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.

The complaint isn’t the beginning.

By the time a customer calls, cancels, escalates, posts, churns, or lights you up in a survey, the experience has usually been broken for a while.

Someone inside the company saw it.

The app said one thing. The agent saw another. The billing notice confused people. The policy looked reasonable in a meeting and absurd in real life.

Then a customer actually needed help.

That’s when the experience collapsed.

Most companies know where these problems live. The issue isn’t visibility. It’s permission.

Can someone say the journey is broken without getting punished for making the room uncomfortable?

Often, no.

The meeting after the meeting

A billing change confused customers.

The email and app did not match. Agents were left explaining a policy they didn’t fully understand.

So the CX leader raised it in the weekly operating review.

“We’re seeing a spike in repeat contacts tied to the billing change. Customers are confused by the language, and agents are struggling to explain the difference between what the email says and what the account screen shows.”

The room went quiet.

A few people looked down. Someone from operations gave a small nod that said, “You’re right,” with the added subtext of, “Please don’t drag me into this.”

The senior leader moved on.

“Okay. Let’s take that offline.”

After the meeting, the CX leader got pulled aside.

“Don’t ever do that again.”

No one asked how many customers were affected. No one asked how fast the team could fix it or what agents were hearing.

The customer issue had become secondary. The surprise had become the problem.

“You need to give me a heads-up before you bring something like that up in that room.”

And there it was. The new rule.

It wasn’t written down. It spread through what happened next.

The CX leader got careful. The next issue came in softer. More qualified. Easier for the room to absorb. The sharp customer language was gone.

Other people noticed.

The frontline leader stopped bringing raw agent feedback. The product lead waited until the issue was fully documented, which meant it was already stale. The ops team brought trends instead of examples because examples have names, timestamps, and consequences.

Nobody had to say, “Don’t bring uncomfortable customer issues into this room.”

The room learned by watching what happened to the person who did.

That’s how psychological safety disappears.

If this sounds familiar, it’s not because your company is uniquely broken or because you’re bad at your job. It’s what happens in most organizations when discomfort travels faster than accountability.

Usually, it erodes through small corrections that teach people what the room will tolerate. A look. A warning. One sentence in private.

After that, the meeting still looks functional. Dashboards get reviewed. Leaders ask for feedback. Someone probably says, “Please be candid.”

But everyone has learned the new operating rule: bring the customer issue carefully, later, after alignment, and without heat.

Customers keep living with the problem everyone has learned to describe politely.

This isn’t an HR problem. It’s a CX problem.

Psychological safety gets tossed into the HR bucket too easily. That’s convenient. It lets executives treat it like a vibes issue.

In customer experience, psychological safety is operating infrastructure. It decides whether the truth about the journey can move through the company without being sanded down, delayed, or buried.

Dashboards, surveys, VOC reports, call transcripts, complaint themes, and journey maps do not mean the truth is moving.

Your frontline might know the pattern. Support might hear the confusion every day. Product might know the defect. Operations might know the workaround has quietly become the real process.

The question is whether anyone can say it clearly without taking a hit.

When naming the pattern becomes politically risky, the story gets cleaned up on the way up. The customer pays for the edit.

They don’t know your org chart. They don’t care which team owns which system. They’re thinking, “Why is this so hard?”

Starting over because two teams never agreed on the handoff. Waiting because a policy was written for internal control instead of customer clarity. Getting stuck in a rollout that looked finished to the business and unfinished to everyone using it.

The agent feels it too. They know the process is getting in the way. But the system gives them polished language instead of real authority.

That is where trust starts to leak.

Blame will not surface that truth any faster. It teaches people how to package it.

Culture shows up in the moment after someone says the uncomfortable thing.

The CX leader’s job is to make truth usable

You may not own the billing system, product roadmap, policy, agent workflow, app, email, or field process.

But you can own how customer truth enters the room.

That means the issue has to be specific enough to act on, safe enough to discuss, and visible enough that it cannot disappear into “offline” purgatory.

This is where pre-wiring gets misunderstood.

Giving a leader context before a meeting can be responsible. Nobody needs surprise theater. A leader should not have to learn about every serious issue cold in a public forum.

The problem starts when pre-wiring turns into pre-censorship. The conversation shifts from “Help me prepare the room to solve this” to “Can I say this without creating trouble?”

CX leaders need to manage the room without weakening the truth. Give people context. Bring evidence. Respect the fact that nobody likes being surprised in public. Then make sure the customer issue still arrives intact.

If the truth has to be softened beyond recognition before it can be discussed, the organization is protecting itself from the customer.

Think of what follows as a truth-safe CX operating pattern—a simple set of habits that make it safer to bring the unedited customer story into the rooms where decisions get made.

The standard

A healthy CX culture does not require everyone to agree.

It requires the truth to survive the room.

People need to be able to say, in plain English, that a policy is driving repeat calls, a launch made the agent experience worse, or the metric improved while the experience got worse.

When those signals surface early, the company still has a chance to fix the journey while the damage is contained.

When they get edited, the truth finds another route. It shows up as churn, complaints, refunds, escalations, bad reviews, and executive surprises.

