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

In this issue:

→ Utilities are letting AI get closer to live account context
→ Platform owners want to own the front door to customer intent
→ Scale is showing up faster than most control models
→ DigitalGenius still looks more useful than flashy
→ The opportunity is better service with clearer operating lines

🔎 Deep dive

Utility AI just moved closer to the account

Kraken Technologies is partnering with Sierra to bring generative AI customer service into utility environments. This is not the usual chatbot setup. Utility service sits inside billing questions, outage moments, tariff confusion, payment pressure, and account anxiety. When it works well, it can remove a lot of friction from moments customers already find stressful.

Axios reports that the system is meant to combine Sierra’s conversational AI with Kraken’s utility software, including account, meter, and rate data. So the model is getting closer to the operating system behind the journey, not just the words around it.

Once the conversation can touch live service context, the upside gets bigger and so does the need for clarity. You need to know what the AI can explain, what it can steer, when a human steps in, and how the whole experience stays easy to trust. In a utility setting, that can mean faster answers, cleaner guidance, and less customer effort, as long as the lines are clear.

Bottom Line: The customer conversation is becoming part of the operating layer. If AI can see the account, your team needs to know where explanation stops and action begins so the experience gets easier, not murkier.

OPERATOR PLAYBOOK

Audit the moments where conversation can turn into action

Most teams are still reviewing prompts and containment rates. Here, that is only part of the job.

Audit every service flow where AI can shape a customer decision for four things:

1. The exact data the model can see, and how fresh it is

2. The moments where explanation turns into guidance or a recommended next step

3. The handoff points that keep the experience smooth when a human needs to step in

4. The owner responsible for keeping the whole experience coherent end to end

Then test whether your current QA process can tell you if the experience is actually getting easier for the customer.

Ask your team: Where does our AI currently shape a customer decision, and who owns that experience end to end?

Signal: If your team can name the assistant but not the owner of the experience around it, you still have work to do.

📈 Market Reality Check

Walmart is treating AI prompts like customer context

Walmart’s Sparky shows what shopping prompts can reveal before the customer ever clicks a filter or types a keyword. A customer who says they need help finding a snack for a child with allergies is giving you context that normal search often never captures.

That makes AI shopping bigger than a retail media story. It becomes a customer-understanding story. If conversational prompts carry richer intent, preference, and constraint data, retailers have a chance to make guidance more useful. They also have a new temptation to over-monetize the moment.

Why it matters: The real shift is not just that AI can recommend products. It is that customers may explain what they need in fuller, more human language, and that changes how brands should think about discovery, relevance, and interruption.

🧰 Tool Worth Knowing

DigitalGenius

What it does: DigitalGenius is a customer service AI platform built around orchestration across email, chat, and voice, with agent copilots, autonomous flows, knowledge search, analytics, and a headless SDK for embedding support experiences.

CX use case: It is useful when the real problem is not writing prettier answers. It helps clean up the gaps between channels, automation, agent assist, and workflow execution in high-volume support environments.

Worth watching because: A lot of AI support tooling still feels like a thin wrapper around response generation. DigitalGenius is more useful when you need routing, reasoning, human handoff, and deployment flexibility to work together in a way that makes service feel more connected.

Bottom line: If your service operation is dealing with handoffs, channel fragmentation, and inconsistent assist behavior, this is the kind of stack I would pressure-test.

⚡ 90-Second CX Radar

Apple wants Siri to feel less clingy and more useful

On the surface, this is about personality tuning. In practice, assistant design is now a CX decision. Tone, interruption style, and response posture all shape trust, friction, and repeat use.

Why it matters: Customers are judging whether AI works, and whether it feels bearable to live with.

🧭 Your Move

The question underneath this whole issue is simple: where can your AI influence a customer decision before a human or a brand-owned channel gets a second look?

That is usually where the work gets messy, and where the customer notices the experience first.

Next week, map one service or commerce journey where AI can see live customer context. Then mark the exact point where the answer becomes a recommendation, a nudge, or an action trigger.

If nobody clearly owns that line, sort that out before you chase another launch.

The real test for CX AI is simple: does it make the next step easier for the customer, while still being clear about who owns what?

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:

→ Checkout moves inside the assistant
→ Voice trust gets very fragile, very fast
→ Confident wrong answers get expensive
→ Brands get judged inside AI answers
→ Smart displays learn to stop being so literal

🔎 Deep dive

The assistant just moved closer to the buy button

Visa has embedded its payment network inside ChatGPT, which means AI agents can help users shop and complete purchases at merchants that accept Visa.

That is a real line-crossing moment.

Yesterday’s assistant helped compare options. Tomorrow’s assistant may pick the brand, check the price, choose the merchant, and ask for permission to pay. Useful? Absolutely. Also the kind of useful that can get weird in a hurry if the customer cannot see the guardrails.

Groceries, diapers, travel, repeat household items, subscriptions, gifts, repairs. These are not abstract “commerce workflows.” They are the little recurring decisions where customers already hate friction, but still want control.

The CX question now gets bigger: did the customer buy, what did the customer approve, what did the assistant infer, what did the assistant do, and how fast can the business unwind it when the result is wrong?

Spending limits, approval prompts, merchant rules, fraud monitoring, receipts, and recovery flows are not boring back-office details anymore. Congratulations to them. They made it to the front of the customer journey.

Bottom Line: Agentic commerce works only when convenience still leaves the customer feeling like the adult in the room.

OPERATOR PLAYBOOK

Find the moment AI stops suggesting and starts acting

Pick one AI-assisted purchase, booking, service, or account flow.

Now find the handoff.

Audit every AI-assisted action for four things:

1. The exact decision the customer is delegating.

2. The approval step before money, access, status, or commitment changes.

3. The receipt or explanation the customer gets after the action.

4. The recovery path when the assistant buys, books, routes, or recommends the wrong thing.

Then test whether a normal customer can tell the difference between suggestion, approval, and completed action without reading a policy page written by three departments and a lawyer.

Ask your team: Where does this experience cross from helpful guidance into delegated authority?

Signal: Strong consumer AI makes the next step easier without making control fuzzy.

📈 Market Reality Check

The confident wrong answer is the expensive one

The dangerous AI answer is not the obviously broken one. Customers can spot nonsense.

The dangerous answer is wrong in a way that sounds polished, specific, and calm. It has the tone of authority. It arrives quickly. It feels like it belongs in the journey.

That is where CX gets exposed.

If AI touches refunds, eligibility, product fit, billing, travel disruption, policy interpretation, or account access, a friendly tone is not enough. The business needs grounded answers, confidence thresholds, escalation paths, and correction loops customers can actually find.

Otherwise the customer acts on bad guidance, the contact center cleans it up later, and everyone pretends this was a one-off. It rarely is.

Why it matters: A wrong answer that sounds certain creates repeat contact, policy confusion, and trust damage. The business may not notice until the customer has already acted on it.

🧰 Tool Worth Knowing

Sprinklr LLM Insights

What it does: Sprinklr LLM Insights helps brands see how they show up in AI-generated answers, including visibility, sentiment, recommendations, share of voice, and competitive positioning.

CX use case: When customers ask an AI assistant what to buy, who to trust, or which provider fits their need, brands need to know whether the answer is accurate, incomplete, distorted, or quietly leaving them out of the room.

Worth watching because: Customer discovery is moving into synthesized answers. That means the customer may form an opinion before visiting the brand’s site, seeing the brand’s content, or entering any channel the brand actually measures.

That is a very uncomfortable place to be blind.

Bottom line: If AI answers are becoming the new front door, brands need to know where that door is sending customers before the journey starts crooked.

⚡ 90-Second CX Radar

Your Google smart display is finally learning how to hold a real conversation

Gemini for Home is getting more conversational across weather, media, news, reminders, and shopping lists. Translation: customers may be able to stop talking to their smart display like they are trying to appease a very literal toaster.

Why it matters: Voice and home assistants are becoming everyday service surfaces. The bar is moving from “understands commands” to “handles normal human follow-up.”

Gemini Gets a World Cup Stress Test

Gemini is moving into the World Cup fan experience, including real-time search answers, match analysis, stats, and AI-generated fan content. That is a brutal testing ground: emotional, global, multilingual, and very online.

Why it matters: Big live events are becoming test beds for AI-assisted customer journeys. The failure mode is a bad answer at peak attention.

🧭 Your Move

This issue is about consumer AI crossing the authority line.

The assistant is moving toward checkout, product choice, payment, account action, and everyday task completion.

Pick one consumer-facing AI experience this week and map the authority line.

Where does it suggest?

Where does it decide?

Where does it act?

Where can the customer pause, reverse, or recover?

If that line is fuzzy inside your own team, it will be even fuzzier to the customer. And the customer will not grade you on your architecture diagram.

The future of consumer AI depends on making the next step easier without making control harder to find.

Until tomorrow,

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

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

In this issue:

→ AI support gets a stress test
→ UX metrics move beyond model scores
→ Drive-thru AI gets another shot
→ Autonomous ops needs rollback discipline
→ Customer trust starts before launch

🔎 Deep dive

The support agent is only as good as the mess it survived

Nubank researchers published a useful look at what it takes to run customer-support AI at serious scale. The headline isn’t “big bank uses AI.” Fine. Everyone has that slide now. The useful part is the operating discipline around it: structured context, human review, evaluation checks, and validation before the agent gets near live customers.

That changes the CX conversation. The question isn’t, “Can the agent answer?” The question is, “Can we prove this thing behaves well when the customer is confused, impatient, locked out, late, charged incorrectly, or stuck in a policy corner?” That’s where customer experience gets real. Demos usually skip the ugly corners. Customers live in them.

The paper connects offline simulation quality with online customer outcomes. That is the bit to steal. If your AI support roadmap doesn’t have a stress test tied to live journey metrics, you’re basically asking customers to QA your ambition.

Bottom Line: AI service quality comes from testing the messy customer moments before customers are forced to find them for you.

More from NuBank

OPERATOR PLAYBOOK

Build the stress test before the launch deck

Start with the customer moments where failure gets expensive. The model comes after that.

Audit every AI-assisted support flow for four things:

1. The exact customer intent, emotion, and policy edge cases tested before launch.

2. The human review process for prompts, retrieved context, and answer quality.

3. The online metric that proves the agent helped the customer finish the job.

4. The escalation rule for confusion, frustration, identity risk, money, eligibility, or repeat contact.

Then test whether rehearsal performance predicts what happens when real customers show up with real pressure.

Ask your team: Which customer problem are we comfortable letting the AI handle only after it has failed safely in rehearsal?

Signal: Mature AI service teams treat evaluation design as part of CX design. It’s not a technical appendix someone adds after the champagne slide.

📈 Market Reality Check

Customers are not as ready as leaders think

TechRadar Pro’s June 8 CX piece points to the gap every AI-service roadmap has to face: more than 90% of business leaders believe customers are comfortable with AI-powered service. Only 42% of customers actually are. Another 28% are actively uncomfortable.

That doesn’t mean customers hate AI. It means they hate being forced to absorb the risk of a company’s automation bet. If the AI removes effort, routes faster, cuts repetition, and keeps a human path open, customers may welcome it. If it adds one more layer between them and the answer, the business just paid to make the journey feel worse.

Why it matters: CX leaders need to pressure-test AI service against customer comfort, not internal enthusiasm. The experience has to feel easier from the customer’s side of the screen. That’s the scoreboard.

🧰 Tool Worth Knowing

ArchIQ

What it does: ArchIQ is McDonald’s renewed AI drive-thru ordering system, reportedly built with Google and being tested at five U.S. locations. Current reporting says it has processed more than one million transactions, with about 90% completed without human escalation.

CX use case: High-volume ordering where speed, language handling, repeat-order recognition, and manager alerts can change both the customer line and the restaurant floor.

Worth watching because: McDonald’s already had one public AI drive-thru stumble. This second attempt is a reminder that customer-facing automation doesn’t get graded on lab accuracy. It gets graded at the speaker box, with noise, pressure, accents, substitutions, coupons, kids yelling in the back seat, and a line of cars behind you.

Bottom line: The tool is worth watching because it puts AI into one of the least forgiving CX environments: fast, physical, operational, and instantly visible when it gets weird.

⚡ 90-Second CX Radar

Apple’s Siri AI is both cool and 2 years too late

Apple’s AI assistant update is less aggressive than the agent hype cycle, but the privacy and personal-context angle matters. For customer-facing brands, the lesson is simple: trust becomes a product feature when AI starts reading across personal data.

Why it matters: Customers may accept more AI help when they understand where their data goes, what the assistant can see, and what it’s allowed to do.

Pega Launches Customer Engagement Studio to Transform Marketing Operations with Agentic AI

Pega’s new workspace uses agents to move from campaign brief to personalized customer actions while keeping governance, compliance, and human validation in the flow. That is much closer to the CX fault line: more relevance, faster, without turning the customer journey into a pile of disconnected automated guesses.

