Not burying the disclosure or softening it with a vague persona name and hoping no one asks. Actually telling customers upfront that they’re talking to an AI and giving them an immediate way out if they’d prefer a human.

The result, according to practitioners who attended our latest Virtual Executive Roundtable who’ve deployed this at scale? The vast majority of customers stay and engage.

In an era of debate about whether AI can match human service quality, organizations that lead with full transparency find that only a small fraction of customers opt out. And those who stay report customer satisfaction (CSAT) scores that, while not yet at parity with human agents, are closing the gap fast. Some deployments are seeing AI-handled interactions score 93% compared to 98% for humans.

What Actually Frustrates Customers

Most organizations approach AI deployment with a reasonable-sounding concern: if we tell customers they’re talking to a bot, they’ll disengage, they’ll demand a human, or they’ll leave frustrated. This instinct is understandable.

A Gartner survey found that 61% of consumers want the ability to toggle AI interactions on or off, reinforcing that customers don’t reject AI; they just want agency over it.

What actually frustrates customers isn’t AI; it’s deception. It’s filling out a form, expecting a live agent, only to discover mid-conversation that you’ve been talking to a bot the whole time. It’s being transferred without context, left waiting, or handed off to a human who has no idea what was already discussed.

Transparency short-circuits all of that. When customers know from the start what they’re dealing with, and know they can leave if they want to, they calibrate their expectations accordingly. And when the AI delivers, they’re satisfied.

What Transparency Looks Like

In practice, the organizations seeing these results aren’t just slapping a disclaimer at the bottom of a chat window.

They’re doing a few things deliberately:

• A named AI persona. Not “our virtual assistant,” an actual character with a name that sets clear expectations. This gives customers something concrete to interact with, rather than an ambiguous blob of automation.
• An immediate opt-out. From the very first message, without a maze of options or having to express frustration to trigger a transfer. Just a clear path to a human, available at any point.
• Continuity through handoff. When a customer does choose to transfer, the human agent receives the full conversation history. Nothing is repeated, nothing is lost. The handoff feels seamless rather than punitive.

Salesforce’s 7th edition State of the Connected Customer report, surveying 16,500+ individuals, found that trust in businesses to use AI ethically dropped from 58% in 2023 to just 42% in 2024, a 16-point decline in a single year.

From the same Salesforce report: 89% of customers say it’s important to know when they’re communicating with AI rather than a human, yet most brands still don’t consistently disclose it.

The low opt-out rates practitioners are seeing aren’t a sign that customers are trapped. They’re a sign that the experience is good enough that customers don’t feel the need to leave.

Some Interactions Shouldn’t Be Handled by AI

These findings challenge something deeper than an operational assumption. They challenge a C-suite narrative that has framed AI deployment as an inherently delicate act of customer management, to be introduced gradually, carefully disguised, and constantly hedged.

That framing has it backward. Customers are not fragile or universally hostile to AI. What they are is perceptive. They know when they’re being misled, feel the friction when a bot pretends to be something it isn’t, and they reward honesty with engagement.

There’s also an important exception worth noting: some interactions should never be handled by AI, full stop. Pet loss. Genuine financial hardship. Emotional escalations. These are human moments, and the organizations getting this right have been explicit about drawing that line. Transparency isn’t just about telling customers what they’re talking to; it’s about knowing, as an organization, where your AI should never go.

Are You Solving for the Wrong Problem?

If your organization is still in the pilot phase, debating how visible to make your AI deployment and nervous about customer backlash, the data suggests you may be solving the wrong problem. The question isn’t how to introduce AI without customers noticing. The question is how to build an AI experience good enough that customers don’t mind.

Transparency is the foundation of that. Not because it’s the right thing to do (though it is), but because it’s what works. It sets accurate expectations, builds trust, and creates the conditions under which customers will give your AI a fair chance — and, as practitioners are finding, most of them will decide it earns one.

The brands that will lead in AI-powered CX over the next few years won’t be the ones that hid their AI the longest. They’ll be the ones who trusted their customers enough to be honest and built something worth being honest about.

These are exactly the kinds of candid, leader-level conversations happening inside our Virtual Executive Roundtables. If you want to hear how your peers are thinking through the opportunities and tradeoffs ahead, and contribute your own perspective to the discussion, join us at a future Virtual Executive Roundtable. It is a chance to step into a smaller, high-value conversation with fellow CX leaders navigating many of the same questions in real time.

Join our mailing list to receive event invites.

The post Why Transparency is a Good AI CX Strategy appeared first on Execs In The Know.

Across industries, CX leaders are now facing a familiar tension: strong momentum in adoption, uneven results in execution. AI is embedded in more contact center workflows than ever before, yet achieving consistent, enterprise-wide impact remains difficult. The technology is advancing faster than most operating models can absorb it.

According to our 2026 CX Leaders Trends & Insights: Corporate Edition Report, two-thirds of contact centers now operate with some form of AI in their workflows, more than double the rate in 2022. But only 7% of CX leaders describe themselves as very effectively meeting their AI objectives. Nearly half say they are only somewhat effective.

That gap tells its own story. This is a question of translation, moving from isolated use cases to reliable, repeatable performance inside complex service environments.