By then, the problem is already old.

So ask the harder question:

Where does the truth get edited before it reaches the people who can fix the journey?

If you’re not sure where to start, ask that question to three people in different roles this week—a frontline leader, a product or operations partner, and one executive peer. Don’t debate their answers. Just listen, write down what you hear, and bring one specific example into your next operating review.

That answer tells you more than another dashboard.

If people can name the break in plain English without paying for it, you are building a real CX system.

If they cannot, you have a reporting system with better manners.

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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.

You can have the right CX idea, a clear customer problem, and a better path forward — and still get stuck trying to convince the people least likely to move.

That’s part of the story running through this week’s issue. Whether you’re thinking about trust, AI, payment access, aging at home, or brand value, the biggest CX wins are not coming from polished decks or big promises. They come from fixing the friction customers feel every day and building momentum with the people who are ready to act.

As you read, look for the common thread: trust is built through systems, not scores. Value is shaped by experience, not price alone. And real CX change starts faster when you stop chasing the holdouts and start proving what works.

Let’s dig in.

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This week’s must-read links:

• Trust Is a System, Not a Score

• Luxury Retail Is Selling the Feeling, Not the Price Tag

• Age Tech Is Turning the Home Into the Next Care Channel

• Stop Trying to Convince the Holdouts

• DCX Stat of the Week: AI is funded, but not fully operational

• DCX Case Study of the Week: M.U.D. Protected Access While Modernizing Payments

Trust Is a System, Not a Score

You cannot measure your way into customer trust.

That is the useful punch in Christina Garnett’s Customer Trust Infrastructure work. Most companies track NPS, CSAT, sentiment, and loyalty data after the customer has already formed an opinion. Helpful signals, sure. But they usually tell you trust changed after the damage is done. They do not show you what built it, what weakened it, or who inside the business needs to act.

The trust problem:

• You may be treating trust like a score when it really behaves like a system.

• Customers learn to trust you through repeated signals: consistency, response, connection, and value.

• Friction does the opposite. A bad policy, slow handoff, confusing process, or broken promise can erase trust faster than a dozen good moments rebuild it.

The CX shift:

• Trust is not owned by one team.

• Marketing can promise it. Product can shape it. Support can repair it. Operations can quietly break it.

• That makes trust a design problem across the business, not another dashboard to admire.

The operating lesson:

• Before you chase a better score, find the friction.

• Look for the delays, handoffs, fine print, policy traps, and gaps between what you say and what customers actually experience.

• Remove those first. Then your trust-building work has a shot.

The CX To-Do: Pick one journey and audit it through the trust equation: consistency, response, connection, value, and friction. The weakest spot is probably closer than you think.

🔗 Go Deeper: Christina Garnett, Customer Trust Infrastructure working paper

Luxury Retail Is Selling the Feeling, Not the Price Tag

You do not create value only through price. You create it through what the customer feels they are getting.

That is the useful lesson from Ralph Lauren and Tapestry. Both are leaning harder into experience, storytelling, and personalization instead of racing to discount.

The value shift:

• Ralph Lauren treats value as the mix of story, product quality, and experience against the price.

• Ralph’s coffee shops help turn the brand into a place customers want to spend time, not just a label they buy.

• When the store feels like “welcome to our home,” the visit carries more emotional weight.

The younger customer:

• Tapestry is using experience to keep Coach and Kate Spade relevant.

• Craftsmanship bars let shoppers personalize bags and make the purchase feel social.

• That gives customers a reason to care beyond the product itself.

The CX lesson:

• Your customers do not experience value as a spreadsheet.

• They judge it through emotion, identity, effort, memory, and whether the brand feels worth choosing again.

• When pressure hits, discounting may protect the transaction but weaken the brand.

The CX To-Do: Look at your journey and ask where the experience makes the price feel smarter.

🔗 Go Deeper: CX Dive

Age Tech Is Turning the Home Into the Next Care Channel

If you serve older customers, caregivers, families, patients, policyholders, or subscribers, this shift is already in your lane.

More people want to age at home. Their families want them to be safe. The gap between those two needs is where age tech is growing fast.

The care gap:

• Caregivers are stretched, working, remote, and often managing care between meetings, errands, and their own lives.

• Smart pill boxes, digital reminders, remote monitoring, video tools, and companion robots can help people stay independent longer.

• The best tools do not take over. They give families confidence while letting older adults keep some control.

The business signal:

• AARP says 25% of caregivers now remotely monitor loved ones through apps, video, or wearables, nearly double the rate from five years ago.

• More than 700 companies are in AARP’s AgeTech Collaborative.

• This is becoming a mainstream customer need, not a side category for healthcare.

The trust problem:

• Safety can start to feel like surveillance if the experience is designed only for the caregiver.

• Privacy, consent, affordability, and dignity have to be built in from the start.

• Nobody wants to feel managed in their own home.

The CX To-Do: Build for the person being cared for, not just the person managing the care.

🔗 Go Deeper: NY Times

Stop Trying to Convince the Holdouts

Simon Sinek makes a useful point about change: it gets harder when you aim it at the people least likely to move.

