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

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

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

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

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

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

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

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

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

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

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

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

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

by Mike Lytle, Chief Delivery Officer, TP

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

Here is the uncomfortable truth:

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

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

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

This isn’t progress.
This is repackaged stagnation.

The Industry Is Building Franken-Platforms

Let’s name the problem plainly.

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

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

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

Because:

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

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

Hyperscale AI Isn’t the Answer Either

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

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

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

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

The Market Has Reached Its Breaking Point

Everyone is trying to patch their way into autonomy:

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

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

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

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

Introducing Omilia’s Self-Learning CX Agents

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

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

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

1. The Concierge Agent: Intelligent Routing Without Intents

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

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

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

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

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

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

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

Low Autonomy – Deterministic, predictable, fully explainable Agents

Perfect for regulated workflows.

Medium Autonomy – Agentic Fallback

Two powerful layers of resilience the industry has completely ignored:

• Belief State Updates
• Intelligent Agent Takeover

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

High Autonomy – Full Agentic Task Execution

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

3. FAQ Agents: Precision Knowledge Access

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

Responses adapted for voice and digital cx channel rendering.

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

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

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

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

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

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

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

The Future Belongs to Self-Improving CX Agents

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

But the next era?

Belongs to platforms that learn.

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

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

The industry is changing.
And Omilia is leading it.

About the Author

Claudio Rodrigues, Chief Product Officer, Omilia

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

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

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

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

Where Automation Goes Wrong — and Right

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

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

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

Deciding What Should Stay Human

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

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

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

Supporting the People Delivering the Experience

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

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

Designing Without Compromise

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

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

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

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

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

Written by Alta Resources

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

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

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

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

The Hidden and Measurable Cost of Latency

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

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

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

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

From Voice of the Customer to Voice of the Moment

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

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

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

The Operational Power of Multi-Agent AI

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

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

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

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

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

The CX Control Tower: Governance at Scale

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

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

Strategic Implications and Your Edge

This evolution demands leadership choices:

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

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

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

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

Written by Press’nXPress

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

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

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.

Across industries, executives are feeling the weight of that shift. The pressure to automate faster and show returns is colliding with rising customer intolerance for poorly designed, impersonal experiences. Sloppy AI interactions are no longer just frustrating; they are reputational risks. At the same time, organizations that have invested in strong data foundations, disciplined decision-making, and intentional human handoffs are beginning to see different outcomes—one in which efficiency and empathy are not competing forces but complementary ones. 

The perspectives that follow come from Execs In The Know Advisory Board members who are actively leading CX organizations through this moment. Their predictions reflect what they are already seeing unfold across frontline operations, data strategies, and customer expectations. Together, they offer a grounded view of what is defining CX in 2026, and what it will take to lead with clarity, credibility, and care in the year ahead.

What CX Leaders See Coming in 2026

“I’m no expert in the prediction markets, but if I had to make one call for 2026, it’s this: customers will finally revolt against poorly implemented chatbots and sloppy GenAI interactions. What was once just frustrating will multiply in droves—more bots, more botched experiences, and a bigger backlash. While executives collect bonuses for checking the AI box, customers will get burned by conversations that go nowhere, and companies that prioritize deployment over experience will pay the price in widespread defections.”

Lisa Oswald
Senior Vice President, Customer Service
Travelzoo 

“We stand at the intersection of artificial intelligence and authentic human connections. And many brands are facing a defining question: Technology is moving so fast that AI will soon be able to handle almost any customer interaction, but should it? 

At Michael Kors, we’ve already embraced agent-facing AI, empowering our Style Consultants with real-time intelligence, product knowledge, and customer insights to deliver exceptional service. But in 2026, we’re taking the next step in our AI journey: deploying customer-facing agentic AI that will handle routine interactions such as FAQs, order tracking, returns, price adjustments, and general inquiries: all the low and medium-complexity calls that can be handled in the brand voice and don’t require human nuance. 

Yet as we prepare for this transformation, we’re focused on achieving something more subtle: knowing when technology should step aside for the elevated and empathetic human touch. This Agentic Authenticity is the ability to blend AI efficiency with emotional human intelligence. It’s ensuring the technology recognizes those moments during an interaction when a customer needs more than a solution. They need empathy and understanding. 

Here’s a possible scenario: A customer will call about a handbag from our latest Collection line, one she’s been eyeing as a graduation gift for her daughter. Our agentic AI will handle the basics flawlessly: availability, shipping options, and care instructions. But then it will detect something in the customer’s voice: hesitation or uncertainty.  

