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

Join CX, support, and operations practitioners to compare how AI is changing the way teams understand service demand, improve workflows, and reduce friction at the next DCX AI Roundtable on June 23

In this issue:

→ Service AI asks for more authority
→ Quiq shows the guardrail layer in practice
→ Agent pilots outrun operating design
→ Ownership matters more than automation
→ Radar checks assistants, infrastructure, and trust traps

🔍 DEEP DIVE

The Bot Wants the Keys Now

Salesforce’s planned $3.6 billion acquisition of Fin is a big deal because it says something useful about the next shape of customer service AI.

Here’s the part I’d watch: Fin isn’t just a better chat window. It works across live chat, email, WhatsApp, text, phone, and Slack. Its product page says the agent averages a 76% resolution rate across 12,000+ customers and handles about 2 million weekly resolutions.

That puts the support agent closer to where customer history, policy, account status, and service actions already live.

The service bot wants a seat inside the operating layer.

Once the agent can see more, recommend more, and eventually do more, the old chatbot scorecard gets thin fast. A deflected contact can look good on a dashboard and still leave a customer with the wrong answer or no clear path back to a human.

The deal matters. The bigger operating question is whether your company knows who owns the customer outcome when an AI agent becomes part of the CRM workflow.

Bottom Line: The question is shifting from “can it answer?” to “can we trust what it can see, say, change, and escalate inside the customer system?”

🧭 OPERATOR PLAYBOOK

Map the Blast Radius Before Volume Goes Up

Pick one high-volume journey where the agent is expected to resolve real work beyond FAQs.

Audit the flow for four things:

1. What customer data the agent can access.

2. What actions the agent can trigger or recommend.

3. What conditions force a human handoff.

4. What recovery path exists when the answer fails.

Then test whether your measurement separates a deflected contact from a resolved customer problem. Those aren’t always the same thing.

Ask your team: Where does the agent have enough authority to help, but not enough guardrails to be trusted?

📊 MARKET REALITY CHECK

Demos Don’t Survive Real Customers

Three quarters of enterprise leaders say they’re adopting agentic AI. The problem is that adoption doesn’t mean the work is operational yet.

Most companies are still closer to “we have agent-like experiences” than “we have governed agents running inside redesigned workflows.”

For CX, this is the gap that matters. An agent demo can look impressive when the task is narrow, the data is clean, and the edge cases stay polite. Customer operations bring missing context, weird policy exceptions, angry customers, bad records, partial refunds, identity questions, and handoffs nobody designed.

The hard work isn’t launching the agent. It’s redesigning the workflow around what the agent can do, what it has to explain, what it must escalate, and how the business catches the miss.

Why it matters: The Salesforce/Fin story gets a lot more practical when you put it next to this gap. Buying or deploying the agent is the visible move. Making it operational is the customer experience work.

🧰 TOOL WORTH KNOWING

Quiq

What it does: Quiq is an enterprise agentic AI platform for customer experience. It includes AI agents, AI assistants, voice agents, AI workflows, contact center tools, reporting, integrations, security, and AI Studio for configuring agents.

CX use case: Useful for teams that want agents to resolve customer questions across channels, assist human agents, connect into existing systems, run defined workflows, and show how the agent was guided, tested, scored, and constrained before it reaches customers.

Why it matters now: The useful idea here is less “another AI support agent” and more “show me the guardrails.” Quiq’s product pages emphasize setup, agent guidance, guardrails, simulations, step-by-step visibility, performance scoring, decision logs, claim verification, and compliance.

Operator read: A service agent gets more credible when the team can see how it was trained, what it is allowed to do, when it should escalate, and how its decisions are reviewed after the fact.

📡 90-SECOND CX RADAR

Siri Meets the Messy Middle

A first-day test of Siri AI on the Mac found a more capable assistant, but also clear limits across apps, files, screenshots, and context. That’s probably the realistic near-term state for a lot of customer-facing AI: useful enough to try, uneven enough to manage carefully.

Why it matters: Customers will judge AI less by model benchmarks and more by whether it works inside the actual environment where the task happens.