That’s where CX work gets stuck. You see a better path. A cleaner journey. A smarter fix. Then the room turns into a debate about risk, ownership, budget, and “how we’ve always done it.”

The better starting point:

• Find the people who already see the problem.

• Give them room to test the idea before it is fully polished.

• Let them help shape the work instead of asking them to simply approve it.

The adoption trap:

• Most people are not against the idea. They are waiting for proof.

• A better slide deck will not change that.

• Visible progress from trusted peers usually does.

The CX lesson:

• Start with the early believers.

• Use small pilots to create real evidence.

• Let demand build from inside the business.

The CX To-Do: Before your next CX initiative turns into another alignment meeting, ask one better question: who already cares enough to help prove this works?

DCX Stat of the Week: AI is funded, but not fully operational

82% of senior leaders invested in AI for customer service in 2025, and 87% plan to invest in 2026. But only 10% of teams say they have reached mature deployment, where AI is fully integrated into operations and working at scale.

Takeaway: You may have AI in customer service, but that does not mean you have transformed the service model. The gap is now between teams using AI around the edges and teams rebuilding workflows around it. That is where the next CX advantage shows up.

Source: Intercom, 2026 Customer Service Transformation Report

DCX Case Study of the Week: M.U.D. Protected Access While Modernizing Payments

If you close a customer channel, you still have to replace the job that channel performed.

That is the useful lesson from M.U.D., the Omaha-area public gas and water utility. The company needed to close physical payment offices, but many customers still preferred to pay with cash or in person. That could have turned a cost-saving move into a customer frustration problem.

Problem:

• M.U.D. served more than 600,000 residents, including customers who relied on physical offices for cash payments.

• Closing offices reduced operating costs, but it also created an access gap.

• The utility needed better payment options without forcing every customer into the same digital lane.

Solution:

• M.U.D. worked with Paymentus to add Walmart Bill Pay, PayPal, Venmo, PayPal Credit, digitized cash options, and AutoPay.

• Walmart Bill Pay gave customers 13 local Walmart locations where they could pay with cash, debit cards, or Ucards.

• Payments posted immediately, which gave customers confidence their bill was handled.

Outcome:

• Walmart Bill Pay transactions grew 600% in the first five months.

• PayPal, Venmo, and PayPal Credit transactions grew 35% in 2023.

• Scheduled AutoPay increased 3x since 2018.

The CX lesson:

• Billing CX is access design.

• Payment choice lowers effort because customers can use the method that fits their life.

• Adoption rises when you meet customers where they already are instead of making them learn your preferred path.

The CX To-Do: Before you close a channel, ask what customer need it quietly solved. Then replace that need, not just the channel.

Go Deeper: 🔗 Paymentus, Driving Cost Savings and Greater Adoption with Walmart Bill Pay & Digital Wallets

Have a case study to share? Reply and let me know!

See you next week.

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.

A lot of companies think they’re orchestrating journeys when they’re really just sequencing messages.

The email is timed well. The offer is personalized. The next step is triggered.

But the customer is still repeating the problem. Still switching channels. Still trying to figure out who owns the issue. Still holding the experience together when the company doesn’t.

That’s the gap.

The business sees orchestration.
The customer feels the hassle.

The promise sounds good

Journey orchestration is easy to like on paper.

Better timing. Better coordination. Better follow-up. Less customer effort.

All of that sounds right.

And to be fair, some progress has been real. Companies have invested heavily in journey platforms and orchestration tools, and customers clearly expect a more connected experience than most brands actually deliver.

But better tools do not automatically create a better experience.

You cannot fix fragmentation just by layering orchestration on top of it.

A lot of what gets called journey orchestration is really just better message sequencing.

The company got better at deciding what to send next.

The customer still ends up doing the work.

That’s the part most dashboards never show.

Where it actually breaks

This is usually not a strategy problem.

It is a continuity problem.

Most orchestration efforts are built around company actions instead of customer continuity.

The company asks:

• What should we send next?

• What offer should we trigger?

• What channel should we use?

• What audience is this person in?

Those questions matter.

But they miss the ones that actually determine whether the journey feels connected.

The customer is wondering:

• What’s actually happening right now?

• What have I already told you?

• What did you say would happen next?

• If this goes wrong, who owns it?

• Will the next person I talk to know any of this?

That is where journeys break.

It happens in the handoff. In the exception. In the moment when the customer has to stop and think, Wait, do I really have to start over?

That is the experience they remember.

The company is split up. The customer is not.

Orchestration often sits on top of a company that still works in pieces.

Different teams. Different systems. Different goals. Different measures of success.

Marketing wants response.
Digital wants completion.
Service wants containment.
Operations wants efficiency.

The customer just wants the company to make sense.

When it doesn’t, they feel it immediately. The workflow may be connected behind the scenes. The experience still feels broken from the outside.

How to fix it, one journey at a time

Start smaller than most companies want to.

Skip the big enterprise vision for end-to-end orchestration. That is usually where the language gets polished and the reality gets fuzzy.

Start with one journey that is already causing pain.

