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

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

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.

This issue brings together some of the most forward-thinking perspectives, real-world case studies, and leadership insights shaping the future of CX. From predictive models to emotionally intelligent design, this quarter’s publication gives CX leaders the clarity and context to navigate a year that promises to be both transformative and deeply human.

Inside the January 2026 Issue

The Future Is Predictive: Inside the Shift Toward Anticipatory CX

The cover story breaks down the move from reactive service to anticipatory, predictive care, showing how agentic AI and strong governance turn insights into action before customers ever reach out. CX leaders will find a practical playbook for building a predictive framework rooted in ethics, data integrity, and human-in-the-loop (HITL) oversight.

How Quince Turned Customer Support Data into Predictive Insights

This real-world case study from Quince reveals how the brand replaced manual reporting with an AI-powered, self-service VoC model, unlocking faster decision-making across the business. A standout story for leaders looking to democratize insights at scale.

Brand Spotlight: Fanatics

Fanatics’ Vice President of Global Fan Experience, Maureen Barnett, offers a vivid look into how emotional connection becomes a competitive advantage. From turning service centers into “experience hubs” to redefining frontline roles, Fanatics shows how passion can transform loyalty.

Leading in 2026: Reclaiming Connection in an Age of Intelligent Isolation

One of the most striking pieces in the issue, this article explores the rising phenomenon of executive isolation and why connection has become a strategic imperative for CX leaders navigating AI acceleration, higher stakes, and shrinking reflective space.

KIA Spotlight: CHEP U.S.

James Glover, VP of CX & Quality at CHEP U.S., shares how predictive insights, circularity, and harmonized data foundations are reshaping supply chains with smarter, more resilient customer experiences.

The New Human Touch: Re-Earning Trust in the Age of AI

As AI becomes embedded across customer-facing interactions, from chat and voice to personalization engines, recommendations, and automated decisioning, customers are no longer evaluating technology in isolation. They are evaluating the brand judgment behind it.

Read and Download the January 2026 Issue

The new year brings both challenges and opportunities, and this issue offers the clarity CX leaders need to navigate them with confidence. The future of CX is anticipatory. And it’s still deeply, unmistakably human.

Read and download the magazine.

Want to contribute to a future issue? Visit our Get Involved page.

The post Introducing the January 2026 Issue of CX Insight: The Anticipation Era Has Arrived appeared first on Execs In The Know.

Artificial intelligence is no longer operating quietly behind the scenes. Today, it sits squarely on the frontline of customer experience. Virtual assistants greet customers before agents do. Bots resolve routine issues in seconds. Intelligent routing systems shape how quickly and easily customers reach help.

In many organizations, AI is now the first impression the brand makes.

Yet while AI adoption has accelerated, measurement has lagged. Customer satisfaction (CSAT), a long-standing metric for understanding service quality, has traditionally focused on human-led interactions. Calls, chats, and emails handled by agents are surveyed, scored, and optimized. AI-led interactions, however, often exist outside that framework.

Not because leaders don’t care, but because the rules for measuring AI satisfaction are still being written.

Redefining Quality in an AI-Led Experience

Early success metrics for AI focused heavily on efficiency: containment rates, handle-time reduction, and cost savings. Those outcomes matter. But efficiency alone doesn’t tell us how customers feel.

An AI interaction can be fast and technically correct and still leave a customer frustrated, uncertain, or disconnected. That’s where traditional operational metrics fall short. Measuring CSAT for AI isn’t about compliance or benchmarking for its own sake. It’s about understanding whether automation is actually delivering on the experience customers expect.

Leading organizations are beginning to experiment. Some deploy AI-specific post-interaction surveys. Others analyze sentiment, effort, and open-text feedback to infer satisfaction when surveys aren’t practical. The methods vary, but the intent is the same: listen, learn, and improve.

Why This Matters Now

Customer expectations have evolved. Curiosity about AI has given way to discernment. Customers know when they’re interacting with automation, and they increasingly judge it by clear standards: speed, clarity, accuracy, and the ability to escalate gracefully to a human when needed.

When AI is the front door, its performance shapes trust. Measuring satisfaction isn’t just about accountability; it’s about influence. It determines how AI is tuned, governed, and improved over time.

Recent consumer data underscores a growing tension. Self-service usage is rising sharply, yet satisfaction with those experiences consistently lags behind that of other support channels. The result is an emerging “experience gap”: customers rely on AI more than ever, but trust it less than they should.

When that gap goes unmeasured, it becomes invisible. And invisible problems don’t get fixed.

A Strategic Question

This is not a debate about whether CSAT is a perfect metric. It isn’t. But it remains a powerful signal. What leaders choose to measure sends a clear message about what matters.

Measuring AI satisfaction forces organizations to confront bigger questions:

• What does “good” look like in an automated experience?
• Where does responsibility sit when AI fails?
• How does feedback flow from customers into product, operations, and governance teams?

These are leadership questions, not tooling decisions.