Why it matters: Personalization at scale only helps customers when the business can control what gets recommended, when it gets triggered, and who validates it before it reaches the customer.

🧭 Your Move

This issue is about a quieter AI shift: teams are moving from demo confidence to operating proof.

The practical standard is simple: clear stress tests, clean escalation rules, honest customer metrics, and recovery paths that work when the agent gets it wrong.

Pick one AI-assisted customer journey this week and ask for the evaluation file. Skip the launch deck. Skip the vendor promise. Ask for the actual test set, failure taxonomy, escalation standard, and live metric.

If nobody can show it, you have found the work.

Your customer shouldn’t be the first person to discover your AI agent’s edge case.

Until tomorrow,

The Psychology of CX 101: The Action Lab

Get Your Free 6-Sprint CX Program

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 control is now a CX problem
→ Customer trust is lagging the rollout
→ Onboarding gets an AI nudge
→ Retention models get morally weird
→ Leaders need receipts, not optimism

🔎 Deep dive

AI control is where the customer story gets messy

The agents have left the lab.

They are not just writing emails, summarizing calls, or making meeting notes look a little less tragic. IBM says AI agents are moving into live enterprise work while the control model is still catching up. Two-thirds of surveyed CIOs and CTOs said they are accountable for AI systems they do not fully control. Seventy-seven percent said AI adoption is already moving faster than governance.

That is an operating problem with customer consequences.

For CX leaders, the danger shows up when AI starts touching things customers actually feel: refunds, eligibility, routing, identity checks, complaints, personalization, scheduling, and recovery. The customer does not care which team owns the model. They care that the decision made sense, someone can explain it, and the business can fix it before trust takes the hit.

Bottom Line: If AI can change a customer outcome, CX needs proof of what happened, who can stop it, and how the customer gets made whole when the system gets it wrong.

→ Get the 2026 Tech Leader Study

OPERATOR PLAYBOOK

Put a leash on the customer-visible decisions

Pick one journey where AI can affect time, money, access, trust, or momentum.

Audit every AI-assisted control point for four things:

1. What customer outcome the agent can change.

2. What policy, data, model, and permission it depends on.

3. What signal shows the agent is drifting or overreaching.

4. Who can pause, correct, explain, or reverse the action.

Then run the courtroom test. If a customer challenged the decision tomorrow, could your team explain what happened without pulling four departments into a forensic group chat?

Ask your team: Where are we letting AI move faster than our ability to explain the customer impact?

Signal: Control is real when it helps the business recover the customer while recovery still matters.

📈 Market Reality Check

The trust gap is already on the scoreboard

Diginomica covered a useful trust split from MIT’s Renee Gosline at Salesforce Connections: 81% of professionals trust agentic AI with customer data, while only 36% of customers agree.

That is a big gap. Big enough that the keynote version of the story and the customer version may not be living in the same zip code.

The macro issue is simple. Internal confidence does not automatically become customer trust. Marketers, service teams, product leaders, and executives may be ready to let AI recommend, rank, personalize, and trigger more of the journey. Customers still want control, consent, and a way to challenge the machine when it reaches too far.

Why it matters: If companies scale agentic AI faster than customers can understand it, the efficiency gain may come with trust debt. And trust debt eventually shows up somewhere in the journey.

🧰 Tool Worth Knowing

OnRamp Aero

What it does: OnRamp Aero brings agentic AI into customer onboarding. It monitors active projects, spots stalled accounts, drafts context-aware follow-up, guides customers in the portal, and helps ops teams turn playbooks into live workflows.

CX use case: This is for the awkward post-sale zone where the customer said yes, everyone celebrated, and then the actual setup starts moving slower than the sales deck promised.

Worth watching because: Onboarding is where trust quietly gets won or wasted. Aero treats follow-through as a managed journey, not a checklist that depends on someone remembering to chase people before the customer goes quiet.

Bottom Line: The useful test is whether Aero helps teams catch stalled momentum, assign the right owner, and keep customers moving toward value without making the experience feel abandoned to automation.

⚡ 90-Second CX Radar

AI retention systems are starting to test the ethics of customer value

CX Today takes the idea into near-future territory, but the operating issue is real. Churn models, lifetime value scoring, and retention prioritization already exist. AI just makes them faster, colder, and easier to scale.

Why it matters: Leaders need a policy for how customers are treated when the model says they are no longer worth saving. That is not just analytics. That is brand behavior.

PegaWorld puts human-guided AI into the customer engagement debate

The useful signal is the frame. Customer engagement teams are now debating where human judgment belongs once automation can select, rank, and trigger the next customer action.

Why it matters: The next CX advantage may come from knowing which decisions should stay human, which can be automated, and which need both in the room.

🧭 Your Move

Today’s issue is about AI ambition getting ahead of customer confidence.

The pattern is showing up in governance, marketing trust, onboarding, and retention decisions.

Take one journey where AI is being added and mark the customer-visible decision points.

Approve. Deny. Recommend. Rank. Route. Refund. Retain. Escalate. Explain.

Then ask the plain version of the governance question: if the customer challenged that decision tomorrow, could your team show what happened without sounding like everyone is hiding behind the model?

AI does not earn trust by acting faster. It earns trust when the business can still own what happened.

Until tomorrow,

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

The biggest problems usually aren’t mysteries. They’re tolerated conditions.

The confusing bill, the broken handoff, the second call that leaves the customer with the same problem, dressed in a new case number: none of this is hidden. Even the policies usually make sense somewhere, to someone. The problem is what happens when those policies leave the conference room and land on a customer who’s already done everything the company asked them to do.

Then the CX team walks in with evidence: customer quotes, call clips, data, and customer pain points arranged like crime-scene markers. The room nods, because nobody wants to defend repeat calls or pretend that confusing a customer at the exact moment they’re trying to give you money is a growth strategy.

The meeting ends with agreement.

Yet, the customer keeps calling.

This is the expensive fraud inside a lot of CX work: the business agrees the pain is real before it agrees to fix it. Agreement can feel productive because it has all the right corporate theater: concern, notes, next steps, and the familiar promise that the issue is now “on the radar.”

But no customer experiences the radar. They experience the broken handoff, the surprise bill, the missing context, and the agent who sounds helpful but powerless.

A View Is Not an Owner

This is why I’ve been thinking about a question raised in a recent webinar on the CX tech stack:

What system knows the customer best?

It sounds like a software question. It’s a question about power.

For years, the lazy answer was CRM, because CRM held the account, opportunity, contact, and owner. But the customer’s actual life with the company doesn’t live in one record. The anger may be in support, the surprise in billing, the abandonment in product analytics, and the most useful sentence buried in a transcript.

The company knows fragments and calls it a view. And we’ve spent years pretending that stitching the fragments together is the same thing as fixing the underlying condition.

AI Can Make a Bad Foundation Faster

AI makes this more interesting and more dangerous. It can stitch together tickets, transcripts, surveys, reviews, and usage data. It can find patterns that used to take teams weeks to surface. That’s useful. It’s also seductive because better pattern recognition can make it seem as though the company has become more capable of solving the problem.

It hasn’t. It’s become more capable of seeing it.

That distinction came up in last week’s DCX AI Roundtable on AI and voice-of-customer work. People are using AI to compress work that used to take weeks. But the tension kept coming back: AI is getting better at finding patterns, while organizations still struggle to turn them into owned change.

One participant said AI can classify feedback and organize themes faster, but the disconnect between that and action is still the hard part. Another put it more bluntly: “AI on top of a bad foundation just gets you to the wrong place quicker.”

AI doesn’t fix a weak operating model. It exposes it, and sometimes it accelerates it. A fragmented business doesn’t become aligned because a model found the pattern. If nobody owns the fix, AI just helps everyone admire the problem in high definition.

That’s the real platform fight: not who owns the data, but who owns the decision.

The next customer intelligence system won’t win because it can describe the customer more elegantly. It’ll win if it can answer the question the room is avoiding:

Who owns what happens next?

The Customer Gets the Whole Mess

Take a boring example, because boring examples are where the money leaks. A customer signs up under a promotion, gets a confirmation that feels official, and then receives a first bill that doesn’t match what they thought they bought.

Now the company becomes a scavenger hunt. Old emails, internal systems, offer language, billing logic, terms, product flow, and service apology all have to be reconciled after the fact. That is exactly why the customer ends up holding the whole mess together.

Internally, everyone can point to their piece of the truth. Externally, the customer has the whole problem. Inside, that contradiction becomes a tolerated condition: everyone can justify their slice, while the customer pays for the whole.

This is where companies say they need a “single view of the customer.” Maybe. But a single view of the customer isn’t worth much if it doesn’t lead to a single owner for the next decision.

The issue is not empathy. In most companies, plenty of people care. The frontline absorbs the frustration, CX sees the pattern, and product may even understand the issue, but none of that moves work across a roadmap. Owned action does, and owned action is scarce.

Owned action is scarce because it disturbs the machinery. It changes roadmaps. It forces tradeoffs. It asks someone with a scorecard and a backlog to absorb a problem the customer already paid for. This is where CX work gets political. Not office politics. Real politics: who has the authority to allocate resources, change priorities, and accept the cost of fixing what customers keep paying for?

CX Can’t Just Document Pain

That’s why CX can become the Department of Pain Documentation: a function that finds the problem, names it, quantifies it, socializes it, and builds the deck before handing the work to a business with no structural reason to move.

If CX only documents pain, we’ve accepted the wrong job.

The job is not to document pain. The job is to move CX from witness to operator.

In most companies, plenty of people care and plenty of systems can see the pattern. The frontline absorbs the frustration, CX sees the trend, product understands the issue. The real gap is cross-functional ownership: a customer issue may show up in service, but the cause may sit in product, billing, sales language, or policy design. If CX owns the evidence but another function owns the condition, the question isn’t, “Do we have the data?” The question is, “Can we get the right owner to move?”

Bill framed the older model as survey → dashboard → readout → action plan. The newer model, he said, is closer to signal → inference → workflow → decision → governance → action. In plain English: find the pattern, route it, decide who owns it, and make the work happen.

Put Ownership Before Action

I would insert one word before action:

Ownership.

Signal. Inference. Workflow. Decision. Governance. Ownership. Action.

That’s not a cosmetic edit. Governance tells the business what’s allowed. Ownership tells the business who has to move. Without ownership, governance becomes a conference room where accountability goes to die. The issue has been classified. The pain has been named. The recommendation is valid. And still, nobody changes the condition that keeps producing it.

This is where AI matters: not by generating prettier summaries, but by proving the cost of inaction.

If the business chooses not to fix a known friction point, what happens next? The answer should show the customer volume, call burden, affected revenue, and agent capacity burned on a problem the frontline didn’t create and can’t solve.

That evidence turns feedback from a moral appeal into an operating consequence.

Make Inaction Expensive

Most CX leaders I know aren’t short on evidence. They’re fighting for the authority to turn evidence into operating change. The best customer intelligence systems should help them do that by connecting the pattern to the owner, the decision, the cost of delay, and the next operating move.

Start smaller than the enterprise transformation deck. Pick one journey where the pain is visible and the owner is close enough. Map the customer signal to the internal condition, then make the operating ask explicit: who controls the condition, what decision is required, and what happens if the business does nothing. Then walk into the room with a better question:

Who owns the fix, and what will they do next?

If the room can answer it, the work has somewhere to land. Budget and priority are easier to find when ownership is clear.

If the room can’t answer it, pay attention. That means the company may know the customer well enough to diagnose the pain, but not well enough to change the experience.

The CX leader’s job is to build the mechanism that makes customer pain impossible to ignore and expensive to repeat, because once that exists, nobody needs CX to be the company’s conscience.

www.marklevy.co
Follow me on Linkedin

Leave a comment

Thanks for being here. I’ll see you next Tuesday at 8:15 am ET.

👉 If you enjoyed this newsletter and value this work, please consider forwarding it to your friends and colleagues or sharing it on social media. New to DCX? Sign Up.

Share Decoding Customer Experience

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

In this issue:

→ Containment only counts when the job gets done
→ AI trust needs a real stop sign
→ Service jobs shift toward exceptions
→ Customer communications get agentic
→ Conversation data starts triggering next steps

🔎 Deep dive

The service AI scoreboard is changing

Genesys reported fresh momentum around agentic experience orchestration, but the useful signal sits in the customer examples underneath the revenue number.

Utility Warehouse doubled containment.
CLEAResult cut after-call work by more than 70%.
Carglass reduced repeat calls by 42%.
TELUS reduced both average handle time and call transfers by 20%.

