Proposed measures include requiring English proficiency, limiting certain types of foreign call handling, and allowing customers to request a U.S.-based agent, all of which signal a meaningful shift in how brands conduct their customer care operations. 

For customer experience (CX) leaders, this is not simply a regulatory development — it is a strategic inflection point. For decades, contact center models have been optimized around global delivery, balancing cost efficiency with scalability and coverage.

These proposals introduce a new compliance dynamic that could reshape how brands approach CX, potentially creating operational complexities for verticals subject to heavy regulation, including finance, healthcare, travel/hospitality, and other high-touch industries. Ultimately, this could become an opportunity to differentiate. Furthermore, if customers are given more control over where their support comes from, organizations may need to rethink their delivery models, potentially creating new, flexible approaches that might accommodate both preference and practicality.

This raises fundamental questions about cost structures, staffing strategies, and how brands define and deliver consistency across channels and regions. 

What CX Leaders Should Be Evaluating Right Now

As these conversations begin to take shape, CX leaders should consider engaging with their C-suite counterparts now. Understanding current exposure to offshore support, modeling the financial implications of increased onshoring, and evaluating the role of automation and artificial intelligence (AI) in facilitating necessary changes. In many ways, these proposals could accelerate trends already underway.

As labor constraints tighten and expectations rise, AI-powered self-service and agent-assist technologies may play an even more central role in maintaining efficiency while supporting a potentially more localized workforce. At the same time, organizations will need to carefully consider how they preserve experience quality, particularly as customer expectations around clarity, communication, and trust continue to evolve. Now is the time to analyze and understand both the go-forward plan and the eventual impact on customers. 

Transparency, Tradeoffs, and the Customer Experience

Communication with customers will also become a more strategic lever. If implemented, these proposals (particularly those related to consumer control over call routing) offer brands an opportunity to reframe parts of the service experience around choice and transparency, giving customers greater control over how they receive support. But that choice must be paired with clear expectations.

If requesting a U.S.-based agent introduces longer wait times or different service pathways, those tradeoffs will need to be communicated proactively and thoughtfully. Done proactively and well, this change could strengthen trust and reinforce a brand’s commitment to both service quality and customer assurance — an increasingly important differentiator in a landscape shaped by automation, AI, and heightened scrutiny around safety and security. Most leaders tasked with making these changes might benefit from getting ahead of the curve and engaging their counterparts and various stakeholders now. 

Preparing for 2027 Starts Now

Looking ahead to 2027, the organizations best positioned will be those that begin adapting (or planning steps to do so) now. This means evolving routing logic to account for customer preferences and regulatory requirements, reassessing workforce strategies to include more flexible domestic capacity, and strengthening quality assurance frameworks to align with emerging expectations around communication and performance.

Perhaps most importantly, it means shifting the mindset from reactive compliance to proactive design. These proposals are not just about where support happens … they are about how trust is built and maintained in every interaction. And in that sense, they may ultimately define the next chapter of customer experience. 

Look for more on this topic in the months to come, and practitioners can connect with peers in the Know It All (KIA) community to compare notes on getting ready for these imminent shifts. 

The post What CX Leaders Need to Know (and Do) as FCC Call Center Rules Advance 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.

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.

The post The New CX Question: What Should AI Own? appeared first on Execs In The Know.

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

The Execs In The Know Expert Network

Want to connect with Nick or learn more about his expertise? You can do that through the Execs In The Know Expert Network, a curated group of trusted CX professionals, each selected for their proven leadership and deep subject matter expertise across key areas of the customer experience discipline.

Access to the Expert Network is available exclusively to Know It All (KIA) members. Not a member yet? Join the free KIA community today and start tapping into CX expertise you can trust.

About John

The post Evolving CX Operating Model: Why Traditional Contact Center Structures Are Breaking Down 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.

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.

While most organizations have seen what AI can do operationally, a more important question is shaping the future of CX: Can AI earn customer trust? That question matters more than ever. Today, customers are not just reacting to a tool or a channel. They are reacting to the judgment behind the experience. Every automated message, recommendation, escalation path, and decision says something about the brand itself. When those moments feel clear, helpful, and respectful, trust grows. When they feel cold, confused, or overly automated, trust slips fast.

That is why trust is becoming one of the most important currencies in modern CX.

