Your daily signal on AI and CX — minus the hype.

Announcing:

A practical peer conversation for CX pros sharing what they’re trying, testing, and learning with AI to improve customer insight, journeys, operations, and support.

When: May 7 at 9:30 AM PDT
Length: 90 minutes
Location: Virtual
Cost: Free
Hosted by: Me

Reserve Your Spot
Space is limited, so everyone gets a voice

In this issue:

→ Hotel robots test guest trust
→ Automation needs a human exit
→ Delivery bots become brand moments
→ Airport robots move behind the curtain
→ AI service funding moves into messier work

🔎 Deep dive

Hybrid hospitality is really a handoff test

National Geographic’s piece on “hybrid hospitality” is the best robot and CX story of the day because it does not treat robots as the point.

The examples are more interesting than that: Avatar Robot Café DAWN uses remotely piloted robots to let people with disabilities work and interact with guests. Hotels use delivery robots for amenities and room service. Aescape has robotic massage tables in hotel and spa settings. Cruise lines use AI and robotics behind the scenes to manage logistics, reservations, routes, and operational flow. (National Geographic)

That is not novelty. That is service capacity being redesigned.

The customer consequence depends on the moment. A robot delivering towels may feel convenient. A robot-guided café interaction may feel surprisingly human because there is an actual person behind it. A robotic wellness experience may give a guest more privacy and control.

But travel is full of uncertainty. Bags go missing. Rooms are not ready. Weather breaks plans. People get tired, embarrassed, confused, or stuck. That is where the handoff matters.

OPERATOR PLAYBOOK

Design the edge of the robot’s job

The mistake is designing the robot task and calling it the experience.

That is how you get a hallway robot that delivers the towel but leaves the guest stranded when the room key fails, the order is wrong, or the situation needs judgment.

Audit every robot-supported journey for four things:

1. What job should the robot never handle?

2. When does the customer need a human without asking twice?

3. What context moves from robot to person?

4. Who owns recovery when the robot creates confusion?

Then test whether the guest can escape the automated path without starting over.

Ask your team: Where does this robot make the experience feel more human, and where does it quietly make the customer do more work?

📈 Market Reality Check

Customers are fine with AI until the risk feels personal

SurveyMonkey’s 2026 customer service research found that 79% of Americans strongly prefer a human over an AI agent. The same research found customers are more open to AI for lower-risk tasks, including ordering food and drinks at 65% and returning an item at 59%.

That split matters.

Customers are not rejecting automation across the board. They are rejecting being trapped inside it when the stakes rise.

A robot can carry towels, deliver food, guide a guest, or support a cruise operation. Fine.

But when the bag is missing, the room is wrong, the charge looks off, or the traveler is already stressed, customers want a person with judgment. Not a loop. Not a polite dead end. A person.

🧰 Tool Worth Knowing

Avride Autonomous Delivery Robot

What it does: Avride builds autonomous sidewalk delivery robots for food, groceries, and small retail orders. The company says the robot can carry six 42 cm pizzas and five 1.5 liter bottles, travel up to 31 miles on one charge, and move at up to 5 mph. It uses lidar, cameras, and ultrasonic sensors, and Avride says faces and license plates are blurred for privacy.

CX use case: This is for local delivery moments where the customer experience depends on clarity, timing, access, safety, and recovery. The app may create the expectation, but the robot is where the promise becomes visible on the sidewalk.

Worth watching because: Delivery robots turn the last mile into a public customer experience moment. The customer is no longer judging only the app, the restaurant, the store, or the ETA. They are judging whether the handoff feels safe, clear, predictable, and recoverable when something goes sideways.

That matters because delivery has always hidden plenty of CX pain inside third-party labor, traffic, unclear drop-off instructions, building access, and customer blame. Robots make that pain more visible. If the bot gets stuck, blocks access, arrives in the wrong place, or confuses the customer, the experience does not feel automated. It feels abandoned.

Avride says its robots are primarily autonomous, but remote support can step in when human intervention is needed. That is the part CX teams should care about. The robot is only as good as the recovery model wrapped around it.

Bottom line: The CX value is not the robot. It is whether the robot makes delivery more consistent, trackable, and easier to recover when the handoff breaks. Otherwise, you have a very visible operational failure rolling down the sidewalk at 5 mph.

⚡ 90-Second CX Radar

JAL will test humanoid robots in airport ground handling

JAL Ground Service and GMO AI & Robotics will begin a phased airport demonstration in May 2026. The stated goal is labor savings and workload reduction in ground handling, with possible future use in baggage loading, cabin cleaning, and ground support equipment. Customers may never see the robot, but they will feel the result if baggage reliability, cleaning speed, or delay recovery improves. Or if it doesn’t.

Netomi raises $110 million as AI service moves into harder work

Reuters reports Netomi raised $110 million in a Series C led by Accenture Ventures. Netomi says it focuses on medium-complexity service issues, including questions inside United Airlines’ mobile app. Watch that phrase: medium complexity. That is where service gets expensive, customers get impatient, and weak handoffs start showing up in retention, complaints, and repeat contacts. (Reuters)

Brain Corp’s shelf-scanning pilot shows where robots may quietly change retail CX

This is not the lead story, but it is still worth watching. Shelf-scanning robots are closer to inventory execution than guest experience, but customers feel the result when the item is missing, the price is wrong, or the promotion fails. The CX question is simple: when the robot finds the miss, who fixes it before the customer gets annoyed?

🧭 Your Move

Robots will keep showing up in places customers can see.

Hotels. Airports. Sidewalks. Restaurants. Cruise ships. Store aisles.

The useful move is to stop asking whether the robot is impressive and start asking where it reaches the edge of the experience. That edge is where customers need judgment, context, empathy, recovery, or simply a person who can say, “I’ve got this.”

Build that layer now.

Otherwise, the robot becomes a very expensive way to reveal the handoff your team never designed.

A robot can complete a task. The experience still depends on what happens when the task is not enough.

Until Monday,

Your daily signal on AI and CX — minus the hype.

Announcing:

A practical peer conversation for CX pros sharing what they’re trying, testing, and learning with AI to improve customer insight, journeys, operations, and support.

When: May 7 at 9:30 AM PDT
Length: 90 minutes
Location: Virtual
Cost: Free
Hosted by: Me

Reserve Your Spot
Space is limited, so everyone gets a voice

In this issue:

→ Amazon turns product pages into live Q&A
→ Travelers care more about resolution than channel
→ Retail AI traffic is starting to convert
→ Agentic commerce gets a trust layer
→ Uber keeps stretching the consumer app

🔎 Deep dive

The product page is turning into a sales associate

Amazon launched Join the chat, a new interactive layer inside its Hear the highlights audio shopping feature. Customers can ask AI hosts product questions by text or voice while listening to an AI-generated product summary. The hosts pause, answer in real time, then keep going. It is now available to U.S. customers on iOS and Android.

That moves online shopping closer to the thing stores used to do well: answer the question sitting between interest and purchase. “Is this coffee maker good for a beginner?” “Does this sweater feel itchy?” “Is it dishwasher safe?” Small questions. Big hesitation.

The CX implication is practical. Your product content, reviews, specs, policies, and comparison logic are becoming part of the service experience. If those inputs are thin, outdated, or written for search instead of decision-making, AI will not fix the gap. It will package the gap more confidently.

Want to try it yourself? Open any of the products below in the Amazon Shopping app and Join the chat with these sample questions—or ask your own.

• La Roche-Posay Toleriane Purifying Foaming Facial Cleanser: “What is the scent like?”

• BISSELL Little Green Mini Portable Carpet and Upholstery Deep Cleaner: “Is it good for pet owners?”

• Soundcore P30i by Anker Noise Cancelling Earbuds: “How do they work for phone calls?”

• Ninja Luxe Café Premier 3-in-1 Espresso Machine: “Is it easy to clean?”

• Dreo Smart Humidifiers for Bedroom: “Can it be used with essential oils?”

OPERATOR PLAYBOOK

Audit the questions customers ask before they buy

Treat product discovery like a support journey. Because it is one now.

Audit every product evaluation flow for four things:

1. The questions customers repeatedly ask in reviews, search, chat, calls, and returns.

2. The specs, compatibility notes, fees, exclusions, or usage limits that are easy to miss.

3. The places where AI can give a clear tradeoff, not a generic recommendation.

4. The escalation path when the customer is asking about risk, fit, money, safety, or timing.

Then test whether a shopper can get a clear answer in two turns without leaving the buying flow.

Ask your team: Which customer question keeps showing up before purchase that our site still answers badly?

📈 Market Reality Check

AI shoppers are showing up with intent

Adobe found that traffic from AI sources to U.S. retail sites grew 393% year over year in Q1 2026. In March, AI traffic converted 42% better than non-AI traffic. That is a sharp reversal from March 2025, when AI traffic converted 38% worse.

The catch: many retail pages are still not fully readable by machines. Adobe found average machine readability scores of 75% for homepages, 74% for category pages, and 66% for individual product pages.

🧰 Tool Worth Knowing

Rep AI

What it does: Rep AI is an ecommerce shopping and support agent for Shopify and retail brands. It helps shoppers find products, ask questions, add items to cart, and handle post-order tasks like tracking, cancellations, and returns.

CX use case: Use it where pre-purchase hesitation and repetitive support collide. Product fit questions. Returns. Order status. “Can this work for me?” moments that quietly decide conversion.

Worth watching because: It connects sales help, support automation, and shopper behavior data in the same interaction layer.

Bottom line: Worth a look if your store treats conversion and support as separate worlds. Treat the performance numbers as vendor claims until you validate them against your own baseline.

⚡ 90-Second CX Radar

FIDO starts building the trust layer for AI agents

The FIDO Alliance announced new workstreams for agentic authentication and agent-initiated commerce. The practical issue is simple: if AI agents are going to authenticate, act, and transact for customers, brands need proof of user intent and clear limits on authority.

Uber adds hotels inside the app

Uber is adding hotel booking through Expedia inventory, giving U.S. users access to more than 700,000 hotels in the Uber app. It also announced AI-powered voice booking and unified search across rides, food, and retail. The consumer app keeps becoming the journey wrapper.

Social scams are now a $2.1 billion trust problem

The FTC says reported social media scam losses reached $2.1 billion in 2025, about eight times the 2020 figure. This is not just a fraud story. It is a trust tax on every legitimate brand trying to sell, serve, or support customers in the same digital spaces.

🧭 Your Move

This issue has one thread running through it: AI is moving closer to the customer’s moment of decision. Shopping, travel, support, payments, booking, fraud prevention. Same pressure. Different rooms.

Your move this week: pick one customer decision point and audit the AI layer around it. Ask what the customer is trying to decide, what information they need, what the system is allowed to do, and where human support needs to stay visible.

If AI is now part of the buying journey, the content behind it has to be treated like an operating asset.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Solidroad brings AI into support QA
→ KPMG shows the adoption gap
→ BAND tackles agent-to-agent coordination
→ Khoros rethinks community as support infrastructure
→ Miravoice turns surveys into AI-led conversations

🔎 Deep dive

Solidroad is going after the QA blind spot

Solidroad raised $25 million to expand its AI quality assurance and training platform for customer support teams. The company says its platform evaluates 100% of customer interactions across human and AI agents, then turns those reviews into coaching and training simulations. Customers include Ryanair, ŌURA, ActiveCampaign, and Crypto.com.