That’s where the AI service conversation gets more serious. Containment by itself can hide a bad experience. A customer who gives up, repeats the story, or calls back later can still look like a neat dashboard win. Annoying, but neat. The stronger question is whether AI preserved context, completed the task, and gave the human team a better handoff when judgment was needed.

For CX leaders, this moves AI out of the chatbot lane and into operating design. The first place customers will feel it is in routine service journeys: booking, scheduling, status checks, claims, repairs, collections, and billing. The hidden risk is familiar. If each function celebrates its own metric, the customer still experiences one broken journey.

OPERATOR PLAYBOOK

Audit the handoff, not just the bot

Pick one high-volume service journey where AI is already involved or likely to be added next.

Audit every AI-assisted service flow for four things:

1. What the AI is allowed to resolve by itself.

2. What context moves with the customer at escalation.

3. What signal tells the team the customer is stuck.

4. What metric proves the issue stayed solved after contact.

Then test whether the customer could switch from chat to phone, agent to human, or self-service to branch without restating the problem.

Ask your team: Where are we measuring AI activity when we should be measuring customer completion?

Signal: Repeat contact is the smell test. If it isn’t improving, the AI may be moving work around instead of removing friction.

📈 Market Reality Check

The workforce shift is already showing up in service

Verizon CEO Dan Schulman said AI will take over “a large percentage” of customer service work, and the company is already testing agents that replace some service reps. He also said those agents are producing customer satisfaction rates 1,280 basis points better than before.

That is a serious operating signal, but it should not be read as a permission slip to cut first and design later. The more customer service shifts toward AI, the more human work concentrates around messy exceptions, emotional moments, and broken handoffs. If leaders automate the easy work without redesigning escalation, knowledge, and authority, the remaining human work gets harder and customers feel the strain.

🧰 Tool Worth Knowing

MARCIEAssist

What it does: Messagepoint’s MARCIEAssist is an agentic AI capability for customer communications management. Users describe the work they want done, and the system can plan and execute supported actions across content, rules, data, and templates.

CX use case: Regulated teams can update complex customer communications faster while keeping roles, permissions, audit trails, and rollback controls in place.

Worth watching because: Customer communications are one of the quiet places where CX breaks. A wrong notice, unclear benefits letter, outdated policy message, or inconsistent template can create calls, complaints, compliance risk, and lost trust.

Bottom line: MARCIEAssist puts AI into the content operations that shape what customers are told and what employees have to explain. No public performance metric was shared.

⚡ 90-Second CX Radar

Zoom pushes contact centers toward resolution, not deflection

The useful idea is simple: redirected work still leaves the customer holding the problem. Watch for CX leaders to pressure-test self-service metrics against repeat contact, first-contact resolution, and context preservation.

Eltropy releases a Safe AI guide for community financial institutions

Credit unions and community banks are moving from AI curiosity to deployment questions. The customer impact shows up in lending, servicing, collections, branch operations, and member trust when teams can’t explain or govern assisted decisions.

Aircall acquires Piper AI to connect conversations to revenue workflows

This points to a broader shift: customer conversations are becoming inputs to next-step execution across sales and service workflows.

🧭 Your Move

When AI handles more of the journey, the operating question is whether the customer still gets a clean path from need to done.

AI is getting closer to the work customers actually feel, so the measurement has to get closer to the outcome customers actually wanted.

Take one AI-assisted journey this week and follow it past the first contact. Did the customer get the thing done? Did the next employee have the history? Did the customer have to repeat, re-explain, or recover from a half-solved answer?

That's where the real AI service scorecard lives: in the customer's next step, and in the moment the system knows enough to stop.

If AI touches the journey, CX has to own the proof that the journey still works.

Until tomorrow,

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

Customers rarely judge the whole experience at once.

They judge the moment in front of them.

The train that shows up closer to schedule. The cart that helps them keep track before checkout. The person who gets them through a confusing airport. The hotel arrival that feels more like being expected than being processed.

That is where CX gets less theoretical.

Not in the strategy deck. Not in the brand promise. In the small operating choices that make a customer feel oriented, helped, recognized, or less on their own.

This week’s links all point to the same idea: better experiences often come from making the ordinary moment work harder.

Let’s dig in.

Subscribe now

This Week’s Must-Read Links

Transit satisfaction improved where riders could feel the basics

RTD Denver’s latest customer and community surveys show how quickly experience improves when the basics become more dependable.

What’s happening: Riders reported stronger perceptions of timely arrivals: buses up 15%, commuter rail up 10%, and light rail trains up 26%. RTD also says bus and rail customer satisfaction levels are 26% and 31% higher than the national average, respectively. The release ties the gains to fare value, helpful operators, safety, community access, and reliability.

Why it matters: Riders do not need a service thesis. They need the bus to show up, the fare to feel fair, the station to feel safe, and the operator to be helpful when something goes sideways. That is CX at its least glamorous and most important.

The bottom line: Reliability is not just an operations score. It is how trust becomes visible.

Worth asking this week: Which basic service promise would customers notice fastest if we actually improved it?

The store aisle is getting its own personalization layer

Instacart and Weis Markets are putting AI-powered Caper Carts into stores, but the important move is not the screen. It is the timing. The help appears while the customer is still choosing what to buy, what to skip, and how much the trip is going to cost.

What’s happening: The carts let shoppers track spend, clip location-based coupons, sign up for Weis Rewards, redeem loyalty benefits, and use Buy It Again recommendations while they shop. Instacart says prompts such as “Got everything you need?” are lifting basket size by nearly one percentage point on average.

Why it matters: The store trip is no longer separate from the digital journey. Loyalty, savings, product memory, retail media, and budget confidence are moving into the physical cart, exactly where customers are making tradeoffs.

Reality check: Helpful can turn intrusive quickly. The test is whether the cart makes shopping easier, cheaper, or more confident for the customer, not just more measurable for the retailer.

Worth asking this week: Where could we put useful guidance closer to the customer’s actual decision point?

Miami airport is turning concierge into a confidence tool

CLEAR’s new concierge service at Miami International Airport puts human help into one of the least forgiving customer journeys: the airport trip.

What’s happening: Concierge Powered by CLEAR is launching at MIA with arrival and departure support, baggage help, airport navigation, and coordination for international travelers. Miami is already a high-volume airport, and travel pressure will rise as major events bring more visitors through the region.

Why it matters: Airports are full of moments where customers do not need delight. They need confidence. Where do I go? What happens next? Who can help if something changes? That kind of reassurance is service design, especially when language, luggage, timing, and family stress are layered together.

Between the lines: The point is not premium concierge everywhere. It is knowing which anxious moments still need a person because self-service shifts too much interpretation back onto the customer.

Worth asking this week: Which high-stress customer moment needs clearer human help instead of another digital instruction?

Hotel arrival is becoming a loyalty moment

Millennium Premier Hotel New York Times Square is reopening after a renovation that changes one of hospitality’s most familiar moments: arrival.

What’s happening: The hotel replaced the traditional check-in desk with a welcome lounge and greeters who can help guests with local recommendations, transportation, and neighborhood context. The renovation also includes redesigned rooms, meeting spaces, wellness amenities, and loyalty-member offers tied to the reopening.

Why it matters: Arrival is not admin. It is the first test of whether a hospitality brand feels organized, generous, and ready for the guest. The old check-in line treats arrival like processing. A better arrival design treats it like orientation, reassurance, and relationship setup.

Reality check: A lounge and greeters only matter if they reduce uncertainty, not just if they photograph well. The CX value is in helping guests feel placed, informed, and expected.

Worth asking this week: Which first moment in our experience still feels like processing when it should feel like welcome?

DCX Stat of the Week

Identity checks are quietly turning good customers away

PYMNTS Intelligence and Trulioo report that nearly two-thirds of enterprises now lose legitimate customers because identity checks create too much friction. Internal teams also say onboarding abandonment tied to verification hurdles has climbed above 60%, and the report estimates that weak identity verification systems cost businesses nearly $100 billion annually through fraud losses and missed opportunities.

Takeaway: Trust controls are part of the customer journey now. If verification blocks real customers, CX, risk, product, and growth teams all own the damage.

Use this in a meeting: Fraud prevention can’t be judged only by what it stops. We also need to measure how many good customers it slows down, rejects, or teaches to leave.

DCX Case Study of the Week

UMassFive Turned Scheduling Into a Member Experience Channel

CX Challenge: UMassFive needed more than a basic scheduling tool as members shifted toward digital self-service and branch teams needed better visibility across appointments, contact center scheduling, virtual engagement, and staff preparation.

Action Taken: The credit union implemented Coconut Software’s enterprise-wide platform for appointment scheduling, contact center scheduling, analytics, and virtual engagement across branches and the contact center.

Result: In 2025, UMassFive completed 13,079 appointments with a 99.5% completion rate. Certified financial coaching appointments rose 12.3%, and average Google ratings across branches rose from 2.73 to 4.56 stars through post-appointment reviews.

Lesson for CX Pros: Scheduling is journey design when it gives customers clearer access and gives employees context before the conversation starts.

Quote: “Our teams can prepare in advance, members arrive ready, and every interaction feels more efficient, empathetic, and aligned to their goals.” - Alexis Derosier, Manager of Marketing at UMassFive

Use this in a meeting: Better scheduling isn’t admin cleanup. It can become a managed service channel with measurable member, staff, and reputation impact.

One Last Thing

The customer usually does not care how impressive the system is behind the scenes.

They care whether the moment works.

Did the ride arrive when expected? Did the cart help before the bill surprised them? Did the airport feel less confusing? Did the first hotel interaction make them feel welcomed instead of processed?

That is the practical CX test hiding in plain sight: make the ordinary moments easier to trust.

Have a great week ahead!

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

In this issue:

→ AI moves into physical experience
→ Comfort becomes a CX signal
→ Agent access becomes a CX risk
→ Productivity needs shared work context
→ Governance moves into the Radar

🔎 Deep dive

Physical experience is still customer experience

Axiom Cloud expanded its AI platform beyond refrigeration into autonomous HVAC optimization. That may sound like a facilities story.

It is not only a facilities story.

HVAC affects whether a grocery store feels comfortable, whether employees can work well, whether shoppers stay longer, and whether a physical location feels cared for or neglected. Customers may never say, “This brand has poor building automation.” They just feel rushed, irritated, tired, or ready to leave.

That is the useful part of this announcement. AI is not just moving into chat, search, claims, marketing, or contact centers. It is moving into the operating systems behind the environment customers walk into. Temperature, uptime, maintenance, energy use, staff comfort, and store reliability all shape the experience before anyone opens an app or asks for help.

The risk is familiar. If no one owns the customer impact, the AI becomes an efficiency tool, not a service improvement. A system can save energy and still make the store feel worse. It can resolve a maintenance issue and still miss the moment customers actually felt the problem. Physical operations need the same CX discipline as digital journeys: clear ownership, visible signals, and a recovery path when the experience slips.

Source: Axiom Cloud

OPERATOR PLAYBOOK

Find the AI decision the customer feels

Start with one journey where a customer could lose money, time, access, trust, or momentum if AI gets something wrong.

Audit every agent-assisted workflow for four things:

1. What customer outcome the agent can influence.

2. Which policy, model, data source, and permission it relies on.

3. Which signal shows drift, misuse, unfair treatment, or a bad handoff.

4. Who can pause, correct, explain, or reverse the action.

Then run a simple test: can your team reconstruct one customer outcome without pulling screenshots and exports from five different systems?

Ask your team: If a customer asked why this AI made that call, could we answer in plain English today?

Signal: AI governance is useful when it helps the business fix the journey while the customer still has a reason to trust you.

📈 Market Reality Check

AI use isn’t the same as AI value

Asana’s latest release includes a useful gap: 75% of knowledge workers use AI at work, but only 5% of companies report meaningful productivity gains.

That sounds familiar.

One person gets faster. A team still waits for context. A customer still gets bounced between systems. A manager still can’t see what happened. The task feels improved, but the experience still breaks in the same old place.

Asana points to several blockers: people can’t find the right agents, agents don’t have enough work context, humans and agents aren’t working together cleanly, and leaders worry about unchecked access and cost. That doesn’t prove Asana has the answer. It does prove the adoption conversation is too shallow.

The question isn’t, “Are our people using AI?” The better question is, “Is AI helping the whole journey work better for the customer?”

🧰 Tool Worth Knowing

Willow

What it does: Willow helps companies see and control how AI agents connect to internal systems. It shows which agents employees are using, which tools and data they can reach, what actions they take, and how that activity is audited.

CX use case: Useful when an agent can touch support tools, CRM records, billing data, knowledge bases, approvals, or operating systems that shape what the customer sees next.

Worth watching because: Willow says it has already been deployed inside Wix across more than 5,000 employees. It can connect agents such as Claude, ChatGPT, Gemini, Codex, Cursor, n8n, and custom agents to enterprise systems with runtime permissions and audit trails.

