction and unnecessary data exposure, which makes the resulting behavior easier to evaluate.
Protocols can standardize the connection between AI applications and external systems. The official [Model Context Protocol documentation](https://modelcontextprotocol.io/) defines MCP as an open-source standard that provides access to data sources, tools, and workflows. Within an orchestrated task, the control system determines which MCP connection the agent may use and limits the information and actions available through it.
### Tool and permission routing
After the system assembles the context, tool and permission routing determines what the agent may do with it. Looking up an order carries less risk than issuing a refund or changing an account, so the system authorizes each tool call according to its consequences and validates the data moving in and out.
Strong routing gives the agent the minimum access required for the task and applies policy checks or approval gates when the stakes rise. If a tool is unavailable or the request exceeds the agent's authority, a structured error tells the orchestration system how to respond.
### Retries, handoffs, and termination
When a step cannot proceed, the orchestration system determines whether the run should recover or end. Depending on the problem, it may allow another attempt, adjust the approach, ask for missing information, or [transfer the case to a person](https://delight.ai/glossary/human-ai-interaction) with the full state attached.
Clear termination rules prevent the model from continuing to generate plausible steps after useful progress has stopped. The orchestration system ends the run only when it reaches a defined outcome, whether that outcome is a completed action, a documented escalation, or a failure with enough context for review.
### Observability
Observability creates a record as the task moves through the system. By connecting each model response to the tool activity, state change, policy decision, and approval that followed, the record helps a team find the source of an incorrect result.
The [NIST AI Risk Management Framework](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/) calls for ongoing monitoring, periodic review, documented risk-management processes, and clearly defined human oversight roles. An orchestration trace gives technical and operational teams evidence they can use to apply those practices to a live agentic workflow.
### Common orchestration patterns
Most production systems reuse a handful of patterns rather than inventing a new architecture for every task.
- **Single agent with tools** — one agent keeps full context and responsibility, calling tools as needed. This covers most workloads.
- **Deterministic workflow with agentic steps** — code owns the overall process, and an agent only handles the steps that genuinely require judgment, such as classifying a request.
- **Manager agent with specialists** — one agent owns the customer-facing result and delegates bounded subtasks to specialist agents, folding their output back into a single response.
- **Handoff** — responsibility transfers entirely to whichever agent, or person, is better suited to what the case now requires.
Agent Steward combines the last two: one agent owns a case end to end and dispatches specialized sub-agents by channel, then hands off to a person the moment a decision crosses a policy or financial limit.
## Why unmanaged agent execution becomes a reliability risk
Unmanaged agent execution creates a reliability risk because an unchecked error can flow into later steps and shape the final result. A confident response may begin with a false assumption, and without a control system, that assumption can influence every action that follows.
A 2026 [Google Research study](https://research.google/blog/towards-a-science-of-scaling-agent-systems-when-and-why-agent-systems-work/) tested 180 agent configurations across financial reasoning, web navigation, planning, and tool use. Independent multi-agent systems amplified errors by up to 17.2 times, compared with the single-agent baseline. Centralized systems contained amplification to 4.4 times because the orchestrator provided a validation point before errors propagated.
The study also showed that coordination strategy must fit the task:
- **Parallel financial analysis** improved by 80.9% with centralized coordination.
- **Sequential planning** declined by 39% to 70% across every multi-agent configuration tested.
- **Tool-heavy workflows** incurred greater coordination overhead as the number of tools increased.
Taken together, these findings support a practical design rule. Use multiple agents for components that can progress independently and keep tightly dependent reasoning in one continuous process. In either architecture, validation should happen before an output becomes the basis for a consequential action.
## What governed agentic orchestration looks like in practice
Governed agentic orchestration combines execution rules with human oversight and a traceable record of each task. These controls become especially concrete in customer service, where an agent's action can immediately affect a customer, account, or transaction. It's also where the market's own hesitation shows up most directly: [McKinsey's 2026 AI Trust Maturity Survey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era) found that nearly two-thirds of organizations cite security and risk concerns as the top barrier to scaling agentic AI.
