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.

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.

The post When AI Starts Acting appeared first on Execs In The Know.

More specifically: Who are you when the title drops, the metrics fade, and the pressure hits? Because, as Travis reminded the room, leading people is complicated. And in a world where every conversation seems to orbit around AI, automation, and scale, the differentiator that will define the next era of customer experience isn’t just technology; it’s emotional intelligence (EQ).

And EQ, he argued, sits on a deeper foundation: identity. Who you are determines what you do. This became the heartbeat of his keynote, “The Standard Is the Strategy.”

Metrics Matter. But They Aren’t the Whole Story.

Travis began with a story from his NFL days, specifically, a moment with legendary linebacker Willie McGinest. After a tough game, Willie posed a question that stuck with him long after he left the field: “Who are you?”

Too often in CX (and in life), Travis said, we obsess about the tangible:

• NPS
• Quarterly numbers
• Service levels
• Efficiency metrics

But in leadership, you can hit the metric and miss the moment. You can meet the standard but violate the principle.

This line landed hard. Because it’s true: in the rush to deliver, leaders can unintentionally drift from the values that made them leaders in the first place.

Standards vs. Principles: The Core Distinction

Travis broke it down simply:

• Standards are what we do.
• Principles are who we are.

Standards matter. They’re measurable, operational, and necessary. But standards alone don’t create culture. Principles do. And in an industry where AI is accelerating faster than any of us expected, Travis reminded leaders of something vital: AI will scale your systems. EQ will sustain your culture. Because CX, at its core, is still about people.

EQ is the skill that helps leaders manage themselves, navigate environments, read the room, and steward their teams through uncertainty. But EQ is built on something deeper: identity. Before you can lead others, you must define who you are going to be.

The Three Principles of Principles

The centerpiece of the keynote came when Travis outlined the three rules that govern how real principles work.

External Conditions Shouldn’t Determine Your Principles

If your principles only hold when conditions are ideal, they’re not principles; they’re preferences.

Travis told stories from NFL training camp, from tough seasons, from moments when every condition felt like a good reason to justify exceptions. But that’s exactly the point: conditions expose principles; they don’t rewrite them.

He challenged leaders to examine whether pressure or convenience has ever swayed their standards, from tone to honesty to accountability.

His reminder hit home: Pressure doesn’t create character. It reveals it.

And this wasn’t just for the boardroom. He drew the line straight into the personal: marriage, parenting, and home life.

Principles Shouldn’t Be Compartmentalized

Leaders don’t get one set of principles for the office and another for home. Character doesn’t clock in and out. Travis spoke about something many leaders quietly wrestle with: the disconnect between the “stage version” of themselves and the “real version” at home. Those gaps erode trust more than any missed KPI ever could.

Authenticity, he said, isn’t about saying whatever you want. It’s about alignment. The version of you in the boardroom, at the dinner table, behind closed doors, and in moments no one sees, should be the same person. And he asked a provocative question: What if authenticity became your most important standard?

Don’t Expect from Others What You Don’t Model Yourself

This one is leadership in its purest form. You cannot expect your team to live at a level you refuse to model. From effort to attitude to integrity, Travis shared: Teams may listen to what you say, but they will replicate what you model.

He reflected on parenting, on coaching, on how often leaders get frustrated with behaviors they actively or passively teach through their example. It was a moment of self-reflection for many in the room. Before you ask, “Why won’t my team rise higher?” Ask: “Have I?”

The Danger of Success in the Wrong Direction

One of the most powerful moments came near the end: a story about scoring what he thought was a touchdown only to realize he ran toward the wrong end zone.
The metaphor was striking. Because in CX, in leadership, and in life, you can hit every target, grow revenue, improve metrics, and still be moving in the wrong direction.

The only success that matters is the kind that aligns with your principles. External success ≠ principled success.