Turning AI into Operational Impact

The reality is that AI in CX is no longer a pilot conversation. It is an operational one. And in operational terms, success depends less on what the technology can do in theory and more on how it behaves under real-world constraints: legacy systems, fragmented data, shifting customer expectations, and frontline teams who must integrate new tools without losing service quality.

The same research underscores the tension:

• 67% of contact centers now use AI (up from 31% in 2022)
• 49% cite customer frustration with AI quality as a growing concern
• AI virtual assistants still resolve only ~60% of interactions, compared with 87% for phone-based service

These are not signs that AI is failing. They are signals that scale is harder than adoption. What separates progress from stagnation is rarely the model or platform itself. It is the clarity of the use case, the discipline of implementation, and the willingness to confront what happens after deployment when customers begin to interact with the system in unpredictable ways.

In many organizations, early AI wins are real but isolated. A reduction in handle time here, a deflection improvement there, or a successful pilot that performs well under controlled conditions. The challenge emerges when leaders try to extend those results across channels, geographies, and volumes without losing performance and customer trust.

That is where the conversation is shifting now: from “what can AI do?” to “what does it take to make it reliable and scalable?”

From AI Possibility to AI Proof

This is precisely the value of examining AI through structured use cases, not as abstract innovation stories, but as operational decisions. What problem was being solved? What trade-offs were made in design? What changed in the workflow? And what happened when the system met real customer behavior?

Our upcoming event, From Investment to Impact: Rapid-Fire AI Use Cases for Customer Experience, is built around that premise. It brings together real implementations from across CX environments, each grounded in a defined business challenge, a specific execution approach, and measurable outcomes.

Facilitated by Chad McDaniel, President of Execs In The Know, the format is intentionally direct. Solution providers present concrete use cases, and CX leaders respond in real time with questions grounded in their own operational realities: what works, where it works, and why.

For leaders navigating AI strategy today, the value is not in another overview of what is possible. It is in seeing what is already working under real constraints, and understanding what it actually takes to replicate it.

Join us and learn how leading organizations are applying AI in real CX environments, the challenges they’ve encountered, and the results they’re achieving.

This session is exclusively for end-user corporate CX leaders responsible for customer experience strategy, operations, and technology decisions.

If you are a vendor interested in participating as a solution provider, please contact Scott Moberly at Scott@execsintheknow.com.

The post What AI Use Cases Are Really Delivering in CX Today appeared first on Execs In The Know.

Consumers Still Favor Live Agents, but the Gap Is Closing

When consumers were asked to compare their interactions with live agents versus AI-powered agents, the preference for humans was clear: 68% rated live agent interactions as “Better” or “Much Better.” By contrast, only 53% said the same about AI-powered agents when compared to their human counterparts.

That’s a meaningful gap. But here’s what’s equally telling: 71% of consumers said they feel neutral or better about AI as their first point of contact when seeking customer care help. Consumer openness to AI isn’t just growing; it’s accelerating.

So what’s driving that tension between preference and openness? It comes down to expectations.

Performance Over Personality

One of the most striking findings in the research is what consumers actually want from AI. While live agents are expected to bring warmth, empathy, and a human touch, AI is held to a different standard entirely.

Consumers ranked “Being Highly Knowledgeable and Accurate” (14%) and “Being Efficient/Quick” (13%) as the top areas where AI-powered agents perform best. Empathy and fairness? Those lagged significantly behind.

The message is clear: consumers aren’t asking AI to act human. They’re asking it to work.

Complexity Changes Everything

Where consumers draw the line most sharply is around issue complexity. For simple tasks (paying a bill, updating account information), the preference split between live and AI agents is nearly even (52% vs. 48%). But when the issue gets complicated?

58% of consumers prefer a live agent for complex issues, and a striking 81% say access to a live agent is “Extremely Important” or “Somewhat Important” in those moments.

This isn’t just a preference; it’s a signal. When the stakes are higher, consumers want a human who can exercise judgment, show empathy, and navigate nuance. AI’s current capabilities in problem-solving simply haven’t earned that level of trust yet.

What Live Agents Need to Get Better At

The research doesn’t let live agents off the hook either. When consumers were asked which soft skills are most critical for human agents to provide excellent care, “Effective Problem-Solving” (16%) and “Being Highly Knowledgeable” (15%) topped the list.

Here’s the catch: those same skills ranked near the bottom when consumers were asked where live agents are currently performing best. That gap between expectation and reality is an opportunity, and a warning, for any brand investing in contact center training.

The Bigger Picture for CX Leaders

The research makes one thing unmistakably clear: the future of customer experience isn’t human or AI. It’s human and AI working together in ways deliberately designed around what consumers actually need at each stage of their journey.

AI excels at speed, availability, and accuracy. Live agents excel at judgment, empathy, and complex resolution. The organizations that figure out how to orchestrate these strengths (matching the right interaction type to the right resource at the right moment) will be the ones that win on customer experience.

The full report goes deep on soft skill differentials, consumer comfort with AI mimicking human qualities, the impact of language and accent barriers, and specific action items for CX leaders.