That matters because most support leaders still manage quality through tiny samples, escalation noise, and the occasional “we should probably look into this” Slack thread. Not exactly a precision instrument.

The first impact shows up in chat, email, phone, QA, onboarding, and AI agent oversight. If every conversation can be reviewed, coaching gets more specific. Risk gets spotted earlier. Policy drift becomes visible. The tradeoff is simple: AI starts judging the work, so leaders need to define what good support actually looks like before the system starts scoring people, bots, and customer moments against a fuzzy standard.

Source: The Next Web

OPERATOR PLAYBOOK

Pressure-test the conversations nobody reviews

AI support does not fail only when the bot gives a bad answer. It fails when no one notices the pattern fast enough.

Audit every support QA flow for four things:

1. Which interactions are currently reviewed

2. Which channels are barely sampled

3. Which AI-agent conversations get scored against customer outcomes

4. Which failure patterns trigger coaching, policy review, or journey fixes

Then test whether your QA program can catch a bad pattern before it becomes a repeat contact problem.

Ask your team: What customer frustration would we miss if we only reviewed the cleanest 2% of interactions?

📈 Market Reality Check

Companies are funding AI before fixing the human adoption layer

KPMG Customer Advisory Research Study found that 53% of organizations prioritize integrating data and analytics, 43% prioritize unified technology platforms, and 40% prioritize cross-functional teams, governance, and processes. But only 19% invest in cross-functional training and development.

That gap is where AI-supported CX quietly stalls. You can fund the data layer, platform layer, and governance layer, but if the people doing the work are not trained across functions, adoption becomes a handoff problem. Support sees one thing. Product hears another. Operations translates it differently. Then everyone wonders why the shiny new system did not change the customer experience.

System investment without human adoption = expensive friction.

🧰 Tool Worth Knowing

BAND

What it does: BAND.ai just came out of stealth with $17 million in seed funding to build an interaction layer for AI agents. Its pitch is that agents across different frameworks, clouds, teams, and organizations need a way to communicate and coordinate in real time.

CX use case: This gets interesting when service, billing, fulfillment, identity, fraud, sales, and retention agents need to work across systems without dropping context.

Worth watching because: Agent coordination sounds technical until billing, fulfillment, fraud, and support all give the customer different answers. Then it becomes a trust problem.

Bottom line: Early signal, not proven CX infrastructure yet. But the problem is real: agent sprawl will create handoff problems inside the business before customers ever see the shiny version.

⚡ 90-Second CX Radar

Khoros turns community into cited AI support

Khoros launched Aurora AI, a reworked enterprise community platform where peer knowledge becomes cited answers and customer communities help with support, product feedback, and engagement. The question is whether the answer reflects lived customer knowledge or turns community wisdom into another sanitized support script.

Miravoice wants AI to run long-form phone research

Miravoice raised $6.3 million to build AI voice agents for structured phone surveys and interviews. More interviews may help teams hear from more customers, but volume is not understanding. The risk is mistaking a bigger research pipe for sharper customer judgment.

Octen is building search for AI agents

Octen launched with $10 million in seed funding to build a search engine designed specifically for AI agents. If AI agents become the first layer of discovery, your help content, product pages, and policies need to be legible to machines before a customer ever sees them.

🧭 Your Move

If AI is exposing the conversations your QA process never saw, your job is to make sure “quality” means more than speed, containment, and fewer tickets.

Pick one support journey this week and trace how quality is actually judged. What does the system reward? What does it miss? Where does coaching happen? Which failure patterns trigger action? And when the customer leaves the interaction annoyed but “resolved,” does anyone see that?

The next support advantage will come from knowing what happened, why it happened, and whether the customer would trust you again.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Wearables reshape consumer expectations
→ Healthcare raises the AI trust bar
→ AI moves closer to the phone screen
→ Retail AI becomes a shopping advantage
→ Service still needs human judgment

🔎 Deep dive

AI is becoming part of the care experience

OpenAI launched ChatGPT for Clinicians, built for the work clinicians actually do: documentation, research, referral letters, prior authorization, and patient instructions. It is free for verified U.S. physicians, nurse practitioners, physician assistants, and pharmacists. It also includes cited clinical search, deep research across medical journals, optional HIPAA support for eligible accounts, and privacy protections that keep conversations from being used to train models.

This is worth paying attention to even if you are nowhere near healthcare.

When AI moves into clinical workflows, it changes what consumers expect around the edges of care. Better explanations. Faster next steps. Less repeating themselves. More help before the visit, after the visit, and between the handoffs where medical experiences usually get clunky.

That connects directly to the wearable signal from EY. Consumers are getting more comfortable with AI interpreting personal health data and shaping the next step. They may not want fully autonomous decisions, but they are starting to expect AI-assisted guidance.

That puts pressure on every consumer medical journey: providers, pharmacies, insurers, labs, wellness apps, and digital health platforms. If AI can help a clinician draft a referral letter, summarize medical research, or prepare patient instructions, consumers will start wondering why the rest of the journey still feels like forms, portals, phone trees, and “please allow 3 to 5 business days.”

OPERATOR PLAYBOOK

Pressure-test the moments where data should trigger help

Look for any place where your company already has a customer signal but waits for the customer to notice the problem, contact support, miss a deadline, abandon a task, or make a preventable mistake.

Audit every data-triggered journey for four things:

1. Signal quality: Is the data strong enough to justify a recommendation, alert, or next step?

2. Customer permission: Does the customer understand how the data is being used?

3. Timing: Does the help arrive before the customer feels the cost?

4. Human escape hatch: Can the customer get help when the recommendation feels wrong, sensitive, or confusing?

Then test whether the experience feels helpful when the recommendation is right, and respectful when the customer ignores it.

Ask your team: Where are we collecting signals that could reduce customer effort, but using them only for reporting?

📈 Market Reality Check

Wearables are raising the bar for every data-rich journey

EY’s Global AI Sentiment Study points to a consumer shift that CX teams should not ignore: 21% of respondents said they would prefer AI to make health decisions based on wearable data, while 48% preferred AI as an assistant in that scenario.

That is not a small behavior signal.

Consumers are getting used to the idea that personal data should lead to something useful. Not another dashboard. Not another passive alert. A recommendation. A warning. A next step. Maybe even a decision, as long as the stakes feel manageable and the boundary is clear.

You will feel this first in journeys built around prevention, reminders, alerts, recommendations, eligibility, upgrades, claims, renewals, payments, and service recovery.

🧰 Tool Worth Knowing

SearchUnify

What it does: SearchUnify is an enterprise agentic AI platform for customer support and self-service. It unifies enterprise knowledge, supports contextual intelligence, and powers task-specific AI agents across support channels. (GetApp)

CX use case: Use it when your support answers are scattered across knowledge bases, community content, docs, tickets, and internal systems. That is where customers and agents start getting different answers to the same question. Same broken knowledge base, faster confusion. Always a treat.

Worth watching because: As you add AI agents, knowledge quality becomes a control point. If the answer layer is weak, automation does not fix the experience. It spreads the bad answer with more confidence.

Bottom line: SearchUnify is worth a look if your team is trying to improve self-service, agent assist, and knowledge findability before scaling more automation.

⚡ 90-Second CX Radar

Skye wants to turn the iPhone home screen into an AI assistant

TechCrunch reports that Skye is building an “agentic homescreen” for iPhone using iOS widgets, with tens of thousands of interested users before launch. The CX angle is simple: consumers may start expecting AI help closer to the moment of need, not buried inside a separate chatbot or app. That makes menu hunting, app hopping, and dead-end help pages feel even older.

Walmart’s annual report points to AI as part of its consumer growth engine

Retail Dive reports that Walmart’s fiscal 2026 annual report highlighted e-commerce growth, store investments, and major AI initiatives, including agentic AI and its customer agent Sparky. Walmart’s U.S. e-commerce contributed 4.3% to comparable sales, and global e-commerce grew 24% to $150.4 billion. This is what happens when AI stops being a side project and starts becoming part of how customers shop, reorder, and get help.

AI is not replacing customer service teams as cleanly as the hype suggests

Customer Experience Dive, citing Gartner research, reports that 74% of service organizations have deployed at least one AI use case, but only 20% have reduced agent headcount. That is the useful reality check. Customers still need human judgment when automation fails, escalates poorly, or cannot read the emotional stakes. AI may reduce effort in routine moments, but messy consumer problems still need a person who can own the outcome.

🧭 Your Move

This issue has one practical thread: customers are being trained to expect their data to work harder for them.

In healthcare, that means wearable data guiding decisions. On the phone, it means AI help moving closer to the home screen. In retail, it means AI shaping search, shopping, and frequency. In service, it means automation handling routine work while humans stay close to the messy moments.

Pick one customer journey this week where you already have the signal.

What does the customer need to know?
When should you intervene?
What should the system recommend?
Where does a human need to step in?

Do not treat customer data like a reporting asset only. If the signal can reduce effort, prevent pain, or help the customer make a better decision, the experience has to carry that responsibility.

Data without timely help is just a better way to watch the customer struggle.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ AI guidance moves deeper into personal finance
→ Financial journeys need stronger guardrails
→ Escalations still lose too much context
→ Dashboards are becoming conversations
→ Parents get more visibility into teen AI use

🔎 Deep dive

Customers are already using AI for financial decisions

EY says nearly half of global consumers have used AI to support savings and investment decisions in the past six months. That should get your attention because financial services has moved past the curiosity phase. Customers are already using AI to interpret money, risk, products, and next steps.

The CX issue is not whether customers will ask AI for help. They already are. The issue is whether your customer experience is ready when AI becomes part of the decision path.

EY also found that 21% of consumers have used AI agents for financial product recommendations, while 14% have allowed AI to select financial service providers on their behalf. That is the real operating shift. AI is moving from advice to selection. Once that happens, your clarity, data quality, policy language, and escalation paths become part of the customer’s decision environment.

For CX leaders, the risk is misplaced confidence. A customer may trust a confident AI recommendation faster than they trust your fine print. That puts pressure on your journey to explain options clearly, show limits, and create a clean human path when the decision has consequence.

OPERATOR PLAYBOOK

Put guardrails around AI guidance moments

If your AI helps customers make decisions, treat that journey differently from a password reset or order status check.

Audit every AI-assisted guidance flow for four things:

1. The assistant explains the basis for its answer in plain language.

2. The customer can tell when the answer is general guidance, not a guaranteed outcome.

3. The customer can reach a person before frustration becomes the trigger.

4. The system keeps the conversation context when escalation happens.

Then test whether a customer can challenge the answer without starting over.

Ask your team: Where could our AI sound more certain than the business can responsibly support?

📈 Market Reality Check

Context loss is still the tax customers pay

Verint’s 2026 agent experience research found that 57% of calls require agents to gather interaction context after an escalation.

That should bother you. Customers do not care that one system handed off to another system. They experience it as repetition, delay, and a pretty clear sign that the company was not really listening the first time.

If your AI strategy improves containment but does not improve memory, you are leaving the most painful part of the journey intact. Your customer may get to the right person faster and still feel like your company forgot them.