Why This Keynote Mattered

At a conference full of strategy, innovation, AI discussions, and operational breakthroughs, Travis did something different. He grounded the week in something unmistakably human. CX leaders today sit at the intersection of exponential technology and deeply human expectations. We’re balancing automation with empathy, speed with care, efficiency with trust. AI is rewriting the rules, but it’s not rewriting our values.

Travis reminded leaders that before you can build a modern, high-performing, AI-enabled customer experience, you must anchor your identity. Because your principles, not your models, not your workflows, not your dashboards, will determine whether your culture thrives or fractures in the years ahead.

He ended where he began, returning to Willie McGinest’s question: Who are you? And is that the person you really want to be?

Because the future of CX is not just AI-powered; it is principle-powered. And as Travis said, the standard is the strategy.

Join Us at CRS Scottsdale

Experience powerful mainstage keynotes, candid leadership conversations, and practical CX insights from the leaders shaping the industry. Registration opens soon!

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The post Travis Brown on the Power of Principles in a Changing CX World appeared first on Execs In The Know.

This question was front and center in the presentation by Meghan Nicholas, Walmart’s Vice President of Customer Engagement Services, at the recent Execs In The Know CRS East Conference. You can watch a preview of her presentation here. At TP, we see this not as a challenge, but as our guiding principle.

We share Meghan’s vision —a people-led, tech-powered future for customer support. This approach recognizes that technology like AI and automation are powerful enablers. They help us operate at scale, remove friction, and deliver faster service. But technology alone is not the answer. The real difference is made by people. When we empower our experts with the right tools and insights, they can provide the empathy, care, and expertise that turn a good interaction into a great one.

In this post, inspired by the insights shared in Amelia Island, we will share the keys to delivering this balanced and resilient customer experience model at scale – shown through our work with a client that needed to transform its CX model. This example provides a model for designing modern service operations that strengthen both empathy and efficiency, use technology to enhance the moments that matter most, and build a foundation of trust that brings you closer to the people you serve.

The client faced a common but significant challenge: their existing customer service model couldn’t keep up with rising expectations. They needed to improve first-contact resolution, boost customer satisfaction, and create a more streamlined, positive experience from end to end. The goal was to not just solve issues but to build lasting trust and loyalty.

Recognizing the need for a fundamental shift, they partnered with TP to co-create a new service model. This collaboration moved beyond a traditional client-vendor relationship into a true strategic alliance focused on shared goals and continuous improvement.

The first step was building a dedicated team of TP experts. These weren’t just general customer service agents; they were specialists who received intensive training tailored to the client’s unique needs. This specialized knowledge empowered them to handle complex inquiries with confidence and accuracy, providing customers with reliable answers on the very first call.

Creating this highly skilled team significantly increased first-contact resolution. Customers no longer had to endure frustrating transfers or repeated calls. Instead, they connected with an expert who could own their issue and see it through to completion.

With a strong foundation of human expertise in place, the next phase was to integrate innovative technology to enhance, not replace, the human element. This included a suite of advanced tools designed to streamline processes and empower the customer experts.

One of the most impactful solutions was a “digital assistant” that provided real-time guidance during customer interactions. This tool offered instant access to knowledge bases, suggested next steps, and ensured compliance with all necessary procedures. By automating the search for information, it freed the experts to focus entirely on the customer—listening with empathy, understanding their emotional state, and providing personalized solutions.

This combination of specialized human talent and smart technology delivered remarkable results. The new model not only met but exceeded expectations, achieving a significant boost in customer satisfaction scores. The client saw firsthand that efficiency and empathy are not mutually exclusive; they are two sides of the same coin.

The success of this partnership demonstrates a clear path forward for any organization looking to elevate its customer experience. It starts with a commitment to understanding customer needs on a deeper level and investing in the people and technology required to meet them.

By building specialized teams, integrating smart digital tools, and fostering a culture of continuous improvement, companies can transform their customer service into a powerful engine for growth and loyalty. This approach doesn’t just solve immediate problems; it builds relationships that last, turning satisfied customers into passionate advocates for the brand.