The Agent Still Needs Pipes

As agentic AI moves into real workflows, the infrastructure problem gets less optional: unified systems, visibility, policy, security, and management across the stack. That’s the unglamorous part customers will feel when it breaks.

Why it matters: Customer-facing AI doesn’t only fail in the model. It fails when the systems around it can’t see, route, secure, or recover the work.

The Fake AI Front Door

Microsoft Threat Intelligence says attackers are impersonating AI platforms such as ChatGPT, Copilot, Claude, and DeepSeek to drive phishing, malvertising, credential theft, and malware. The trust problem isn’t only what your AI says. It’s also what customers believe they’re clicking before they ever reach you.

Why it matters: As customers search for AI tools, brands may inherit confusion, fraud reports, and support demand created before the customer even reaches an official channel.

✅ YOUR MOVE

Here’s the practical shift: the AI service agent is leaving the edge of support and asking for customer-system authority. The Salesforce/Fin deal makes that harder to dismiss.

The operating question is yours: what happens when the agent can see more, do more, and influence more of the customer outcome?

This week, pick one AI-assisted service journey and map authority before features.

What can the agent access?

What can it change?

What has to escalate?

What does the customer experience when it fails?

Then put one named owner next to each failure path.

If the AI agent can touch the customer outcome, the business needs to own the recovery path.

Until tomorrow,

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

Join CX, support, and operations practitioners to compare how AI is changing the way teams understand service demand, improve workflows, and reduce friction at the next DCX AI Roundtable on June 23

In this issue:

→ Customer prompts become product promises
→ Data trust decides AI adoption
→ AI creation needs real recovery
→ Sponsored answers need clear standards
→ Utility bots raise the stakes

🔎 DEEP DIVE

Shopping Is Starting to Make the Product

Amazon is pushing Alexa for Shopping into custom merchandise. Shoppers can describe a design, generate artwork, tweak it, and order it on products like shirts, hoodies, and water bottles.

That sounds like a novelty until you follow the journey. The customer is no longer only searching, comparing, or checking out. They are creating the product, sending it into production, and expecting the normal retail machine to handle the rest.

The Verge noted that Amazon still applies content rules around trademarks and copyright. Good. That is where the CX work starts. Once AI can turn a prompt into a purchasable item, the experience depends on content policy, preview accuracy, production quality, delivery promises, returns, refunds, and customer support.

The first customer pain will not be “AI made a shirt.” It will be “AI made the wrong shirt, the text looks weird, the design got rejected, the gift is late, and now I need help.” Very glamorous. Very real.

Bottom Line: AI commerce gets serious when the assistant can create, order, and ship. At that point, CX owns the promise after the prompt.

OPERATOR PLAYBOOK

Give the Agent a Smaller Job

Pick one service or buying journey where AI is already being tested, promised, or quietly used.

Audit every AI-assisted handoff for four things:

1. The exact customer intent the agent is allowed to handle.

2. The policy, price, eligibility, or product rule it must follow.

3. The point where a human must review, approve, or recover.

4. The customer signal that proves the experience improved.

Then test whether a new employee could read the same rules and reach the same decision.

Ask your team: Where are we asking AI to be confident because our process is vague?

Signal: If the agent needs tribal knowledge to work, the journey is not ready for automation.

📈 MARKET REALITY CHECK

Data Stewardship Is the AI Trust Test

64% of customers believe companies are reckless with customer data.

That is the trust problem under every shiny agent demo. Customers may want personalization, easier service, and smarter recommendations, but they are also watching how much data companies collect, how clearly they explain it, and whether the value exchange feels fair.

The same Salesforce report says only 42% of customers trust businesses to use AI ethically, down from 58% in 2023. That makes AI adoption a trust balance-sheet question, not a product capability question alone.

Why it matters: CX leaders cannot treat data stewardship as a compliance side issue. If customers think your data practices are careless, trust, loyalty, and AI adoption all get harder.