🧰 Tool Worth Knowing

CFO Silvia

What it does: CFO Silvia is an AI personal finance assistant that connects bank accounts, crypto wallets, trading accounts, savings, loans, real estate, vehicles, and other assets into one financial dashboard. The product says it is used by 8,000+ investors tracking more than $15 billion in assets.

CX use case: You should study this because it turns personal finance from a reporting interface into a guidance conversation. The customer does not only see balances. They can ask what changed, where risk is building, what fees may be dragging performance, or how a scenario could affect their financial picture.

Worth watching because: Money is one of the highest-trust customer journeys. If AI guidance works here, your customers will expect similar clarity in insurance, telecom, healthcare, utilities, and every other category where decisions feel personal and consequences feel real.

Bottom line: CFO Silvia is a useful signal for CX teams: dashboards are being pulled into conversation, but the trust bar rises fast when the assistant touches financial decisions.

⚡ 90-Second CX Radar

Yelp is making its AI assistant more transactional

Yelp’s assistant is moving closer to recommendations, quotes, ordering, and booking. That turns local discovery into a guided action layer. If your brand depends on third-party discovery, the assistant’s view of your relevance becomes part of your customer journey.

Microsoft pushes Copilot deeper into commerce

Copilot Checkout and richer product catalog access point to a bigger shift: buying journeys are moving into AI-mediated environments. You will need cleaner product data, clearer policies, and fewer broken post-purchase handoffs because the assistant may become the shopper’s first filter.

Meta gives parents visibility into teen AI conversations

Meta is adding a supervision feature that lets parents see the topics their teens have discussed with Meta AI over the past seven days. The CX angle is trust design. When AI touches sensitive or family-adjacent journeys, your customers will expect visibility, boundaries, and control without needing a policy decoder ring.

🧭 Your Move

The shift in this issue is simple: AI is moving from answering basic questions to guiding customer decisions. That creates a higher bar. When the journey involves money, family, risk, service recovery, or a meaningful choice, your AI has to explain itself, show its limits, preserve context, and hand off cleanly.

Pick one AI-assisted journey where your customer is making a meaningful decision. Financial choice. Plan change. Upgrade. Cancellation. Claim. Care issue. Anything with consequence.

Now pressure-test it like a skeptical customer. Does the answer explain itself? Does it show uncertainty? Can the customer get to a person without performing frustration first? Does the handoff remember what already happened?

If one of those breaks, the experience may still look efficient on your dashboard while making the customer less confident.

AI guidance only works when the customer can understand the answer, question it, and get help without losing the thread.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Home Depot kills the phone tree
→ Scam fear hurts callbacks and reminders
→ Onboarding friction is creeping back
→ Level AI targets the support middle
→ Health AI keeps outrunning trust

🔎 Deep dive

Home Depot is rebuilding service around conversation

If you own service, this is worth your attention. Home Depot is replacing rigid phone menus with conversational AI and pushing that model across chat, SMS, and voice. In January, the company said these support tools were already live across channels.

This week, Google said Home Depot’s new voice system reached solutions four times faster in pilot stores, captured intent in under 10 seconds, and still kept a direct path to a human associate.

The CX point is simple. Customers do not call when they are in the mood for a maze. They call when they need help now. That means the phone channel is starting to behave less like an IVR and more like live triage.

For you, the opportunity is lower effort, faster routing, and less wasted associate time. The risk is also clear. If the system sounds capable but sends people sideways, trust drops fast. In a recovery moment, that damage is hard to win back.

OPERATOR PLAYBOOK

Audit your service and recovery calls

Take one hard look at your phone, chat, and callback flows this week.

Audit every service and recovery journey for four things:

1. Can the system understand intent in the first exchange?

2. Can the customer reach a human without friction or punishment?

3. Does context survive the handoff?

4. Are sensitive actions confirmed in a second channel?

Then test whether a skeptical customer can get through the flow without wondering if the outreach is fake, repeating the issue twice, or giving up before the real help starts. The scam environment is changing the bar for what feels trustworthy.

Ask your team: Which of our service journeys assumes trust we have not earned?

Signal: Better automation is colliding with weaker channel trust.

📈 Market Reality Check

Consumers are starting to dodge legitimate outreach

Truecaller’s latest survey says 82% of Americans have ignored important calls or texts because they feared a scam. It also says 75% were targeted by a scam call or text in the past year, and 84% are more concerned than they were a year ago.

That should get your attention if you own appointments, billing, service recovery, fraud alerts, or delivery communication. The problem is no longer limited to bad actors. The channel itself is getting harder to trust, which means even legitimate outreach has to work harder to be believed.

🧰 Tool Worth Knowing

Level AI

What it does: LevelAI is a CX platform built around the messy middle of support. Its site groups the product around coaching, agent assist, screen recording, AI virtual agent, Auto-QA, voice of the customer, analytics, and integrations.

CX use case: If you are trying to improve service quality without sampling a tiny slice of calls and hoping for the best, this is the kind of system to look at. It is aimed at the points where support usually breaks down: containment, handoff, QA, coaching, and agent guidance. Level also positions the platform across regulated and service-heavy environments including financial services, banking, healthcare, retail, insurance, and collections.

Worth watching because: Level says it has delivered a 25% increase in CSAT, a 45% increase in agent satisfaction, and 90% time saved in QA monitoring. Its site also highlights real-time guidance for live interactions and fully automating QA as part of the efficiency pitch. Those are company claims, so I would treat them as directional until you see your own workflow in a demo.

Bottom line: If your team is buying separate tools for bots, QA, coaching, and insight, Level is worth a look because it is trying to connect those layers instead of treating them like separate software aisles.

⚡ 90-Second CX Radar

MEO turns support into a visual journey

MEO and TechSee say their visual AI service flow helps customers show what is wrong instead of trying to describe it. The company says the experience reached a 96.4% customer satisfaction rating. If your issue is visual, the old call flow is already the wrong interface.

Consumer health AI is moving faster than reliability

KFF says five technology companies have launched dedicated consumer-facing AI health tools so far in 2026. It also says 32% of adults have used AI for health information and four in ten of those users uploaded personal medical information for personalized advice. The demand is real. The trust model is still shaky.

unitQ is trying to tie customer pain to business pain

unitQ launched a unified platform that pulls six products into one system and connects customer signals to revenue, retention, and risk. The bigger point is the one you are probably living with already. CX teams are under pressure to prove which issues matter before the KPI deck turns ugly.

🧭 Your Move

Pick one trust-sensitive journey this week. Billing. Service recovery. Fraud alerts. Appointment reminders. Onboarding. Then ask a blunt question: if the customer got this call or message today, would they know it was really you?

That is where the next round of CX work is heading. Less interface polish. More proof, clarity, and confidence.

If your channel feels smarter but less believable, you did not improve the experience.

Until Monday,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Ulta moves shopping into AI
→ Discovery shifts away from your site
→ Home Depot cuts phone friction
→ DICK’S builds a digital coach
→ Cars become service surfaces

🔎 Deep dive

Ulta is betting discovery will start somewhere else

Ulta Beauty and Google announced two moves that should get your attention. Ulta products are becoming shoppable inside Google’s AI Mode in Search and the Gemini app, with streamlined checkout for eligible purchases. Ulta is also launching Ulta AI across its own digital experience, using data from its 46+ million members to help shoppers narrow choice and buy faster.

Here’s the part that matters to you. Discovery, comparison, and even purchase are starting to happen inside an AI layer your brand does not fully control. You will feel that first in discovery and evaluation, where the assistant starts shaping what gets surfaced, compared, and trusted.

For your customer, that can mean less hunting and fewer dead ends. For you, it raises a harder question. If your product data is thin, your advice is generic, or your checkout handoff breaks, you may lose the customer before they ever reach your owned experience.

OPERATOR PLAYBOOK

Pressure-test your discovery-to-checkout handoff

Audit every AI-assisted shopping journey for four things:

1. Can the assistant access accurate product facts, availability, and eligibility in real time?

2. Can it explain tradeoffs clearly enough to help the customer decide?

3. Can the customer move to a human or owned channel without starting over?

4. Can checkout finish cleanly, with no surprises on price, stock, or fulfillment?

Then test whether your brand still plays a meaningful role after the assistant narrows the choice set.

Ask your team: If discovery keeps moving upstream into AI assistants, what are you doing right now to stay visible, useful, and easy to buy from?

📈 Market Reality Check

AI discovery traffic is no longer a side channel

Adobe said AI traffic to U.S. retail sites rose 269% year over year in March 2026. That does not tell you everything, but it does tell you this is no longer fringe behavior. If AI systems are becoming a real channel for discovery and evaluation, you now have to care about what those systems understand about your products, offers, and brand truth.

What this does not prove is conversion quality, loyalty, or margin. More AI traffic can still mean weaker intent or more price-driven comparison. But the channel is clearly growing, and it is starting to influence the front end of the consumer journey faster than many teams expected.

🧰 Tool Worth Knowing

Adobe Brand Concierge

What it does: Adobe positions Brand Concierge as a conversational AI layer for brand-owned digital properties. In the DICK’S example, it is being used to guide customers from discovery to decision with personalized recommendations and coaching-style interactions.

CX use case: This is useful when your customers need help narrowing choice, building confidence, or matching a goal to the right basket.

Worth watching because: It gives you a middle path. You do not have to give all discovery to Google, ChatGPT, or the next assistant that shows up. You can build guided buying into your own experience, with your own data and rules.

Bottom line: Worth a look if your problem is guided decision-making at scale. Less interesting if your basics, product data, or handoffs are still broken.

⚡ 90-Second CX Radar

Home Depot adds AI phone agents to store support

This matters because it goes after a customer moment people still hate: calling a store. Home Depot said the pilot let callers describe what they needed in natural language, kept a path to a human, and even started carts based on the project described on the call.

DICK’S wants a digital coach inside its app

The interesting part is not the phrase “digital coach.” It is the attempt to connect product knowledge, customer data, and journey orchestration so the guidance feels relevant across touchpoints. No rollout metrics were shared.

Volkswagen is turning the car into an AI service surface

Volkswagen said new China-built vehicles will get onboard AI agents from the second half of 2026, with the ability to handle tasks like finding top-rated restaurants and confirming reservations. That matters because post-purchase experience is expanding into daily decision support.

🧭 Your Move

Pick one consumer journey where an AI layer is already sitting between you and the customer. Product discovery is the obvious place to start. Store calls are another. Then ask the uncomfortable question: if the assistant became the first touch tomorrow, would it understand your offer well enough to sell it and recover cleanly when things go wrong?

Focus on three things.

1. Clean up the data that powers discovery.

2. Tighten the handoff into checkout or human help.

3. Decide which moments you want to own directly instead of renting from someone else’s interface.

You are no longer competing only on experience. You are competing on how well an AI can explain you.

Until tomorrow.

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Automation is scaling faster than resolution
→ Customers are spotting deflection quicker
→ Integration is still mostly aspiration
→ A new tool wants intent sooner
→ Orchestration is becoming the battleground

🔎 Deep dive

Customers feel CX is getting more automated, not better

You can see the pattern pretty clearly. In a current r/customerexperience discussion, people keep coming back to the same complaint: automation is fine when it removes effort, but it gets ugly fast when it adds steps, repeats the issue, and delays a human handoff.