As Meghan mentioned, the future of customer experience is one where innovation serves humanity, and every interaction is an opportunity to create a meaningful connection.

by Mike Lytle, Chief Delivery Officer, TP

The post Delivering CX Without Compromise at Scale appeared first on Execs In The Know.

Here is the uncomfortable truth:

Most of what’s being sold today isn’t autonomy. It’s layering shiny wrappers on top of brittle, deterministic systems.

Startups with twelve months of experience say they have “Agentic AI”.
Orchestrators glue together third-party components and call it a “platform”.
Generic AI vendors offer huge models, but not the precision, control, or governance that enterprise CX actually requires.

Meanwhile, enterprises are still stuck manually building intents, designing flows, tuning NLUs, and fixing failures one painful edge case at a time.

This isn’t progress.
This is repackaged stagnation.

The Industry Is Building Franken-Platforms

Let’s name the problem plainly.

Today’s orchestrator platforms have become Frankenstein systems: a speech engine here, a third-party NLU there, an LLM from somewhere else, layered with connectors, crutches, rules, and duct-tape logic – all stitched together inside a routing engine that still relies on static queue maps.

Their favorite proof point is often “bring your own key” (BYOK). But BYOK isn’t flexibility,  it’s abdication. It signals that the platform itself is indifferent to the intelligence doing the work. When AI components are treated as interchangeable commodities, integrations remain shallow, optimization never happens, and production performance predictably collapses the moment the system leaves the demo environment.

This approach can never produce a truly autonomous agent.
Why?

Because:

• No part of the system understands the full interaction.
• No component sees end-to-end context.
• Nothing learns organically from outcomes.
• And no one owns the failures.

Compare that to the Omilia Self-Learning CX Agents – a unified solution where perception, reasoning, routing, task execution, and lifelong learning operate cohesively, not as rented parts.

Hyperscale AI Isn’t the Answer Either

Yes, generic LLMs are powerful. But they are not built for:

• high-precision voice automation
• strict regulatory environments
• controlled, auditable decision paths
• domain-specific customer journeys
• deterministic guardrails and safe autonomy

They hallucinate.
They drift.
They over-generalize.
And they cannot deliver enterprise-grade containment without massive manual tuning.

CX is not a playground for generalist AI
It requires surgical accuracy.
It requires governance.
It requires vertical learning from real customer interactions – something hyperscale LLMs were never designed for.

The Market Has Reached Its Breaking Point

Everyone is trying to patch their way into autonomy:

• The orchestrators bolt on LLMs.
• The hyperscalers bolt on industry blueprints.
• The startups bolt on integrations.

But none of them can escape their architecture.
None of them can evolve from stitched tech into truly self-learning systems.

To reach real autonomy, CX must move beyond orchestration and into end-to-end Agentic AI – a unified system capable of perceiving, reasoning, acting, and learning continuously from both AI-led and human-led conversations.

This is exactly what we built.
And it is something only a vendor with 20+ years of enterprise CX experience could build.

Introducing Omilia’s Self-Learning CX Agents

This is not a wrapper.
This is not a prompt over a chatbot.
This is not a flow-builder with marketing lipstick.

This is a new class of enterprise AI:
CX agents that learn, adapt, and improve autonomously through a closed learning loop.

And it’s powered by four native, deeply integrated capabilities in our Agentic platform.

1. The Concierge Agent: Intelligent Routing Without Intents

While others still use rule sets and intent maps, our Concierge Agent uses:

• zero-shot routing
• dynamic agent queue awareness
• real-time reasoning
• automatic disambiguation

…to route every call or chat optimally, with >90% accuracy, and with zero training required.

Not one intent.
Not one workflow.
Not one labeled dataset.

This is the beginning of true autonomy.
Not a menu.
Not a bot.
Not a router.
An out of the box, reasoning agent for the front door of CX.