Bottom line: A customer-facing AI problem may start with an internal agent that had too much access, too little supervision, and no clear owner.

⚡ 90-Second CX Radar

IBM and Google Cloud expand production AI delivery

IBM and Google Cloud announced a new practice focused on scaling AI, modernizing core systems, and building industry-specific agents for Gemini Enterprise. The useful part is the move from pilot talk to production work in regulated functions where customer service, operations, and compliance all have to hold together. That is where AI stops being a demo and starts becoming a promise.

Cognizant and ServiceNow push AI governance into daily operations

Cognizant and ServiceNow are connecting Cognizant Neuro AI Trust with ServiceNow AI Control Tower. The CX signal is not the governance branding. It is the move toward live visibility, controls, and accountability when AI operates inside regulated or customer-facing workflows.

🧭 Your Move

Today’s edition is about one simple shift: AI is moving into the operations customers feel before they ever talk to support.

The same pattern shows up in Axiom’s HVAC launch, Asana’s productivity gap, Willow’s access controls, and the push to run production AI inside real operating systems.

Pick one experience signal customers feel but rarely describe well: comfort, cleanliness, wait time, noise, availability, order readiness, or staff attention.

Then ask who owns that signal when AI touches the operation behind it.

Who sees the evidence? Who can correct the setting? Who knows whether the customer felt the improvement? Who fixes it when the efficiency gain makes the experience worse?

If the answer is unclear, the problem isn’t the automation. The problem is ownership.

AI improves CX only when someone owns the part of the experience the customer actually feels.

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 moves closer to booking decisions
→ Reliability beats pure delivery speed
→ Marketing agents need visible guardrails
→ Risk agents follow the whole customer
→ Vertical bots meet contact center complexity

🔎 Deep dive

The AI travel agent now has a point of view

Priceline just moved Penny from helpful travel bot into something closer to a decision partner. The new version can compare live travel options across flights, hotels, cars, maps, pricing, traveler preferences, and booking context inside one conversation.

That matters because travel is not a simple search problem. Customers are not only asking, “What is available?” They are asking, “What makes sense for this trip, this budget, this risk, and this moment?” When an AI starts surfacing its own pick, explaining the tradeoff, and carrying preferences from one trip to the next, the journey shifts from browsing to guided commitment.

Here’s where it breaks. If the AI gets the preference layer wrong, the customer does not experience a bad recommendation as a model issue. They experience it as a brand that misunderstood the trip. So this is not just a chatbot question anymore. Once AI shapes choice before checkout, product, service, legal, data, and brand all have a stake in what the agent recommends.

Source: Priceline

OPERATOR PLAYBOOK

Audit the moments where AI narrows choice

Before you celebrate an AI agent for reducing clicks, look at the choices it is quietly removing. Convenience can absolutely help the customer. It can also hide the wrong tradeoff.

Audit every AI-guided purchase or booking flow for four things:

1. What the agent is allowed to optimize for.

2. What customer preference data it remembers.

3. How it explains why one option is recommended.

4. Where a customer can challenge, reset, or escalate.

Then test whether a reasonable customer would understand the recommendation well enough to trust it without feeling pushed.

Ask your team: If this AI pick goes wrong, who owns the customer explanation?

📈 Market Reality Check

AI discovery raises the standard for fulfillment

This week’s Locus survey gives this travel story a wider commerce reality check. AI is already changing how people shop, with 45% of consumers using AI as a primary or secondary shopping tool. Among those shoppers, 39% say they are more likely to try new brands or products, and 37% are more likely to buy more items when using it.

That does not prove AI makes customers loyal. It proves AI can move demand into places your operation may not be ready to support. The important detail comes after discovery: 68% of shoppers say a fast refund makes them more likely to shop with a retailer again. AI may create the intent, but delivery, returns, and service still decide whether the relationship survives.

🧰 Tool Worth Knowing

MoEngage Merlin AI Custom Agents

What it does: MoEngage launched Merlin AI Custom Agents for lifecycle and CRM teams. Marketers can build agents that run on MoEngage data, define audience, channel, content, and budget guardrails, and see an activity log of what each agent did.

CX use case: A retention team could ask an agent to review campaign performance, draft a journey, QA an in-app message, or point out which segment is not responding before another generic campaign goes out.

Worth watching because: The useful part is visibility. MoEngage is not asking teams to trust a mystery box in the middle. It is giving them a way to see the data pulled, decisions made, channels touched, and content sent. No public performance metrics were shared in the launch.

Bottom line: Marketing AI gets more useful when the team can inspect it, limit it, and stop it.

⚡ 90-Second CX Radar

Oscilar launches Agent Hub for fraud, compliance, credit, and onboarding

Risk is part of CX whether we like it or not. Oscilar is trying to coordinate more than 30 agents across the customer risk lifecycle, which matters because fraud checks, onboarding, disputes, and credit decisions are often where customers feel the cost of internal silos.

Vonage adds vertical AI agents to its contact center

Vonage is embedding healthcare, financial services, and retail-trained agents into Vonage Contact Center through Avaamo and Syndeo. The useful question is not whether a vertical bot sounds smarter. It is whether it can handle compliance, handoff, and context without fragmenting the customer.

🧭 Your Move

The pattern today is simple: AI is moving upstream. It is not waiting for a customer to ask for help after the journey breaks. It is helping the customer choose, buy, book, return, verify, and decide.

That is useful. It is also where sloppy ownership gets expensive. When AI starts shaping the customer’s options, the work can no longer sit with one platform owner or one innovation team. Someone has to own the promise the agent is making on behalf of the business.

If AI helps the customer decide, CX has to help the business decide what the AI is allowed to value.

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:

→ Contact centers become mixed workforces
→ AI needs coaching and visibility
→ Trust depends on handoff design
→ Feedback moves closer to action
→ Financial agents get guardrails

🔎 Deep dive

The contact center now has a management problem

The contact center used to be easier to manage, at least in theory. People answered customers. Supervisors coached people. Workforce teams forecasted demand. Quality teams reviewed conversations. It was messy, but the roles were visible.

AI agents change that. Now part of the work may be done by a system that answers, routes, remembers, summarizes, recommends, or acts before a person ever steps in. Cisco’s latest Webex Contact Center announcements are one signal of that shift, but the bigger point is not the product. It is the operating model.

Customers do not care whether a human agent, AI agent, routing model, or knowledge system created the failure. They feel one conversation and one promise. If context disappears, if the AI takes the wrong action, or if a supervisor cannot see what happened, the customer pays in repetition, delay, and lower trust. That is why CX leaders need management practices for the mixed workforce, not just deployment plans. Who measures the AI? Who coaches the humans around it? Who intervenes when the journey starts to bend?

OPERATOR PLAYBOOK

Manage the mixed workforce before customers meet it

Pick one journey where AI and humans already share the work: billing questions, fraud checks, appointment scheduling, claims, account updates, onboarding, or complaint handling.

Audit every AI-assisted service flow for four things:

1. Which customer outcome the AI agent is allowed to affect.

2. Which performance standard applies to both AI and human agents.

3. Which handoff moments require full context, not a summary.

4. Which manager can intervene when the AI creates risk.

Then test whether a supervisor can reconstruct the customer journey without asking three teams for screenshots.

Ask your team: If this AI agent were a new employee, what would we expect its manager to see every day?

Signal: The risky moment is not only the bad answer. It is the invisible action nobody reviews until the complaint arrives.

📈 Market Reality Check

Consumers are using AI, but trust is still conditional

Corporate Insight’s new financial services AI study says 74% of survey respondents have used AI, and nearly a third use it multiple times per day. That sounds like adoption has already crossed the line. But the same research says 43% are more concerned than excited about AI’s role in society, and 66% believe their financial and insurance providers already use AI moderately or extensively.

That matters because trust is not created by usage alone. Corporate Insight points to three comfort drivers: clear disclosure when AI is used, an easy path to a human at any point, and human review of AI decisions. For CX leaders, that is the practical design brief. Do not hide the AI. Do not trap the customer inside it. Do not let high-stakes decisions feel unreviewed.

🧰 Tool Worth Knowing

AskNicely Insights Agent and Response Agent

What it does: AskNicely launched two AI agents for multi-location service businesses. Insights Agent scans surveys, reviews, and operational feedback, then sends summaries and recommendations to inboxes, Slack, or Microsoft Teams. Response Agent drafts and sends personalized replies to customer reviews and feedback using brand tone and guardrails.

CX use case: Useful when regional managers, location leaders, or frontline teams are drowning in dashboards but still need to know where complaints, risks, and recovery opportunities are showing up.

Worth watching because: This is feedback management moving closer to daily work. The release does not share public performance metrics, so the buyer question is simple: do the recommendations actually change behavior, or do they become another stream of advice people ignore?

Bottom line: Customer feedback becomes more valuable when it reaches the person who can act before the pattern turns into a scorecard problem.

⚡ 90-Second CX Radar

Experian launches an Agent Operating System for financial services AI

Experian is positioning agentic AI around data, decisioning, governance, and controls across the lending lifecycle. That matters because loan approvals, fraud checks, and credit decisions are customer moments where speed only helps if explainability and human oversight are designed in.

Centrical raises $39 million for AI-era frontline performance

Centrical’s release points to AI role-play, coaching, and performance intelligence for human and digital workers. The useful signal is that frontline readiness is becoming part of the AI operating model, not a training task bolted on after launch.

Snowflake adds governance and context controls for enterprise AI agents

Snowflake is pushing trusted business context, agent identity, and security posture management. CX teams should care because a customer-facing agent can only be as reliable as the definitions, permissions, and sensitive-data controls sitting behind it.

🧭 Your Move

Look at one AI-enabled journey and stop asking only whether the agent works. Ask whether the work around the agent is managed well enough for a customer to trust the outcome.

That means visibility, coaching, recovery, escalation, and decision review. Unexciting words. Very useful ones.

The customer does not need to know your workforce model. They need to feel that someone still owns the experience.

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:

→ Agents move behind the guest journey
→ Workflow design becomes the CX risk
→ Financial advice leaves the bank
→ Agent control towers get real
→ Contact centers get practical

🔎 Deep dive

Guesty is putting agents behind the guest experience

Guesty launched Agent Hub, a set of AI agents for short-term rental operators. The interesting part is not that hospitality software now has AI. Everyone has AI. The interesting part is where these agents sit: revenue, guest messages, operations, finance, marketing, reviews, fraud checks, check-in readiness, and turning messages into tasks.

That matters because the guest does not experience those as separate workstreams. They experience one stay. If the payment fails, the cleaning task slips, the check-in form is incomplete, or the reply misses the tone of the moment, the guest does not say, “Ah, that was an operations issue.” They feel one broken promise.

So here is the real CX question: when AI starts acting across the journey, who owns the combined effect? Guesty says operators can set guardrails and stay in control. Good. But guardrails only help if someone has already mapped which AI actions can affect trust, revenue, safety, and recovery.

OPERATOR PLAYBOOK

Look at the work behind the promise

Pick one journey where customers depend on fast coordination: booking, onboarding, renewal, repair, claim, refund, or check-in.

Audit every AI-assisted workflow for four things:

1. Which customer-facing promise the agent can affect.

2. Which internal system, rule, or team it depends on.

3. Which actions require approval before they happen.

4. Which failure sends the customer to a human.

Then test whether the customer still gets a coherent experience when two agents touch the same journey.

Ask your team: Where could an AI action be technically right and still damage trust?

📈 Market Reality Check

Customers are already asking AI for money advice

JD Power says 53% of customers reported turning to AI for financial advice in the past three months. That is not fringe behavior. That is customers taking a high-trust moment and testing whether AI gives them a clearer answer than their bank.

This should make banks and credit card issuers uncomfortable. JD Power also says 47% of customers recall receiving advice from their bank, and fewer than one in six are aware that new financial health and support resources are being introduced. So the issue is not only whether banks have advice tools. It is whether customers know they exist, trust them, and find them useful when money feels stressful.

For CX leaders, the warning is simple: if your advice experience is hard to find, generic, or disconnected from the customer’s next decision, customers will look elsewhere. And “elsewhere” may be an AI answer box with no relationship to your brand, risk standards, or recovery path.

Data Source: JD Power

🧰 Tool Worth Knowing

Hyland Control Tower

What it does: Hyland introduced Control Tower as the command center for its Enterprise Agent Mesh. In plain English, it helps teams see, measure, and control AI agents across document-heavy processes.

CX use case: Useful for journeys where documents, approvals, and customer impact are tangled together, such as banking onboarding, healthcare referrals, insurance claims, and government service cases.

Worth watching because: Hyland is treating agent management as an operating issue, not a feature toggle. Its release also describes Agent Passport, Agent Library, and lifecycle controls. That is the plumbing CX teams will need once agents touch real customer outcomes.