That matters because your internal story and your customer’s story can drift apart fast. Your dashboard may show fewer contacts. Your customer may feel like you built a system to wear them down before helping them. That gap usually shows up first in support, billing, pharmacy, and other journeys where the exception matters more than the happy path.

If your automation layer hides failure instead of handling it, you are training customers to trust you less every time the issue gets a little messy.

For more CX Insights from Reddit, check out the DCX Insight Finder GPT

OPERATOR PLAYBOOK

Start with the journeys where your customer arrives stressed, confused, or already late in the process. Support. Billing. Returns. Claims. Outages. Pharmacy. Cancellations.

Then audit every automation flow for four things:

1. Can your customer solve the issue in fewer steps than before?

2. Can they get to a human without restarting the story?

3. Does context move with them when the handoff happens?

4. Are you measuring repeat contact and abandonment, not just deflection?

Then test whether your “successful” self-service flow would still feel successful if you were the customer on your worst day.

Ask your team: Where are we saving labor by shifting effort onto the customer?

📈 Market Reality Check

Integration is still the missing middle

KPMG’s new Total Experience Report is useful because it cuts through the usual optimism. Leaders know integration matters. Most still have not done it. The same study says integrated companies are three times more likely to achieve higher revenue growth, and leaders tie integration to stronger CX impact, business outcomes, consistency, personalization, and efficiency.

The contradiction is the interesting part. Technology shows up as both the top enabler and the top blocker. Legacy systems and siloed data are still getting in the way, even while AI and automation remain the top investment priority over the next 24 months.

That is the market reality you are working in. More money is going into AI. The connective tissue is still weak.

🧰 Tool Worth Knowing

Malachyte Understand

What it does: Malachyte says its MiQ engine can recognize customer intent from the start of a session, even for unknown visitors, and keep updating that understanding as the customer clicks, searches, scrolls, and adds to cart.

CX use case: If you are trying to improve product discovery, search, recommendations, or first-session relevance before sign-in, this is the kind of capability worth paying attention to.

Worth watching because: The interesting angle is the cold-start problem. Plenty of personalization stacks still struggle when the customer first shows up. Malachyte is going straight at that gap. It also has public customer stories pointing to measurable impact with retailers including Saks OFF 5TH and Neiman Marcus.

Bottom line: Solid idea with clear CX relevance. The promise here is simple: make the first few minutes of the customer journey feel smarter, more relevant, and less generic. The public proof is still mostly vendor-published, but there is enough signal here to justify a closer look.

⚡ 90-Second CX Radar

Adobe turns AI visibility into a CX issue

Adobe is making the case that AI interfaces are now part of the discovery journey. Its release says AI traffic to U.S. retail sites rose 269% year over year in March. That should get your attention. Discovery quality now matters before the customer even reaches your site..

Ascension frames responsible AI as a navigation problem for patients

Ascension’s take is practical. It points to AI helping people find the right care more easily, get more relevant support, and move through the care journey with less friction. Healthcare is a tough proving ground. If AI cannot reduce confusion there, you should be skeptical everywhere else.

🧭 Your Move

Pick one journey where automation looks efficient on paper and stressful in real life. Listen to five calls. Read five chats. Walk the flow yourself. Then ask the hard question: where did you save cost by making the customer work harder?

If your team is celebrating lower contact volume, pair that with repeat contact, abandonment, and handoff quality. Otherwise, you are grading the machine on how well it hides demand.

Customers will forgive a lack of magic. They are less forgiving when they can feel you wasting their time.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Why Adobe’s move matters to you
→ Where this can go wrong first
→ A simple audit to run this week
→ A service tool worth watching
→ Three signals to keep on your radar

🔎 Deep dive

Adobe just made AI discovery your problem

You can feel the shift now. Customers are no longer always starting with your site. They are asking AI tools to compare options, summarize products, explain differences, and narrow the field before they ever get to you.

That is why Adobe’s latest move matters. It is a signal that discovery is being pulled upstream into AI surfaces that rank, recommend, and interpret on the customer’s behalf. Which means your job is changing. It is no longer enough to make the journey work once someone lands on your site. You also need to make sure your content, product data, and policies are clear enough for an AI system to describe you correctly before the click.

That is where this gets risky. A customer can get a fast answer that sounds clean and confident, even when it is wrong or incomplete. If your pricing logic is messy, your policies do not line up, or your content leaves room for guesswork, the failure may happen before your team ever sees the customer.

For you, that means losing trust or consideration earlier in the journey. For the customer, it means confusion dressed up as convenience.

OPERATOR PLAYBOOK

Run this audit before AI does it for you

Pick one high-intent journey this week. Keep it simple. Plan selection. Product comparison. Booking. Checkout. Returns. Billing help.

Now audit that journey for four things:

1. Can an AI tool find the right answer from your public content without making assumptions?

2. Do your pricing, policies, product facts, and eligibility rules match across channels?

3. If the answer is incomplete, can the customer move to a person without starting over?

4. Can your team tell which source content is shaping what the customer sees before they arrive?

Then push it one step further. Test the journey as if the customer is showing up already half-informed by an AI answer you did not create.

Ask your team: Which journey would fall apart fastest if customers stopped browsing and started outsourcing comparison work to AI?

📈 Market Reality Check

Your customers are already using AI, but they still want control

Customers are moving faster than most companies. McKinsey says 63% of European consumers already use AI tools to compare options. More than half use them to learn about a product or category. Nearly half use them for discovery or inspiration.

That should get your attention. People are already using AI to reduce effort in the early part of the journey. They are not waiting for companies to decide the experience is polished enough.

But there is an important catch. They still want the final decision to stay with them.

That is the line to watch. Your customers will gladly use AI to do some of the legwork. They are far less comfortable handing over judgment. So if your experience feels helpful, clear, and easy to verify, you win. If it feels slippery, opaque, or over-automated, you have a problem.

🧰 Tool Worth Knowing

Amazon Connect AI agents

What it does: Amazon Connect AI agents are built to handle customer requests across voice and chat, reason through the issue, and take action inside service workflows.

CX use case: What makes this worth your time is that it is not only about answering questions. It can pull in customer history, case data, knowledge content, and connected systems so work actually moves. That matters when you are trying to cut repeat contacts and stop making customers carry the experience for you.

Worth watching because: A lot of teams still have a patchwork setup. One tool for self-service. One for agent assist. A bunch of context lost in between. This points toward a tighter model where the customer and the agent are working from the same core intelligence.

Bottom line: The promise here is not speed for its own sake. It is less repetition, better handoffs, and fewer moments where the customer has to do the stitching.

⚡ 90-Second CX Radar

Choice Hotels moves from AI pilots to enterprise-wide deployment

Choice says it is embedding AI across guest experience, hotel operations, and distribution. The interesting part for you is the scope. This is not a small experiment off to the side. It is a bet that AI belongs across the full journey.

The FTC fallout around OkCupid data is a reminder that bad data lineage becomes a CX problem later

Reuters reports Clarifai deleted millions of OkCupid user photos and related training data after FTC scrutiny. When data practices get sloppy, the damage does not stay tucked away in legal or compliance. It eventually shows up in customer trust.

Adobe is also attacking the visibility problem, not only the workflow problem

Its new brand-visibility move aims to help companies show up accurately across AI-led discovery surfaces. That matters because customers are increasingly meeting your brand through someone else’s summary first.

🧭 Your Move

Take one journey where your customers compare options before they buy. Search it the way they would. Then ask an AI tool the same question. Put that answer next to your site, your policy, and your agent script.

You are looking for drift. Conflicts. Gaps. Anything that would cause the customer to head in the wrong direction with total confidence.

The teams that handle this well will not be the ones with the flashiest AI demo. They will be the ones who cleaned up the messy stuff first.

The next CX fight starts before the click.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ AI is moving into live trust moments
→ Search is starting to act on the customer’s behalf
→ Consumer comfort still trails AI capability
→ A new tool for faster CX concept work
→ Ads are spreading into chat interfaces

🔎 Deep dive

The journey is getting weird in new places

CX is getting stranger because AI is no longer sitting quietly in the back office. It is starting to show up inside the moment itself. It can verify who is real, shape what the customer sees, steer what they do next, and even act on their behalf. Zoom’s partnership with Tools for Humanity is a good example. It brings human verification into Zoom Meetings for higher-stakes moments. That means trust checks are moving into the live interaction, not just the login screen.

That shift matters because more of the journey is now being shaped in places the brand does not fully own. Search can call stores. Browsers can guide research. Video platforms can decide how trust gets signaled.

For customers, the experience starts to feel less direct and more filtered.

For companies, the risk is blunt: when the system gets it wrong, the brand still gets blamed.

There is upside here, too. The teams that build clear disclosure, proof, recovery, and a clean human fallback will look steadier than the teams that chase convenience and hope for the best.

Source: Zoom

OPERATOR PLAYBOOK

Audit the moments where AI is shaping the decision

Pick one journey this week where AI is doing more than helping in the background. Look for the places where it is influencing what the customer sees, believes, or does.

Audit it for four things:

1. What system is shaping the decision before the customer even reaches you?

2. What proof does the customer get that the answer, recommendation, or person is credible?

3. Where does recovery happen when the system gets it wrong?

4. Who owns the experience when your brand does not own the surface?

Then ask whether your team is managing the actual experience, or just the channel you happen to control.

Ask your team: Which customer decisions are now being influenced upstream by AI systems outside our direct environment?

📈 Market Reality Check

People are using AI more. That does not mean they feel great about it.

ACSI’s new AI Platforms Study puts overall satisfaction across six major AI platforms at 73 out of 100. More than half of U.S. adults say they have no recent experience using any AI platform. Among people who do use them, 52% say they use AI at least once a day. So yes, the behavior is getting real. It just is not evenly spread yet.

The tension is emotional, not technical. ACSI found 43% say reduced human-to-human interaction is their top concern. That is the part CX teams should not ignore. AI can get more capable every quarter and still make the experience feel shakier when the moment matters.

🧰 Tool Worth Knowing

Claude Design

What it does: Anthropic launched Claude Design as a research preview for creating polished visual concepts such as prototypes, slides, mockups, and one-pagers through conversation. Anthropic says it can also use a team’s design system when provided.

CX use case: Quick mockups for onboarding flows, support pages, escalation screens, service concepts, and internal storytelling assets when you need people to see the idea fast.

Worth watching because Teams that can clearly explain these new mediated experiences will have an edge. Sometimes the fastest way to get alignment is to show the future state rather than keep talking about it.

Bottom line: Good for speed and early alignment. Just keep a human eye on quality before anything touches a customer.

⚡ 90-Second CX Radar

Google adds hotel price tracking and store calling to AI Mode

Google says AI Mode can now track hotel prices, support agentic restaurant booking, and call nearby stores to check inventory and deals. That means search is starting to do customer work, not just point to options. When that layer gets stock, hours, or pricing wrong, the cleanup still lands on the brand.

Google brings side-by-side AI Mode to Chrome

Google’s new Chrome experience keeps AI Mode open beside the webpage while people browse. That brings research, comparison, and interpretation into a single assisted surface. The site still matters, but now it increasingly works as evidence for the model, not always as the place where the journey begins.

🧭 Your Move

This week, stop looking only at the journeys you own from start to finish. Look harder at the moments being shaped before the customer gets to you, beside your experience while they are in it, or inside another company’s interface after you think the handoff is done.