Get the Free Psychology of CX 101 Read-Along Workbook

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🧰 TOOL WORTH KNOWING

Elastic Path Product Experience Manager

What it does: Elastic Path’s Product Experience Manager helps teams publish structured product, pricing, hierarchy, and bundle data for AI-driven discovery and agentic commerce.

CX use case: Useful for B2B, distributor, and complex-commerce teams where the buying journey depends on contract pricing, account-specific catalogs, compatibility rules, bundles, quoting, and reordering.

Worth watching because: Agentic commerce is only as smart as the product truth it can access. If AI shoppers, search tools, sales reps, and customers are all pulling from different product logic, the experience gets expensive fast.

Bottom line: This is not the shiny bot layer. It is the plumbing that keeps an AI-assisted buying journey from turning into a very polite guessing machine.

⚡ 90-SECOND RADAR

HGS says service is moving out of the cost-center box

HGS CEO Venkatesh Korla told The Economic Times that AI is shifting business process work toward customer experience and value creation. The useful tension: personalization gets better when industry knowledge, workflows, and human judgment travel with the model.

Why it matters: Service AI becomes more credible when it improves the customer outcome and the economics beyond handle time.

Walmart tests ads inside AI shopping conversations

Business Insider reported that Walmart is testing sponsored listings inside Sparky, its AI shopping assistant, with fewer ads than a standard search-results page. The interesting part is the restraint. Retail media can help the customer, or it can turn the assistant into another cluttered shelf.

Why it matters: If AI becomes the shopping front door, CX leaders need standards for when a recommendation is helpful, sponsored, biased, or just noise with better grammar.

Sierra plugs utility service agents into Kraken

Axios reported that Sierra is partnering with Kraken Technologies to bring AI customer support agents into utility customer systems. The agent gets the conversation. Kraken supplies the account, meter, rate, and service context behind it.

Why it matters: Regulated service AI gets serious when the agent can resolve issues instead of stopping at summaries. That makes data access, action limits, and recovery ownership the whole ballgame.

🧭 YOUR MOVE

This issue is about what happens after the AI moment looks impressive.

The prompt, recommendation, or agent answer is only the front door. The customer still judges the promise by the data used, the rule applied, the product shipped, the ad shown, and the recovery path when something goes sideways.

Pick one journey where AI is being asked to create, recommend, route, sell, renew, or resolve. Then write down the decision rule in plain English. If that rule takes three departments, five exceptions, and one person named Linda to interpret it, you found the work.

Start with the customer promise the AI is making. Then inspect the data, policy, fulfillment, approval, and recovery work required to keep it.

The best AI experience may be the one where the customer never has to learn how many rules had to behave.

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:

→ 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

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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.

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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.

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.

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.

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.

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

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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 daily signal on AI and CX — minus the hype.

In this issue:

→ Airbnb widens the trip promise
→ Customers judge resolution, not channel preference
→ Sibbi connects commerce and post-purchase support
→ Volkswagen doubles down on AI-enabled care
→ Collections becomes a customer relationship test

🔎 Deep dive

Airbnb is making the full trip its problem

Airbnb’s 2026 summer release pushes the company further into the trip itself. It added grocery delivery, airport pickups, luggage storage, car rentals, boutique and independent hotels, shared itinerary tools, AI review highlights, AI comparison, and AI-powered customer support that knows trip details.

That is a bigger promise than “book a stay.”

This will show up first in the messy moments: the early arrival, the delayed flight, the missing groceries, the luggage gap before check-in, the hotel expectation mismatch, the support chat when the customer just wants the trip fixed. The customer will not care which partner touched the experience. They will remember who made the journey feel easy, and who made them chase the answer.

For executives, the tradeoff is simple enough to make uncomfortable. Airbnb gets more journey data, more loyalty surface area, and more chances to become the trip operating layer. It also inherits more ways to disappoint people.

OPERATOR PLAYBOOK

Audit the handoffs customers think you own

AI expands the promise surface. That sounds useful until a partner, policy, or bot gets between the customer and the fix.