Download the Full Report →

The post What Consumers Really Think About Live vs. AI-Powered Agents appeared first on Execs In The Know.

Uber decided to do something different.

The company’s Global Digital Experience team, the group sitting at the crossroads of customer support operations and engineering, started asking a harder question: what if you could infer how every single customer felt, even the ones who never filled out a form?

The answer became an artificial intelligence (AI) engine that analyzes the full universe of support interactions in real time, surfacing satisfaction signals that traditional surveys simply can’t see. No waiting for voluntary feedback, no sampling bias, and no blind spots.

The Three Measuring Levers 

The model is built around three pillars: Resolution, Effort, and Sentiment.

Resolution is the foundation. Did the customer’s problem actually get solved? Effort is the friction audit. How hard did the customer have to work to get there? And Sentiment is the hardest piece: tracking the emotional arc of an interaction from first message to final reply, measuring whether someone left feeling better or worse about the brand than when they arrived.

None of these is new in isolation. What’s new is the synthesis, such as weaving together transactional data, real-time trip telemetry, conversation logs, turn counts, and tone signals into a single, coherent picture of what a support experience actually felt like.

The Lessons Were Hard-Won

Building this wasn’t a clean sprint. Uber’s team quickly discovered that years of CSAT data had given them a false sense of understanding. Once they started peeling back layers to define more nuanced sub-metrics, they found complexity that legacy surveys had been quietly papering over all along.

Aligning stakeholders across different business lines, languages, and markets on one shared definition of “satisfaction” required iteration after iteration. Teaching an AI model not just that it failed, but why, within a specific cultural or operational context, turned out to be genuinely hard work.

It’s the kind of friction that only makes the output more valuable.

What’s Next for Digital Experience at Global Scale

The near-term unlock is significant: for the first time, Uber can compare performance across fundamentally different support technologies (legacy automation and modern conversational AI) using a normalized metric. Apples to oranges, finally made comparable.

But the longer-term vision is more ambitious. The team sees a future where AI doesn’t just measure satisfaction; it anticipates friction before customers feel it, resolves issues without a single click, and transforms every support interaction from a transaction into a trust-building moment.

That future isn’t fully here yet. But the infrastructure being built now is what makes it possible.

Want the full story?  Access the complete case study, including how Uber’s team structured the cross-functional build, what broke along the way, and how they see AI reshaping customer experience on a global scale.

The post What Happens When You Stop Waiting for Customer Feedback? appeared first on Execs In The Know.

Those things still matter. But they are no longer enough.

Today, customers are looking for something deeper from the brands they choose to do business with. They want confidence and clarity. They want to know that when something goes wrong, they will be supported by a brand that is competent, consistent, and worthy of their trust. In other words, they want assurance.

That is why Customer Assurance is such an important idea for CX leaders right now.

What Is Customer Assurance in Customer Experience?

Customer assurance is not just about making promises. It is about creating experiences that leave customers feeling informed, protected, respected, and certain they are in good hands. It is what happens when trust, empathy, operational excellence, and intelligent innovation come together in a way the customer can actually feel.

And in this moment, that matters more than ever.

Why Customer Assurance Matters More in Today’s CX Environment

Across industries, leaders are navigating enormous change. Artificial intelligence (AI) is rapidly reshaping the contact center, automation is taking on more routine tasks, and self-service has become the norm. Customers are moving faster, expecting more, and giving brands less room for error. At the same time, organizations are operating in an environment marked by cost pressure, shifting workforce expectations, economic uncertainty, and constant pressure to modernize.

That combination has created a new kind of challenge for CX teams. It is no longer enough to ask, “How do we make service faster?” The more important question is, “How do we make customers feel confident in this new experience?”

How AI Is Raising the Stakes for Customer Trust and Confidence

Because as AI becomes more visible across customer journeys, confidence becomes the differentiator. Customers may appreciate faster answers and 24/7 support, but speed alone does not build trust. If an AI agent gives incomplete information, if a handoff to a human feels disconnected, if policies are enforced without empathy, or if customers are left wondering who or what they are actually dealing with, trust begins to erode. And once trust slips, loyalty often follows.

This is where customer assurance becomes a leadership imperative. For CX leaders, assurance is about setting a higher standard for how experiences are designed and delivered. It means building operations that work responsibly, ensuring customers have clear information and support that feels grounded in both intelligence and care, and preparing teams to lead through change, not just react to it.

Why Customer Assurance Is a Competitive Advantage

For organizations, customer assurance is becoming a business advantage. In a market where products can be copied and prices can shift overnight, trust is one of the few differentiators that grows stronger over time. Brands that create confidence through every touchpoint are better positioned to retain customers, protect reputation, and grow long-term value.

For teams, assurance creates alignment. It gives frontline leaders, agents, technologists, and executives a clearer shared purpose. Not just to reduce handle time or to deploy new tools. But to deliver experiences that customers believe in. That is a much stronger rallying point, especially in a time when many teams are being asked to adapt quickly to new technologies and new expectations.

And for the industry, customer assurance may be one of the most important ideas shaping the road ahead.