The Bigger Picture

AI does not operate in isolation. Its success depends on back-office workflows, escalation paths, fulfillment systems, and cross-functional coordination. Measuring satisfaction at the surface without understanding what happens behind it creates blind spots and risk. The future of CX won’t be defined by how much AI is deployed, but by how intentionally it’s measured, governed, and improved.

And that starts with a simple question: If AI is representing your brand, do you really know how customers feel about it?

 Read the full article, Are You Measuring CSAT for AI?, to explore real-world data, examples, and the leadership questions shaping the next era of AI-driven customer experience.

The post Measuring Customer Satisfaction in AI-Led CX appeared first on Execs In The Know.

Customer experience is at a true inflection point. Customer and associate expectations continue to rise, and technology is accelerating faster than ever. And CX leaders everywhere are navigating a critical question: How do we scale efficiency without sacrificing empathy?

A People-Led, Tech-Powered Approach

Meghan’s keynote sits squarely at that intersection. With oversight of a vast, complex customer engagement ecosystem, she brings a rare blend of operational rigor and people-first leadership to the conversation, exactly what today’s evolving CX landscape demands.

In her session, Meghan will explore how Walmart is evolving its support model through a people-led, tech-powered approach that uses innovation to enhance, not replace, human connection. She’ll unpack how CX leaders can thoughtfully adopt new tools and capabilities while staying grounded in what matters most: trust, clarity, and care at every customer touchpoint.

Attendees can expect a real-world perspective on what it takes to modernize service operations at scale while keeping empathy at the center of the experience. Her keynote will dive into the real tension many leaders feel daily: the push for speed and efficiency versus the pull toward deeper, more human engagement.

As automation, AI, and self-service continue to reshape the customer and associate journeys, Meghan will offer insight into how leaders can design service experiences that are not only faster and smarter but also more meaningful. The result is a model of care that delivers operational excellence without losing the heart of the experience.

CX Without Compromise

CRS Amelia Island brings together senior CX leaders from across industries to share what’s working, what’s not, and what’s next. This year’s theme, CX Without Compromise: Setting the Standard for a New Era of Care, reflects a collective commitment to raising the bar on how organizations show up for their customers and their teams. Meghan’s perspective perfectly embodies that charge, connecting high-performance service strategy with purpose-driven leadership.

We’ll gather February 25–27, 2026, at The Ritz-Carlton, Amelia Island, for three days of immersive learning, candid peer conversations, and forward-looking insight from some of the most respected leaders in customer experience. Meghan joins a growing lineup of featured speakers who are actively shaping the future of CX across global organizations.

We’re offering $100 off Early Bird registration with promo code Jolly100, valid now through December 31. If you’re ready to explore how CX leaders are redefining care through the balance of innovation and humanity, CRS Amelia Island is where those conversations come to life.

You can learn more and register here.

We look forward to welcoming you to Amelia Island as we continue to set the standard together for customers, teams, and the future of customer experience.

The post How Walmart Is Redefining the Future of Care appeared first on Execs In The Know.

Agentic artificial intelligence (AI) represents a defining moment for customer experience (CX) transformation. Unlike traditional automation or FAQ-driven bots, agentic systems can understand intent, initiate and complete multi-step tasks, learn from outcomes, and support both human agents and customers in real time. Done well, agentic AI becomes an orchestration layer rather than a gatekeeper, giving frontline teams more time, clarity, and emotional bandwidth to focus on high-value engagements. 

But as this technology accelerates, one core tension has become visible: customer adoption is rising rapidly, and trust is not keeping pace. 

AI Adoption Is Growing — Trust Is Not 

Across consumer segments, more people are using AI for everyday interactions, from billing disputes and troubleshooting to scheduling, subscription management, account changes, and proactive alerts. Younger demographics are especially comfortable using AI as a first-touch resolution tool, and many report positive experiences when systems are intuitive, fast, and accurate.  

Yet our latest research report, Ethics, Adoption, and Opinion: Consumer Perspectives on AI for CX, developed in partnership with Microsoft, reveals a paradox every CX leader needs to understand.   

Consumer perception of companies that expand AI usage has shifted more negatively since 2023, even as adoption has grown. Consumers are demonstrating apparent willingness to engage with AI, but they are also becoming more critical, more discerning, and more vocal about where AI succeeds and where it falls short. 

Three patterns now define consumer confidence:

• Ease of use 
• Accuracy and comprehension 
• Access to a human when needed 

If AI is clear, fast, contextual, and correct, trust builds quickly. If it misunderstands intent, over-routes, forces repetitive clarification, or blocks escalation, trust erodes almost instantly. 

The research is conclusive: customers are not resistant to AI. They are resistant to feeling misunderstood, constrained, or unsupported. 

Performance Expectations Have Changed

The modern consumer no longer evaluates AI by novelty or interface sophistication. They assess it by outcome:

• Did it resolve the issue correctly on the first attempt? 
• Did it reduce friction rather than add it? 
• Did it feel transparent, respectful, and human-aware? 
• Could they reach a live agent without a battle? 