Then ask the harder question: if AI is now mediating the moment, what part of that experience still belongs to your team to design, explain, recover, and defend?

The next CX edge may come from handling mediated experiences better than everyone else.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Starbucks shifts AI into discovery
→ Trust becomes the real commerce test
→ Drive-thru AI keeps moving forward
→ Stellantis goes bigger on AI
→ Walmart tightens the care journey

🔎 Deep dive

Starbucks is testing more than a feature

Starbucks launched a beta app inside ChatGPT that helps people discover a drink, customize it, choose a store, and move into Starbucks checkout. That sounds like a nice convenience play. It is more important than that. Starbucks just moved AI into the part of the journey where intent gets shaped, before the customer ever hits the menu wall or starts hunting through filters.

That is the shift. Most AI in CX has lived downstream, cleaning up friction after the customer is already confused or annoyed. This puts AI upstream, in the moment where preference gets formed. The customer upside is clear: less searching, less menu fatigue, faster movement from curiosity to purchase. The business upside is clear too: better guided discovery and a shorter path to checkout.

The hard part is what happens next. Once another layer starts shaping choice, the handoff becomes the experience. If recommendations do not carry cleanly into checkout, loyalty, inventory, pricing, and recovery, the whole thing starts to feel flimsy. A smart front end will not save a messy journey.

OPERATOR PLAYBOOK

Pressure-test the handoff, not the demo

If AI is helping customers browse, configure, or order, do not spend all your energy on whether the assistant sounds smart. Inspect the seams.

Audit every AI-assisted flow for four things:

1. Can the customer move from recommendation to checkout without redoing work?

2. Do preferences and customizations carry through cleanly?

3. Do loyalty, payment, inventory, and service rules still hold up when the journey starts outside your app?

4. When something breaks, does recovery stay simple or does the customer get bounced around?

Then run one ugly test on purpose. Break the path. Try an out-of-stock item, a bad substitution, a broken promo, or the wrong store. That will tell you more than ten polished demos ever will.

Ask your team: Where does the customer get punished for trusting the AI?

📈 Market Reality Check

Agentic commerce is already running into the trust question

American Express launched its ACE developer kit for agentic commerce and paired it with protections for registered agent purchases. That is the useful tell. The market is already moving past the novelty phase and into the harder question: what happens when an AI agent is allowed to act on behalf of a customer in a real transaction?

This is where AI and CX really meet. The moment an agent can browse, decide, and buy, customer experience stops being only about convenience. It becomes about confidence, control, accountability, and what recovery looks like when the machine gets it wrong. The smart move is not just enabling the transaction. It is building the trust layer at the same time.

🧰 Tool Worth Knowing

Presto Voice AI

What it does: Presto powers voice AI for drive-thrus. Dairy Queen tested it in corporate stores and is now expanding the pilot to select franchisees.

CX use case: High-volume ordering environments where speed matters, repetition matters, and getting the order right matters more than sounding clever.

Worth watching because: This is a clean signal of where practical AI adoption is heading. Not toward novelty. Toward bottlenecks.

Bottom line: Voice AI is getting promoted from experiment to operating layer.

⚡ 90-Second CX Radar

Stellantis and Microsoft are pushing AI deeper into customer care, sales, and operations

The headline here is scale. The companies said they are working across more than 100 AI initiatives, including customer care and sales. That is what it looks like when AI stops being a side project and starts becoming operating infrastructure.

Walmart is folding AI support into a broader health journey

Walmart expanded its Better Care Services offer and added Curai Health’s GLP-1 AI agent for customers exploring or using GLP-1 medications. The interesting part is not the tool itself. It is the journey design. Care, pharmacy, nutrition, and support are being tied together instead of handed to the customer as separate chores.

Adyen says AI shopping assistant use has jumped from 12% to 35% in a year

That is a sharp reminder that this is moving faster on the customer side than many companies assume. People are already experimenting with AI in commerce. The gap now is whether the trust, payment, and recovery design is ready for them.

🧭 Your Move

Take one journey where AI now shows up before purchase. Map the handoff into checkout. Then map the recovery path when something goes wrong. That is where the weak spots usually are.

Most companies do not have a model problem. They have a journey ownership problem. AI is getting closer to the customer’s first decision. That raises the stakes for everything that happens after it.

A smart front door means nothing if the hallway is a mess.

Until Monday,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue

→ AI pricing gets more accountable
→ Support metrics get harder to fake
→ Personalization keeps climbing
→ Privacy assumptions look shakier
→ Messaging platforms may get pricier

🔎 Deep dive

The pricing shift matters more than the product launch

The cleanest signal in today’s news is not a shiny new AI feature. It is the pricing model.

HubSpot said its Breeze Customer Agent moved to outcome-based pricing, charging $0.50 per resolved conversation instead of pricing around conversation volume. It also said Customer Agent resolves about 70% of conversations on average, with some teams reaching 90%.

That matters because per-conversation pricing rewards motion. Per-resolution pricing forces a tougher question: did the customer actually get helped? That will show up first in support, where plenty of teams have been calling containment a win when the customer still leaves annoyed, confused, or back in queue ten minutes later.

The upside is obvious. Vendors now have a stronger reason to care about the finish, not just the interaction. The risk is obvious too. A weak definition of “resolved” can still turn into dashboard fiction. Customers do not care that the case was marked closed. They care that the problem is gone.

OPERATOR PLAYBOOK

Pressure-test your definition of “resolved”

This is the week to audit one support flow that looks good in a dashboard and shaky in real life.

Check four things. First, does “resolved” mean the customer confirmed the answer, or just that the chat ended? Second, can the bot complete the task, or does it just explain policy in a friendly voice? Third, when a human takes over, does the context come with it? Fourth, are refunds, disputes, damaged orders, and edge cases being measured separately from easy FAQ traffic?

Then ask one uncomfortable question: if you stripped out the easy contacts, would your automation story still sound as good?

Ask your team: Which high-volume flow looks efficient but still creates repeat contacts?

Signal: The cheap part of AI support was answering questions. The expensive part is owning the outcome.

📈 Market Reality Check

Agent experience is becoming a CX risk, not just an HR problem

Verint’s new agent experience report makes the point pretty clearly. Agent frustration is not some side issue sitting off to the left of customer experience. It is starting to look like a direct operating risk. Verint says 31% of agents surveyed are likely to leave within six months, 94% expect AI to change their roles within three years, and 61% expect that work to become more complex and technical.

The part I would pay attention to is this: in 45% of calls, agents spend an average of three minutes searching for answers. That is not just wasted labor. That is slower service, shakier confidence, rougher handoffs, and more customer patience burned for no good reason.

The market signal here is simple. If companies keep talking about AI while leaving agents stuck in clunky workflows, they are going to get the worst of both worlds: higher attrition and weaker CX.

🧰 Tool Worth Knowing

Intercom Fin Procedures

What it does: Intercom is pushing Fin further into complex support work with Procedures, aimed at queries that are a small share of the queue but eat a disproportionate share of team time.

CX use case: Think refunds, account investigations, damaged orders, policy exceptions, or any case where a canned answer is not enough, and the handoff quality decides whether the customer stays calm or spirals.

Worth watching because: A lot of support AI still looks best on simple requests. This move goes after the ugly middle, where service memory is made. It is still a vendor claim, so treat it like one until you see your own repeat-contact and failure-rate data.

Bottom line: Support for AI gets more useful when it can handle processes, not just language.

⚡ 90-Second CX Radar

Tesco leans harder into AI personalization

Tesco expanded its partnership with Adobe to use AI and agentic AI capabilities with Clubcard data to better interpret customer needs and deliver more personalized experiences. Clubcard serves more than 24 million UK households. This is loyalty data turning into operating infrastructure, not just a marketing asset.

Lawyers are warning people that AI chats are not private

U.S. lawyers are warning clients not to treat chatbot conversations like confidential advice after a federal judge ruled certain Claude-generated materials were not protected by attorney-client privilege. For CX leaders, that raises the pressure on disclosure, retention, and how clearly companies explain what happens to customer conversations with AI.

Meta’s WhatsApp AI fee is running into EU pushback

The European Commission intends to force Meta to roll back a fee for rival AI assistants on WhatsApp while an antitrust probe continues. That matters because messaging channels may become gatekeepers with pricing power, which changes the economics of customer access fast.

🧭 Your Move

Pick one AI-enabled support flow and review it with fresh eyes. Not from the bot log. From the customer’s side. Look at repeat contacts, messy handoffs, and whether the task actually got finished.

AI in CX is getting more serious now. Good. That usually means the easy stories are over.

The question is getting simpler: did the customer get the outcome?

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Banking AI moves behind the frontline
→ Service data starts shaping next action
→ Segmentation gets easier for your team
→ CX teams get faster agent deployment
→ AI shopping keeps moving upstream

🔎 Deep dive

CIMB Niaga is using AI to help bankers show up better in the moment

CIMB Niaga, Google Cloud, and Artefact launched AI agents for relationship managers and contact center teams. The signal here is where the AI sits. It is being used to give employees better context, better guidance, and better information right when a customer needs help.

You will likely feel this first in onboarding, assisted service, and advisory moments where customers want clarity and confidence. That is where staff-facing AI starts to earn its keep. The customer gets less repetition and more relevant help. Your operation gets more consistency from one interaction to the next.

The risk sits in the same place it usually does. Weak context and shaky recommendations will speed up the wrong move. AI can tighten execution, but it can also make sloppy judgment travel faster.

OPERATOR PLAYBOOK

Look for the lag between what customers tell you and what your company does next

Pick one journey where customers are already giving you the signal, but your business still acts too late for it to help. Complaint handling. Save offers. Loyalty recovery. Onboarding friction. Billing confusion.

Check four things:

1. Where does the signal show up first?

2. How long does it take before someone can use it?

3. Which team owns the next move?

4. Does the next touchpoint reflect what the customer just told you?

Then look at whether the customer gets a better experience in the same session, not three days later after the data gets cleaned up, routed around, and buried in a dashboard.

Ask your team: where are you still treating live customer intent like old news?

📈 Market Reality Check

The contact center is starting to look more like a decision engine

Later this month at the Adobe Summit 2026, TELUS Digital will be showing how contact center transcripts and agent notes can trigger personalized web journeys and loyalty actions, with the company framing the value as cutting response time from days to minutes. The shift here is straightforward. Service data is moving out of the reporting bucket and into the action bucket.

If that pattern holds, your contact center becomes a live source of intent, sentiment, and next-step signals that can shape retention and digital experience while the signal is still fresh. That changes the role of service. It becomes part of how you steer the journey, not just how you clean up after it.

The pressure point is speed. Most companies already collect plenty of insight. The weak spot is turning it into action before the moment passes.

🧰 Tool Worth Knowing

Springbig AI Audience Builder

What it does: Springbig launched a natural-language audience segmentation tool that lets marketers describe the audience they want in plain English instead of building segments through filters and query logic.

CX use case: If you run loyalty or retention work, this can help you move faster on win-back campaigns, churn-risk targeting, and promotional audiences without waiting on a technical handoff.

Worth watching because: a lot of customer marketing gets stuck in the messy middle between insight and execution. Tools like this cut some of that drag.