CX is entering a period where the conversation is expanding beyond efficiency and into responsibility. Beyond implementation and into impact. And beyond what technology can do and into what experiences should feel like when they are done well. The brands that lead in this next chapter will not simply be the ones adopting AI the fastest. They will be the ones using it in ways that strengthen trust, empower employees, and create more confident customer relationships.

That is the opportunity in front of us.

Attend CRS Scottsdale, Sept. 30-Oct. 2, 2026

At CRS Scottsdale, this is the conversation we are bringing to the forefront. CX leaders will come together to explore what it really takes to build trust, confidence, and clarity in a new era of care. From AI and leadership to service strategy, customer expectations, and operational transformation, the focus is not just on what is changing, but on how leading organizations are responding with intention.

Want to learn more about what Customer Assurance looks like in practice? Visit the CRS Scottsdale website to learn more. 

The post Customer Assurance: The Strategy CX Leaders Can’t Afford to Ignore appeared first on Execs In The Know.

A customer encounters an issue, reaches out, and the business reacts. For years, success has been defined by how well that recovery happens: how quickly teams respond, how efficiently they resolve, and how satisfied the customer feels afterward.

But that model is starting to show its limits.

Not because companies don’t care about experience, but because customers increasingly expect something different. They don’t want fast recovery. They want the issue handled before it ever becomes their problem.

Across industries, CX leaders are rethinking what it means to deliver great service: shifting from reacting to anticipating, from resolving to preventing, and ultimately, from servicing customers to staying ahead of them.

The gap isn’t intention — it’s execution

There’s no shortage of ambition. Most organizations already recognize that proactive, anticipatory service is the goal. The challenge is turning that ambition into something operational.

What consistently gets in the way isn’t a lack of vision, but a breakdown between insight and action.

Data lives across disconnected systems, making it difficult to see the full customer journey in real time. In regulated industries, even when the signals are clear, compliance requirements can slow or limit how teams respond. And internally, ownership isn’t always defined — who decides when a signal is strong enough to act, and what action should follow?

The result is a familiar pattern: companies are getting better at identifying potential issues, but they still struggle to do something about them before the customer feels the impact.

This isn’t a strategy problem. It’s an execution gap.

What proactive CX actually looks like

When organizations successfully make the shift, it doesn’t come from trying to predict everything. It comes from identifying the moments that matter — where timely intervention can meaningfully change the outcome.

In practice, this means recognizing friction early and stepping in before the customer has to.

• A payment likely to fail is addressed before service is interrupted
• A delayed order is communicated before the customer checks on it.
• A drop in product usage triggers outreach before a cancellation decision is made.

But what makes these moments effective isn’t just the prediction — it’s the experience that follows.

Customers aren’t simply notified; they’re given clear, immediate paths to resolution. The interaction feels helpful, not intrusive. And most importantly, it removes effort instead of adding to it.

Over time, these interventions do more than reduce contact volume. They build trust, reinforce reliability, and shift the relationship from reactive support to something that feels intuitive, seamless, and aligned with customer needs.

Rethinking how success is measured

One of the more nuanced challenges in moving to proactive CX is measurement.

Most organizations are still wired to evaluate success based on what happens after something goes wrong. Metrics like handle time, speed to answer, and post-interaction satisfaction all assume that the interaction has already occurred.

But in a preventative model, the most meaningful outcome is often the absence of that interaction.

That requires a different way of thinking about success. Instead of asking how well an issue was resolved, teams begin to look at whether it was avoided altogether. Instead of focusing solely on efficiency, they start to connect proactive interventions to outcomes like retention, reduced churn risk, and long-term value.

It’s a subtle but important shift. The goal is no longer just to fix problems faster; it’s to design experiences where those problems don’t surface in the first place.

The role of AI (and the reality behind it)

AI is often positioned as the driver of anticipatory CX, but the reality is more grounded.

AI is most valuable when it helps teams make sense of signals at scale. It can surface patterns, highlight risk, and recommend actions that would be difficult to identify manually. But it doesn’t deliver the experience on its own.

What enables proactive CX is the combination of data, AI, and human execution. Data provides the signal, AI interprets it, and people (agents, operators, and CX teams) deliver the response in a way that builds trust.

Where organizations tend to struggle is not in deploying AI, but in operationalizing it. If teams don’t trust the output, they won’t act on it. If systems aren’t connected to execution, insights stay theoretical. The value only materializes when those signals are embedded directly into workflows and decisions.

What progress actually looks like

For organizations looking to move forward, progress tends to come from focus and practicality rather than sweeping transformation.

The most effective approach isn’t to build a fully predictive model across every touchpoint. It’s to start with a small number of high-impact scenarios where the path from signal to action is clear. Addressing something like payment failures, delivery disruptions, or early churn indicators can create immediate value and, just as importantly, build internal momentum.

From there, progress comes through iteration rather than perfection. Waiting for flawless data or fully unified systems often delays action indefinitely. Organizations that make meaningful gains are the ones willing to test, learn, and refine.

And throughout that process, the anchor remains the same: outcomes. The goal isn’t to implement new technology for its own sake, but to reduce avoidable contacts, improve retention, and create more seamless customer experiences.

From cost center to revenue driver

At VXI, this shift is central to how we think about the role of CX.