Audit every AI-assisted customer journey or partner-supported flow for four things:

1. The moment the customer believes your brand owns the outcome

2. The data the AI needs before it gives advice or takes action

3. The partner failure most likely to create the first complaint

4. The team with authority to recover the moment

Then test whether the customer can resolve the issue without repeating the story across channels, partners, or bots.

Ask your team: Which handoff would make us look careless even if another company caused the problem?

📈 Market Reality Check

Customers will use automation when it finishes the job

Verint’s 2026 consumer research is a useful check on the lazy “customers hate bots” narrative. Yes, 61% of consumers still prefer a human agent. But 69% say they would switch to automated service if it fully resolved their issue.

That changes the operating question. You are not choosing between bot and human as a matter of preference. You are choosing which path can actually finish the job. Speed helps. Deflection hurts. The customer knows the difference.

🧰 Tool Worth Knowing

Sibbi by Marqo

What it does: Sibbi is a commerce agent built to handle product discovery, visual search, recommendations, cart actions, order tracking, returns, and exchanges in one shopper conversation.

CX use case: This is for retail teams trying to connect the pre-purchase and post-purchase journey. A shopper can describe what they want, upload an image, refine the result, add to cart, check order status, and start a return without being pushed into a separate support flow.

Worth watching because: Most commerce AI still treats conversion and service as separate problems. Sibbi is aimed at the gap after checkout, where the customer still expects the brand to remember what they wanted, what they bought, and what went wrong.

Bottom line: Worth watching if your ecommerce journey is split across search, recommendations, order tracking, returns, and support tickets. The risk is familiar: it only works if inventory, policies, customer history, and recovery rules are clean enough to trust.

More: Marqo

⚡ 90-Second CX Radar

Volkswagen Group UK extends AI-enabled customer care across six brands

Volkswagen Group UK extended its customer care partnership for another seven years, covering customer care, case management, connected vehicle support, and AI-enabled omnichannel contact center work across Audi, Škoda, SEAT, CUPRA, Volkswagen Passenger Cars, and Volkswagen Commercial Vehicles. The CX signal: automotive support is becoming lifecycle management, especially as connected vehicles turn ownership into a longer service relationship.

Expedia brings AI deeper into travel comparison and trip planning

Expedia Group announced AI Property Compare, Property Expert, Activity Planner, and natural language search for Vrbo. This sits right in the decision anxiety part of the journey, where customers are trying to compare options, avoid regret, and make a trip feel less risky before they spend.

Collections gets an Agentforce layer

Salesforce’s Summer ’26 release includes Collections with Agentforce, using predictive AI to score invoices and recommend outreach cadence, escalation timing, fees, service holds, and tier-based treatment. Collections is one of those journeys where a company’s relationship language either holds up or collapses. A clumsy dunning flow can turn a payment issue into a trust issue fast.

🧭 Your Move

Pick one journey next week where AI has widened the promise your brand makes. Booking, checkout, appointment scheduling, delivery, support after purchase. Map the promise before you map the tech.

Then find the handoff nobody wants to own. That is where the customer will judge the whole thing.

AI does not reduce accountability. It moves it closer to the moment of disappointment.

Until Tuesday (Have a great Holiday),

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:

→ Google makes the cart proactive
→ AI support gets a wait-time reality check
→ Service agents move from answers to workflows
→ Search agents start calling businesses
→ Checkout data gets stress-tested

🔎 Deep dive

Google wants the cart to do more of the thinking

Google introduced Universal Cart at I/O, and the CX shift is easy to miss if you only look at the shopping feature. The cart can now work across merchants and across Search, Gemini, YouTube, and Gmail. It can watch for price drops, flag stock changes, read payment perks, and spot product incompatibilities before the customer checks out.

That means the buying journey is moving into an AI-managed layer. The customer may get less effort. That’s useful. But you inherit a new operating standard. Your product data, availability, offers, shipping promises, payment rules, and return paths have to be clean enough for an agent to act on them without guessing.

This will show up first in discovery and checkout. A messy product feed used to hurt conversion quietly. Now it can trigger the wrong purchase, a failed checkout, or a support issue your team has to clean up after the fact. Fun little mess.