2. Task Agents: Autonomy on Demand, from Low to High

While competitors force enterprises to choose between rigid determinism or wild LLM improvisation, Omilia gives customers graduated autonomy, safely and with full governance:

Low Autonomy – Deterministic, predictable, fully explainable Agents

Perfect for regulated workflows.

Medium Autonomy – Agentic Fallback

Two powerful layers of resilience the industry has completely ignored:

• Belief State Updates
• Intelligent Agent Takeover

Your system no longer breaks when your workflow breaks.
It recovers.
It reasons.
It fills gaps.
It completes tasks.

High Autonomy – Full Agentic Task Execution

Planning.
Reasoning.
Multi-step workflows.
Enterprise integrations guided by MCP.
This is what autonomous CX actually looks like. Everyone else talks about “autonomous cx agents”.
We ship them — safely.

3. FAQ Agents: Precision Knowledge Access

Lightweight but powerful, optimized for voice latency and RAG-based reasoning.
No hallucinations.
No generic answers.
Just context-aware precision extracted from enterprise knowledge.

Responses adapted for voice and digital cx channel rendering.

This is how knowledge retrieval should work — not as a bolted-on LLM search feature.

4. The Lifecycle Management Agent: The holy grail of Agentic CX

This is the real revolution. Closing the Loop. The self-learning engine.

Where everyone else stops: at deployment, Omilia’s Lifecycle Management Agent starts:

• Analyzes AI + human interactions
• Detects emerging tasks
• Discovers and reports new automation opportunities
• Learns tribal knowledge from real agents
• Autonomously creates new Task Agents
• Evolves knowledge
• At the touch of a button, closes the loop, safely and transparently

This is the backbone of Self-Learning CX Agents.
This is autonomy with governance.
This is evolution at enterprise scale.

No startup has this.
No orchestrator has this.
No hyperscaler has this.
Only an end-to-end CX platform evolved over decades could achieve it.

The Future Belongs to Self-Improving CX Agents

The first era of conversational AI was about building bots.
The second was about stitching components together.
The third, today, is filled with hype and shortcuts.

But the next era?

Belongs to platforms that learn.

That adapt.
That evolve.
That unify deterministic control with agentic intelligence.
That eliminate the need for hand-built flows and manual NLU maintenance.
That turn enterprise CX into a dynamic, self-improving ecosystem.

This is not a feature.
This is the new foundation of customer experience.

The industry is changing.
And Omilia is leading it.

About the Author

Claudio Rodrigues, Chief Product Officer, Omilia

With a strong background in product strategy, design, and delivery of Conversational AI solutions, Claudio leads Omilia’s product vision across Conversational AI and GenAI. Claudio connects his expertise in AI with a proven track record of scaling platforms globally and aligning product development with customer needs, ensuring Omilia’s solutions deliver measurable impact across industries.

The post The AI Arms Race is Misleading You – Self-Learning CX Agents Are the Only Path to Real Autonomy appeared first on Execs In The Know.

That concern makes sense. For years, many automation efforts were built around one clear goal: lowering costs. Teams were measured on call deflection and handle time, and speed became the focus while experience sometimes took a back seat. When that happens, customers feel it. They get stuck in systems that don’t understand their issue, they repeat information because tools aren’t connected, and they struggle to reach a person when their situation becomes more complicated. In those moments, it’s easy to assume that efficiency and empathy are working against each other.

But the real issue isn’t efficiency, it’s design.

Where Automation Goes Wrong — and Right

When automation is layered onto a journey that already has friction, it just speeds up the frustration. When it’s built into a thoughtful, well-designed experience, it removes barriers and strengthens the human moments instead of weakening them.

AI is very good at handling simple, repetitive questions, pulling up customer history quickly, reducing manual after-call work, and routing people to the right team faster. Those tasks don’t require emotional judgment, they require consistency and speed. When technology handles that operational work well, agents get time and focus back, and that shift changes how conversations unfold.