Proactive service isn’t just about efficiency or deflection. It’s about recognizing that every moment (whether it results in a customer interaction or not) has the potential to influence revenue.

When organizations combine AI-driven insight with human delivery, they move beyond support and into something more strategic. They prevent churn before it happens, identify opportunities to engage at the right time, and create experiences that feel consistent and effortless.

It’s a model built on both automation and expertise (what we often describe as “bots + brains”) where technology identifies the opportunity, and people ensure it’s delivered in a way that resonates.

Designing for what doesn’t happen

The organizations that stand out in modern CX aren’t defined by how quickly they recover. They’re defined by how often customers never need to reach out at all.

Anticipatory CX isn’t about predicting every possible outcome. It’s about consistently preventing the moments that matter most.

For organizations willing to rethink how experience is designed, measured, and delivered, that shift is already within reach.

Written by Lauren Kindzierski, Head of Strategy & Innovation, VXI

About VXI

VXI Global Solutions is a leading global BPO providing omnichannel customer service, CX, and digital solutions for top brands, with 40,000+ employees across 43 locations worldwide. For more information, visit www.vxi.com

The post Preventing Customer Recovery with Anticipatory CX appeared first on Execs In The Know.

At the Customer Response Summit in Amelia Island, I shared the stage with three other experts to understand their strategies for tying customer care excellence to revenue growth, reduced churn, and operational efficiency.

I was joined by Jen Joyce, Senior Vice President of Digital Customer Operations at Frontier Communications; Craig Barnes, Senior Vice President of Customer Care at Retail Store Operations at Williams-Sonoma; and Tanisha Parker, Associate Director at Grubhub.

The conversation centered on three pillars – each laid out here as a playbook to help you plan, measure, and communicate:

1. Reframing the contact center as a value creator
2. Securing executive and board buy-in
3. CX as a revenue system

From Cost Center to Value Creator

Execs In the Know members are in various stages of shifting the narrative around contact centers from cost center to value creator. The panel offered four recommendations:

• The first is that customer trust and growth is earned, not won. AI alone does not do that. AI + humans does. We agreed on the guiding principle: “Automate the noise, humanize the moments that matter.” AI is ideal for high volume, low emotion, and predictable interactions. Humans, however, have empathy and understanding when the stakes are higher – emotionally and financially. While automation removes noise, humans deliver the impact.
• Second, the contact center is the best source of customer insights. Because it sits at the real-time intersection of customer experience, behavior, and feedback, contact centers are uniquely able to identify what customers are thinking. By turning our rich data into actionable information, we can help organizations spot trends immediately and respond faster.
• Third, service excellence consistently correlates with higher Customer Loyalty. In practical terms, if a client reaches out for technical support, and we solve that quickly and effectively, chances increase that the customer will not churn and, instead, will become an advocate.
• This dovetails with the fourth key of modern CX: Revenue growth. We are on the front-lines of upsell, cross-sell, and repeat purchases. Our speed, efficiency, and effectiveness directly impact brand, product, and service resonance. The best growth strategies are centered on customer experience.

Securing executive and board buy-in

Securing executive buy-in for CX initiatives requires inspiring leadership to act. There are three levers to do this:

• Contact centers experience customer pains daily. Senior management will support transformation initiatives when you highlight that friction, communicate the urgency, and show them first-hand the broken workflows that the customer experience experts overcome in each interaction. This connects them emotionally to the problems we solve.
• Turning micro-pilots into macro wins. This means beginning with small experiments that reduce risk while proving ROI – important metrics for the C-suite. Having this support is required for larger-scale implementations.
• Aligning with executive priorities: This is an exercise in translation – framing CX in terms of revenue and risk. Often, it is effective to work collaboratively across functions to present a unified case to the executive team for how the recommendations will help the organization achieve its strategic goals, faster.

Driving action requires more than sharing anecdotes: Delivering quarterly evidence (grounded in data and storytelling) wins commitment and budget.

CX as a revenue system

We are confident that delivering on the points in this blog will make CX a revenue system in the next 12 months. Accomplishing this requires that CX leaders make three strategic bets:

1. Integrated intelligence and intent-led automation: The most successful contact centers design automation around customer goals rather than keywords, scripts, or rigid workflows. Applying an intent strategy combined by AI creates compounding value. This, in turn, requires data rigor: a) Unifying data from disparate data sources b) Consolidating customer data post-acquisition c) Having a unified, end-to-end view of the customer across systems and operations.

2. Structured experimentation and proof-based scaling: Begin with small pilots with clear KPIs backed with quarterly proof. Then, increase the scale. Specifically, around AI, this has been echoed at previous Execs In The Know events: Because AI aspirations are not meeting CX realities – and because most deployments still do not move beyond the pilot phase – it is important to focus on discrete use cases before expanding its scope.

3. Enhanced workforce and human-centered differentiation: Smarter customer experts outperform automation-only models. Leverage AI to make them more effective through real-time assistance, recommendation engines, and decision support. 

Final Thoughts

The panel concluded with a powerful reminder: “Experience isn’t a cost—it’s the multiplier that grows every part of the business.”  By reframing CX as a growth engine and aligning it with organizational priorities, leaders can secure the resources needed to elevate customer experience initiatives and drive long-term business value.