OPERATOR PLAYBOOK

Pressure-test the AI-assisted checkout path

This is where CX, product, ecommerce, finance, legal, and care need to get in the same room.

Not for a brainstorm. For a failure drill.

Audit every agentic buying flow for four things:

1. Product data an AI agent can understand without filling gaps.

2. Inventory, price, delivery, promo, and substitution logic.

3. Payment, tax, loyalty, financing, and return handoffs.

4. Recovery paths when the wrong item gets purchased.

Then test whether a customer can move from discovery to purchase to support without losing context.

Ask your team: Where does the customer regain control if the agent makes the wrong call?

📈 Market Reality Check

Customers prefer humans until waiting costs them

Lorikeet surveyed 1,083 consumers across the US and Europe and found the headline tension every CX team should care about: 68% say they prefer human support, but AI preference jumps from 7% to 44% when the choice becomes “wait 15 minutes for a human or get help from AI right now.”

That does not mean customers suddenly love bots. It means the stated preference for humans gets weaker when the human path is slow, repetitive, or inconvenient. Same old story, sharper numbers. If your queue is long and your bot is useless, customers get the worst version of both channels. If your AI can resolve the issue cleanly, speed becomes part of trust.

🧰 Tool Worth Knowing

Zendesk Agent Builder

What it does: Lets service teams build, test, deploy, and tune custom AI agents tied to their own policies, workflows, data, and business logic.

CX use case: Useful for teams that want AI agents to do more than answer FAQs. Think order updates, account changes, refund paths, knowledge retrieval, issue routing, and governed handoffs across messaging, email, voice, and external AI surfaces.

Worth watching because: Zendesk is pushing toward a full “service workforce” model, with AI agents, copilot tools, quality scoring, workflow connectors, and MCP support sitting inside one operating layer. That matters because most AI service failures are not answer failures. They’re context, permission, workflow, and handoff failures.

Bottom line: This is worth watching if your support operation is past the chatbot phase and now needs AI agents that can act inside real service workflows without creating cleanup work for humans.

⚡ 90-Second CX Radar

Google Search agents start calling businesses for customers

Google says Search will expand agentic booking and, for select categories like home repair, beauty, and pet care, let users ask Google to call businesses on their behalf. That sounds convenient until your frontline team starts receiving AI-mediated calls with partial context and very human expectations.

CAVA puts AI behind the bowl line

CAVA is building CAVA Core and CAVA Current to connect data, operations, staffing, inventory, and personalization. The CX point is not “restaurants are using AI.” It is that consistency, wait times, prep accuracy, and personalization are starting to depend on systems customers never see.

Consumers are starting to stop noticing AI

PYMNTS says AI adoption is starting to look less like a destination and more like a layer inside everyday tasks: shopping research, service interactions, writing, planning, and trip support. That is the customer expectation shift. Once AI feels normal somewhere else, your static search box, clunky FAQ, or dead-end chatbot feels older faster.

🧭 Your Move

If your AI roadmap still starts with “where can we automate?” you’re probably aiming too late in the journey.

The better question this week: where will an AI agent depend on our data, policies, inventory, content, workflow rules, or recovery paths to make a customer-facing decision?

That applies to the cart. It applies to support. It applies anywhere the customer expects the company to know what just happened.

Audit the journey for the agent now, before the customer teaches you where it breaks.

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 service agents are now mainstream.
→ CSAT is beating handle time.
→ Failed AI hits support and brand.
→ PolyAI opens voice agent building.
→ Google pushes agents closer to purchase.

🔎 Deep dive

The service agent story just got more interesting

Salesforce’s new service research says AI agents are no longer hanging around in pilot purgatory. Sixty-six percent of customer service organizations now use them, and 70% of those teams say they see measurable value within 60 days.

That part is useful. But this is the part I’d pay attention to: the top improved KPI was customer satisfaction. Not rep productivity. Not average handle time. Not first response time.

That changes the conversation.