The brands getting this right understand something simple but easy to miss: the human touch has not gone away. It has just moved. It now lives in how AI experiences are designed, when automation is used, how clearly it is explained, and where human judgment steps in. This is where many organizations are feeling the pressure. AI can absolutely create efficiency. But efficiency alone does not create confidence. And customers can tell the difference.

AI may deliver speed and scale, but trust is what determines whether customers stay.

The real leaders in this space are asking sharper questions now. Does this interaction reflect how we want customers to feel about our brand? Are we being transparent enough? Do customers know when AI is involved? Have we made it easy to reach a human when the moment calls for nuance, empathy, or accountability?

The tension between efficiency and empathy is also overdue for a reset. Too often, these are treated like opposing forces. They are not. AI is powerful at speed, pattern recognition, and consistency. Humans are powerful at judgment, emotional nuance, and repair. The strongest CX strategies are not choosing one over the other. They are deliberately designing the relationship between them.

That means trust-first AI is not just about technology; it is about governance, training, measurement, and restraint. It is about helping teams build AI-assisted emotional intelligence and giving customers more visibility into what is happening and why. It is also about knowing when not to automate, when not to personalize, and when to let the customer lead.

That is the bigger idea behind The New Human Touch: Re-Earning Trust in the Age of AI, featured in the latest issue of CX Insight magazine.

AI may deliver speed and scale, but trust is what determines whether customers stay.

In the full article, we explore how CX leaders can design AI experiences that feel more human, why governance is now a frontline CX responsibility, how trust should be measured differently, and what customers really expect from brands using AI today.

Because AI may be transforming the mechanics of CX. But trust is still what determines whether customers come back.

Read the full article in CX Insight to explore what trust-first AI looks like in practice.

The post Trust Is the Real CX Differentiator appeared first on Execs In The Know.

As Derek Bell, VP of Product at Procedureflow, recently observed: ‘AI is being asked to operate, not just assist, but most organizations are still running document-based knowledge systems.’ This disconnect is creating what I call the execution gap, and it’s where many AI initiatives are stalling.

Procedureflow’s three-pillar approach: treating knowledge as a system, enabling agents by design, and providing controlled autonomy.

The Evolution: From Responder to Actor

Stage 1: AI as Responder (Chatbots) – Answer questions, deflect tickets, surface knowledge articles. Reactive and limited in scope.

Stage 2: AI as Assistant (Copilots) – Sit alongside agents, suggesting responses and pulling information. Humans make final decisions.

Stage 3: AI as Actor (Agentic Systems) – Execute tasks autonomously processing returns, updating accounts, coordinating across departments. They don’t just recommend; they do.

Most organizations manage all three stages simultaneously. A chatbot handles initial contact; a copilot assists escalations, and agentic workflows trigger in the background. According to McKinsey’s State of AI research, 23% of organizations are already scaling AI agents, yet operational governance remains the primary barrier to broader deployment.

The Hidden Gap: Intelligence Without Structure

Here’s the paradox: As AI becomes more capable, the need for operational clarity intensifies. A chatbot can mask knowledge gaps. A copilot can surface options without committing. But an agentic system that processes a refund incorrectly creates compliance risk, erodes trust, and violates policy.

As Derek Bell puts it: ‘AI doesn’t fail because it lacks intelligence; it fails because it isn’t connected to structured, executable knowledge.’

Consider a customer contacting support for a defective product outside the return window. An agentic system needs to know: Does this customer qualify for an exception? What is the approval threshold? Who gets notified? Which system gets updated first? Without structured decision logic, AI either freezes or acts on incomplete information.

What Makes Agentic AI Different

• Decision Authority vs. Decision Support – Copilots suggest. Agents decide. AI must operate within clear governance frameworks defining approval of thresholds, escalation triggers, and exception handling.
• Cross-System Orchestration – Agentic systems coordinate actions across multiple platforms. A simple ‘update my subscription’ might require changes in billing, CRM, fulfillment, and communication systems in a specific sequence.
• Accountability in Ambiguity – Humans handle edge cases through judgment. Agentic AI requires explicit rules for ambiguity. Organizations that haven’t defined these boundaries find their AI either over-escalates or under-escalates.

Four critical capabilities: guided execution following defined paths, deterministic outcomes for predictability, embedded guardrails for safety, and real governance for traceability.