Pick the place where customers are most likely to bounce between channels, repeat themselves, get stuck in a handoff, or hear different answers from different parts of the business. Billing issue. Service problem. Onboarding. Claims. Save flow.

The point is to start where the breakdown is real and visible.

Map the promises, not just the process

This is where a lot of teams lose the plot.

They document steps, systems, and touchpoints.

Fine.

What you really need to map are the promises.

What does the customer think you told them?
What are they expecting you to remember?
Where do they believe ownership sits?
At what point are they quietly being asked to hold the whole thing together on your behalf?

That is where the truth usually is.

A lot of broken journeys are not broken because the workflow is impossible.

They break because the company made a promise it did not realize it was making. Or it made several small promises that quietly conflict.

Decide what context must survive the handoff

Do not settle for vague language like “we need better data.”

That does not help.

Get specific about what has to carry forward every time.

• The reason for contact

• What already happened

• The last action taken

• Any constraint that matters

• What the customer was told

• Who owns the issue now

If that context does not move with the customer, the orchestration is failing.

It may still look impressive from the inside.

It is still failing.

Put one person on the hook for the journey

Someone has to own what happens across the seams.

Not just the campaign. Not just the channel. Not just the platform.

The journey.

Otherwise, every team will do its part, and the customer will still get a broken experience.

That is one of the quiet reasons orchestration disappoints. Plenty of people are accountable for pieces. Almost nobody is accountable for the moments in between.

And that is exactly where customers get stuck.

Build for recovery, not just progression

Real journeys do not stay clean for long.

A payment fails. An appointment moves. A case gets misrouted. A message fires at the wrong time. A customer ignores one step and shows up somewhere else. An employee steps in without enough context.

That is normal.

So the question is not only whether the system can move the customer forward.

You also need to know what happens when the journey goes sideways.

Can the next channel see the problem?
Can the employee pick up the thread?
Can the company recover without making the customer repeat the whole story?

That matters more than most trigger logic.

Watch for effort transfer

This is one of the easiest mistakes to miss.

A company automates a step and calls it progress.

But did the work actually go away?

Or did it move to the customer?

If the customer has to repeat information, chase updates, translate between departments, or figure out who owns the issue, then the company did not remove effort.

It pushed it outward.

That is not orchestration.

That is cost shifting with nicer language.

Test in the messy middle, not the happy path

Do not test orchestration only in workshops or polished demos.

• Use the late payment.

• The missed technician visit.

• The angry callback.

• The exception case.

• The channel switch.

• The half-complete application.

• The customer who did everything right and still got stuck.

That is where you find out whether the journey holds together.

The messy middle tells the truth.

The standard is simple

Journey orchestration works when the customer no longer has to carry the experience for the company.

They shouldn’t have to keep reintroducing the problem.
They shouldn’t have to bridge the gap between channels.
They shouldn’t have to guess who owns the issue.
They shouldn’t have to repair the disconnects between your teams.

If they still do, the orchestration isn’t working.

No matter how good the dashboard looks

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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.

Most companies say they are getting smarter with AI. A lot of them are just getting better at hiding the same old problems.

This week’s stories all point to the same tension. Companies want faster service, cleaner operations, and more personalized experiences. Fair enough. But speed is useless if the agent cannot resolve. Personalization is weak if it just dresses up a clunky decision process. And better dashboards do not matter if leaders still cannot see where customers are getting stuck.

That is what makes these pieces worth reading together. One shows why most AI agents still fail the real frontline test. Another gets at a harder truth: the biggest drag on outcomes is often buried in handoffs, dependencies, and work nobody can fully see. Starbucks offers a more practical example of where this is going, using AI to help customers figure out what they want before they ever hit checkout. Then the stat brings it back to earth. Executives think the experience is strong. Customers are a lot less convinced.

The common thread is simple. The gap between what companies think is happening and what customers actually live is still wide. AI can help close that gap. It can also make it worse by adding polish where the real issue is still unresolved.

So here is the standard: is your team using AI to remove friction customers actually feel, or to make internal blind spots look more sophisticated?

Let’s dig in.

This week’s must-read links:

• Most AI agents still fail the job interview

• AI is getting better at finding the stuff leaders miss

• Starbucks is making drink discovery feel more like a chat

• Your brain is not built to be locked in all day

• DCX Stat of the Week: Leaders think CX is stronger than customers do

• DCX Case Study of the Week: Cebu Pacific scaled support without losing the human touch

Most AI agents still fail the job interview

A lot of companies are buying “AI agents” the way people buy kitchen gadgets on late-night TV. The demo looks slick. The real thing disappoints. This Ada Guide on How to evaluate AI customer service agents is useful because it gives you a better test: stop admiring the pitch and start interviewing the agent like it’s applying for a frontline job.

What to pressure-test:

• Ada says a real service agent should handle eight basics well, including multi-part questions, follow-ups, consistent answers, relevance, semantic understanding, and direct responses instead of article links.

• That matters in the real world because customers do not speak in neat, one-intent tickets. They ask messy questions, stack requests, and expect the system to keep up.

Where the money gets wasted:

• If the agent cannot hold context, answer the same issue consistently, or resolve without dumping links, you did not buy automation. You bought friction with a bigger invoice.