Agentic AI is uniquely positioned to meet these expectations because it enables complex, end-to-end orchestration without requiring the customer to navigate menus, repeat information, or wait for callbacks. The breakthrough is not automation; it is continuity, intelligence, and context. 

But one insight stands above the rest: Accuracy and comprehension are now the baseline for trust.

Customers are not rejecting AI. They are rejecting bad AI that creates friction or uncertainty. 

If the system cannot reliably understand intent, interpret nuance, or distinguish between routine and high-risk scenarios, the overall experience will feel unstable, even if the interface is polished.

Consumer Concerns Have Expanded Beyond CX

In 2023, most AI concerns centered on traditional CX frustrations:

• Lack of accuracy 
• Poor escalation paths 
• Scripted or robotic tone 
• Dead-ends or data repetition 
• Excessive containment 

In 2025, concerns have expanded. Consumers now raise broader questions about:

• Data ethics and privacy 
• Transparency in how systems learn 
• Fairness, safety, and information accuracy 
• Job displacement and societal impact 
• Automated decision-making beyond customer care  

77% of consumers say understanding a company’s AI ethics is extremely or very important.

Trust in AI is no longer a single-channel sentiment. It is a holistic reflection of how AI shows up in life, at work, in media, and in society. CX leaders suddenly find themselves shaping not only service interactions, but consumer perception of AI as a whole.

That is a powerful responsibility and a pivotal opportunity.

Human + AI: A Balanced Design Philosophy

The most forward-looking CX organizations are no longer pursuing automation as a replacement strategy. They are building orchestration models where agentic AI handles complexity, repetition, and structured tasks, while humans focus on emotional connection, judgment, and exception handling.

Examples of where agentic AI excels:

• Authentication and verification 
• Form completion and policy exploration 
• Billing adjustments or subscription changes 
• Claims intake and scheduling 
• Complex information retrieval 
• Routine troubleshooting or process navigation  

Examples of where human connection is irreplaceable:

• Moments of emotion, well-being, or vulnerability 
• Multi-party decisions with financial or personal impact 
• Situations where empathy, reassurance, or nuance matter 
• Escalations where accuracy alone is insufficient 
• Long-term loyalty building

AI should assist, not replace agents. When consumers know that help is available without friction, their willingness to use AI grows dramatically. Agentic systems build trust not by being perfect, but by being clear, capable, honest, and safely hand-off ready.

Responsible Adoption: A Leadership Framework

Responsible AI adoption is rapidly becoming a leadership imperative. As organizations introduce agentic capabilities, they must think not just about performance, but about:

• Confidence thresholds 
• Model improvement and governance 
• Transparency in intent and escalation 
• Data lineage and ethical learning 
• Human enablement and emotional well-being  

The most effective organizations are developing internal criteria to answer two essential questions:

1. Should this interaction be automated? 
2. Under what conditions, escalation paths, and quality controls?

This alignment transforms AI deployment from a technology decision into a trust and experience discipline.

A New Playbook for the Modern Contact Center

Agentic AI invites leaders to rethink service not as a queue, but as a dynamic engagement ecosystem. Contact centers that embrace this model are discovering:

• Deeper loyalty driven by reduced effort and increased accuracy 
• Higher agent satisfaction as cognitive load decreases 
• Faster knowledge cycles and lower error rates 
• Greater ability to prevent, not just resolve, issues 
• More relationship-building conversations and value creation 
• Clearer insight into behavioral patterns, needs, and sentiment

The contact center becomes a growth engine, not just a cost center. When automation handles complexity and humans focus on connection, both the business and the customer experience gain a strategic advantage.

Download the New Research 

Our brand-new research volume, developed in partnership with Microsoft: Ethics, Adoption, and Opinion: Consumer Perspectives on AI for CX, reveals groundbreaking consumer data and insight that CX leaders need to understand, including:

• Generational comfort gaps that shape channel preference, AI adoption patterns, and willingness to self-serve 
• Consumer concerns are expanding beyond CX, into ethics, data transparency, fairness, and workplace implications 
• Higher performance expectations, where AI must be not just fast but intelligent, accurate, and friction-free 
• Trust levers that matter most: accuracy, comprehension, transparency, and the right balance between AI and humans  

This research explores the widening trust gap, why it matters, and how CX organizations can design AI-powered experiences that feel reliable, respectful, and customer-centric.

You can download the full report here.

Experience the Research Live in Dallas — January 29, 2026

Be among the first to experience these insights in person, ask questions, and explore how other CX leaders are designing responsible, trust-centric AI.

We’re bringing our newest CX findings to Dallas, featuring:

• Exclusive analysis from our latest research 
• Real-world implications for AI adoption 
• Moderated, executive-level discussion led by Execs In The Know’s President & Co-Founder Chad McDaniel  

Where: Dallas, TX, at the Microsoft Innovation Hub
When: Thursday, January 29, 2026

Seats are limited. Register today. 

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