Delight.ai applies this model by giving each case one accountable owner, governing its decisions, and encoding operating procedures in reviewed workflows. Agent Steward, Trust OS, and Actionbooks provide those capabilities.
### Trust OS: permissions and graduated autonomy
Teams use [Trust OS](https://delight.ai/trust-os), Delight.ai's governance and observability layer, to set the boundaries around agent execution. It records conversations, decisions, and tool calls, and role-based access determines who can change the system. Teams can then test an approved change before it reaches full production traffic.
Those controls support a graduated path to autonomy for Agent Steward. Teams begin with human review for defined decisions, then expand autonomous handling after a case type meets the required performance and policy thresholds. The approval rules and audit trail stay visible throughout that progression.
### Agent Steward: case ownership across sessions
When a customer case spans several sessions or systems, [Agent Steward](https://delight.ai/autonomous-agent) gives it one accountable owner from start to finish. Specialized sub-agents can work through APIs, email, voice, and desk channels, and Steward coordinates their work within the same case record.
The same case record carries the evidence Steward gathers and the recommendation it prepares. If a decision crosses a policy or financial limit, the workflow routes it to the appropriate person for approval. Every action, API call, and response remains available in the audit trail.
### Actionbooks: the executable, reviewed step
The case owner also needs clear instructions for carrying out approved work. Teams can encode an operating procedure as a structured Say, Ask, and Call flow, which Delight.ai calls an [Actionbook](https://delight.ai/blog/product/introducing-actionbook-editor). The flow guides the agent through a task such as booking an appointment, updating a record, or resolving a service issue.
Operations teams can author and edit Actionbooks directly, so the people responsible for the procedure can maintain it. Each new or revised Actionbook remains a draft until a person approves it for deployment, creating a deliberate checkpoint before the behavior reaches a customer.
Together, these controls create a continuous record from the original request through the final outcome. Teams can follow what happened, understand the basis for each action, and refine permissions or workflow logic when the outcome falls short.
Agent Steward brings that governed execution into one customer-facing system. Trust OS applies the decision rules, and each team chooses where human approval enters the workflow. [Explore Agent Steward](https://delight.ai/autonomous-agent) to see how end-to-end case ownership works in practice.
## Conclusion
Agentic orchestration is what turns a capable model into a system a business can actually run in production: a control layer that decides what an agent can try, what state it can trust, and when a person needs to step in. Whether a workflow uses one agent or several, the added complexity only earns its cost when it demonstrably reduces risk or covers work a single agent genuinely can't.
Delight.ai's Trust OS, Agent Steward, and Actionbooks show what that discipline looks like built into a live, customer-facing system, not left as a design principle on a whiteboard. [Explore Agent Steward](https://delight.ai/autonomous-agent) to see governed orchestration applied to real customer cases.
- [What is agentic orchestration?](#section-anchor-id-0)
- [Agentic orchestration vs AI orchestration vs single agent loop](#section-anchor-id-1)
- [How does agentic orchestration work?](#section-anchor-id-2)
- [Why unmanaged agent execution becomes a reliability risk](#section-anchor-id-3)
- [What governed agentic orchestration looks like in practice](#section-anchor-id-4)
- [Conclusion](#section-anchor-id-5)