Building the Foundation: From Knowledge Chaos to Operational Clarity

Leading organizations approaching agentic AI start with knowledge architecture, asking:

• Where does our operational knowledge live? For most companies, it’s fragmented across SharePoint, Slack, email threads, and tribal knowledge. Agentic AI requires centralized, structured, and version-controlled operational knowledge.
• How do we translate expertise into executable logic? The best agents make dozens of micro-decisions per interaction. Enabling AI to replicate this requires breaking down judgment into decision trees and conditional workflows. Organizations succeeding here treat processes as living systems, not static documents.

(Learn more about transforming process workflows.)

• How do we maintain consistency across channels? When policy changes, does it update everywhere AI needs it? Organizations with mature operational structures build single sources of truth that all systems including AI reference.
• How do we govern what AI can do? Define confidence thresholds (‘If certainty is below 85%, escalate’), impact boundaries (‘No AI decision exceeds $500’), and audit requirements.

A Framework for Leaders: The Four Pillars of AI-Ready Operations

Procedureflow serves three audiences: Knowledge Managers accelerate creation, Human Agents get increased automation, and Customers benefit through agentic APIs.

Pillar 1: Structured Decision Logic

Move from ‘here’s what we usually do’ to ‘here’s exactly what to do when X, Y, or Z occurs.’ Document decision trees, define variables, and codify exception handling.

Pillar 2: Governed Knowledge Systems

Establish a single source of truth for policies and procedures. Implement version control so changes propagate systematically. Create ownership accountability.

Pillar 3: Workflow Visibility

Map how work actually moves through your organization. Identify hand-offs, dependencies, and bottlenecks. Make implicit steps explicit. The most sophisticated deployments expose workflows through structured APIs that AI systems can query in real-time. Procedureflow’s Agentic API exemplifies this, enabling AI agents to follow defined steps while maintaining full traceability.

Pillar 4: Continuous Feedback Loops

Build mechanisms to capture when AI decisions work and when they don’t. Create review processes where edge cases inform knowledge updates.

The Leadership Imperative: Designing for Human-AI Collaboration

The goal isn’t to remove humans from customer experience, it’s to position them where they add most value. Agentic AI should handle routine execution, freeing humans to manage complexity, build relationships, and solve novel problems.

The maturity journey: Organizations progress from documented (text-based, SME reliant) through guided, collaborative, and strategic phases to innovative (AI chat bots, agent assist, agentic API).

This requires leaders to rethink workforce development. As AI takes on transactional work, the human role shifts toward judgment, empathy, and strategic intervention. Training must evolve from ‘how to process a return’ to ‘when AI guidance may not serve this customer’s best interest.

Transparency with customers is equally critical. Organizations leading in AI deployment are upfront about when AI is acting and how customers can request human review.

Starting Points: Where to Begin

Audit your knowledge infrastructure. Where are operational policies and decision rules documented? How easily could an AI system access them?

Map one end-to-end journey. Choose a common request password reset, return processing and document every decision point, system touch, and hand-off.

Define AI governance principles. What decisions should always involve humans? What are your risk tolerances? Establishing these boundaries prevents reactive policy making.

The Opportunity Ahead

The companies that will lead aren’t those with the most advanced AI they’re those with the operational clarity to deploy it safely and effectively.

The shift from copilot to agent requires building organizations where intelligence, automation, and human judgment work in concert. Where customers receive faster, more personalized service without sacrificing accuracy. Where frontline teams feel empowered rather than replaced.

Consider what becomes possible: A health insurance company automating complex eligibility determinations across 50 states while ensuring every decision follows current regulations. A financial services firm enabling 24/7 account servicing while maintaining audit trails. A utility provider orchestrating multi-system processes without workflow fragility.

These aren’t hypothetical. Organizations building on structured knowledge foundations are already achieving them. The early results from companies deploying governed agentic systems show dramatic improvements because when AI follows the right process, it works better, not just faster.

The question isn’t whether agentic AI will reshape customer experience it already is. The question is whether your organization will lead this transformation or react to it.

by Sarah Jeanneault, Procedureflow

About the Author

Sarah Jeanneault is VP of Marketing at Procedureflow

Sarah brings over 20+ years of experience leading growth-focused strategies and building customer-centric ecosystems that drive revenue, strengthen engagement, and increase long-term value. She has guided teams across startups and enterprises to achieve multi-million-dollar growth. Outside of work, Sarah enjoys skiing, biking, trail running, gardening, and baking sourdough bread.