Guest post written by Mike Lytle, Chief Delivery Officer, Americas at TP

TP is a global leader in digital business services, which consistently seeks to blend the best of advanced technology with human empathy to deliver enhanced customer care that is simpler, faster, and safer. For more information, visit tp.com.

The post Proving CX’s Value in the Boardroom appeared first on Execs In The Know.

And across industries, that pressure is showing up in the same places: how to move faster without losing trust, how to create efficiency without eroding the experience, and how to separate what is genuinely transformative from what is simply new. Those were among the biggest undercurrents in this week’s Virtual Executive Roundtable, where leaders came together to talk candidly about what AI is changing within their organizations right now.

What made the conversation so valuable was not that anyone claimed to have it all figured out. It was that the discussion stayed grounded in the tension leaders are actually managing every day. What does agentic AI really mean in practice? Where is it delivering measurable value today? Where do human teams still matter most?

And perhaps most importantly, are organizations using AI to modernize the same old service model or to build something better from the ground up? The conversation made one thing clear: this moment is bigger than automation. It is a very real opportunity for CX leaders to rethink what great service looks like next.

What does “agentic” really mean?

One of the most useful parts of the conversation was the effort to separate true agentic AI from the catch-all language that’s flooding the market. Leaders repeatedly came back to one simple distinction: traditional AI reacts, while agentic AI acts. Traditional AI answers the question, and agentic AI helps complete the task. It is more conversational, more proactive, and better at taking action across systems than simply surfacing information.

That distinction matters because confusion is still slowing organizations down. Several leaders noted that many companies say they are building agentic capabilities when, in reality, they are still enhancing scripted bots. That disconnect is important. If leaders use the same word to describe radically different levels of maturity, it becomes harder to benchmark progress, align teams, and make smart investment decisions.

The industry is still early

A striking theme from the discussion was how low current adoption still is. Estimates in the room placed true agentic AI adoption somewhere below 15%, with some leaders putting it closer to 3%-5%. And yet nobody sounded relaxed about that. Quite the opposite. The tone was urgent. Leaders acknowledged that while the industry is still early, the pace of change is accelerating fast enough that waiting now could create a real competitive gap later.

That is the tension many organizations are sitting in right now. They know they do not have all the answers. They also know they do not have the luxury of standing still. If competitors have already spent the last two years learning, testing, failing, refining, and scaling, what happens to the organizations that are just beginning to ask foundational questions now?

Where leaders are seeing real value today

The conversation stayed grounded in practical use cases, and that was refreshing. Leaders discussed high-volume, repeatable work such as account updates, password resets, and status checks. They also discussed more advanced uses: proactive service interventions, automated quality auditing, and knowledge management that updates in near real time based on live interactions.

One example stood out: instead of simply telling a customer that an order is delayed, an agentic system can recognize that the customer paid for expedited shipping, predict the miss before the customer complains, and proactively issue a refund. That’s not just efficiency; that’s service with foresight.

Leaders also pushed back on a narrow definition of value. This is not only about removing cost or reducing headcount. The more mature conversation is about containment, speed, routing accuracy, reduced friction, better use of human expertise, and stronger experiences overall. In other words, if your scorecard for AI is only labor reduction, are you measuring the wrong thing?

The human-in-the-loop is a strategy

If there was one point the group returned to again and again, it was this: human oversight still matters, especially in emotionally charged moments. Leaders shared examples from industries where the issue is not a simple transaction but a life event, a safety concern, or a vulnerable moment. In those cases, the role of AI is not to replace empathy. It is to recognize when empathy is required and make the handoff smarter, faster, and more informed.

That raises an important leadership challenge. Are organizations training AI only on business rules? Or are they also teaching it context, tone, and the signals that should trigger human intervention? Because the risk is obvious: a system can be technically correct and emotionally disastrous. And in customer experience, that still counts as failure.

A few shared perspectives

• “The old AI is ‘what do you want to know?’ agentic AI is ‘what do you want to do?’”
• “Instead of AI being a helper, AI is now your accountable digital labor. It’s part of your workforce.”
• “Standard AI was reactive… agentic AI is more proactive in how we solve for the customer.”
• “Traditional AI is scripted responses. Agentic AI can go out, pull from multiple sources, and make decisions.”
• “The AI would say your order is confirmed. Agentic AI will predict it won’t arrive on time and refund the expedited shipping in real time.”
• “It’s moving from supervised learning to autonomous decision-making.”
• “Agentic AI isn’t just answering questions; it’s completing the transaction end-to-end.”

This is bigger than automation

The biggest takeaway from the discussion was not that AI is moving quickly. Leaders already know that. It is the organizations making the most meaningful progress who are asking sharper questions as they move. They are not just chasing use cases. They are thinking carefully about trust, orchestration, customer expectations, employee impact, and where human connection still matters most. That is the real work in front of CX leaders now; not simply adopting AI, but shaping how it shows up in the experience in a way that is thoughtful, responsible, and genuinely useful.

What should AI handle? What should stay human? Where can we remove friction? Where should we add reassurance? And how do we use this moment not just to do service cheaper, but to do it better?