• Ada puts it plainly: an agent without semantic understanding is basically an expensive search bar. Hard to argue with that.

The CX To-Do: Run your vendor bake-off on real customer contacts. Same issue. Different wording. Follow-up included. Does it resolve, or just perform?

🔗 Go Deeper: Ada

AI is getting better at finding the stuff leaders miss

This one lands because it names a problem a lot of companies live with every day. People are working hard. Meetings are happening. Dashboards are full. Results still feel stuck. The point here is that the problem often is not effort. It is the hidden patterns underneath the work.

What’s going on:

• Most outcomes are shaped by a mess of decisions, handoffs, and dependencies spread across teams.

• No single leader can really see all of that clearly while the work is happening.

What AI changes:

• AI can look across time, teams, and data and spot patterns people miss.

• That makes it useful for seeing why work that looks productive does not always produce a better outcome.

Why a CX leader should care:

• This is not just an operations story. It is a customer story.

• If AI helps you see which steps actually drive trust, speed, and resolution, you can fix the journey before customers pay the price.

• That is a lot more valuable than using AI to just make the same broken process move faster.

🔗 Go Deeper: Microsoft

Starbucks is making drink discovery feel more like a chat

This is one of those moves that looks simple on the surface but says a lot about where CX is going. Starbucks is testing a ChatGPT experience that helps people find a drink by describing a mood, a craving, or even sharing a photo. That matters because most people do not walk around thinking in menu language. They think in moments. I want something cold. I want a little boost. I want something that fits today.

What stands out:

• Starbucks is meeting customers where their thinking actually starts, which is usually fuzzy, not precise

• The experience helps turn that loose intent into a drink suggestion without making the customer do the work

What it means:

• This makes discovery feel easier and more personal

• It also gives Starbucks a new way to shape demand before the customer ever gets to checkout

The CX angle:

• This is a good example of AI being useful before the transaction, not just during service

• The bigger idea is clear: brands can use AI to help customers decide, not just help them search

🔗 Go Deeper: Starbucks

Your brain is not built to be locked in all day

This one hit home for me because it pushes back on a bad modern assumption: that every quiet moment needs to be filled. Darius Foroux argues that mind-wandering is not the enemy of good work. In many cases, it is what helps good work happen. When your brain has a little room, it can connect ideas, process what you’ve taken in, and surface thoughts you were too busy to notice.

What makes it useful:

• Darius separates focused attention from reflective attention and makes the case that you need both

• He also makes a smart distinction between anxious mental spinning and healthy wandering driven by curiosity

Why it matters for CX people:

• If your job depends on judgment, pattern recognition, and insight, constant input can actually get in the way

• Some of your best ideas about customers, friction, and what to fix next probably will not show up while you are cranking through Slack and meetings

The practical takeaway:

• Protect a few small pockets of real off-time

• No podcast, no scrolling, no filling the silence

• Sometimes the most useful thing you can do for better thinking is back off a little

🔗 Go Deeper: Darius Foroux

DCX Stat of the Week: Leaders think CX is stronger than customers do

84% of executives think customers would recommend their company. Only 45% of customers actually would.

Takeaway:
That gap is expensive. It means a lot of companies are managing CX from the conference room, not from reality. If your team thinks the experience is working but customers are far less likely to recommend you, you have a perception problem before you have a loyalty problem.

Source:
Capgemini Research Institute, Reimagining customer experience: Human-led, AI-powered (2026). The report draws on surveys of 9,500 consumers across 16 countries and 1,200 senior leaders and frontline staff across 13 markets.

DCX Case Study of the Week: Cebu Pacific scaled support without losing the human touch

CX Challenge: As the Philippines’ largest airline by passenger volume, Cebu Pacific had to support large volumes of travelers quickly and consistently. The risk: long waits, uneven answers, and frustrated passengers during stressful travel moments.

Action Taken: Cebu Pacific partnered with Ada to deploy AI customer service across 11 languages, automating routine questions so support could be available at scale while freeing human teams for more complex cases.

Result: The airline now resolves 75% of customer interactions autonomously and increased customer satisfaction scores by 50%.

Lesson for CX Pros: The best automation does not feel like avoidance. It removes friction from repetitive journeys, gives customers faster answers, and creates more space for agents to show empathy where it matters most.

Quote: “Partnering with Ada allows us to set a new benchmark in customer service, leveraging AI to better serve our passengers.” — Candice Iyog, Chief Marketing and Customer Officer, Cebu Pacific

Further Reading: Ada

Have a case study to share? Reply and let me know!

See you next week.

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.

Most customer surveys fail before the customer ever sees them.

Not because the wording is bad, the survey tool is weak, or because customers suddenly stopped caring.

They fail because the survey shows up in the wrong moment, for the wrong reason, chasing the wrong insight.

That is the quiet flaw in many CES, NPS, and CSAT programs.

It is not mysterious.

I've sat in rooms where teams were measuring a dozen touchpoints and making decisions based on none of them. The problem is almost never the data. It's what triggered the survey in the first place.

The survey was built around an internal event rather than a customer experience.