## What is an AI Concierge?
Source: https://delight.ai/glossary/what-is-an-ai-concierge
- [Glossary](/glossary)\n- What is an AI Concierge?\n\n## What is an AI Concierge?\n\nAugust 20 2026\n\n### Key takeaways\n\n- **Concierge AI** handles a customer's request from first message through resolution.\n- A concierge AI can **use prior context, reach out proactively, and take approved actions** in connected business systems.\n- Human escalation remains essential for **ambiguous, sensitive, or high-stakes requests**.\n\n## What is an AI concierge?\n\n**Concierge AI,** also called an AI concierge, **is a customer-facing AI agent that uses conversation history and account context to manage a request from start to finish.** Like a hotel concierge, it brings the relevant information and actions into one continuous service experience.\n\n## How does an AI concierge work?\n\nTurning a customer's message into a completed outcome takes four connected stages:\n\n- **Understand the request -** The AI concierge identifies the customer's goal, including requests that contain multiple details or use indirect phrasing.\n- **Retrieve context -** It then brings in relevant account history, prior conversations, order details, or permissions from connected systems.\n- **Take an approved action -** Next, it follows the business's rules to update an account, check a booking, process a refund, or complete another defined workflow.\n- **Close the loop -** Lastly, it confirms the outcome, asks whether the customer needs further help, and escalates with context when a person needs to step in.\n\nPersistent memory keeps all four stages connected across repeat visits and channel changes. Delight.ai's [Agent Memory Platform](https://delight.ai/agent-memory-platform-amp) preserves memory across chat, SMS, email, and voice, so a customer can continue the same conversation without repeatedly describing their situation.\n\n## How is an AI concierge different from a chatbot?\n\nA standard chatbot usually responds within the current session and directs the customer to information or a support queue. An AI concierge carries that context and responsibility across the whole customer journey instead.\n\n| Capability | Standard chatbot | AI concierge |\n|---|---|---|\n| Trigger | Responds after a customer asks | Can respond and initiate timely outreach |\n| Memory | Commonly uses the current session | Can retain context across visits and channels |\n| Scope | Supplies an answer or resource | Completes approved actions and workflows |\n| Escalation | Transfers the conversation at a stopping point | Transfers the conversation with history and attempted actions attached |\n\nThe practical test is what the customer must do next. Actions like repeating the issue, opening another ticket, or switching tools leave the work with the customer, whereas a concierge experience carries the customer's context forward and keeps ownership clear until the request is resolved.\n\n## Where are AI concierges used?\n\nRetail, B2B software, and travel companies can all lean on an AI concierge when customers need continuity across questions, systems, or channels.\n\n### Retail\n\nBJ's Wholesale Club built an AI shopping assistant named Bev with Delight.ai to unify product discovery, coupon savings, and membership guidance, as well as handle member care moments like order status updates, returns, and account questions, all in [one continuous conversation](https://delight.ai/customers/bjs).\n\n### Software\n\n[Mixpanel](https://delight.ai/customers/mixpanel) uses Delight.ai to authenticate users and retrieve account history, tier, access level, and workflow context before responding. That context removes about 10 minutes of manual account verification from each support interaction.\n\n"Our support agents were spending the first ten minutes of every interaction just figuring out who they were talking to… The actual problem-solving came second." — Kathleen Matthews, Global Support, Mixpanel\n\n### Travel\n\n[Norse Atlantic Airways](https://delight.ai/customers/norse-atlantic-airways) replaced its legacy chatbot with an AI agent named Freya in under 2 months. With structured tuning and operator feedback, Freya's containment rate rose from 60% to 80% in 2 weeks.\n\nThese examples show how the same model adapts to different customer journeys. Retail members get help with shopping and membership questions, software users receive account-aware support, and airline passengers keep one service thread as their needs change.\n\n## The takeaway\n\nAn AI concierge gives customers continuity from question to resolution through memory, approved actions, and informed human handoffs. The strongest evaluation examines what context the system retains, what actions it can complete, and how your team governs its decisions. [Explore Delight.ai's AI concierge for customer experience](https://delight.ai/ai-customer-experience-platform).\n\n- [What is an AI concierge?](#section-anchor-id-0 "What is an AI concierge?")\n- [How does an AI concierge work?](#section-anchor-id-1 "How does an AI concierge work?")\n- [How is an AI concierge different from a chatbot?](#section-anchor-id-2 "How is an AI concierge different from a chatbot?")\n- [Where are AI concierges used?](#section-anchor-id-3 "Where are AI concierges used?")\n- [The takeaway](#section-anchor-id-4 "The takeaway")\n
## Buy-side agentic commerce
Source: https://delight.ai/glossary/buy-side-agentic-commerce
## Buy-side agentic commerce
October 20 2026
### Buy-side agentic commerce overview
- **Buy-side agentic commerce** is a shopper's own AI agent finding, comparing, and buying products for them across many sellers at once.
- Shoppers already use these agents through AI assistants like ChatGPT and Microsoft Copilot, agentic browsers like Perplexity's Comet, and marketplace tools like Amazon's Rufus.