These are exactly the kinds of candid, leader-level conversations happening inside our Virtual Executive Roundtables. If you want to hear how your peers are thinking through the opportunities and tradeoffs ahead, and contribute your own perspective to the discussion, join us at a future Virtual Executive Roundtable. It is a chance to step into a smaller, high-value conversation with fellow CX leaders navigating many of the same questions in real time.

Join our mailing list to receive event invites.

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Performance metrics may look stable — service levels met, occupancy steady, costs controlled — yet something feels structurally off.

AI pilots launch but stall. Digital volumes increase, but accountability blurs. Customer expectations rise faster than internal capability.

For decades, the traditional contact center operating model delivered exactly what it was designed to deliver: efficiency, predictability, and cost control.

It worked — until it didn’t.

Customers began carrying expectations from every interaction they have — across industries, across platforms — into every new experience. The standard is no longer set by direct competitors. It is set by the most seamless interaction they had yesterday.

Today’s CX environment is structurally different. Customers move fluidly across channels. AI is embedded into workflows. Regulatory scrutiny has intensified. Data from every interaction is strategically valuable.

Yet many organizations are still operating on a structure built for queue management — not experience orchestration.

The issue isn’t effort.

It’s architecture.

Traditional contact center operating models were engineered for a different era.

And that architecture is beginning to fracture under modern CX demands.

The Model We Built — and Why It Worked

For years, the contact center was optimized around a clear set of assumptions:

• Voice was the dominant channel
• Demand patterns were forecastable
• Workforce management drove precision
• Performance was measured by speed and cost
• Technology cycles were relatively stable

It made sense for an environment defined by concentrated voice demand, slower technology cycles, and clearer functional boundaries.

It was structured. It was disciplined. It delivered measurable efficiency.

But it was built around queues, not journeys. That distinction now matters.

The Emerging CX Operating Model

The future operating model will require evolving tools. AI will advance, automation will expand, and digital ecosystems will grow more complex. But technology alone will not determine success.

The differentiator will be how organizations redesign accountability, governance, and workforce structure to integrate those capabilities. Structural alignment will determine whether those investments create lasting value.

If structural alignment is the differentiator, redesign cannot be abstract. It requires deliberate structural shifts.

Across industries, four are becoming clear:

1. Unified Experience Ownership
2. Embedded AI Governance
3. Workforce Redesign — Not Just Upskilling
4. Real-Time Data Integration

Together, these shifts represent more than incremental improvement. They signal a structural redesign of how CX organizations must operate to compete.

The Bottom Line

Traditional contact center structures are not flawed. They were optimized for a less complex era.

They worked — until they didn’t.

The question now is not whether the model was effective. It is whether it still fits.

The future of CX will not be won by those who install better tools.

It will be won by those who redesign how the organization works.

Guest blog post written by John Sorenson

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Let me be honest with you: I’ve been to enough industry conferences to know when a room is just going through the motions. At CRS 2026 in Amelia Island, something was different.

When I stepped on stage to lead “When AI Acts: Leading Through the Shift from Copilot to Agent,” I wasn’t there to talk about tools or trends, or which model is in market right now. I was there to have the harder conversation; the one about whether our organizations are actually ready for what’s coming.

Because here’s the reality: AI isn’t waiting for us to be ready. It’s already acting.

“By 2028, agentic AI is expected to handle 68% of customer experience interactions with technology partners.” — Cisco Global Research, 2025 (7,950 decision-makers across 30 countries)

→ Source: Cisco Newsroom, May 2025

Sarah Jeanneault presenting at CRS 2026, Amelia Island

The Question That Shifted the Room

I opened with a simple question: “When AI starts acting on behalf of your team – who is accountable?”

You could feel it land. Not because it’s new, but because most of us haven’t answered it yet. And the gap between how fast AI is advancing and how prepared our operations are to support it? That’s the gap I’ve been thinking about every single day.

As long as AI is a chatbot or a copilot, humans are the final decision-makers. The moment AI becomes agentic – resolving transactions, routing cases, applying policy logic without waiting for someone to click approve, the responsibility structure changes entirely. AI becomes embedded in your operations, and your operations had better be ready to hold it.

“93% of IT leaders report intentions to deploy autonomous AI agents within two years, and nearly half have already started.” — MuleSoft 2025 Connectivity Benchmark Report (1,050 IT leaders surveyed)

→ Source: MuleSoft / Salesforce Connectivity Benchmark Report, 2025

From Suggesting to Executing

Here’s the progression I walked through, and it’s one that most organizations are living in real time right now:

Stage 1 – AI as Responder: Chatbots that react to prompts, deflect tickets, answer straightforward questions. Reactive. Limited.

Stage 2 – AI as Assistant: Copilots that sit alongside your agents, surfacing information and suggesting next steps. Humans still make the call.

Stage 3 – AI as Actor: Agentic systems that execute. They process requests, make decisions within defined parameters, and move work across systems — independently.

Most organizations I talk to are managing all three simultaneously. And that third stage? That’s not just a technical upgrade. It’s a change in risk, accountability, and operational design.