The AI will recognize this reluctance and know when to transfer the customer to a Style Consultant who can help them overcome any concerns about the product. It may say something like: “This sounds like a really special gift. Let me connect you to one of our Style Consultants who knows our Collection line and can answer any questions you may have.” But the handoff won’t be blind. Our Style Consultant will receive real-time context – purchase history, style preferences, and the occasion. When they answer the call, they’ll already know this is about celebrating a milestone.  

Here’s what we’re learning as we build this future: The more sophisticated our AI becomes, the more valuable the human connection becomes. 

Looking ahead, CX won’t be measured by how much AI can do. It’ll be measured by how wisely we choose when it shouldn’t. By how seamlessly we blend efficiency with empathy and how we use technology to deliver more humanity, not less.”

Ebrahim Hyder
Vice President, Customer Experience
Michael Kors

“I believe the proliferation and implementation of Agentic AI tools will be the ‘Big Story’ in CX in 2026. For many, 2025 was spent exploring and planning Agentic AI solutions, with some limited deployments, but I believe 2026 is where we see these tools become embedded throughout. This will be impactful in all aspects of our daily work lives: back-end reporting & analytics, agent support systems, customer-facing interactions, etc.”

Paul Brandt
Chief Operations Officer
GoodLeap

“The single most important priority for CX in 2026 will be turning data into decisive, AI-powered action that measurably improves sales and service outcomes. Everything else, channels, journeys, and tech stacks, will be secondary to whether organizations can rapidly unlock and operationalize their data to fuel meaningful transformation. 

What Leaders Are Still Trying to Do
Every CX leader is chasing the same North Star: better business results through lower friction and higher loyalty. Teams want to eliminate effort, anticipate needs, and deliver proactive, personalized experiences that feel effortless to customers while still driving revenue, retention, and wallet share. The ambition is clear and consistent across industries; what varies wildly is the ability to execute. 

The Persistent Execution Gap
Despite major investments in platforms, data lakes, and journey orchestration, many organizations still struggle to connect insights to action in a repeatable, scalable way. Dashboards are full, but playbooks are empty. Teams know what is happening in the experience, but they lack a reliable, closed-loop mechanism to decide what to do next, who should do it, and how to measure impact in terms the C-suite cares about. This is the core CX execution gap that will define winners and losers in 2026.  

AI Pressure Without a Data Strategy
That execution gap is now colliding with intense pressure from the C-suite to “use AI” to transform customer experience. The risk is that organizations rush to deploy AI assistants, copilots, and automation without first building the data foundation and operating model those tools require. Without the right data strategy, AI is an empty shell: impressive demos, thin real-world impact. Leaders who treat AI as a strategy instead of a capability within a strategy will end up with sophisticated tools and no fuel to power them. 

Data as the CX Holy Grail
In 2026, the real differentiator will not be who has the most AI, but who can unlock, connect, and activate data the fastest to achieve clearly defined business outcomes. That means:  

• Unifying the right data across channels and systems around the customer and the journey, not the org chart.

• Defining explicit links between signals (behaviors, intents, friction points) and actions (offers, interventions, routing, outreach) that can be automated or guided in real time.

• Building feedback loops so every interaction makes the next one smarter for associates, for customers, and for the models themselves.   

Leaders who get this right will use data to orchestrate experiences that feel tailored, timely, and effortless, not just personalized in name only. 

Building the Foundation to Win with AI
Those that move fastest to build a strong data and decisioning foundation will extract outsized value from AI, whether through internal tools or external vendors. Their AI will be grounded in rich, trustworthy signals and embedded into workflows that actually change frontline behavior and customer outcomes. Those who skip this foundational work will build AI tools with no fuel: impressive user interfaces and clever capabilities that never scale beyond pilots because they lack the data, governance, and action frameworks to matter.  

The True Lever: Actionable Transformation
The organizations that win in 2026 will treat CX not as a reporting function but as a transformation engine. They will measure success by how effectively they translate insight into action at speed and scale actions that reduce friction, drive conversion, grow lifetime value, and deepen loyalty. The technology, including AI, will be an accelerator. The real advantage will come from a clear transformation strategy, a disciplined approach to data, and an operating model that makes it easy for teams to act on what they know. In other words, the future of CX belongs to those who build AI-ready foundations and then relentlessly focus on turning customer insight into customer impact.” 

Alvin Stokes
Vice President, Global Reservation and Service Operations, Guest Services
Princess Cruises

What This Means for CX Leaders 

Taken together, these predictions point to a defining truth for 2026: the future of CX will not belong to the organizations with the most advanced AI, but to those with the clearest judgment. Leaders who win will be the ones who know when to automate and when to escalate, when to trust the model and when to trust the human, and how to turn insight into action without losing empathy along the way. 