And that distinction matters more than most teams think, because it decides whether your data can be tied back to revenue, churn, and cost-to-serve or whether it just decorates a dashboard.

If you want better feedback and more influence in the business, start with the journey. The most useful feedback programs are anchored in what the customer was trying to do, what got in the way, what they felt in the moment, and whether the company made progress easier or harder. Feedback gets sharper when it is tied to a specific moment in that journey and timed to the interaction you actually want to understand.

Survey the moment the customer forms a judgment

Not every touchpoint deserves a survey.

The ones worth measuring cluster in four places. Each is tied to a business outcome your CFO already tracks:

• Choice

• Handoff

• Friction

• Recovery

Choice is when your customer decides whether to stay. Buy, upgrade, renew, or walk away — every conversion and retention outcome your business tracks runs through these moments. If you are not listening here, you are measuring after the decision has already been made.

Handoff is when the customer crosses a boundary your org chart created — from sales to service, from digital to human, from one support tier to another. From their view, it is one company. From yours, it is a seam. That seam is where repeat contacts, rework, and operational costs hide.

Friction is where your customer is working harder than they should. Billing errors, password resets, cancellation flows, return processes — these are the moments where effort spikes and patience runs out. Customers rarely complain loudly. They just churn, stop referring, and tell someone else.

Recovery is where your company either earns back trust or loses it permanently. Something went wrong — how you respond determines save rates, future spend, and what the customer says about you afterward. Most treat it as cleanup. The ones who get it right treat it as the most important sales call they never scheduled.

These are the moments when customers decide what kind of company you are.

Pick the metric with intent

The wrong metric gives you an answer to a question nobody asked.

They use NPS, CSAT, and CES interchangeably, as if all customer questions do the same job.

They do not.

Here is how to keep them straight:

• Use NPS to understand the relationship.

• Use CSAT to understand an interaction.

• Use CES to understand effort.

You are measuring without making a choice.

And when you do not choose, it is very hard to stand in front of a CFO and say, “Here is the signal. Here is the risk. Here is the investment I am asking for.”

Map the touchpoint before you write the survey

Before you write a single question, answer these four. Most programs skip at least one — and then wonder why the results sit in a deck and nothing changes.

1. What is the customer actually trying to do? Be specific — not "get support" but "fix a billing error without having to call twice." The more precise you are here, the more useful the feedback will be.

2. What is actually at stake here? Revenue, churn, cost, regulatory exposure, brand trust. If the answer is "nothing critical," this touchpoint probably does not need a survey.

3. What metric fits this moment? NPS for the relationship. CSAT for a specific outcome. CES for effort. Pick one and commit. Using all three on the same touchpoint just means nobody made a call.

4. Who owns the fix? If you cannot name them, you are collecting feedback with nowhere to go.

That last question is the one many organizations avoid.

Because a survey with no owner is not a listening system.

It is documentation.

If you are the CX leader, your job is not to ship more surveys. Your job is to name the owner, agree on the metric, and ensure the insight has a path to roadmaps, budgets, and performance goals. Otherwise, you are curating frustration instead of change.

This map is a business asset. It gives you standing in conversations about roadmaps, budgets, and priorities that spreadsheets full of survey scores never will.

Respect the customer enough to keep it short

Most survey design problems are not technical. They are political.

Someone wanted to add a question. Nobody wanted to cut one. The result is a survey that burdens the customer and dilutes the signal you were trying to collect.

The standard I'd push your team to hold:

• Keep it short. One core metric and one open-text follow-up is often enough.

• Use neutral wording. Customers can spot self-congratulation instantly.

• Avoid double-barreled questions. If you ask whether something was fast and friendly, you learn very little when it was one but not the other. Separate the ideas or accept muddy data.

• Ask the score question before the rest. Sequence influences responses, and many firms start with the core rating question before asking customers to elaborate.

• Always give customers a place to explain the score in their own words. That is where the real signal lives.

Most surveys get too long for a simple reason:

Nobody wanted to prioritize.

And customers can feel that indecision in every extra question.

Stop measuring everything

This should be common sense, but it is surprisingly rare in practice.

Good survey design minimizes respondent burden and keeps requests to a reasonable length and frequency. Ignore that, and you do not just annoy customers.

You weaken the signal you are trying to collect.

So:

• Throttle invites.

• Use suppression rules.

• Choose the moments that matter most.

If a customer completed a service survey on Monday, do not send another one on Thursday because a different team wants its own metric.

That is not customer listening.

That is internal politics dressed up as research.

As a CX leader, this is one of your leverage points with the C‑suite. When someone asks for “one more quick survey,” you can respond with:

“If we add it, here is where we will increase opt-outs and dilute the signal from the moments that actually drive renewals and growth. Which do you want to prioritize?”

The next time someone asks for "one more quick survey," that is your answer. Make the trade-off visible. That conversation is yours to own.

A 30-day playbook for CX leaders

The principles are helpful. But your influence comes from what you do with them.

Here is how you can use this approach over the next 30 days.