Bottom line: this is the kind of AI tool that earns its keep by being useful, not impressive.

⚡ 90-Second CX Radar

8x8 brings AI Studio natively into its CX platform

8x8 says AI Studio lets teams build, test, and deploy AI agents directly on its platform using natural-language instructions. The useful part is the shorter path from idea to live workflow in service environments where rollout lag has quietly slowed a lot of progress.

David’s Bridal partners with Shopify for agentic commerce

David’s Bridal is using Shopify’s agentic commerce capabilities as part of its wider AI push, and the retailer says AI already drives 90% of its customer communications. You can expect the effect to show up earlier in the journey, closer to discovery and purchase, where AI starts shaping choice before service ever enters the picture.

Scotiabank launches Scotia Intelligence

Scotiabank says Scotia Intelligence gives employees a unified AI foundation built for secure and scalable adoption. It sounds internal, and that is fine. A lot of CX progress depends on whether your company has the plumbing to support better decisions at scale.

🧭 Your Move

Take one customer journey where your company already sees the warning signs, but still reacts too late to change what happens. Maybe customers are telling you they’re confused on billing, threatening to cancel, or repeatedly calling about the same issue. The data is there in surveys, complaints, call notes, and digital behavior—but by the time someone reviews it, the customer has already decided to leave or downgrade.

That’s the gap this wave of AI is built to close.

Instead of just collecting more feedback, use AI to notice those signals faster, route them to the right team, and trigger a better next step while the customer is still in the moment. When you use AI to shorten the time between “customer tells you something” and “company does something useful about it,” that’s when it stops being a demo and starts being practical.

The next AI edge in CX may come from acting on the signal while it still matters.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Tesco puts AI closer to the loyalty engine
→ Consumers want AI help, not paid influence
→ Banks face a sharper AI fraud threat
→ Scrunch treats AI agents like a new front door
→ Trust is becoming the cost of convenience

🔎 Deep dive

Tesco puts AI where customers will actually feel it

Tesco’s new Adobe partnership matters because this is not some back-office experiment. It puts AI closer to the part of the retail experience that already shapes what customers buy, notice, and ignore. Reuters reports Tesco is tying Adobe’s AI tools more closely to Clubcard, which reaches more than 24 million UK households, as part of a wider digital push across offers, content, delivery, marketplace, and retail media.

That means AI is moving into the wallet. Customers will feel it first in discovery, promotions, and repeat purchase. That can make the experience more relevant. It can also get annoying fast if the recommendations feel pushy, mistimed, or a little too eager to steer the decision.

The executive takeaway is pretty straightforward. When AI starts influencing what customers see first, trust becomes part of the operating model. Relevance helps. Pressure backfires.

OPERATOR PLAYBOOK

Pressure-test the AI offer layer before it starts wearing people out

If your team is putting AI into promotions, recommendations, or shopping guidance, look at the journey like a customer who is busy, skeptical, and not in the mood.

Audit every loyalty and offer flow for four things:

1. Can the customer tell why they are seeing this?

2. Does the recommendation fit the moment?

3. Can they dismiss it, correct it, or move past it easily?

4. Would the logic still look fair if somebody outside the company reviewed it?

Then test whether the experience still feels useful when the customer is in a hurry, confused, price-sensitive, or already irritated.

Ask your team: Where are we using AI to help the customer, and where are we using it to push a result?

📈 Market Reality Check

Adoption is flying. Trust is not keeping pace.

Stanford HAI’s 2026 AI Index says generative AI reached 53% population adoption within three years, faster than the personal computer or the internet. It also estimates annual value to U.S. consumers at $172 billion by early 2026.

That is the growth story. The trust story is messier.

The same Stanford summary says the Foundation Model Transparency Index dropped to 40 from 58 last year. Public sentiment is mixed too. Fifty-nine percent reported optimism about AI’s benefits, while 52% also reported nervousness.

AI is getting folded into daily life fast. Customers are not giving it a blank check.

🧰 Tool Worth Knowing

Scrunch Agent Experience Platform

What it does: Scrunch builds an AI-ready version of your site for retrieval bots, serving clean, structured content to AI agents without touching the human-facing experience.

CX use case: When customers meet your brand through ChatGPT, Perplexity, or other AI tools first, Scrunch improves what those systems can actually see and return.

Worth watching because: It treats AI agents as a real experience surface, not an afterthought — and that is where discovery is heading.

Bottom line: Smart fit for teams that care about AI-led discovery, accurate citations, and the story customers hear about them before they ever land on the site.

⚡ 90-Second CX Radar

75% of Americans say they would lose trust in AI shopping if results were sponsored

Quad and The Harris Poll found that 74% of Americans are aware of agentic AI shopping, and 51% say they would rather use AI shopping tools to reduce the risk of a bad purchase. Good signal for adoption. The catch is trust. Seventy-five percent say sponsored results would reduce it. Consumers are fine with guidance. They are much less fine with invisible influence.

AI-boosted hacks with Anthropic’s Mythos could have dire consequences for banks

Reuters reports experts see Anthropic’s Mythos as a serious new threat to banks because it can identify vulnerabilities and help exploit them, especially across older and mixed tech environments. For CX leaders in financial services, this lands well beyond security. Fraud spikes, outages, verification friction, and service recovery failures tend to show up together.

Perplexity expands its personal finance tool with Plaid

EMARKETER says Perplexity has expanded its personal finance capabilities through Plaid connections for checking, savings, credit card, and loan accounts. The bigger point is that more financial guidance may happen outside the bank experience. Banks still own the relationship in many cases. They may not own the first moment of analysis for much longer.

🧭 Your Move

Pick one journey where AI is already shaping attention before purchase or before support. Then ask one blunt question: does this feel helpful, or does it feel like the company is trying to get a little too clever?

That is the line. Consumers will use AI when it saves time, lowers effort, or helps them make a better call. They start pushing back when it feels murky, manipulative, or hard to challenge.

The next strong AI experience will feel useful. The weak one will feel like someone quietly tilted the table.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

📌 DCX Stat of the day: Only 42% of customers trust businesses to use AI ethically, down from 58% in 2023.

Takeaway: CX teams cannot treat AI rollout as a pure efficiency play. Customers are signaling that better personalization does not automatically earn trust, so transparency, consent, and human escalation matter more than ever.

In this issue:

→ Grab pushes AI deeper into daily consumer moments
→ Where AI should cut effort, not customer control
→ Convenience keeps rising, but trust gets taxed
→ Air India puts AI into disruption recovery
→ Revolut shows the cautious side of AI help

🔎 Deep dive

Grab and the guided everyday journey

Singapore-based Grab’s latest product push is useful because it is not built around one chatbot stunt.

It spreads AI across small consumer moments that usually live in different products: finding food, planning a route, booking a stay, shopping from a list, and paying while traveling. That is the shift. The guided journey is starting to matter more than the single transaction.

The CX upside is obvious. Fewer redirects. Less re-entry of information. Less hunting around when the customer already has intent. The risk is just as obvious. The more the app anticipates, suggests, and routes, the more damage it does when it guesses wrong or hides the easiest way out. Convenience feels smart right up until it starts acting like a trap.

OPERATOR PLAYBOOK

Where AI should remove friction without removing control

This is the test. If AI is stepping into a customer journey, it should shorten the path without making the customer feel managed.

Audit every guided flow for four things:

1. Does the AI reduce steps or just rearrange them?

2. Can the customer easily override the recommendation?

3. Is the next best action actually the customer’s next best action?

4. When the system is wrong, is recovery immediate or buried?

Then test whether the customer can switch channels, restart, or reach a human without losing context.

Ask your team: Where have we made the journey feel smoother for us, but more opaque for the customer?

📈 Market Reality Check

Trust is becoming the hidden cost of AI convenience

Salesforce’s latest State of the AI Connected Customer report puts a hard number on the trust problem.

Only 42% of customers trust businesses to use AI ethically, down from 58% in 2023. The report also says 72% of customers think it is important to know when they are communicating with an AI agent. That is a clean signal that trust is not a side issue anymore. It is part of the product experience.

The useful takeaway for CX leaders is pretty simple. As AI gets woven into everyday moments like discovery, support, and booking, customers are not just judging speed or convenience. They are judging whether the system feels honest, visible, and safe. If that confidence slips, the gains from smoother journeys get harder to keep. This is not just a governance problem. It is a design problem.

AI convenience up. Ethical trust down.

🧰 Tool Worth Knowing

AIR by Revolut

What it does: Revolut’s AIR is an AI tool that answers questions across money tasks and comes with a plain warning that its information may not always be accurate.

CX use case: It is a good example of conversational finance that tries to be useful without pretending to be flawless. That matters in categories where a bad answer can cost the customer real money.

Worth watching because: The interesting part is not that Revolut launched an AI helper. Plenty of firms have done that. The interesting part is the mix of utility and explicit caution. A lot of brands still want the magic without the label on the box.

Bottom line: This is less a wow story than a trust design story.

More: 🔗 Revolut

⚡ 90-Second CX Radar

Air India puts AI into disruption recovery
Air India said active Air India Express bookings can be rebooked through its AI-powered assistant Tia on WhatsApp. This is one of the better journey examples in the mix because it puts AI into a stressed moment where speed and clarity matter more than novelty.

AI scams are becoming part of the customer journey whether brands like it or not
Consumer Reports coverage warns that AI is helping scammers personalize outreach with fake voices, images, and messages. That means every brand-owned interaction now competes with better fraud theater in the same channels customers already use.

🧭 Your Move

The short version: AI is starting to show up in more ordinary consumer moments, not just headline-grabbing chatbot demos. Discovery, booking, service recovery, and financial help are all getting more guided. At the same time, the trust burden is rising right alongside the convenience.

Key takeaways:
AI guidance works best when it cuts effort without trapping the customer in the flow.

Trust is becoming a design problem, not just a compliance problem.

The strongest use cases are still the plain ones: help me choose, help me book, help me recover, help me know this is real.

Until tomorrow,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ AI can influence the choice, but not the belief
→ Trust is getting harder to earn in digital journeys
→ Returns are becoming a fraud and fairness fight
→ Loyalty is shifting from points to habit design
→ The next edge may come from better proof, not more output

🔎 Deep dive

The consumer trust gap just got harder to ignore

The sharpest story today is not that people use AI in shopping. It is that they do not fully trust what it tells them.

A new Digital Commerce 360 / Omnisend report found that 84% of Americans trust online product reviews, 33% trust them more than they did two years ago, and 86% still have concerns about AI-generated product recommendations. It also found that 93% double-check AI recommendations before buying.

That matters because a lot of brands are treating AI recommendations as a conversion layer when customers are still treating them as a draft. The recommendation may get attention. The proof still has to come from somewhere else. Reviews, verified purchase signals, and specific feedback are still carrying the final burden of belief.

This shows up first in discovery and evaluation. If customers think the recommendation engine might be biased, paid for, or manipulated, the experience does not feel helpful. It feels rigged.

OPERATOR PLAYBOOK

Audit every recommendation surface like it has to win a cross-examination

If AI is influencing product discovery, your job is not only to improve relevance. It is to make the logic feel credible.