When agents aren’t juggling disconnected systems or buried in repetitive tasks, they can slow down. When a conversation calls for care, they can listen more closely and respond with greater understanding. In that environment, efficiency doesn’t reduce empathy because it creates space for it.

Deciding What Should Stay Human

The better leadership question isn’t how much we can automate; it’s where automation truly helps and where a person makes the biggest difference.

Every customer journey includes moments that carry emotion, whether it’s a product that didn’t work, a billing issue that affects someone’s budget, or a loyal customer who feels let down. Those situations require judgment and flexibility, and trying to automate every part of those interactions often creates more harm than good.

Operating without compromise means being clear about those boundaries and designing accordingly. It means building simple, fast escalation paths so customers can reach the right support when they need it. It means routing complex issues to skilled agents quickly, and then giving those agents the authority and information they need to fully solve problems instead of passing them along.

Supporting the People Delivering the Experience

There’s also an internal side to this conversation that matters just as much, because empathy can’t scale if the people delivering it are overwhelmed. If agents are navigating disconnected systems and repetitive manual work all day, it becomes much harder for them to show patience and care. Friction inside the organization almost always shows up in the customer experience.

Thoughtful automation can ease that burden by connecting systems, surfacing the right information at the right time, and reducing unnecessary manual steps. When agents feel prepared and supported, conversations become more focused, issues are resolved more completely, and trust grows more naturally over time.

Designing Without Compromise

The organizations getting this right aren’t racing to automate everything they can because they understand that speed alone isn’t the goal. They’re taking the time to design carefully, and they recognize that efficiency removes friction while empathy builds loyalty, and both are necessary for long-term success.

Customer expectations will continue to rise, and the pressure to operate efficiently isn’t going away. At the same time, people still want to feel heard, understood, and respected. The good news is that we don’t have to choose between those outcomes because when we design with intention, efficiency supports empathy instead of replacing it.

The trade-off only exists when design is an afterthought. When design leads, efficiency and empathy work together, and what emerges is a stronger, more human approach to care.

In our work with brands across industries, we’ve learned that balancing innovation and humanity isn’t about moving slower or spending more. It’s about being deliberate. It’s about knowing where automation creates value and where human connection creates trust.

CX without compromise isn’t just a theme, it’s a leadership choice. And the organizations that make it will define what modern care looks like in the years ahead.

Written by Alta Resources

The post The False Trade-Off: Why Efficiency and Empathy Aren’t Opposites appeared first on Execs In The Know.

This simple truth captures the structural inflection point customer experience (CX) has reached in 2026. Over the past decade, enterprises poured resources into formalizing CX management -maturing Voice of the Customer (VoC) programs, standardizing Net Promoter Score (NPS) and CSAT metrics, and rolling out dashboards across global regions and business units. Weekly operational reviews and quarterly board deep-dives became routine. This was the era of measurement discipline: organizations gained the power to quantify sentiment, benchmark performance, and track trends with sophisticated analytics.

But these systems were inherently retrospective, analyzing closed journeys after the fact. The new era? Time-sensitive intelligence. The burning question for leadership has shifted: not “How satisfied were customers last month?” but “How quickly can we detect, diagnose, and mitigate emerging dissatisfaction before it escalates into churn?”

Artificial intelligence isn’t just polishing analytics; it’s rearchitecting CX as a core pillar of enterprise operations and risk management. Platforms like Press’nXPress (PXP) lead this charge, delivering real-time experience intelligence tailored for high-volume environments like retail chains, airport operations, healthcare networks, and contact centers. In complex ecosystems, friction spreads fast, and reputational impact compounds in hours.