Bottom line: This is not a shiny front-end tool. It is a reminder that customer-facing AI gets safer when the back-office agents have owners, metrics, and stop buttons.

⚡ 90-Second CX Radar

NiCE World 2026 is being framed around agentic AI at enterprise scale

NiCE says Citi, Hyatt, Aetna, GEICO, Lowe’s, and others will share how they are using AI for self-service, journey orchestration, and workforce performance. Enterprise CX is moving from pilot talk to operating proof.

Customer Contact Week is making AI strategy and frontline readiness central themes

CCW’s June announcement points to benchmarking across AI strategy, optichannel CX, frontline readiness, leadership development, and operational performance. That mix matters because AI failure in contact centers is rarely just a model problem. Usually, the team was not ready.

🧭 Your Move

Ask where AI is being allowed to act. Then ask whether that action changes a promise, a policy, a price, a handoff, a recovery path, or a moment of trust.

That is where CX leadership belongs now: where the work changes and the customer feels the result.

The customer does not care which agent did the work. They care whether the experience still holds together.

Until tomorrow,

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

Inside the organization, the workflow may be moving. A request was submitted, a ticket was created, an escalation happened, and someone can usually point to the next step.

They’ve advanced in the relationship. They assume the company has advanced with them. They expect the promise, the context, and the responsibility to travel forward, too.

When that doesn’t happen, what looks internally like a process delay becomes a trust issue externally.

When does a process problem become a customer problem?

I ran into this personally this week.

I had added my wife to our new medical coverage. The enrollment had been submitted, the payment had cleared, and the carrier had received the information. Then we learned she needed an MRI, and the imaging center needed to verify active coverage before scheduling the appointment.

From my perspective, the transaction was complete. I had done what the system asked me to do.

Everyone I spoke with agreed that the coverage should be active. The representatives were helpful, the issue was escalated, and nobody was confused about the intended outcome. Yet when the moment required proof, the system still showed nothing.

Inside the company, it was a timing issue: the file was in, the update was pending, and coverage would show up eventually.

In our reality, the question was different: can we rely on the company right now?

That’s the customer question leaders need to sit with longer. The answer affects more than satisfaction. It affects whether the customer trusts the company, whether they call again, whether employees spend time explaining a problem they can’t fully fix, and whether the business understands the cost it just created by making uncertainty part of the experience.

The company saw a delay. We experienced risk. We weren’t worried about a claim being denied a month from now. We were worried about whether she could get the scan this week.

What does the customer have to carry?

Uncertainty is not a neutral thing to hand to a customer. It becomes especially heavy when the issue touches health, money, access, or any commitment the customer made because they believed the company’s promise.

In our case, we still had to decide what to do next. Do we postpone the appointment? Pay out of pocket? Call again? Wait? Trust that the system would catch up before the gap created a larger problem?

That’s emotional work, logistical work, and risk management handed back to the customer after the customer had already fulfilled their obligation. The policy can be correct, the employees can be competent, and the process can be underway. The experience can still fail if the customer is left carrying the uncertainty between those moving parts.

You see versions of this in subscription businesses all the time. A customer signs up under an offer that sounds clean during the sale. The first bill arrives and doesn’t match what they understood. Now they’re searching old emails while an agent searches internal systems, both trying to reconstruct a promise that disappeared somewhere between purchase and billing. The problem isn’t how hard people are working. It’s that continuity broke somewhere in the middle.

The customer moves through the company as one person with one story. The company experiences the same journey as separate transactions handled by separate owners. If no one carries the story across those transitions, the customer ends up doing it themselves.

That’s not just frustrating. It’s expensive.

Where does the business feel the cost?

Customer friction rarely stays contained. It moves into the operating model.

The second call lands in service. The unresolved issue becomes handle time. The manual correction becomes a credit. The confusing bill creates early-life distrust. Product learns too late that the offer made sense in the meeting, but didn’t survive contact with the actual customer journey.

By the time leadership sees the pattern, the original issue may have been relabeled as churn, digital abandonment, service volume, leakage, or poor adoption.

Zoomed out, those “one-off” problems show up as early-life churn that won’t budge, repeat contact rates that stay stubbornly high, and digital adoption curves that flatten long before they should.

This is why I’m skeptical when companies focus too heavily on the visible layer of experience. A better interface matters. A cleaner checkout matters. A chatbot that doesn’t make people want to throw their phone across the room definitely matters. But none of that solves the deeper problem if responsibility disappears during handoffs.

A polished digital journey can still damage trust if the promise can’t survive the move into billing, fulfillment, support, or recovery. That’s the moment when customers become operators of the company’s system. They save screenshots, archive emails, maintain timelines, and remember names because the business doesn’t have a reliable enough memory of its own.

Highly capable customers can even mask the problem. Their diligence compensates for fragmentation, so the company appears more functional than it actually is. Employees cover for it too, especially support teams that inherit complexity they didn’t create and are asked to be calm, fast, accurate, and empathetic while working with partial visibility.

That’s how employee heroics get mistaken for a working process.

Then call volume rises, and everyone wonders what changed.

What will AI expose?

AI will make this harder to ignore.

There’s a tendency to think of AI as a solution layer. In some cases, it will be. It can summarize conversations, surface information, route requests, and speed up responses. But AI also acts like a diagnostic tool because it reveals whether the business actually has the context, policy clarity, data quality, and ownership model needed to serve the customer well.

When data and accountability are fragmented, AI tends to produce polished, incomplete answers. The chatbot can say, “I see that must be frustrating. Your case has been escalated to our specialist team,” while the specialist queue has no record of the promise, no authority to fix pricing, and no clear owner when the customer calls back two days later. The customer doesn’t need a beautifully worded explanation of why the company can’t resolve the issue. They need someone, or something, to own the next move.

In a better system, the same AI doesn’t just empathize—it validates the promise, checks the latest source of truth, and either confirms the fix or routes to a human with clear authority and a specific next action.

That requires more than a model. It requires journey context. What happened before? What was promised during the sales call that the customer still remembers word for word? Which system has the latest truth when billing and CRM disagree? What recovery action is allowed without a manager override? Who is accountable when the normal path fails?⁠

Those are enterprise questions before they’re technology questions. If leaders skip them, AI readiness becomes another version of the same old problem: a better front end sitting on top of weak operating discipline.

There are real constraints here—regulation, legacy systems, risk policy. Ownership doesn’t mean ignoring those. It means being explicit about what the company will and won’t do inside those constraints, instead of letting the customer discover the limits the hard way.

What should leaders inspect?

If I were looking at this from within a business, I’d start with a journey that already creates visible friction. The one employees talk about quietly. The one customers call about repeatedly. The one surrounded by workarounds. The one where experienced people say, “This happens all the time,” and then keep moving because they’ve learned to live with it.

Follow actual cases from beginning to end. Don’t start with the process map. Start with the customer’s path and look for the moment when they believed the company had taken ownership, while the company had only created a ticket, assigned a task, or recorded an update.

That’s often where the story changes.

The goal isn’t to find a team to blame. Most teams are operating within constraints they inherited. The better goal is to find where activity has been mistaken for ownership.

One pattern I’ve seen repeatedly: a leader asks, “Did we open a ticket?” instead of, “Who owns this until the customer is whole?” The room relaxes after the status update, even though nothing in the customer’s world has actually changed. It’s easier to manage tickets than outcomes, but the customer only measures outcomes.

A simple way to reset the conversation is to ask, “Before we move on, who wakes up owning this if nothing changes?” That question shifts the room from reporting activity to taking responsibility for an outcome a real customer will feel.

When I’ve run this exercise inside companies, the breakthrough usually comes when leaders see the full thread of a single case on one page. It becomes painfully clear how many times the customer reasonably believed, “Okay, they’ve got it now,” right before the handoff dropped them.

That’s where experience connects to what shows up on the dashboard later: the renewal that doesn’t happen, the extra contacts into care, the digital funnel that never quite takes off, the product team learning the same lesson for the third time.

When you want to stress-test a journey, pick a painful one and ask, “Could a reasonable customer explain who owns this for them, end to end?”If the honest answer is no, the company is still asking the customer to do work the business should be doing.

When the customer moves, ownership has to move too.

The customer should never become the connective tissue holding the organization together.

That’s the company’s job.

www.marklevy.co
Follow me on Linkedin

Leave a comment

Thanks for being here. I’ll see you next Tuesday at 8:15 am ET.

👉 If you enjoyed this newsletter and value this work, please consider forwarding it to your friends and colleagues or sharing it on social media. New to DCX? Sign Up.

Share Decoding Customer Experience

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

DCX Stat of the day: Physicians and staff spend an average of 13 hours each week completing prior authorization requests. That is a CX problem because the customer feels that admin drag as delay, uncertainty, and one more moment where trust can slip. Cognizant

In this issue:

→ AI is moving from talking to doing
→ Ownership gets fuzzy right where risk gets real
→ Brands may lose ground before checkout even starts
→ Better testing is becoming a CX requirement
→ Travel keeps showing where this goes next

🔎 Deep dive

Cognizant just showed where this gets real

Cognizant’s latest move matters because it takes AI out of the safe demo zone and drops it into a workflow people already hate. Its first live use case is prior authorization, where AI can check what is needed, gather the right documentation, and submit the request. That is not a nicer chatbot. That is AI stepping into real work that already creates delay, cost, and frustration.

The interesting part is not speed. It is responsibility. Once AI starts doing work inside the flow, the old questions about handoffs, permissions, approvals, and cleanup are not back-office details anymore. The customer feels them. If the AI gets stuck, oversteps, or sends the work into a dead end, the customer does not care that the workflow looked elegant on a slide.

The useful part is bigger than healthcare. Every company has journeys where the customer is really waiting on internal coordination, not on another message. Claims. Onboarding. Returns. Service approvals. That is where the work gets stuck. If AI is going to touch those moments, the better question is not “Where can we automate?” It is “Who owns the decision when the AI goes one step too far?”

OPERATOR PLAYBOOK

Check where AI is being allowed to do too much

Pick one customer journey where the customer is really waiting on internal coordination, like onboarding approval, return handling, claim intake, order exceptions, or service eligibility.

Audit every action step in that flow for four things:

1. What the AI is actually allowed to trigger, submit, change, or approve.

2. Where the rules still need to stay obvious to the team.

3. Where a human should step in before the customer feels the impact.

4. How you will trace the decision when the outcome is wrong, delayed, or challenged.

Then test whether the AI is being treated like a supervised helper or a vague new owner nobody wants to name.

Ask your team: Which customer-facing workflow would create the biggest mess if an AI agent acted one step too far?

📈 Market Reality Check

Luxury brands may lose the experience before the customer reaches the brand

McKinsey’s point is especially sharp in luxury. A lot of the value is not just the product. It is the feeling of guidance, discretion, reassurance, and being understood. If AI starts shaping demand earlier, inside general assistants or third-party shopping tools, then part of that high-touch experience may get decided before the customer ever reaches the brand’s own site, store, or adviser.

That is where the CX risk shows up. Luxury brands have spent years trying to control pacing, tone, service quality, and the overall feel of the relationship. If AI becomes the first layer interpreting what the customer wants, the brand risks losing control of the very part of the experience that makes luxury feel different. And if that AI gets the nuance wrong, the customer does not blame the system. They blame the brand for making an expensive experience feel generic.

🧰 Tool Worth Knowing

Cresta Synthetic Customers

What it does: Cresta turns your conversation history into realistic customer personas so teams can test AI, pressure-test messaging, and train agents against situations that sound more like real life.

CX use case: Useful when you want to test AI agents, policy changes, or frontline messaging before real customers run into the rough edges, especially in service and retention journeys.

Worth watching because: This gets at the real problem behind a lot of shaky AI launches. Most teams test the happy path, or they test against the way they imagine customers behave. Real customers interrupt, repeat themselves, get impatient, lose trust, and change direction halfway through. That is the stuff that breaks the experience.

Bottom line: If AI is going to play a bigger role in the journey, better testing becomes part of the CX job. Cresta is worth watching because it treats customer behavior as something to learn from before launch, not something to clean up after launch. No public performance metrics were shared beyond its reference to a blind evaluation methodology.

⚡ 90-Second CX Radar

Gainsight launches an agentic stack aimed at retention, not just support

This is a useful reminder that the AI conversation is moving deeper into post-sale experience. Once AI starts reaching into onboarding, education, communities, and product usage signals, retention stops being a reporting metric and starts looking more like a daily operating system.

AI and Content Reshape Attractions & Tours at Trip.com Group’s Global Partner Forum

Travel is a good early signal because discovery, comparison, and booking already get messy fast. Trip.com is betting AI can tighten that path, which is probably where a lot of other experience-heavy sectors are heading too.