- For a business, the shopper's agent becomes a new kind of customer to win, one with no brand loyalty and no patience for missing product data.
### What is buy-side agentic commerce?
Buy-side agentic commerce is a shopper's own AI agent finding and buying products for them. The agent takes the shopper's goal, such as a budget and delivery date, and searches across retailers. It compares the options, then completes or prepares the purchase within limits the shopper sets.
It's one of the 2 sides of [agentic commerce](https://delight.ai/blog/industry/agentic-commerce). On the other side is [sell-side agentic commerce](https://delight.ai/agentic-commerce), where a business's own [AI agent](https://delight.ai/glossary/ai-agent) helps shoppers and their agents choose from its catalog.
Three traits separate a buy-side agent from a retailer's on-site shopping assistant:
- **It works for the shopper**, so its job is the best outcome for that person rather than for any one store.
- **It carries the shopper's mandate**, the budget, preferences, and approval rules the shopper set before the agent started looking.
- **It acts across many sellers**, comparing a dozen stores' prices and delivery dates in the time it takes a person to open one product page.
That changes who a business has to persuade. A person can be won over by a well-designed product page, but **a shopper's agent judges every store by the same data at the same time**. It has no loyalty to the brand the shopper liked last year unless the shopper tells it to.
### What are the types of buy-side agents?
Buy-side agents come in four main forms today, and they differ mostly in where the agent lives and how far it can go without the shopper. All four are built on [agentic AI](https://delight.ai/glossary/agentic-ai), which can plan and take steps toward a goal instead of only answering questions.
| Type | Where it lives | Examples | How far it goes today |
|---|---|---|---|
| **AI assistant platforms** | Inside a general AI chat or search product | ChatGPT, Google's agentic checkout, Microsoft Copilot Checkout, Perplexity's [Instant Buy with PayPal](https://newsroom.paypal-corp.com/2025-11-PayPal-and-Perplexity-Launch-Instant-Buy) | Recommends products, then completes checkout in the chat after the shopper confirms or sends the shopper to the merchant's own checkout |
| **Agentic browsers** | A web browser that can click and type for the shopper | Perplexity's Comet | Shops directly on retailer sites using the shopper's own saved logins |
| **Marketplace agents** | Inside a marketplace app | Amazon's Rufus | Buys a chosen item automatically once it drops to the shopper's target price |
| **Personal agents** | A general assistant connected to the shopper's accounts and wallet | Meta's Muse | Shops across the web and checks out with a stored wallet once the shopper approves the total |
The same model is starting to show up in business purchasing, where a company's procurement agent buys supplies within its purchasing policy. Most buy-side activity today, though, is consumer shopping.
An agent funded by ads or referral fees has a reason to favor sellers that pay it. One the shopper or their employer pays for answers only to that buyer, so **winning an agent's recommendation can depend on the platform's incentives as much as on the product**.
### What can buy-side agents do?
Most shoppers let buy-side agents research and compare, but few let them finish a purchase alone. Across 7 markets, 74% of shoppers open to AI shopping would let an agent compare prices, but **only 21% would let it complete a purchase on its own**. And 59% would require approval for every purchase, according to [Global Payments' 2026 agentic commerce research](https://www.businesswire.com/news/home/20260923900333/en/Consumers-Expect-AI-to-Make-15-of-Their-Purchases-Within-Five-Years-Global-Payments-Research-Finds).
Our own consumer research points to the same gap between using AI and trusting it. The [Delight AI Index](https://delight.ai/reports/ai-index/) surveyed 1,001 US consumers and found that 71% already use AI customer service. Only 57% like it.
The gap between comparing and buying shows up as three levels of autonomy:
1. **Research only** — the agent compares options and recommends one, and the shopper buys it.
2. **Buy with approval** — the agent builds the cart and the shopper confirms the total, the way Google's agentic checkout and Meta's Muse work today.
3. **Buy within a mandate** — the agent buys on its own inside preset limits, such as [Rufus Auto Buy](https://www.modernretail.co/technology/marketplace-briefing-amazons-shopping-bot-rufus-can-now-automatically-buy-products-for-you-when-prices-drop/) purchasing an item once it hits a target price.