The Hidden and Measurable Cost of Latency

Experience risk doesn’t accumulate in reporting cycles; it builds continuously. Consider real-world scale:

In a busy retail chain, a staffing imbalance during peak hours can spawn hundreds of negative interactions before shift end – driving up refund rates and eroding loyalty. Aviation operators know this intimately: a single gate delay cascades into poor boarding perceptions, inflight dissatisfaction, baggage mishaps, and scathing public reviews by day’s close. Healthcare providers face it too; communication gaps or surging wait times quietly dismantle patient trust long before a performance summary hits inboxes.

The interval from friction to awareness is pure exposure. Days of latency let dissatisfaction snowball: churn spikes (retail averages 25.4%, telecom 21.5%), complaints escalate, and negative sentiment amplifies online. Detection within hours? Corrective action stays contained, preserving revenue. McKinsey case studies show airlines using predictive CX systems achieved 800% satisfaction uplifts and 60% churn reductions on priority routes by intervening early. For enterprises at scale, these fractional gains translate to material P&L impact – millions in protected revenue, stabilized brand equity, and optimized operations.

Press’nXPress slashes this latency. By ingesting omnichannel signals instantly, it empowers teams to act before issues compound, directly safeguarding your bottom line.

From Voice of the Customer to Voice of the Moment

Traditional VoC is post-mortem analysis: surveys on completed journeys, insights delayed by days or weeks. AI-enabled experience intelligence operates in the present tense. It pulls continuous streams of structured (ratings, scores) and unstructured (reviews, transcripts) data from physical kiosks, digital journeys, contact centers, and social platforms.

Rather than recapping last week’s performance, it flags deviations in real time. Correlating them with context like traffic surges, staffing levels, or policy tweaks and forecasts downstream outcomes like churn probability or revenue leakage. This embeds CX as a live monitoring layer in your infrastructure, compressing the decision cycle from weeks to minutes. Management intervenes at the speed of customer perception, not calendar cadence.

Press’nXPress exemplifies this shift. Its touch-free kiosks, QR codes, SMS, and web collectors capture unbiased, in-the-moment feedback across touchpoints. A facility services firm using PXP for gym and pool maintenance uncovered client pain points via kiosk data, boosting satisfaction through targeted fixes. Retail chains integrating real-time kiosks saw 30% satisfaction jumps in six months by addressing issues on the spot. No more waiting for quarterly aggregates—get hyper-responsive insights that drive loyalty and growth.

The Operational Power of Multi-Agent AI

Enterprise multi-agent AI isn’t a single super-model; it’s a symphony of specialized agents collaborating in parallel, orchestrated for precision. Here’s how it breaks down:

• Sentiment agents: Monitor polarity shifts, clustering emerging dissatisfaction across channels.
• Trend agents: Deploy statistical anomaly detection to spot baseline deviations.
• Predictive agents: Model churn risk, escalation odds, and operational ripple effects.
• Driver agents: Quantify root causes tied to performance drops (e.g., new scripts tanking calls).
• Action agents: Translate insights into workflows, alerts routed, owners assigned, protocols triggered.

An orchestration layer enables seamless data sharing and unified outputs, mimicking mission-critical systems like cybersecurity or fraud monitoring. In CX, this means continuous governance: not improved reports, but a proactive intelligence fabric.

Press’nXPress harnesses this architecture natively. Its powerful analytical engine processes feedback from physical, digital, and voice touchpoints, generating actionable dashboards and automations. A workplace experience firm used PXP to monitor multi-location service quality, identifying gaps that drove service excellence. In dining services, real-time analytics revolutionized menus and ops, proving the competitive edge of instant feedback. Aviation leaders at FTE already leverage similar setups for seamless passenger journeys.

Press’nXPress multi-agent workflow: From signal to action in seconds.

The CX Control Tower: Governance at Scale

Air traffic thrives on live radar, not summaries. CX is evolving similarly. A true CX control tower ingests omnichannel data, fuses it with operational metadata (staffing, volumes), benchmarks across sites/segments, and auto-activates workflows on threshold breaches.