🧭 Your Move

The thread running through this issue is pretty simple: AI is starting to do more of the work, not just talk about the work. That sounds efficient until ownership gets fuzzy, testing gets shallow, and the customer ends up feeling the cost of a workflow nobody really thought through.

Pick one workflow this week where AI is likely to move from answering questions to actually doing something. Then get very clear on checkpoints and ownership before anybody starts celebrating speed.

The teams that handle this well will not be the ones with the most AI in market. They will be the ones that know where the AI stops, where a human steps in, and how the customer stays protected when the flow gets messy.

If AI is going to do part of the job, ownership has to be designed into the experience.

Until tomorrow,

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

The policy choice. The handoff. The judgment call. That’s usually where customers decide what your experience is actually worth.

These stories cover different ground, but they all hit the same sore spot.

Customers don’t run into your CX strategy first. They run into the operating choices underneath it.

Sometimes it shows up in a visible tradeoff a company is willing to stand behind. Sometimes it shows up when AI handles the easy stuff, then freezes the minute something gets sensitive. And sometimes it shows up when feedback actually changes a decision instead of getting buried in a listening program.

That’s why this week’s mix is worth your time. It’s less about positioning and more about where trust holds up or falls apart. The handoff. The policy choice. The data problem. The judgment call. The stuff customers actually run into.

Let’s dig in.

Reserve now - Only a Few Spots Left

This Week’s Must-Read Links

Costco is showing that employee experience can be part of the member promise

Costco is making the kind of tradeoff most brands would rather not name. It’s accepting some short-term customer inconvenience in order to protect the employee experience behind the member promise. That matters because CX leaders are constantly told to remove friction without always being allowed to ask which tradeoffs actually build trust over time.

What’s happening: Costco’s store-closure decision is a reminder that not every convenience cut is a CX failure. Sometimes a company is protecting the workforce conditions that make the rest of the experience possible.

Why it matters: Customers don’t judge convenience in isolation. They judge whether the brand makes sense. If the operating model supports service, trust, and consistency, some tradeoffs can strengthen the value proposition instead of weakening it.

Reality check: This is where a lot of CX conversations get mushy. Experience isn’t just about adding access, speed, or options. It’s also about being clear on which tradeoffs you’re willing to let customers see.

The real work in agentic commerce is still operational

Forrester is making a point that gets lost in most agentic-commerce talk: the hard part isn’t the interface. It’s the operating system underneath it. If your product data is messy, inventory visibility is weak, and coordination across teams is slow, smarter agents will just expose those problems faster.

State of play: The market is still treating agentic commerce like a front-end story. Forrester is pushing it back where it belongs: data quality, fulfillment accuracy, content structure, and operational readiness.

Why it matters: This gives CX and digital leaders a better frame for the meeting that usually goes sideways. The question isn’t whether to chase the demo. It’s whether your stack can support trustworthy automation without making the experience murkier.

The bottom line: The strongest brands here are unlikely to be the ones with the loudest AI story. They’ll be the ones that make product, inventory, and delivery information reliable enough that customers and machines are working from the same set of facts.

Sam’s Club is showing what a stronger customer voice loop can look like

Sam’s Club is doing something more useful than another “we listen to customers” message. It’s building a member community that looks less like a research panel and more like a working input into product and experience decisions. That shift is more important than it sounds.

What’s happening: The Member’s Mark community has grown to more than 150,000 participants, roughly triple its size a year ago. The interesting part isn’t the audience number by itself. It’s the chance to move customer input closer to the people making product and experience calls.

Between the lines: A lot of customer listening still dies in dashboards, summary decks, and tidy closed-loop rituals that never shape a real decision. This points to a tighter model where customer voice gets closer to merchandising, product choices, and experience changes.

Why it matters: CX teams say they want a seat earlier in the decision flow. This is what that looks like when it’s real: customer input treated less like commentary and more like something the business can actually use.

AI helps with routine service until trust enters the moment

JD Power’s latest banking-app research draws a line that a lot of service teams still blur. Customers are fine with AI when the job is simple. They’re far less comfortable when the issue involves fraud, disputes, or anything that feels risky.

What’s happening: Virtual assistants are helping with routine tasks, but they are still weak in the moments where customers need judgment, reassurance, and a clean handoff to a human.

Why it matters: This isn’t just an AI story. It’s a service-design story. A lot of customer frustration starts when companies automate the front of the interaction without fixing escalation, ownership, and issue resolution underneath it.

The CX To-Do: Audit your highest-stakes service journeys. Then ask a hard question: where is the assistant actually helping, and where is it just delaying a real answer?

DCX Stat of the Week

Loyalty is starting to carry more of the growth burden than acquisition

Brands with mature loyalty programs now generate 71% of repeat revenue from program members, up from 54% in 2022, while customer acquisition cost across the same categories has risen 67% over the same period.

Takeaway: This is the kind of number that can change a budget conversation. If repeat revenue is concentrating inside loyalty while acquisition keeps getting pricier, CX starts looking a lot more like margin defense and growth leverage.

Source: The Loyalty Premium. A 2026 State of Consumer Loyalty Study

DCX Case Study of the Week

Royal Caribbean’s Digital Insights Team Drove Better App Performance

CX Challenge: Royal Caribbean needed faster, more scalable customer insight to improve its mobile experience and make better digital product decisions.

Action Taken: The company built a new Digital Insights team and expanded research usage through UserTesting so customer feedback could flow more directly into app and experience decisions.

Result: Research usage scaled 10x in one year. App NPS rose 15 points, engagement increased 26%, and conversion improved by nearly a full percentage point.

Lesson for CX Pros: Customer insight works best when it acts like operating infrastructure, not an occasional research step. Faster evidence loops can improve experience quality and commercial performance at the same time.

Further Reading: User Testing Illumi Award Winners

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

One Last Thing

The next CX edge probably doesn’t come from another message refresh or another automation rollout.

It comes from tighter operating choices.

Cleaner handoffs help. Better judgment in risky moments helps. So do stronger links between customer input and real decisions. The bigger point is simpler: get clearer on which tradeoffs actually make the experience stronger instead of just making it look busier.

Have a great week ahead!

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

Reserve your spot - Filling Up Fast

In this issue:

→ Retailers are building branded AI shopping assistants
→ Consumers still want the final buying call
→ Messaging speed is now a CX operations issue
→ Onboarding delays are becoming churn risk
→ Hotels are reducing handoff risk across guest journeys

🔎 Deep dive

Retailers are being told to build their own AI shopping front door now

This AWS move matters because it turns AI shopping into a more practical question for retailers: do you want that first product conversation happening inside your own experience, or inside a general assistant that does not know your catalog, margins, or brand voice the way you do.

AWS says more than 300 million customers used Amazon’s shopping assistant last year, driving nearly $12 billion in incremental sales, and it is now packaging that learning into Agentic Shopping Assistant on AWS. The pitch is pretty simple: use the foundation, layer in your own data, rules, and brand voice, and launch faster than you could on your own. If AI becomes a real shopping interface, the brands that keep recommendations and follow-through inside their own system have a better shot at reducing effort without giving away the relationship.

Gift buying is exactly the kind of customer moment where people are unsure, under a little pressure, and not in the mood to click through endless filters. That is where this gets interesting. The value is not generic automation. It is helping someone describe what they need, then guiding them toward a useful next step that still feels on-brand. That is the pattern worth watching: AI earns its place when it makes progress easier for the customer and keeps relevance, brand voice, and conversion closer to the operator.

Source: AWS

OPERATOR PLAYBOOK

Audit the moments where customers still do translation work for your business

Pick one consumer journey where customers still have to translate their need into your system, like product discovery, booking, order follow-up, appointment setting, or guest support.

Audit every step in that flow for four things:

1. Where the customer is forced to search in your language instead of their own.

2. Whether the AI can turn intent into a useful next step, not just another answer.

3. Where accuracy or policy gaps would make the experience feel risky or annoying.

4. How context carries forward when the customer moves from AI to a human or from one channel to another.

Then test whether the AI is reducing effort for the customer, or simply shifting work into a shinier interface.

Ask your team: Which customer journeys would feel dramatically easier if people could describe the task naturally instead of navigating our structure?

📈 Market Reality Check

Consumers are drawing a clear line between assistance and autonomy

Gartner’s new consumer survey is a useful reality check for every brand racing toward agentic commerce. Consumers showed far more interest in AI that helps narrow choices than in AI that makes the purchase decision for them. Gartner found 31% would let AI narrow household-supplies options and 28% would do the same for personal electronics, but only 11% were willing to let AI make purchase decisions on their behalf.

That gap is more than a trust story. It is an operating design instruction. Brands can earn permission faster by using AI to reduce cognitive load at the messy part of the journey, comparison, fit, price, and relevance, while leaving the final choice with the customer. That is especially important because Gartner also found 54% of AI-shopping users said they had to double-check all the information, and 62% said the information ended up wasting their time. Accuracy is not a model issue sitting in the background. It is now part of the customer experience itself.

🧰 Tool Worth Knowing

Aircall AI Messaging Agents

What it does: Aircall launched AI Messaging Agents that handle inbound SMS and WhatsApp conversations on a company’s existing business numbers, using the same workspace and knowledge base as its AI voice agents.

CX use case: Useful for brands that lose sales or frustrate customers when a simple text question about delivery, availability, booking, or service timing sits unanswered after hours or gets trapped in a separate queue.

Worth watching because: This is a practical fix for a very consumer problem. Customers do not care which internal team owns voice, SMS, or WhatsApp. They care whether the business answers quickly and keeps the conversation intact when a human needs to step in. Aircall is betting that one number, one workspace, and one shared context can remove some of the friction companies created by splitting those interactions across tools.

Bottom line: Messaging AI becomes useful when it protects momentum. If the answer is immediate and the handoff is clean, the business keeps the sale, the booking, or the trust. If not, it just automates the dead end.

⚡ 90-Second CX Radar

OnRamp is pushing agentic AI deeper into onboarding and post-sale activation

This matters because time-to-value is a customer experience issue, not just a revenue metric. When onboarding drags, the customer feels uncertainty long before the finance team feels churn.

Mews is collapsing hotel booking, guest messaging, and operations into one AI-native stack

The interesting move is not more hotel software. It is reducing the number of places guest context can break. When booking, messaging, payment, and operational data sit closer together, fewer customer moments die in the handoff.

🧭 Your Move

Choose one high-intent consumer moment this week where customers still have to do too much work before they can buy, book, or move forward. Then ask a harder question than “Where can we add AI?” Ask where natural-language guidance, cleaner context, or faster handoff would remove actual effort from the journey.

The brands that win this phase will not be the ones that automate the most. They will be the ones that make AI feel like clearer progress for the customer and cleaner execution for the operator at the same time.

In consumer experience, the best AI does not replace the customer. It reduces the work standing between the customer and the outcome.

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.

Reserve your spot - Space is limited

In this issue:

→ Proactive AI is moving beyond reactive support
→ Outreach logic now shapes conversion and collections
→ Faster digital growth raises identity risk
→ Guest booking is becoming an AI workflow
→ Trust still breaks at the handoff

🔎 Deep dive

Talkdesk is pushing AI from service resolution into revenue recovery

Talkdesk’s announcement matters because it moves AI from handling inbound demand to shaping customer behavior before the customer asks for help. Its new retail and financial services agents are built for abandoned carts, recall communications, deposit growth, pre-qualification, and delinquency outreach, which means AI is now showing up earlier in revenue and risk-sensitive moments.

That changes the CX conversation. Reactive service can sit inside the contact center. Proactive AI reaches into acquisition, onboarding, recovery, compliance, and collections, where the message timing, customer context, and next best action all carry commercial consequences. This is likely to show up first in journeys where hesitation is expensive and delay quietly kills conversion.

The hidden risk is false confidence. Once the outreach is automated, teams may confuse faster contact with better judgment. If the trigger is weak, the message tone is off, or the handoff fails when the customer needs help, the business does not just lose efficiency. It scales bad timing, weak context, and avoidable distrust.

Source: Talkdesk

OPERATOR PLAYBOOK

Audit the trigger before you automate the outreach

Pick one journey where delay quietly destroys value, like cart recovery, loan follow-up, onboarding activation, or payment recovery.

Audit every proactive outreach flow for four things:

1. What signal actually triggers the outreach.

2. Whether the system has enough context to make the message useful.

3. Where a human needs to step in before the outreach feels intrusive or wrong.

4. Which team owns the result across CX, operations, revenue, and risk.

Then test whether the journey feels more helpful to the customer, or simply more efficient to the business.

Ask your team: Which outreach moments would still feel smart if the customer could see every rule behind them?