Audit every recommendation and review the surface for four things:

1. Whether verified purchase signals are obvious enough to matter

2. Whether mixed feedback is easy to find, not buried under volume

3. Whether the recommendation is separated clearly from paid placement or promotion

4. Whether the customer can see enough product detail to verify the suggestion without leaving the page

Then test the moment after the AI suggestion. What proof does the customer get that this is the right product, not just the easiest one to surface?

Ask your team: If customers assume your recommendation engine is biased, what in the interface proves otherwise?

📈 Market Reality Check

Consumers are questioning what is real

Gartner found that 50% of consumers prefer brands that avoid GenAI in consumer-facing content. It also found that 61% frequently question whether the information they use to make everyday decisions is reliable, and 68% frequently wonder whether the content and information they see is real.

That is the bigger commercial problem. Even as AI use rises, comfort with AI-shaped customer experiences does not rise accordingly. Consumers are getting more verification-minded, not less. That means every customer-facing use of AI now carries a trust tax. If the value is not obvious, the skepticism shows up fast.

AI adoption may rise. Automatic trust will not.

🧰 Tool Worth Knowing

Mastercard Return Risk Intelligence

What it does: Mastercard uses AI to analyze return, refund, and dispute patterns, enabling merchants to score return risk in real time.

CX use case: Helps brands spot suspicious returns before they turn into chargebacks, losses, or broad policy crackdowns that punish good customers too.

Worth watching because: Returns are one of the easiest places to wreck trust while trying to reduce fraud. A blunt policy saves money in the short term and burns loyalty in the process.

Bottom line: The value here is not catching bad actors. It is protecting the return experience from becoming hostile for everyone else.

⚡ 90-Second CX Radar

Points alone are losing their grip on loyalty
The stronger loyalty programs are leaning on AI analytics, targeted messaging, gamification, and tiered rewards. The interesting part is not the tech. It is the shift in logic. Loyalty is becoming less about counting transactions and more about shaping the next one.

Meta wants shopping help inside the apps people already use
Meta says its upgraded assistant can help people discover what to wear, how to style a room, or what to buy, while also comparing scanned products to alternatives. That matters because product discovery is drifting into feeds, chats, and camera-led moments long before the customer reaches a brand site.

🧭 Your Move

Pick one journey and strip the AI story out of it for a minute. Ask a simpler question: what makes this experience believable to the customer? In discovery, that may be review integrity. In returns, it may be fair treatment. In loyalty, it may be whether the program feels personal before it feels promotional.

A lot of teams are still asking where to add more AI. The sharper question is where customers are already skeptical, and what proof they need before they trust the experience.

“The next edge in consumer CX may come from better evidence, not smarter output.”

Until Monday,

I’m obsessed with Wispr Flow Pro! Get a Free Month on me.

Your daily signal on AI and CX — minus the hype.

In this issue:

→ Hippo improves claims response
→ Citi cuts onboarding delay
→ Visa gets ready for AI buyers
→ Parloa goes after enterprise scale
→ CX teams need clearer handoffs

🔎 Deep dive

Claims is where AI can actually make CX feel better

Hippo says it expects more than 70% of homeowner claims to be filed digitally, with first contact happening in under two hours on average.

That is worth paying attention to because claims has always been one of the hardest moments in the customer journey. Customers are stressed, the issue is expensive, and the experience can go sideways fast if communication is slow or unclear. So when a company makes that moment faster and easier, it matters.

What I like about this story is that it points AI at a real customer problem. Faster intake, better routing, quicker updates, and less administrative drag can make a rough moment feel more manageable. That is where AI earns its keep in CX. Not by sounding clever. By reducing friction when the customer is already under pressure.

The bigger opportunity is what happens next. If AI keeps improving the front end of claims and supports cleaner handoffs to humans when needed, this is the kind of use case that can raise confidence, not just efficiency.

📬 Copy-Paste Take: Send this to your COO

Hippo is a good example of AI being used where customers can actually feel the benefit. In claims, faster intake and quicker response are not small wins. They can make a stressful moment feel more organized, responsive, and human.

OPERATOR PLAYBOOK

Look for the places where AI can reduce stress, not just labor

Pick a journey where the customer already feels pressure. Claims is the obvious one, but the same logic applies to fraud review, identity verification, service recovery, and billing disputes.

Audit every intake and escalation flow for four things:

1. Does the customer get started quickly and easily?

2. Does the experience make the next step clearer?

3. Do updates make the customer feel informed and supported?

4. Does the human step in with context and continuity when needed?

Then ask whether the journey feels more reassuring after AI enters the picture.

Ask your team: Where can we use AI to make a stressful moment feel simpler, clearer, and more responsive?

📈 Market Reality Check

Customers are watching the machine now

A new CX Network survey says 36% of practitioners see customer awareness of how AI works and uses data as the behavior shift shaping their work most.

That changes the brief. Customers are not only judging speed and convenience now. They are judging intent. Does this feel fair? Does it make sense? Can I tell what is happening? Do I trust this company with my data and my time?

A lot of teams are still treating AI like a quiet efficiency layer. Customers have already dragged it into the relationship. That is where the pressure moves from performance to perception, and from automation to trust.

Speed without clarity gets suspect fast.

🧰 Tool Worth Knowing

Parloa

What it does: Parloa is an AI agent management platform built for enterprise customer experience. It covers the full lifecycle, from designing and integrating agents to testing, deploying, scaling, and improving them over time.

CX use case: Useful for teams that want one system for voice, chat, messaging, and agent assist instead of treating each channel like its own science project. The practical value is not just automation. It is being able to build once, manage centrally, and keep improving across channels.

Worth watching because: A lot of AI customer service tools still feel like point solutions with good demos. Parloa is going after the bigger operational problem. How do you design, test, govern, deploy, and monitor AI agents at enterprise scale without creating a mess across channels, teams, and markets? That is a more serious CX question.

Bottom line: This is less chatbot tool, more operating system for conversational AI in CX. That makes it more useful, and a lot more consequential, if you are trying to scale AI without losing control.

⚡ 90-Second CX Radar

Citigroup cuts account-opening document review from over an hour to 15 minutes with AI

This sounds operational. It is. It is also customer experience. Customers feel compliance drag as waiting, uncertainty, and friction. Citi is going after one of the dullest parts of onboarding, which is often where the real pain lives.

Visa opens the door to AI-driven shopping for businesses worldwide

If AI starts acting on behalf of customers, checkout stops being a page and starts becoming a system of permissions, data quality, and trust. That is a bigger shift than it sounds. A lot of commerce teams are not built for it.

Ask Yuma adds a conversational layer to support operations

This one is interesting because it goes after the work behind support, not just the conversation in front of it. The next useful wave of AI in CX may have less to do with chat and more to do with helping teams change workflows without six tickets and a quarter of delay.

Quick read

Today’s issue is a useful reminder that AI is moving into the parts of CX that customers actually feel. Claims, onboarding, checkout, and support operations all get more valuable when the payoff is less confusion, less waiting, and more confidence.

That is the thread running through all of this. Efficiency matters. But the better story is reassurance. The teams that get the most from AI will be the ones that make the journey feel clearer and steadier, not just faster.

🧭 Your Move

Take one high-friction journey and review it from the customer side after AI enters the picture. Not the dashboard view. The lived experience.

A better question than “is the AI working?” is this: where is it making the experience feel easier, and where is it still leaving the customer unsure what happens next?

AI gets real in CX when it makes hard moments feel easier to navigate.

Until tomorrow,

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Your daily signal on AI and CX — minus the hype.

📌 DCX Stat of the day: Nearly 1 in 5 consumers who used AI for customer service said they saw no benefit, a failure rate Qualtrics said was almost four times higher than AI use in general.

In this issue:

→ Exit journeys are now the stress test
→ Returns data shows the trust gap clearly
→ One playbook for pressure-testing leave flows
→ A fraud tool with a better instinct

🔎 Deep dive

Refund chatbots are making a bad moment worse

AI can get away with a lot in low-stakes moments. A weak answer during product discovery is irritating. A weak answer when someone is trying to get their money back feels like stalling. That is a different kind of failure.

That is the risk showing up here. When a refund bot loops, deflects, or slows down access to a human, the customer does not experience that as innovation. They experience it as resistance. The bot becomes the company’s way of saying no without saying no.

That is why refund and complaint journeys deserve more scrutiny than the front end of the experience. If AI makes those moments feel harder, the savings story falls apart fast. You may cut contacts in the short term. You also raise the odds of repeat complaints, trust damage, and churn.

Consumer AI is starting to get judged at the point where the customer wants out. That is where weak service design gets exposed.

More: CNBC

OPERATOR PLAYBOOK

Audit one journey where the customer is trying to leave

Pick one exit-stage flow this week. Returns. Refunds. Cancellation. Billing reversal. Something with money, urgency, and elevated emotion.

Audit that flow for four things:

1. Can the customer see status, rules, and next steps clearly?

2. Can they reach a human quickly when the case involves money, timing, or exceptions?

3. Is fraud control targeted, or are you spreading suspicion across everyone?

4. Are you tracking true resolution, not just containment or deflection?

Then run the tougher test. What happens when the customer is angry, confused, or already on their second contact?

Ask your team: Where does AI reduce effort for the customer, and where does it mainly reduce effort for us?

Signal: If your AI makes exits feel harder, the savings story is probably overstated.

📈 Market Reality Check

Consumers will use AI in returns. They just will not forgive a bad experience.

Ada found that 55% of shoppers had already made or planned to make a return after the holiday season. It also found that 60% would be likely to use AI-powered customer service if it could instantly answer questions and process a return. That is the opening.

The warning sits right beside it. Only 36% said they were very satisfied with the returns process, and 57% said a poor returns experience would reduce their likelihood of buying from that brand again. Consumers are open to automation here. They are not giving brands a pass for making returns slower, colder, or harder to resolve.

AI acceptance + poor recovery = preventable churn

🧰 Tool Worth Knowing

Return Vision

What it does: Happy Returns, owned by UPS, offers an AI tool called Return Vision to flag suspicious returns for human audit.

CX use case: It gives retailers a way to tighten fraud review without turning the entire returns process into a hassle for everyone else.

Worth watching because: This is a smarter pattern. Apply scrutiny at the edges, not across the whole journey. That protects both trust and cost-to-serve better than blanket friction.

Bottom line: Returns AI gets more useful when it helps teams isolate bad actors without punishing good customers.

⚡ 90-Second CX Radar

Airbnb says a third of its customer support is now handled by AI in the U.S. and Canada

This matters because it puts a real consumer support brand at scale, with AI already handling roughly a third of support issues in North America. The useful CX question is not whether AI can take volume. It is what happens when those issues move from simple support into exceptions, disputes, credits, and recovery. That is where the leave-stage risk starts to show up.

NGL refund claims show how AI-adjacent deception can turn into a customer recovery problem

The FTC says NGL used fake messages to push users toward paid subscriptions and the earlier order halted deceptive claims around AI content moderation. Now the agency is running a refund claims process for users affected by unauthorized charges.

🧭 Your Move

The smartest next step is not another shiny pilot.

Pick one leave-stage journey where AI is already involved. Pressure-test it. Look at trust, cost-to-serve, repeat contact, and service quality together. That is where the real answer is.

Good AI in CX should make leaving feel clear, fair, and fast. Anything else will be felt as resistance.