If AI agents are now moving CSAT, they’re no longer just a cost play. They’re part of the customer experience. They’ll show up first in proactive outreach, routing, case resolution, product recommendations, and those messy handoff moments where customers either feel helped or feel trapped.

For executives, the implication is pretty simple: AI agents need to be managed like service infrastructure, not shiny automation. That means better data, clearer recovery paths, smarter workforce planning, and actual ownership when the agent gets it wrong.

OPERATOR PLAYBOOK

Design the miss, not just the magic trick

The demo is always the easy part.

The customer does not care that the AI handled 80% of the flow beautifully if the last 20% leaves them confused, stuck, or repeating their story to a human who has no context.

Audit every AI service flow for four things:

1. Does the agent know when the customer intent is unclear?

2. Is the knowledge base current enough for policy, billing, account, and exception questions?

3. What triggers escalation when confidence drops or frustration rises?

4. Does the recovery language explain the next step without making the customer feel like they’re training your system?

Then test whether a frustrated customer can get to a human with context intact.

Ask your team: When the AI gets it wrong, where does the customer feel the cost first?

Signal: The best AI service teams will be the ones that make failure boring, visible, and recoverable.

📈 Market Reality Check

AI failure does not stay in the contact center

Sinch’s early findings put a harder edge on this. When an AI agent fails, enterprises say the cost lands in the support queue 35% of the time and in brand perception 34% of the time.

That second number is the one I’d circle.

Support volume is easy to see. Brand damage is quieter. It shows up when customers double-check every answer. When they abandon self-service. When they post the screenshot. When the next interaction starts with less patience than the last one.

The customer may still get helped.

But now they trust you a little less.

🧰 Tool Worth Knowing

PolyAI Agentic Dialog Platform

What it does: PolyAI opened its Agentic Dialog Platform to more builders. It includes a natural language Agent Builder, an Agent Development Kit, and shared testing environments for voice and conversational AI agents.

CX use case: Teams can build and test voice agents for billing, bookings, order management, authentication, troubleshooting, and routing before customers ever hear them.

Worth watching because: Voice is where weak AI design gets exposed fast. Customers interrupt. They ramble. They change their mind halfway through the sentence. They also get annoyed quickly when a system sounds confident but cannot actually help.

Bottom line: Worth a look if you have meaningful call volume. Just don’t treat voice like chat with a microphone. The customer’s patience works differently there.

⚡ 90-Second CX Radar

Google turns the cart into a shopping agent

Google’s Universal Cart will work across Search, Gemini, YouTube, and Gmail. It can handle price alerts, stock updates, compatibility checks, merchant checkout options, and Google Pay support. For retailers, that is useful and slightly uncomfortable. The brand may still own the sale, but more of the shopping decision is moving into Google’s layer.

Gemini Spark brings agents into the daily workflow

Google is positioning Gemini Spark as a personal AI agent that can manage tasks across connected apps. That matters because customer expectations do not stay inside one product. Once people get used to agents that remember, monitor, and act, static self-service starts to feel dated pretty quickly.

Alibaba service experiment gives AI agents a gut check

A new field experiment on Alibaba’s Taobao platform found agentic AI reduced average chat duration but lowered ratings for AI-eligible chats. That is the useful warning. Faster is not always better if the customer leaves irritated. Escalation design matters, especially when the failure feels personal.

🧭 Your Move

AI service agents are starting to prove they can improve the customer experience.

Good.

Now comes the less glamorous work.

Pick one live or planned AI service flow this week. Map the first moment where the customer might get confused. Then map the handoff, the recovery owner, the message the customer sees, and the metric that tells you whether trust was repaired.

If you cannot name those pieces yet, the flow is not ready. It may work in a demo. Customers are less polite.

AI agents earn trust in the recovery path. The demo just gets the applause.

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:

→ Consent becomes the AI trust test
→ Customers still want judgment at risk
→ Readiness moves before go-live
→ Payment failures get flagged earlier
→ Produce quality gets machine vision

🔎 Deep dive

Consent is no longer a policy form

A Melbourne psychiatrist reportedly refused new patients unless they agreed to AI note-taking during sessions. The registration form told patients who objected to arrange for their doctor to refer them somewhere else. The Guardian also reported that two in five Australian GPs are now using AI scribes, with uptake doubling over the past 12 months.