📈 Market Reality Check

Faster onboarding now comes with a deeper trust burden

AU10TIX reported on May 27 that AI-generated identity fraud has now surpassed physical document forgery, and that nearly 1 in 11 verification attempts showed signs of AI involvement. It also found a major biometric coverage gap in the travel segment it analyzed, with no deepfake detection or face comparison observed.

That does not prove every consumer journey is equally exposed, or that every business needs the same level of verification friction. It does tell leaders something important: the same flows being redesigned for faster signup, booking, payments, and activation are becoming easier to test, spoof, and scale from the outside. If growth teams optimize only for speed, they may be creating a cleaner front door for synthetic traffic too.

🧰 Tool Worth Knowing

ROLLER AI Agent

What it does: ROLLER launched a guest-facing AI agent that answers questions and helps capture bookings and upsell opportunities across phone, chat, and email.

CX use case: Useful for consumer-facing attractions, leisure, and venue operators that lose demand when a guest asks a pre-visit question after hours, hesitates on a ticket option, or drops out before completing the booking.

Worth watching because: This is a cleaner example of AI moving into the live decision moment, not just support deflection. For consumer brands, that matters. The pre-purchase question is often the experience. If the agent can reduce uncertainty and help close the booking in the same interaction, CX and conversion stop behaving like separate teams.

Bottom line: The useful pattern is simple: remove wait time at the exact moment a customer is deciding whether to buy. The real test is whether the AI can keep guidance accurate, specific, and trustworthy enough to improve conversion without creating a more polished version of confusion.

⚡ 90-Second CX Radar

BCU uses Glance inside Salesforce to reduce friction in digital support

This is a good reminder that better AI support still depends on better human visibility. Letting members show what they see, then storing that context in the same record, is the kind of practical fix that makes handoffs less painful.

Travelport, Cognizant, and Anthropic are trying to turn AI travel intent into confirmed bookings

The interesting part is not the model choice. It is the infrastructure claim. Travel search is getting conversational faster than booking systems are getting executable, which means the experience breaks when intent is easy to express but hard to complete.

🧭 Your Move

Choose one customer journey this week where the business wants AI to act before the customer asks for help. Map the trigger, the customer context, the trust checkpoint, and the handoff owner before you optimize for scale.

The stronger operators in this phase will know which intervention protects revenue, which one protects trust, and where those two goals start fighting each other.

Proactive AI only creates customer value when timing, trust, and ownership arrive together.

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.

Reserve your spot

In this issue:

→ AI is entering exception-heavy workflows
→ Voice automation is getting more ambitious
→ Confidence still lags behind adoption
→ Community data is becoming action-ready
→ Trust is moving into the execution layer

🔎 Deep dive

Microsoft is turning AI workflows into customer-facing operating systems

Microsoft’s Copilot Studio update matters because it puts AI closer to real customer work, not just chat. The release adds real-time voice, a simpler workflow builder, and a way to blend rules, approvals, and AI steps in one flow.

The useful example is Graebel. Microsoft says the company built a Service Order Agent that can read messy emails, check them against business rules, work inside a system that has no API, and pass the issue along when a human needs to step in. That is much closer to day-to-day CX work than a simple bot demo.

This will show up first in workflows where requests come in incomplete or inconsistent, like scheduling, claims, service changes, and support intake. The upside is speed. The risk is that teams trust the automation before they have fixed the handoff and exception path. That is where customers usually feel the pain.

OPERATOR PLAYBOOK

Audit the exception path before you add another agent

Pick one workflow where customer requests often arrive messy or incomplete.

Audit every exception-heavy workflow for four things:

1. What the system still has to figure out before work can start.

2. Which rules are written down, and which still live in someone’s head.

3. Where the workflow stops and needs a human handoff.

4. How the team can see why the system made a decision.

Then test whether the automation is truly saving work, or just pushing confusion further down the line.

Ask your team: Where would a customer feel the cost first if this workflow gets the exception wrong?

📈 Market Reality Check

Adoption is rising faster than operator confidence

GetYourGuide’s new travel operator research offers a useful reality check. The company said 64% of operators are using AI more than a year ago, but 56% still say the tools feel overwhelming. It also said 44% have received inaccurate or misleading information from AI tools. At the same time, travelers coming from AI-powered search show a 45% lower bounce rate.

That does not prove the same pattern will hold in every industry. It does show the pressure CX leaders should expect. Customers may start showing up with AI-shaped expectations before internal teams feel ready to serve them clearly and consistently.

🧰 Tool Worth Knowing

Higher Logic Vanilla MCP

What it does: Higher Logic launched Higher Logic Vanilla MCP, giving customers a native way to connect community data to AI tools like ChatGPT, Claude, and Cursor through the Model Context Protocol.

CX use case: Useful for teams that use community as a support and feedback channel but still treat that data like a separate island. Instead of exporting posts and reports, teams can ask questions about live community activity and turn the answers into follow-up work faster.

Worth watching because: A lot of customer signal sits outside the ticket queue. This is a practical way to bring product questions, support patterns, and recurring friction into the same workflows teams already use elsewhere.

Bottom line: The appeal here is simple: easier access to useful customer signal. No public performance metrics were shared. The real test is whether teams use that signal to fix ownership and response speed, not just create another dashboard.

More: Business Wire

⚡ 90-Second CX Radar

Mastercard says agentic commerce needs a trust and execution layer

This is a useful signal for CX leaders because it frames AI shopping as a full journey problem, not a discovery widget. If agents can influence product choice, checkout, and post-purchase activity, trust controls move closer to the customer moment.

Optimizely and Deloitte Digital are pushing AI transformation past tool adoption

The interesting part is not the partnership itself. It is the reminder that AI personalization still falls apart when teams skip workflow redesign and shared success measures.

Attentive is expanding AI from message generation into marketing orchestration

That matters for CX because the next pressure point in personalization is not more content. It is deciding when AI should send a message, wait, or stay quiet.

🧭 Your Move

Choose one live AI workflow this week and review it with the people who own CX, operations, and risk. Focus on the edge cases, the handoffs, and the proof trail, not the headline efficiency claim.

The teams that win this phase will not be the ones with the most agents. They will be the ones that know where automation helps, where human judgment still matters, and how to explain both clearly.

Trust in AI does not come from the demo. It comes from what happens when the workflow gets messy.

Until tomorrow,

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

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

In this issue:

→ AI usage pricing changes CX math
→ Response speed is now a revenue issue
→ Payments teams expect agentic buying
→ Conversational sales gets more specific
→ Trust still depends on human design

🔎 Deep dive

Target’s AI pricing rethink is a warning for every CX team

Target India is pushing deeper into AI while reassessing deployment as providers shift from subscriptions to usage-based pricing. That matters for CX because the cost of AI is no longer a background software line. It is moving closer to the interaction itself.

This changes the operating question. CX leaders now have to decide which moments deserve live AI spend, which ones can run on cheaper automation, and where a human fallback protects more value than another model call. That shows up first in high-volume journeys like product discovery, service triage, and internal assist.

The hidden risk is lazy scale. When AI feels cheap, companies spray it everywhere. When usage pricing lands, they discover they never defined which interactions create margin, reduce effort, or improve recovery. That is not a model problem. That is an operating discipline problem.

OPERATOR PLAYBOOK

Price the journey before you automate more of it

Pick one AI-assisted journey with real interaction volume and work backward from the business value of the outcome instead of the novelty of the model.

Audit every usage-priced AI journey for four things:

1. Which customer outcome justifies the interaction cost.

2. Where lower-cost rules or templates can do the same job.

3. When the workflow should hand off before more AI spend adds little value.

4. Who tracks cost, recovery, and customer effort together.

Then test whether the journey is cheaper because it is better, or just cheaper because costs are still hiding in another budget.

Ask your team: Which AI interactions would we still pay for if every call, prompt, and handoff hit the same P&L line?

📈 Market Reality Check

Payments leaders are treating AI agents as real buying actors

PYMNTS says that payments executives now describe AI assistants moving from advisers to purchasing agents, making the payments layer the point where intent turns into action. That is a real business signal. Once AI influences checkout, approvals, or payment choice, CX, fraud, and payments teams stop being adjacent functions.

This does not prove consumers are ready to let agents buy freely on their behalf. It does show where enterprise attention is going: trust, speed, and transaction controls now sit inside the customer journey, not after it.

🧰 Tool Worth Knowing

EngageLab Conversational Sales

What it does: Aurora Mobile launched EngageLab Conversational Sales as an AI-assisted sales and engagement workflow that combines omnichannel intake, lead qualification, and fast handoff to human reps.

CX use case: Useful for teams that lose high-intent inbound demand because nobody responds in the channel or time window the customer expects. The workflow is aimed at web, app, and messaging interactions where delay kills conversion before a human even joins.

Worth watching because: The product is not just another bot pitch. It makes a more specific claim: if response speed is now expected inside one minute, teams need a cleaner AI-to-human relay and a pricing model that does not punish scale through idle-seat licenses.

Bottom line: Aurora shared one customer result, not broad benchmark data, so treat the performance claim as directional. The useful idea is the design pattern: qualify fast, transfer context cleanly, and use humans where they earn their keep.

⚡ 90-Second CX Radar

Forbes argues AI is turning customer feedback into a real-time operating input

The useful takeaway is not the AI layer by itself. It is the shift from post-interaction surveys to always-on feedback capture that can actually change the next customer experience, not just describe the last one.

CIO makes the case that trust and relationships are now core AI transformation infrastructure

That matters for CX because AI projects now cut across service, digital, payments, merchandising, operations, and risk. If ownership and trust are weak, the customer will feel the seams long before leadership sees the dashboard.

🧭 Your Move

Review one AI journey this week with finance, CX, and operations in the same room. Decide which interactions deserve premium intelligence, which ones need simpler automation, and where human recovery matters more than another layer of AI.

The winners here will be the ones that know where AI creates measurable customer value and where it simply creates a more expensive version of ambiguity.

If AI cost is becoming variable, CX design has to become more intentional.

Until tomorrow,

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

Your next customer may never touch the journey you spent months improving.

They may not visit your homepage, open your app, read your comparison page, scroll through your FAQ, or follow the flow your team fought to simplify.

They may ask an AI agent to do the work for them.

• “Find the best internet plan for my home.”

• “Compare these insurance options.”

• “Check whether I can return this, and tell me what to do next.”

Useful for the customer.

A little uncomfortable for CX.

For years, we treated the owned journey as the center of customer experience: the website, the app, the chatbot, the contact center, the store, the account portal. That made sense when customers were doing the work themselves.

AI agents change the front door.

The customer may still make the final decision, but the research, comparison, filtering, scheduling, and prep work may happen somewhere your team does not control.

Which means the work underneath the journey starts to matter more than the journey itself.

Product truth. Policy clarity. Knowledge quality. Recovery paths. Handoffs. Permission rules. All the things customers usually have to fight through quietly.

Agentic CX will bring that work much closer to the surface. And your work is just beginning.

What Happens When the Customer Is Not the One Clicking?

When customers delegate tasks to AI agents, the experience becomes whatever the agent can understand, retrieve, compare, trust, and act on. The journey no longer starts only on the website, app, chatbot, or contact center. It starts wherever the customer asks for help, and the company’s clarity becomes more important than its designed path.

Most CX work assumes the customer is present.

We map what the customer sees. We measure where they click. We study where they abandon. We improve the form, rewrite the help article, reduce the steps, and celebrate when task completion moves.

That work still matters. It just may not be where the decision starts.

When a customer delegates the task to an AI agent, the experience becomes whatever the agent can understand, retrieve, compare, trust, and act on.

A different design problem.

The agent does not care that your homepage feels warm and aspirational. It does not appreciate the brand system. It is not moved by the lifestyle photography. It will not sit through your clever product naming architecture and say, “Wow, what a thoughtful narrative.”

It wants the truth.

• What does this cost?

• Who is eligible?

• What is included?

• What happens if I cancel?

• Is the appointment available?

• What does the policy actually say?

• Where do I go when something breaks?

If those answers are scattered, stale, inconsistent, or buried in a PDF from three product launches ago, the experience is already fragile. The customer may never know where it broke. They will only know the answer was wrong, the recommendation was confusing, or the next step created more work than it removed.

Then the company gets the call.

Why Does Agentic CX Reward Clean Operating Truth?

Agentic CX rewards companies whose product data, policies, pricing, eligibility rules, recovery paths, and knowledge sources tell the same story. AI agents assemble answers from whatever they can find and trust. If a company’s operating truth is scattered or inconsistent, the agent may expose those gaps before the customer ever reaches the owned journey.

Agentic CX is a stress test of how clean your company’s truth actually is.

A human customer can work around messy systems. They can read three pages, call support, compare notes with a friend, open five tabs, squint at the fine print, and make an educated guess.

That does not mean the experience worked.

It means the customer did the cleanup.