Press’nXPress is this tower in action. Mot just visualizing sentiment, but detecting exposure early, escalating intelligently, and baking accountability into processes. Facility managers maintaining high-traffic venues used PXP’s kiosks and QR to reveal preferences and bottlenecks, elevating standards across portfolios. In retail and hospitality, it’s turned feedback into 30% satisfaction surges and sales lifts. For operators in aviation or healthcare, it means governed experiences that scale without breaking. Traditional feedback tools measure but PXP manages risk and performance.

Strategic Implications and Your Edge

This evolution demands leadership choices:

1. Elevate CX to infrastructure: Position it alongside finance, operations, and risk as a core system driving enterprise resilience.
2. Compress decision cycles: Shrink signal-to-action from weeks to minutes, matching the speed of customer perception.
3. Govern with rigor: Ensure data integrity, AI transparency, and clear cross-functional accountability to sustain trust and performance.

Markets now amplify instantly; reviews go viral, loyalty flips in hours. Laggards chase damage after it spreads; leaders preempt it with precision. Over time, these incremental speed advantages compound into durable dominance: lower churn, higher revenue, and operations that adapt faster than competitors.

Customer experience has outgrown dashboards. It’s time to operationalize it as enterprise intelligence – a real-time system that detects friction as it emerges, forecasts its impact, and mobilizes your teams to win back loyalty before a single review posts.

Don’t let latency erode your edge. Turn CX into your revenue shield and growth engine today.

Written by Press’nXPress

Press’nXPress: Experience intelligence at the speed of your customers.

The post AI in Customer Experience: From Measurement Discipline to Enterprise Intelligence appeared first on Execs In The Know.

In a recent consulting engagement, we encountered a scenario that’s becoming increasingly common across enterprises: a complex organization with multiple business units struggling to deliver consistent customer experience through an aging contact center platform.

The Perfect Storm for Transformation

The company—a $5 billion industry leader with seven distinct business units—was facing a critical inflection point. Their current contact center platform, implemented in 2019, was fundamentally a “like-for-like” replacement of their previous 30-year-old system. It had solved the immediate technical debt problem but created new challenges:

• Customer hold times averaged 30-40 minutes
• Agents spent 5 minutes on post-call wrap-up
• Self-service capabilities were limited
• Business units couldn’t track where customer orders were in the system
• System changes required vendor involvement, creating bottlenecks

What made this situation particularly compelling was the CEO’s personal involvement. After hearing consistent customer complaints about hold times and order visibility, he created a task force and set an ambitious goal: reduce hold times to under 1 minute by 2026.

Beneath the Surface: The Real Challenges

While technology replacement was the obvious solution, our analysis revealed deeper organizational challenges:

1. Business Alignment Gap: Seven business units with different operational models needed to agree on common requirements
2. Process Over Platform: Reducing 5-minute wrap-up times required process transformation, not just new technology
3. Self-Service Deficit: The “where’s my stuff?” problem wouldn’t be solved by agent tools alone
4. Transformation Readiness: The organization needed a clear roadmap connecting technology changes to the CEO’s ambitious goal

The Transformation Opportunity

The most successful contact center transformations address both technology and operational realities. Based on our experience guiding dozens of similar transformations annually, the path forward requires:

• Framing the initiative as customer experience transformation, not platform replacement
• Creating a multi-phase roadmap showing clear progress toward the CEO’s goal
• Leveraging the initiative to standardize customer experience across business units
• Emphasizing self-service capabilities that directly address customer pain points

Organizations facing similar challenges should ask themselves: “Are we replacing a platform, or transforming our customer experience?”

For enterprises with complex organizational structures, the answer to that question can mean the difference between a costly technology refresh and a genuine competitive advantage.

Want to learn how we’re helping organizations transform their contact centers into strategic customer experience assets? Contact our team to discuss your specific challenges.

Written by Simplify

The post 40-Minute Hold Times: How One Company’s Customer Experience Crisis Created the Perfect Storm for Transformation appeared first on Execs In The Know.