Until tomorrow,

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Your daily signal on AI and CX — minus the hype.

📌 DCX Stat of the day: Carrefour Israel and A2Z Cust2Mate plan to deploy 4,000 smart shopping carts nationwide under a five-year agreement, with rollout starting in Q3 2026 across six flagship stores.

In this issue:

→ AI is now shaping the shelf
→ Where retail AI can go wrong fast
→ The human gap AI still has not closed
→ A simple test for AI-led journeys

🔎 Deep dive

Smart carts just made AI part of the store

For a while, consumer AI mostly lived around the journey. Search. Recommendations. Chat. Carrefour’s rollout is different. AI is moving into the basket itself.

Carrefour Israel and A2Z Cust2Mate signed a deal to deploy 4,000 smart carts with charging, software, implementation, training, and long-term support included. The deal also ties the rollout to retail media and data monetization rights. That matters because it moves AI from a support layer into the purchase moment itself.

The CX question changes fast when that happens. If the cart helps shoppers decide faster, the trip feels easier. If it starts pushing the wrong item, the wrong offer, or too much noise, the experience gets worse in the most valuable part of the journey. Once AI reaches the point of purchase, weak judgment starts to feel like pressure.

OPERATOR PLAYBOOK

Pressure-test one journey where AI can influence a sale

Pick one journey this week where AI is already shaping customer choice. Search to product page works. In-store assisted discovery works too.

Check four things:

1. Who owns the recommendation rules and priority logic?

2. Can you see where customers reject, ignore, or override AI prompts?

3. What happens when the recommendation is wrong, irrelevant, or misleading?

4. Can the customer recover without friction or staff rescue?

Ask your team: If our AI pushed the wrong choice today, who would know first and who would fix it?

Signal: Consumer AI gets expensive when nobody owns the correction loop.

📈 Market Reality Check

Emotional connection is a top customer value, and the weakest AI progress area

Only 16% of organizations report making meaningful progress with AI in building emotional connections with customers.

That is the sharpest signal in the ServiceNow study,The CX Shift: A Study of Customer Expectations in the AI Era. The company says the research covers more than 34,000 customers, service reps, and executives globally, and points to a widening gap between customer expectations and AI delivery.

For CX leaders, the implication is pretty clear. The near-term advantage may come less from replacing more human moments and more from using AI to clear low-value work so humans show up better in the moments that actually shape trust.

AI investment + weak emotional progress = a CX gap customers will notice

🧰 Tool Worth Knowing

Genie by Tote.ai

What it does: Loop Neighborhood Market is deploying Tote.ai’s Genie across its convenience store network. The assistant handles employee questions on customer service, POS operations, maintenance, payments, and more.

CX use case: This is internal AI with external consequences. If store staff get faster answers and rely less on managers for routine questions, customers usually get quicker help and more consistent service.

Why it matters: A lot of retail AI value may come from making the front line less hesitant, not from adding another flashy customer-facing layer.

Bottom line: The useful tools in consumer AI may be the ones customers barely notice because the store suddenly works better.

⚡ 90-Second CX Radar

Sezzle is bundling AI shopping and AI support into one consumer app

Sezzle said today that its app now includes an AI shopping assistant and 24/7 AI support as part of a broader push to become an all-in-one shopping, rewards, and financial education platform. The CX signal is that discovery, decision support, and service are starting to collapse into one consumer surface. That raises the trust bar because the same interface is now shaping both what people buy and how they get help.

Magicpin just launched an AI assistant for local retail

Economic Times reported today that Magicpin launched an AI assistant called Vera and committed $1 million to build out its AI stack for local commerce. The interesting part is not the assistant name. It is the direction of travel. Hyperlocal commerce players are trying to use AI to make smaller merchants feel easier to search, compare, and buy from. That could improve convenience, but it also puts more pressure on data quality and recommendation accuracy.

Alexa+ turns food ordering into one conversation

Amazon says Alexa+ can now place delivery orders through Grubhub and Uber Eats using a more natural back-and-forth flow, with browsing, customization, and order changes handled in one conversational window. The CX signal is pretty clear: task completion is moving away from command-based interfaces and toward adaptive, transaction-ready AI.

🧭 Your Move

The theme today is simple. AI is moving closer to where customers decide, buy, and ask for help.

Pick one journey where AI is already doing more than drafting or support search. Then map who owns the logic, who monitors bad outcomes, and how recovery works when the system gets it wrong. That is work a CX leader can do this week without waiting for budget season.

Closing Signal

The companies that manage AI well will look calm from the outside because the control layer will be doing its job.

Share this with one operator who still thinks consumer AI is mostly a marketing story.

Until tomorrow,

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📌 DCX Stat of the day: 91% of customer service leaders say they are under pressure to implement AI in 2026. Gartner

In this issue:

→ IBM shows the next control layer
→ Why observability is moving into CX
→ The metric shift hybrid teams will force
→ Basecamp opens its doors to agents
→ Voice bots just rang 3,000 pubs

CONTEXT

The new AI problem is supervision

The first wave of AI was “can we build agents?” The next wave is “who’s watching them?”

IBM’s live dashboard is an early clue. Its consulting arm now uses AI workers on more than 150 client engagements, and in one January security use case agents completed 52,000 investigations after cutting task time from about 45 minutes to a few minutes.

Once agents are doing that kind of work, the hard part stops being the model demo. It becomes the supervision model around it.

WHY IT MATTERS

CX leaders are about to inherit the same problem in service and digital journeys.

Think about a billing adjustment, a cancellation save, or a fraud flag. If an agent makes those calls without clear visibility and guardrails, you feel it in complaints, chargebacks, and churn.

As agents start handling real customer tasks, the weak point shifts to the control layer: who can see what the agent did, when a human steps in, how decisions get audited, and how fast you can recover when it goes wrong.

IBM’s Enterprise Advantage offer leans heavily on governance, brand control, accuracy, cost, and human validation. That’s a signal: autonomy by itself is no longer an interesting story. Governed autonomy is the story.

EXEC SUMMARY

🎯 Exec Briefing: Why this should be on your agenda

IBM is pushing a real-time operating model where humans monitor digital workers and intervene on important outputs, and its consulting platform already supports hundreds of client engagements. Its Enterprise Advantage service turns that into a packaged way of working for customers.

For CX leaders, that creates a fast tension. More agent autonomy can cut effort and cost. It also increases the number of places a customer can get a wrong answer, a messy handoff, or a decision nobody can clearly explain.

The edge now goes to teams that manage agents like a workforce, not a feature.

📬 Copy-Paste Take: Send this to your COO

AI agents are moving into customer work, which means the next advantage will come from who can see them, govern them, and step in before a bad answer becomes a service problem.

🔎 Deep dive

The control room is becoming the product

The interesting shift isn’t that vendors can spin up agents. It’s that they’re now building the management layer around them.

IBM’s Enterprise Advantage pitch is basically a playbook and control room for digital workers: structure, context, execution, and built‑in human oversight for workflows that rely on agents, including customer self‑service and regulatory reporting. That’s the tell. Autonomy alone is hard to buy. Governed autonomy is what gets funded.

Talkdesk is coming at the same problem from the contact center. Its CXA Operations Center is built around observability, evaluation, and live monitoring so AI and human agents can be managed as one workforce. That’s a cleaner mental model for CX leaders than “AI handles simple stuff.”

The simple stuff is not your risk. The risk is:

• When you can’t see what the agent decided or why

• When nobody owns the thresholds for escalation

• When recovery takes three transfers and the customer repeating their story

The control room – the place where you see agent behavior, intervene, and repair – is becoming the real product. If you buy agents without that control room, you’re buying blind.

Operational takeaway: Treat “agent observability and oversight” as its own capability, budget line, and owner, not a byproduct of a vendor demo.

OPERATOR PLAYBOOK

Audit your hybrid service model before volume arrives

Pick one live journey where AI is already doing more than drafting – for example, password resets, appointment changes, billing questions, or order status.

For that one flow, answer four questions:

1. Authority: Where can the agent act without explicit human approval – and is that level of authority written down anywhere?

2. Observability: Where can a human reconstruct what the agent did, what it saw, and why it chose that path?

3. Escalation: Where does escalation happen only after confusion (multiple transfers, “agent not helpful” surveys) instead of before real risk (billing disputes, compliance exposure, churn‑likely events)?

4. Recovery: Where does recovery depend on the customer repeating context, because your systems don’t preserve the agent’s decisions and prior steps?

Then pressure‑test the handoffs:

• Can a human jump into the journey mid‑stream, see the agent’s history, and fix the issue without forcing the customer to start over?

• Do you have at least one metric or alert that would tell you, within a day, that this agent has started making bad decisions at scale?

Ask your team: If this agent is wrong for 1,000 customers this week, where do we see it first – and who is accountable for acting on that signal?

Signal: If you cannot trace the handoff and can’t name the owner of the fix, you don’t control the experience.

📈 Market Reality Check

The pressure is already here

Gartner reports that 91% of customer service leaders say they face pressure from executive leadership to implement AI in 2026. It also says nearly 80% of organizations plan to transition at least some agents into new roles, and 84% expect to add new skills to the agent role.

The metric shift follows. Hybrid teams make some of the old comfort metrics less useful on their own. Average handle time and containment don’t tell you whether AI made things better or just hid problems. You need to see:

• how often humans are correcting agent decisions

• how long it takes to detect a bad pattern

• what happens to NPS or churn when an agent, not a person, is the first touch

Those are management metrics, not model metrics.

Pressure + weak oversight = expensive speed.

🧰 Tool Worth Knowing

Basecamp Agents

What it does: Basecamp has launched a technology preview that lets teams bring their own AI agents into Basecamp through an official CLI, agent skill, and SDK. Agents can write docs, manage to-dos, answer check-ins, and work through the terminal with tools like Claude, Codex, Cursor, and others.

CX use case: Useful for internal CX and journey teams that want agents to handle project hygiene, updates, follow-through, and status capture inside an operating system people already use.

Worth watching because: This is what happens when agent access moves out of the lab and into the tools where work actually gets managed. That can remove admin drag. It can also create quiet messes if permissioning, audit trails, and ownership stay fuzzy.

Bottom line: The interesting part is not the AI. It is that a work platform is preparing for agents to behave like teammates.

⚡ 90-Second CX Radar

When AI Called Every Pub in Ireland

An AI voice agent made more than 3,000 calls to Irish pubs asking the price of a Guinness, and only a handful of people realized they were speaking with a machine. The stunt is funny. The CX signal is not. Voice agents can now collect real-world commercial data at scale, which means local businesses are getting pulled into AI-mediated discovery whether they planned for it or not.

80% of Fortune 500 use active AI agents

Microsoft says more than 80% of Fortune 500 companies use active AI agents built with low-code or no-code tools, and 29% of employees have turned to unsanctioned AI agents for work tasks. That is a useful warning. Agent sprawl is going to hit customer-facing work faster than many governance teams expect.

🧭 Your Move

Pick one customer journey where AI is already doing more than drafting. Then map who owns the agent, who can see its actions, and who fixes the fallout when it gets something wrong.

That sounds less glamorous than another demo. It is also where trust, cost-to-serve, and service quality are actually won or lost.

The companies that manage AI best will look boring from the outside. That is usually a good sign.

Until tomorrow,

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