An AI agent may expose those gaps faster. It might pull from your policy page, a support article, a third-party comparison site, a Reddit thread, and the customer’s own email history. Then it may summarize the answer as if your company has one clear version of reality.

That is where the risk shows up.

Take home internet. A customer asks an agent to compare providers based on speed, price, installation date, equipment fees, promotional expiration, contract terms, cancellation rules, and customer reviews.

The agent does not move through the journey the way your UX team designed it. It assembles the decision from whatever it can find and trust.

One provider has the nicer website but vague fee language. Another has less polish but clear pricing, structured plan data, visible install windows, and cancellation terms that do not require a scavenger hunt.

The recommendation may favor the company with cleaner truth over the company with cleaner visuals.

That should get our attention.

A lot of companies have spent years improving the surface of the experience while the operating layer stayed messy underneath: outdated policy pages, conflicting product data, unclear ownership, internal exceptions, and rules that only a few people know how to explain.

AI does not make those issues disappear.

It moves them closer to the customer.

Why Is Control the Wrong Question for CX Leaders?

The better CX question is not how to control every AI agent, but how to make the company’s truth accurate, consistent, findable, and safe to act on. Leaders cannot control every interface or external summary. They can influence the systems, policies, knowledge, handoffs, and recovery rules that determine whether agents represent the company correctly.

A lot of CX leaders will start with the same concern:

“How do we control what the agent says?”

Fair concern. Too narrow.

No company is going to control every agent, interface, connector, comparison site, external summary, and place where a customer asks for help. The experience is already moving outside the channels companies built.

That does not make CX less important.

It makes CX more operational.

The work shifts from page-by-page ownership to truth-level ownership: product data, policy language, knowledge management, consent, identity, recovery rules, service handoffs, and decision rights.

Not exactly the glamorous side of customer experience.

But it is where trust gets made or broken.

Agentic experiences will not only test the quality of your digital design. They will test whether your systems, policies, and teams can tell the same story when the customer is not patiently clicking through the path you designed.

CX teams do not need to own every system. They do need to know where customer trust depends on those systems lining up.

That takes a different kind of influence.

Less defending the journey map.

More helping the business see where the customer promise depends on internal clarity.

How Do You Run the Agent Layer Field Test?

Run the Agent Layer Field Test by choosing one high-value customer task and checking what an AI agent would need to know, access, trust, explain, and recover from. Evaluate findability, comparability, trust, permission, and recovery across real sources, including product pages, policies, knowledge bases, reviews, transcripts, and service rules.

Traditional journey maps show what the customer does, thinks, and feels.

Keep that.

For agentic journeys, add one more layer:

What does the agent need to know, access, trust, explain, and recover from?

Start with one high-value customer task. Choose something customers already research, compare, or hesitate over before acting.

Buying a plan. Filing a claim. Changing service. Returning a product. Booking an appointment. Renewing a contract.

Then run a practical test.

Do not answer these questions from memory. Pick the actual journey. Look at the actual sources an agent might use: product pages, help articles, policy documents, chat transcripts, public reviews, comparison sites, internal knowledge bases, customer emails, and service rules.

Mark every place where the agent would have to guess, infer, reconcile conflicting information, or ask the customer to do extra work.

Use five checks:

1. Findability

Can the agent find the correct answer without guessing?

Is the information public, current, structured, and easy to interpret?

Or is it scattered across product pages, help articles, policy PDFs, and internal knowledge bases?

2. Comparability

Can the agent compare options clearly?

Are prices, fees, limits, eligibility rules, exclusions, timelines, and tradeoffs clear enough to compare?

Or does the customer still have to decode the fine print?

3. Trust

Can the agent tell which source is reliable?

Is there one obvious source of truth?

Or do the website, support team, sales copy, chatbot, and third-party summaries tell slightly different stories?

4. Permission

Can the customer approve the action safely?

Do they understand what the agent is about to do, what permissions they are giving, and what could change?

Or are they being asked to hand over too much control too early?

5. Recovery

Can the customer recover if something goes wrong?

If the agent makes the wrong move, can the customer reverse it without starting a support archaeology project?

You know that version.

Three transfers. Two policy interpretations. One supervisor callback. A ticket number nobody can find.

Same old pain. New entry point.

The goal is not to make the journey map look more advanced. The goal is to find the places where customer trust depends on information, systems, and teams telling the same story.

So do not ask only:

“Can the agent complete the task?”

Ask:

“Can the customer understand, approve, and recover from what the agent did?”

That is where agentic CX becomes real.

It is also where the journey map starts showing the parts of the experience that were already fragile.

What Role Does CX Play When Agents Shape the Journey?

CX has not lost the experience. AI agents make the customer journey faster, more distributed, and less dependent on a person doing the work manually. CX still has to see across the fragile points where agents, data sources, permission models, external summaries, internal policies, and customer trust meet.

I do not think CX has lost control of the experience.

I think the idea of control was always a little inflated.

Customers have always built their own version of the journey. They asked friends. They searched forums. They read reviews. They compared competitors. They called twice to see if two agents gave the same answer. They took screenshots because they did not fully trust the company to remember what it promised.

AI agents make that behavior faster, more powerful, and less dependent on the customer doing the labor personally.

The customer is still trying to get something done. The company is still responsible for whether the promise holds up. CX is still the discipline that should see across the seams.

But the seams are moving.

They are no longer limited to the screens we own or the calls we handle. They now sit between agents, data sources, permission models, external summaries, internal policies, and the customer’s final moment of trust.

If your experience only works when a patient human clicks through your screens, reads the content in the right order, catches the exception, understands the fine print, and calls the correct department when something breaks, you may not have a strong customer experience.

You may have a dependency on customer labor.

AI agents will expose that quickly.

So the better question is not, “How do we control the agent?”

It is:

What must be true for an agent to represent our company accurately, helpfully, and safely when the customer is not there doing all the work?

That answer will take you straight to the fragile parts of the experience.

Which is exactly where CX should be.

www.marklevy.co
Follow me on Linkedin

Leave a comment

Thanks for being here. I’ll see you next Tuesday at 8:15 am ET.

👉 If you enjoyed this newsletter and value this work, please consider forwarding it to your friends and colleagues or sharing it on social media. New to DCX? Sign Up.

Share Decoding Customer Experience

The bar for CX is no longer “say the customer matters.” It is proving your experience helps, performs, and holds up when the business has to make tradeoffs.

This week’s stories all point to the same shift: polished messaging is losing ground to measurable experience. Whether it is Spotify quietly raising switching costs through personalization, Etsy moving product discovery into conversation, or Daily Harvest turning support into a revenue lever, the pattern is clear.

There is also a sharper question running through all of this: are you making life easier for customers, or just building systems that are harder to leave, harder to question, and harder to measure honestly? That tension shows up everywhere right now—in loyalty, leadership alignment, social engagement, public accountability, and the growing role of AI in discovery.

Let’s dig in.

Subscribe now

This week’s must-read links:

• Spotify Shows the Cost Customers Don’t See

• Alignment Has to Survive the Tradeoff

• Customer-Centricity Needs Receipts

• Etsy Moves Shopping Search Into the Conversation

• DCX Stat of the Week: More Posting Does Not Mean More Connection

• DCX Case Study of the Week: Daily Harvest Turns CX Into a Revenue Channel

Spotify Shows the Cost Customers Don’t See

Spotify’s 20th anniversary listening timeline feels like a fun little gift. Your first song. Your most-played artists. A playlist built from years of listening history.

Nice idea. Also a pretty clean lesson in switching costs.

There’s no cancellation fee. No contract. No obvious penalty. But if you leave, you’re walking away from playlists, listening habits, trained recommendations, device connections, and the small bits of personal history that now live inside the product. That is the cost customers don’t see on a statement, but absolutely feel.

The useful pattern: Spotify has made the experience better by remembering you. That memory creates convenience, familiarity, and emotional pull. It also makes starting over somewhere else feel like work.

The CX lesson: Personalization earns loyalty when it helps customers feel understood. It creates dependence when customers stay mostly because rebuilding their experience sounds exhausting.

The operator test: Look at the places where your experience stores customer effort, history, preferences, and progress. Are you making the relationship more useful, or just harder to leave?

The CX To-Do: Audit where your personalization creates value versus quiet lock-in.

🔗 Go Deeper: The Monday Morning Economist

Alignment Has to Survive the Tradeoff

Rhona Bradshaw, former Chief Digital Officer at Comcast, makes a point CX leaders will recognize fast: leadership alignment is not the same as everyone nodding at the strategy meeting.

The actual test comes later, when budgets tighten, timelines slip, and every function starts protecting its own roadmap.

The useful part: Bradshaw argues that transformation work has to bring leadership on a journey grounded in outcomes. That means speaking in financial terms: margin, cost to serve, revenue growth, and the business benefit behind the North Star. CX work can’t live on soft language alone if the company is being run against hard numbers.

The operating shift: Her push is for tighter planning. Set the end state, track progress in shorter cycles, and do not move the goalposts every time the work gets uncomfortable. The path can change. The outcome should not.

The bottom line: Alignment gets real when leaders can agree on what matters most and what gets done first. That is where prioritization, budget, and ownership finally stop competing with each other.

The CX To-Do: Bring one CX initiative back to one financial outcome, one operating milestone, and one decision leaders must own.

Customer-Centricity Needs Receipts

The Consumer Test is trying to make customer-centricity harder to fake. It scores public companies based on what they actually publish: customer language, CX programs, feedback loops, customer metrics, and evidence of executive-level commitment.

The useful part: This pushes past the usual “customers are at the center of everything we do” language. If a company says customers matter but does not show how it listens, what it measures, or what changes because of feedback, the public record gets thin fast.

The catch: This is not a perfect measure of customer experience. It is a public evidence audit. A company can have strong CX work behind the scenes and still score low if it does not publish the proof.

The CX takeaway: Visibility creates pressure. When customer metrics, ownership, and improvement loops are public, leaders have less room to hide behind warm language and vague intent.

The CX To-Do: Look at your own public story. Could someone see how your company listens, learns, and acts?

Etsy Moves Shopping Search Into the Conversation

Etsy’s new ChatGPT app puts a real CX question on the table: what happens when the shopping journey starts before the customer ever reaches your site?

The beta lets shoppers tag @Etsy inside ChatGPT and search for products through natural language, like asking for a Mother’s Day gift under $100 for a mom who loves gardening. That matters because Etsy’s inventory is messy in a good way: millions of unique items, different seller descriptions, and products that do not fit neatly into standard catalog logic.

Search gets more human: Shoppers often don’t know the exact keyword. They know the situation, the recipient, the room, the mood, or the constraint.

The seller risk: Discovery now depends on how well AI understands listing data, item descriptions, context, and intent. That puts pressure on sellers and marketplaces to make product information clean, useful, and specific.

The CX takeaway: Conversational search moves the journey closer to the customer’s actual thought process. The test is whether it helps them find better matches faster, or just adds another layer between intent and action.

The CX To-Do: Audit whether your product content answers how customers actually describe what they need.

DCX Stat of the Week:

More posting does not mean more connection

Sprinklr found that 78% of retail brands fail to drive real engagement, even though retail posts more than any other sector. The retail category’s median Social Index score is just 1.9 out of 10, showing that high activity does not automatically create customer connection.

Takeaway:
Your customer does not care how active your brand calendar is. They care whether you show up in the moments that actually help them decide, trust, buy, ask, recover, or come back.

The CX problem is familiar: teams confuse volume with presence. More posts. More campaigns. More channel activity. But if the feed turns into noise, service spillover, or generic promotion, customers scroll past it or use it as another place to complain.

The better question: where does social actually shape the journey?

Source: The 2026 Sprinklr Social Index Report

🔗 MORE STATS: Daily Stats on Substack Notes

DCX Case Study of the Week: Daily Harvest Turns CX Into a Revenue Channel

CX Challenge: As Daily Harvest expanded from DTC subscriptions into retail channels like Target, Kroger, Costco, and Wegmans, its homegrown support system couldn’t easily centralize customer data, scale inquiries, or connect service interactions to revenue.

Action Taken: Daily Harvest kept Kustomer during its migration to Shopify and Klaviyo, with Create CX helping configure workflows. Agents gained one view of order history, upcoming subscription deliveries, communication history, VIP status, and cancellation signals. The team also added self-service FAQs and AI voice automation through Flip.

Result: 2x agent productivity, 10% increase in weekly revenue, CSAT maintained in the high 80s to mid-90s, and 31% of inbound calls now involve automation. Agents can proactively save subscriptions, upsell, and offer targeted discounts.

Lesson for CX Pros: Connect support tools to commerce data. When agents can see customer intent, timing, and value, service becomes retention and revenue—not just issue resolution.

Further Reading: Kustomer Daily Harvest case study.

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

See you next week.