1. Audit your triggers.
   Pull a list of every CX, NPS, CSAT, and CES survey currently going out. Tag each as:

   • Internal event (system milestone, workflow completion)

   • Customer moment (Choice, Handoff, Friction, Recovery)
     Note where you are asking often, learning little, and struggling to tie results to revenue, churn, or cost.

2. Map your top moments that matter.
   With your team and one or two key partners (operations, product, or marketing), identify your top 3–5 moments in each category:

3. Kill or throttle low-value surveys.
   Use suppression rules, sunset dates, and success criteria. Propose:

   • Fewer total invites

   • Higher relevance

   • Stronger linkage to churn, repeat calls, and upsell
     Bring a one-page view to your CMO/COO that shows, “Here is what we are stopping. Here is how it improves customer experience and the credibility of our metrics.”

4. Redesign one key survey end-to-end.
   Pick one high-impact touchpoint (for example, onboarding, a major support channel, or cancellations). Redesign:

   • Trigger based on the actual customer moment

   • One core metric (CSAT or CES)

   • One open-text question

   • Clear routing of feedback to the owner team
     Set upfront how you will use the data in roadmap or policy discussions.

5. Report back in business terms.
   When you share results, lead with:

   • “Here is what we are changing, who owns it, and how we will track it.”
     Make it impossible for leaders to see CX as “soft” by keeping the conversation anchored in money, risk, and trust.

You are no longer the person who runs surveys. You are the person who curates which moments deserve measurement and turns those insights into decisions.

The real question

The question is not:

Should we send a survey?

The real question is:

Is this moment important enough to deserve one?

That is the shift.

The goal is not to collect more feedback.

The goal is to identify the moments where trust is won or lost, ask one smart question, and route that insight to someone who can actually improve the experience and the economics.

Better survey programs are not built by asking more often.

They are built by asking with intent.

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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.

AI is finally changing CX work in real ways—not just with faster content or cheaper tickets, but by pulling CX closer to the live operation of the business. Done well, that means spotting friction earlier, responding faster, and shaping outcomes before customers feel the pain.

There’s a catch: AI will scale whatever you point it at. Aim it at cost per contact and you risk industrializing bad experiences. Aim it at customer value—with clean data and clear guardrails—and you remove effort, improve satisfaction, and free teams to focus on the problems that genuinely need human judgment.

This week’s edition leans into that fork in the road. You’ll see:

• What real‑time CX design looks like in practice.

• How optimizing the wrong metric quietly erodes loyalty.

• Where AI breaks down as a “researcher.”

• Why agentic AI depends more on your data plumbing than your demos.

• A support stat line and case study you can steal as a playbook.

My take: AI isn’t the problem. Most CX teams are just pointing it at the wrong outcomes.

The thread running through all of it: is your AI strategy improving customer experience—or just making friction cheaper?

Let’s dig in.

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From rearview CX to real‑time design

Most CX teams weren’t built for real-time. They were built for post-mortems: long survey cycles, NPS readouts, churn analyses, and “what went wrong” decks. Emma Sopadjieva, Head of Customer Experience Strategy at Samsara, is arguing for something different — CX that sits much closer to the live operation of the business, where AI helps you see signals as they happen and adjust the experience before it turns into churn.

Why it matters

Instead of just digging through old data, CX teams can start acting like experience designers. Real-time signals, product telemetry, and cross-functional customer data mean you can spot friction while it’s forming and intervene before a customer opens a ticket or cancels.

The mindset move is from “data archaeology” to “experience architecture.” Less explaining what happened last quarter, more shaping what happens this afternoon.

What stands out

You get a picture of CX where the listening is mostly ambient. Click patterns, usage drops, support pings, even tone of voice all become early-warning signals for customer health. That’s very different from waiting on a quarterly survey readout.

The bottom line

The role of CX is expanding. The job isn’t just to measure the customer experience and report the score. It’s to help orchestrate that experience in something close to real time, with a tighter line to churn, retention, and revenue.

🔗 Go Deeper: CXO Outlook

When AI hits the wrong target, customers pay the price

Here’s the uncomfortable truth: AI will happily optimize whatever you tell it to, even if that goal quietly erodes your customer base.

Why it matters

A lot of leaders still treat each touchpoint like its own little optimization project. So you end up with a perfectly “efficient” IVR, strong deflection in chat, tight authentication rules, and a support script that hits all the checklist items. On paper, each piece looks great. In reality, the customer journey is miserable and loyalty tanks.

AI takes this pattern and speeds it up. If your north star is cost per contact, it will run hard at that – and may be incredibly effective at degrading long-term customer value.

What stands out

The Booking.com example is the contrast: they use AI to remove friction and add context, which drives both lower cost per reservation and higher satisfaction. That’s what happens when the goal is customer value, not just expense reduction.

The bottom line

Treat this like a guardrail for your AI roadmap. If your primary metric is efficiency, don’t be surprised when the machine gets efficient at damaging the relationship. The smarter move is to point AI at outcomes that matter over time: loyalty, quality of customers, and lifetime value—not just cheaper tickets. If your AI roadmap is obsessed with efficiency but vague on customer value, you don’t have a technology gap—you have a strategy gap.