About Procedureflow
Procedureflow simplifies knowledge management by turning complex processes into visual guides that are easy to navigate, while ensuring they remain accurate, collaborative, and compliant. Our visual and strategic approach to managing standard operating procedures helps organizations deliver trusted knowledge that scales and drives operational efficiencies. Clearly guided processes combined with task automation help employees quickly grasp critical details and execute requests with confidence. Create a unified knowledge source that anyone can navigate and count on to deliver exceptional service. Discover more at www.procedureflow.com.

Related Resources:

• Process Workflow Automation Guide – Practical approaches to transforming static processes into executable workflows
• Procedureflow Product Overview – Explore how leading organizations are building AI-ready operational systems

The post When AI Acts: Leading Through the Shift from Copilot to Agent appeared first on Execs In The Know.

This is anticipatory customer experience (CX).

And while the concept isn’t new, a powerful combination of predictive analytics, agentic artificial intelligence (AI), and disciplined governance is turning it from an aspiration into a competitive advantage.

Predictive Signals Are Becoming the New Starting Point

For years, CX teams focused on reacting well and resolving issues quickly, smoothing over friction, and optimizing the moments after something goes wrong. But predictive analytics has changed what’s possible. By analyzing patterns, signals, and customer behavior, organizations can now forecast:

• Likelihood to contact
• Churn or risk behaviors
• Sentiment shifts
• Next-best actions or offers

These insights are no longer interesting; they’re actionable. And when paired with agentic AI, they unlock anticipatory experiences that quietly remove effort from the customer’s day.

Examples include:

• A bank alerts a customer about a low balance before a fee hits.
• A healthcare provider sends check-in links hours before an appointment.
• An airline offers rebooking options before a connection is missed.

These moments aren’t flashy. But they build trust in ways reactive service never will.

Agentic AI Is Accelerating the Shift From Insight to Action

AI used to be assistive, including supporting agents, summarizing interactions, or helping customers self-serve. Today, agentic AI is elevating the model.

These systems can analyze data, recognize patterns, initiate tasks, and orchestrate workflows with little to no human intervention. That means brands can:

• Fix issues before customers notice
• Trigger resolution workflows automatically
• Deliver proactive communication at scale
• Reduce escalations and customer frustration

But here’s the nuance: the technology works only when leaders set guardrails around where AI belongs, where it doesn’t, and how human oversight ensures fairness, accuracy, and sanity checks. Without governance, predictive and agentic systems risk becoming chaotic rather than customer-centric.

When Done Well, Anticipatory CX Elevates the Human Experience

An overlooked benefit of anticipatory CX is the impact on frontline work. When AI handles the repetitive, predictable, or preventable moments (status updates, reminders, verifications), agents can focus on complex issues that truly require empathy and judgment.

The result?

• Better employee engagement
• Faster resolutions
• Lower operational costs
• More authentic interactions
• Higher customer trust

Anticipatory CX doesn’t replace humans. It makes space for them to be at their best.

Building an Anticipatory CX Playbook

The organizations that are getting this right aren’t simply deploying technology. They’re redesigning operating models, rewriting workflows, establishing experimentation labs, and ensuring human-in-the-loop (HITL) governance that keeps AI aligned with brand values.

Anticipatory CX is becoming the hallmark of mature, customer-centric organizations. And as technology accelerates, brands that don’t build these capabilities will fall behind those that do.

Read the full magazine article inside the latest issue of CX Insight to explore how forward-thinking organizations are moving from reactive to anticipatory CX, and what it takes to build your own playbook.

The post Why the Future of Customer Experience Happens Before the Customer Ever Asks appeared first on Execs In The Know.

URBN — the parent company to lifestyle brands including Anthropologie, Free People, Urban Outfitters, and Terrain — recently replaced a fragmented system of decision-tree bots with a single, unified agentic AI Agent.

This wasn’t a small pilot. It was a global rollout across seven countries and five languages, serving hundreds of thousands of customers monthly.

For leaders watching the AI space with a mix of interest and caution, URBN’s story provides a blueprint for shifting from simple automated answers to true, reliable resolution.