For a business, the approval step is where the sale is still open. Between the agent's shortlist and the shopper's yes, a store that can confirm a delivery date or offer a bundle in that conversation can still win the order.
### What are retailers saying about buy-side agentic commerce?
Retailers are telling Delight.ai that buy-side agents are both a new place to sell and a threat to how shoppers find them.
At Delight.ai, we build AI agents for retail, travel, and marketplace brands, so we hear how these teams are reacting firsthand. Among the businesses that raised shoppers' AI assistants with us in 2026, four concerns came up again and again.
| What they raised | Share of businesses | What it looks like in practice |
|---|---:|---|
| **Being found in, or selling through, AI assistants** | 45% | Getting the catalog and checkout ready so a shopper can discover and buy a product without leaving ChatGPT or another assistant |
| **Losing product discovery to AI assistants** | 36% | Shoppers doing all their research in an assistant and arriving at the store already decided, or never arriving at all |
| **Product data an agent can understand** | 18% | A shopper asking for merchandise tied to a specific band or film, and the catalog not describing products in a way an agent can match to that request |
| **Security of orders an agent places** | 18% | Making sure an order placed through an agent comes from the actual account holder and not someone using a stolen phone |
The discovery shift is already measurable for some of these brands. One ecommerce retailer estimated that roughly 30% of its shoppers' product discovery now starts in an AI assistant, before those shoppers ever reach Google.
The most common response is a brand-owned shopping agent. **About 6 in 10 of these businesses are building or evaluating a sell-side agent of their own**. That way, a shopper or a shopper's agent can get answers from the brand directly instead of only through a third-party assistant.
### How does a buy-side agent decide what to buy?
A buy-side agent decides what to buy by comparing the facts it can read and verify across every seller it checks. That usually means structured product data, live price and stock, delivery dates, and reviews. Page design and brand storytelling carry far less weight with agents than they do with human shoppers.
In one example, an agent is shopping for a carry-on suitcase that has to fit a specific airline's size limit. One store lists exact dimensions in its product data, and another only shows them in a photo. **The agent can only recommend the store whose data it can read**.
When a product drops out, the agent rarely says why. A stale price, a missing spec, or an out-of-stock signal sends it to the next seller, and the business never sees that visit as a lost sale. That makes machine-readable product data the first thing a business needs to get right on the buy side.
### How does a buy-side agent pay?
A buy-side agent pays with a payment token the shopper approved, and proves who it is with a digital signature the merchant can check. Those two steps, plus a record of what the shopper agreed to, let a store accept an order from software instead of a person:
- **The mandate** records what the shopper approved, such as a price limit or an exact cart, which Google's Agent Payments Protocol (AP2) stores as a signed digital record.
- **The identity check** confirms the agent is acting for an actual shopper, which [Visa's Trusted Agent Protocol](https://www.digitaltransactions.net/visa-launches-trusted-agent-an-agentic-commerce-protocol/) does with agent-specific cryptographic signatures.
- **The payment** uses a token with spending limits instead of the shopper's card number, through programs like Mastercard Agent Pay, Visa Intelligent Commerce, and Stripe's Shared Payment Tokens.
The identity check has a direct revenue cost when it fails. Bot defenses built to stop scrapers and fraud can block a legitimate shopper's agent too, and **every good agent a store blocks is a sale it never sees**. The [agentic commerce protocols comparison](https://delight.ai/blog/industry/agentic-commerce) covers how ACP, UCP, AP2, and the card networks fit together.
### Where does buy-side meet sell-side?
Buy-side and sell-side agentic commerce meet when a shopper's agent asks a business a question and the business's own agent answers it. Instead of reading a bare product feed, the shopper's agent gets a reply shaped by the business's catalog, offers, and rules. That reply can also draw on what the business already knows about the customer.
Agent-to-agent protocols such as Agent2Agent (A2A) let agents from different companies find each other and exchange information securely, so this hand-off can happen without a person in the middle.