🔗 Go Deeper: Bain & Company

AI can draft your survey. It still can’t think like your best researcher.

If you’ve ever asked AI to draft a survey, you know it can spit out something that looks pretty polished in seconds. That’s exactly the risk.

Why it matters

AI is strong at the surface-level stuff: clear phrasing, logical sections, clean formatting. Where it struggles is the judgment calls that separate “fine” surveys from ones that produce reliable, decision-worthy data.

Those misses don’t always jump out. You only feel them later when your insights are fuzzy, contradictory, or weirdly easy to misread.

What stands out

AI still walks into the classic survey traps: too many questions, fatiguing grids, inconsistent scales, and demographic questions that show up too early.

The bottom line

For CX teams, the danger isn’t just bad surveys—it’s fake confidence. You end up with charts that look professional but rest on shaky design.

AI is a great drafting assistant. It is not your research brain. You still need someone who understands bias, respondent behavior, and data quality, plus basic steps like pilot testing and friction-aware design.

🔗 Go Deeper: Nielsen Norman Group

Agentic AI doesn’t scale on cool demos. It scales on clean data.

Everyone wants AI agents right now, but this piece throws a bucket of cold water on the hype: clever demos are easy; scaling real agents is brutally hard without solid data foundations.

Why it matters

The headline stat is telling: almost two-thirds of enterprises have tried agents, but fewer than 10% have scaled them to meaningful value. The blocker isn’t ambition or ideas—it’s the plumbing.

Agents live and die on the quality of the data they touch. If your systems are siloed, your lineage is fuzzy, and access controls are loose, those weaknesses show up fast once agents start taking actions instead of just summarizing.

What to do

The advice is refreshingly practical:

• Start with a few high-impact workflows that are genuinely “agentable” instead of announcing a company-wide reinvention.

• Invest in cleaner, more real-time customer data—where it lives, how it’s governed, and who can see what.

This is less about shiny AI features and more about the unglamorous work of data architecture and governance.

The bottom line

For CX leaders, this is a reminder that your agent strategy can’t be better than your data strategy. If your customer data is scattered and inconsistent, your agents will behave that way too. Putting sophisticated AI on top of broken systems doesn’t fix the underlying issues; it just exposes them faster.

🔗 Go Deeper: McKinsey

DCX Stat of the Week: Support costs more time than most brands realize

This dataset reframes support cost in a way most teams need to hear: the real currency is time.

Why it matters

Every ticket is a time sink—both for customers and agents. AI changes that equation quickly when it chews through the simple, repeatable stuff first, freeing humans for the work that actually requires judgment.

The numbers are pretty stark:

• Brands using AI cut median first response time from 11 hours to 5.5 hours.

• Median resolution time collapsed from 18 hours to 2.4 minutes.

That’s not a small tweak in experience; customers feel that difference immediately.

By the numbers

When automation passes the 50% mark, AI is doing the work of about 6.3 agents while the typical human team has 3 people on it.

At 60%+ automation, ticket volume explodes—up 143% year over year—while human hours only climb 6%. That’s what scaling support without scaling headcount looks like.

Even at the lower tiers of automation, the model still shows roughly $73K in estimated net annual savings after platform costs.

The bottom line

The real win isn’t just a cheaper support line. It’s the ability to handle growth without reflexively adding more people every time volume spikes. That opens up headroom to reassign humans to higher-value work and rethink what “great support” looks like when speed is almost instant

🔗 Go Deeper: Gorgias
🔗 MORE STATS: Daily Stats on Substack Notes

📊 DCX Case Study of the Week: How SeatGeek doubled AI CSAT at 51.5% automation

SeatGeek’s story is a good example of disciplined AI adoption in support, not just turning on automation and hoping for the best.

CX Challenge

During big events, their support team was drowning in repetitive questions like “Where are my tickets?” Volume spiked, response times stretched, and escalations piled up. Classic high-growth, high-friction pattern.

Action Taken

SeatGeek rolled out Zendesk AI agents, but they started narrow: one high-volume, well-understood use case.

They leaned on a zero-training model to generate context-aware responses using their existing knowledge, launched first with authenticated users, then iterated based on how agents actually performed. Only after proving value did they expand to more scenarios.

Result

In just 4 months:

• Automated resolution hit 51.5% (up 6 points).

• AI CSAT jumped from 34% to 70%.

• Tens of thousands of customers got instant help with fewer escalations hitting the human team.

Lesson for CX Pros

This is a playbook worth copying: pick one painful, high-volume issue, solve it with automation, validate that customers are satisfied, then expand. And keep humans in the loop—not just as escalations, but as coaches improving the AI over time.

Quote

“The zero-training model has been amazing… it feels very personalized.” — Whitney Thomas, SeatGeek

Further Reading: Zendesk case study

Have a case study to share? Reply and let me know!

If there’s a pattern across these stories, it’s this: AI amplifies whatever mindset you bring to customers. Aim small and short‑term, and it makes friction cheaper. Aim at long‑term value, and it makes better experiences possible at scale.

One question to leave with your team this week: where is your AI strategy genuinely improving the customer’s experience—and where is it just making friction cheaper?

See you next week.

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.