That is where the customer feels the cost.

In mental health, the patient is not making a normal software preference choice. They are trying to get care. If declining AI means restarting a referral, waiting again, or walking away, the choice starts to feel like a toll booth. The business may see efficiency. The customer may feel pressure, exposure, and loss of control.

This will first appear in intake, consent language, privacy disclosures, provider scripts, and escalation paths. The executive implication is blunt: any AI tool that records, summarizes, routes, scores, or stores customer information is now part of the experience. Treating it like an internal productivity tool is how trust quietly leaks out of the journey.

OPERATOR PLAYBOOK

Pressure-test the opt-out

The issue is not whether the AI works. That only gets you to the starting line.

Audit every AI-assisted consent or disclosure flow for four things:

1. Where the customer first learns AI is involved.

2. Whether the choice is plain, visible, and timed correctly.

3. What happens if the customer says no.

4. Who owns recovery when the customer feels pushed.

Then test whether a reasonable customer could understand the choice without needing legal patience or CX-level stubbornness.

Ask your team: Where are we asking customers to accept AI because it makes our process easier?

📈 Market Reality Check

Adoption is moving faster than judgment

LegalZoom’s survey of 1,000 aspiring founders and business owners gives this issue a wider frame. Among AI users, 42% use AI daily, 36% use it weekly, and 77% use it at least weekly.

That is the easy part to celebrate.

The useful part is where people draw the line. Respondents said they would avoid AI for high-risk legal or financial decisions, customer-impacting decisions, employee-impacting decisions, and situations involving regulatory penalties. People are not rejecting AI. They are refusing to hand over judgment when the consequence lands on a person.

That should shape how CX teams govern AI rollouts.

🧰 Tool Worth Knowing

Smart Role

What it does: Smart Role is a pre-production readiness layer for customer support teams. It helps companies build role-play scenarios, virtual customers, scorecards, and readiness gates so agents can practice before they touch live customers.

CX use case: Use it before new hire go-live, BPO transitions, seasonal staffing spikes, product launches, policy changes, and regulated support flows where “completed training” is weak evidence.

Worth watching because: Smart Role is solving the right problem: the gap between training completion and conversation readiness. Its readiness model is simple and useful: pull from real tickets, past QA misses, and SOPs, turn those into simulations, then certify readiness before go-live. Stop using customers as the final QA filter.

Bottom line: The scorecard matters. If the scenarios reflect real customer pressure, policy complexity, and recovery moments, this can prevent damage before it reaches the floor. If the scenarios are soft, you’ve automated a nicer version of the same hopeful sign-off.

⚡ 90-Second CX Radar

Mastercard wants to catch payment failure before the customer feels it

Mastercard’s A2A Payment Intelligence predicts whether an account-to-account payment is likely to clear before it is initiated. Failed payments rarely feel like payment operations to customers. They feel like fees, service interruption, confusion, and one more reason to call.

Albertsons is putting AI upstream of the produce aisle

Albertsons launched an AI-powered Intelligent Quality Control tool for produce inspection. Customers do not care that the quality miss happened in the supply chain. They care that the berries went bad before the receipt stopped feeling new.

SmartBear flags the AI quality debt problem

SmartBear says 70% of software experts are concerned application quality is already suffering as AI speeds up development, and 60% have experienced quality issues because development moved faster than testing could validate. Faster shipping is not a CX win when the API, workflow, or edge case breaks in front of the customer.

🧭 Your Move

Pick one AI-assisted moment this week and look at it from the customer’s side of the glass.

Can they see AI is involved? Can they decline it? Can a frontline employee explain what happens next without sounding like they’re reading from a policy bunker? And if the customer says no, does the journey still work?

That last question is the one to sit with.

The customer does not experience your AI roadmap. They experience the choices your AI roadmap leaves them.

Until tomorrow,

Mark

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