### Buy-side agentic commerce example
A shopper tells her AI assistant to buy a specific espresso machine for under $300, delivered by Friday. Her assistant checks 6 retailers, and only one of them has its own sell-side agent. The table follows the purchase step by step and shows what each kind of retailer sees.
| Step | What the shopper's agent does | Retailer without its own agent | Retailer with a sell-side agent |
|---|---|---|---|
| **1. Set the mandate** | Records the model, the $300 limit, and the Friday deadline | Nothing yet | Nothing yet |
| **2. Search** | Reads 6 retailers' product data and drops 2 whose stock information is out of date | A data request, and no sale if its feed is stale | The same data request |
| **3. Ask** | Asks the remaining 4 about delivery dates and any bundles | No one to answer, so the agent relies on whatever the feed says | Its agent checks live stock, confirms Thursday delivery, and offers a bundle with a milk frother for $289 |
| **4. Confirm** | Shows the shopper the best cart and waits for her approval | A lost sale it can't see in its analytics | An approved order placed within the shopper's limits |
| **5. After the sale** | Moves on to the shopper's next request | Nothing | The full conversation, including the frother she almost bought elsewhere, tied to her customer history |
The retailer with its own agent won on a question the product feed couldn't answer. Its agent could offer the bundle because the retailer had already approved that offer, and it could promise Thursday because it checked live inventory instead of a nightly feed.
In a Delight.ai agent, approved actions like that offer are step-by-step flows a person reviews before they go live, called Actionbooks. Any request outside them goes to a human. Delight.ai's memory feature, [Agent Memory Platform (AMP)](https://delight.ai/agent-memory-platform-amp), ties the exchange to the shopper's history, so the retailer can suggest descaling tablets when she's likely to need them.
Retailers already run this kind of sell-side agent. BJ's Wholesale Club's shopping assistant Bev is powered by Delight.ai and brings product discovery, savings guidance, and member care into one assistant. Members who engaged with Bev converted at a higher rate and showed greater purchasing activity per visit than those who didn't, according to the [BJ's Wholesale Club case study](https://delight.ai/customers/bjs).
> *"Whether a member is browsing for a grill, looking up club hours, or asking about a renewal, they're talking to the same Bev. That's how a real member-team member relationship is supposed to feel."*
>
> *Scott Vandegrift, VP of Product, BJ's Wholesale Club (2026)*
### Key takeaways
- Buy-side agentic commerce is a shopper's own AI agent finding, comparing, and buying across many sellers, within limits the shopper sets.
- Most shoppers still approve the final purchase, so a business that can answer the agent's questions in that moment can still win the order.
- Many retailers we work with are responding by building their own sell-side agents, so shoppers and their agents can get answers from the brand directly.
- Agents choose on facts they can read and verify, like exact dimensions and live stock, so a store with missing product data drops out without knowing it.
Shoppers' agents will keep getting better at buying, and no business controls which one a customer picks. What a business can control is the agent answering on its own behalf, and Delight.ai builds that agent with your catalog, offers, and rules behind every answer. [See what a sell-side agent could do for your business](https://delight.ai/agentic-commerce).
- [Buy-side agentic commerce overview](#section-anchor-id-0 "Buy-side agentic commerce overview")
- [What is buy-side agentic commerce?](#section-anchor-id-1 "What is buy-side agentic commerce?")
- [What are the types of buy-side agents?](#section-anchor-id-2 "What are the types of buy-side agents?")
- [What can buy-side agents do?](#section-anchor-id-3 "What can buy-side agents do?")
- [What are retailers saying about buy-side agentic commerce?](#section-anchor-id-4 "What are retailers saying about buy-side agentic commerce?")
- [How does a buy-side agent decide what to buy?](#section-anchor-id-5 "How does a buy-side agent decide what to buy?")
- [How does a buy-side agent pay?](#section-anchor-id-6 "How does a buy-side agent pay?")
- [Where does buy-side meet sell-side?](#section-anchor-id-7 "Where does buy-side meet sell-side?")
- [Buy-side agentic commerce example](#section-anchor-id-8 "Buy-side agentic commerce example")
- [Key takeaways](#section-anchor-id-9 "Key takeaways")