The “Button-Path” Ceiling

Before this shift, URBN relied on a standard “button-path” model operating on a rigid decision tree.

A customer clicks a button. The bot surfaces a “Top 10” FAQ. If the customer’s need falls outside that pre-programmed scope, the experience breaks.

Due to the nature of this type of system, URBN had to manage different bots for different regions. Each had its own logic, its own maintenance requirements, and its own limitations.

While the bot could service basic tasks and inquiries, it struggled with more complex customer service needs. For a global customer base expecting conversational fluency on WhatsApp and SMS, the button-path model had hit a hard ceiling. It wasn’t acting as an agent; it was acting as a search bar.

The Pivot: One Brain, Many Brands

The most significant part of URBN’s rollout wasn’t the speed but the consolidation.

Most global enterprises assume that complexity requires more layers. They build individual bots for every brand-market-language combination, resulting in a sprawl of “zombie bots” that are hard to update and harder to govern.

Joannah Holmes and her team took the opposite approach. They moved to a unified messaging system powered by a single AI “brain.”

The scope was immense:

• 4 Brands: Urban Outfitters, Free People, Anthropologie, and Terrain.
• 7 Countries: Including an expansion into Canada.
• 5 Languages: English, Italian, Spanish, French, and German.
• Channel Optimization: Replacing old-school bots on SMS and WhatsApp with one intelligent AI Agent, plus adding Apple Business Messaging for iOS users.

By working with Quiq to deploy a single, unified AI Agent capable of handling these variables, URBN eliminated the fragmentation. The AI Agent understands the context of where the customer is coming from, including:

• Which brand it’s engaging with.
• The right language to speak.
• Customer details, such as what they’ve purchased before and how long they’ve been a customer.
• Any previous interactions, like if the customer is picking up on a previous thread about a return they want to finish up.

The AI Agent then uses that information to seamlessly engage with the customer, resolving any issues and building deeper loyalty.

What “Agentic” Actually Means in Retail

There is a lot of noise around “Agentic AI.” At Quiq, we believe the definition is simple: Agentic AI goes beyond retrieving information to execute actions and resolve issues.

URBN’s implementation moves beyond the “read-only” mode of legacy chatbots. For example, their new AI Agent doesn’t just point customers to a shipping policy page. It handles the critical, complex workflows that actually drive resolutions.

For example, if a product arrives in poor condition, it triggers an emotional response from the customer. The AI Agent manages the workflow, collecting necessary details and offering a resolution, turning a negative moment into a neutral or positive one instantly.

The impact of automating these complex transactions was immediate. In the U.S. alone, the system began handling 4,000 to 5,000 conversations per day. 

Key Lessons for Retail Leaders

If you are a customer experience leader hesitant to replace your legacy systems, URBN’s story offers three clear directives.

1. Consolidate to Accelerate

You do not need 24 different bots to serve 4 brands and 6 languages. You need one intelligent AI Agent that understands context and has clear guardrails. Consolidation gives you better data, easier governance, and a consistent voice across your entire portfolio.

2. Move Beyond FAQs

If your bot only answers the “Top 10 Questions,” you are underutilizing the technology. The real ROI lies in transactional workflows. Look at your top contact drivers—claims, returns, adjustments—and ask if your AI Agent has the permission to actually fix them, or if it’s just describing them.

3. Prioritize Unified Messaging

Modern customer experience isn’t just on your website. URBN met customers where they already were: Apple Business Messaging and WhatsApp. By integrating these into a single AI flow, they removed the friction of the distinct “support channel”, creating seamless experiences across the AI Agent, humans, and channels.

The Future is Fast and Unified

The URBN story proves that “global scale” doesn’t have to mean “years of development.” With the right agentic strategy and partner, you can transform the customer experience for four major brands quickly and seamlessly.

As Joannah Holmes and her team have demonstrated, the future of retail CX is about being agentic, unified, and delivering resolution at the speed customers deserve.

I’m excited to join Joannah at the next Execs in the Know Customer Response Summit, where she’ll walk through URBN’s incredible AI Agent transformation. See you there!

By Mike Myer, CEO and Founder of Quiq

The post How URBN Outfitters Cracked the Code on AI Agents appeared first on Execs In The Know.