“23% of organizations are already scaling AI agents — yet operational governance remains the primary barrier to broader deployment.” — McKinsey State of AI, 2025 (1,993 respondents across 105 countries)

→ Source: McKinsey State of AI Report, November 2025

The Foundation Beneath the Intelligence

This is where the conversation really shifted. Because we quickly moved past “is your AI smart enough?” to ask a much harder question: is the foundation beneath your AI structured enough?

Most organizations still rely on document-based knowledge: policies written in long-form PDFs, procedures scattered across shared drives, tribal knowledge living in the heads of your most experienced team members. For years, that’s been fine because humans compensate for ambiguity. We fill in the gaps, ask clarifying questions, escalate when we’re not sure.

AI doesn’t do that. AI executes based on the structure it’s given. If your knowledge is fragmented, outdated, or unclear, those weaknesses don’t disappear when AI touches them. They get amplified.

Attendees engage in the Care Gap exercise

Naming the Care Gap

Midway through the session, we stopped theorizing and got practical. I put people into small groups and gave them three questions:

1. Where does customer care break in your organization?
2. What is the measurable business impact?
3. Why does that gap exist structurally?

Each group had to distill their answers into a single Care Gap Statement: the symptom, the impact, and the root cause.

What struck me was how fast people could do it. They didn’t have to think hard about where things break. Inconsistent responses driving repeat contacts. Escalations triggered by unclear decision thresholds. Copilot suggestions overridden because agents don’t trust the logic underneath them. Extended handle times because knowledge lives in four different places and no one can find what they need fast enough.

These aren’t new problems. What changed was the framing. We stopped calling them performance issues and started calling them what they actually are: structural gaps.

“Service reps spend 66% of their time on non-customer-facing tasks. AI implementation is directly tied to reducing that friction — but only when the underlying knowledge is structured.” — Salesforce AI Agent Research, 2024

→ Source: Salesforce AI Agents Statistics, 2024

Turning Breakdowns Into Opportunities

Once you name the gap, you can reframe it. I asked every group to complete this sentence:

“If we improved ___, we could achieve ___, without compromising customer care.”

The most compelling responses weren’t about acquiring better AI. They were about designing better systems. Improving decision logic clarity to reduce override rates. Centralizing and version-controlling workflows to lower escalations. Clarifying exception paths to improve first-call resolution. The emphasis wasn’t the tool; it was the architecture underneath the tool.

A Real-World Example of Structural Change

I shared a story from a large health insurance contact center that illustrates exactly what I mean. Agents were handling complex benefit and claims inquiries, and escalation rates were high. Leadership assumed it was a training problem.

It wasn’t. The knowledge existed. It was just structured in a way that nobody could use confidently under pressure. Policies lived across documents. Decision paths weren’t mapped. So, agents defaulted to escalation not because they didn’t know the answer, but because they couldn’t find it fast enough to trust it.

The organization rebuilt its processes into clear, visual workflows with defined decision logic and governance controls. The policies didn’t change. The structure did. Escalations dropped significantly. First-call resolution improved.

The transformation wasn’t technological. It was architectural.

The Maturity Journey

Toward the end of the session, I mapped out the maturity journey that resonated most with the room:

Documented: Information exists but remains static and interpretive. You’re dependent on SMEs to fill the gaps.

Guided: Processes are structured step-by-step. People can follow them without context.

Collaborative: Governance and version control are formalized. Knowledge is owned, not assumed.

Strategic: Knowledge is connected to analytics and continuous improvement.

Innovative: AI acts autonomously within defined guardrails. This is where the magic happens but only if the earlier stages are solid.

The sequence matters. I see organizations every week trying to leap directly to “Innovative” because of competitive pressure or boardroom enthusiasm. But automating ambiguity doesn’t eliminate it, it multiplies it. Scaling inconsistency doesn’t fix it, it accelerates it. Delegating judgment to AI without defining the rules introduces risk, not efficiency.

“Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues without human intervention by 2029 — a 30% reduction in operational costs.” — Gartner, March 2025

→ Source: Gartner Press Release, March 5, 2025

What the Room Carried with Them

As we wrapped, conversations kept going at the tables. Leaders were asking each other: Who currently owns knowledge updates in your organization? Is your decision logic formally documented or just assumed? If AI executed tomorrow based entirely on how your systems are structured today, would you feel confident?

That last question is the one I want to leave you with too. Not because the answer is supposed to be “yes” right now most of us aren’t there yet. But because the question itself is the leadership shift this moment demands.

The Real Takeaway

AI will keep advancing. Its role in customer experience will keep expanding. The differentiator won’t be who adopts it first.

It will be who builds the strongest operational clarity beneath it.

When AI suggests, humans can adjust. When AI acts, structure determines trust.

That’s the shift. And it extends far beyond technology.

By Sarah Jeanneault, VP of Marketing, Procedureflow

About the Author

Sarah Jeanneault is VP of Marketing at Procedureflow, where she leads growth strategy and customer-centric programs. With 20+ years of experience across startups and enterprises, she focuses on the intersection of operational clarity and AI-readiness in customer experience. She spoke at Customer Response Summit 2026 in Amelia Island.

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