AI will be an accelerator, but leadership intent will remain the differentiator. In a year where customers are less patient and expectations are less forgiving, CX excellence will be measured not by what technology can do but by what leaders choose to protect, elevate, and act on. 

The post CX Leadership Predictions for 2026 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.

Contact centers are balancing rising customer expectations for speed, accuracy, and personalization against increasing complexity and cost pressure. AI is helping organizations meet those demands at scale. At the same time, the human elements of experience, including trust, understanding, and emotional connection, continue to shape how customers judge interactions and how brands earn loyalty. The result is a new CX equation: one where technology accelerates what’s possible, and empathy still defines what truly resonates.

Rather than viewing empathy and automation as opposing forces, many CX leaders are exploring how both can coexist within modern service ecosystems.

Empathy Has Always Been Part of CX

Customer experience has always involved an emotional dimension. Regardless of industry, customers typically arrive with some combination of urgency, uncertainty, frustration, or expectation. How those emotions are acknowledged often shapes whether an interaction is remembered as positive or negative, even when the outcome is identical.

Historically, this emotional work was carried out almost entirely by frontline teams. Agents listened, interpreted tone, adjusted their approach, and made decisions in real time, all without extensive automation support. Today, that dynamic is shifting. AI systems can now:

• Detect sentiment in voice and text interactions
• Adjust conversational responses dynamically
• Predict escalation risk
• Surface relevant history to agents in real time
• Automate post-interaction summaries and follow-ups

These capabilities create new possibilities for how empathy is delivered, supported, and measured within CX operations. What once depended solely on individual skill now often involves a partnership between human judgment and machine intelligence.

Artificial Empathy: Simulation, Support, or Substitute?

One of the most visible developments in this space is “artificial empathy,” AI systems designed to recognize emotional cues and respond with language that signals understanding, concern, or reassurance. In live chat and virtual assistance environments, empathetic phrasing has been shown to reduce customer friction, de-escalate tension, and improve conversational flow.

From an operational perspective, this capability can:

• Improve containment rates without sacrificing tone
• Create more consistent service language across interactions
• Reduce the emotional burden on agents for routine inquiries
• Enable 24/7 responsiveness at scale

At the same time, artificial empathy remains a simulation. AI does not experience emotion; it identifies patterns associated with emotional expression and generates responses based on training data and probabilities. For many routine or low-stakes interactions, this distinction may be irrelevant to outcomes. Customers often prioritize speed, clarity, and resolution above all else.

For other interactions like those involving financial hardship, health concerns, account recovery, or emotionally charged disputes, the presence of a human remains vital to many customers. In these moments, empathy is not simply linguistic; it’s expressed through judgment, discretion, and shared understanding of context that current systems cannot fully replicate.

As a result, organizations are increasingly designing hybrid models in which artificial and human empathy coexist.

Human-Centered CX in an Automated Environment

As automation handles an increasing share of routine interactions, the nature of human-agent work is changing. Contacts that reach live agents today tend to be:

• More complex
• More emotionally charged
• More policy-driven
• More likely to involve exceptions or escalations

This concentration effect has significant implications for workforce design, training, and support. Human-centered CX in an AI-enabled contact center often focuses on:

1. Agent Enablement

AI is increasingly used to support agents with real-time guidance, next-best-action prompts, compliance monitoring, and automated documentation. The goal is not to replace empathy, but to remove the friction that prevents agents from focusing on the customer.

2. Cognitive and Emotional Load Management

With higher-complexity interactions becoming the norm, agent well-being, recovery time, and emotional resilience become operational concerns.

3. Skills Evolution

The value of human agents is shifting from information recall to interpretation, judgment, negotiation, and relationship management. These are skills that remain difficult to automate at scale.

In this environment, empathy becomes less about repeated soft phrases and more about situational awareness, discretion, and trust-building in moments that truly matter.

Empathy as a Design and Operating Principle

Many CX leaders now approach empathy not only as a frontline behavior but as a system-level design principle. This shift influences how AI is implemented across the service ecosystem.

Organizations taking a human-centered design approach often focus on:

• Journey orchestration: Defining when automation is optimal and when human engagement should be prioritized
• Escalation design: Building intentional handoffs between virtual and live agents
• Context preservation: Ensuring customers do not repeat themselves across channels
• Transparency: Clearly signaling when customers are interacting with AI versus a human

From a governance standpoint, this also affects how success is measured. In addition to operational key performance indicators (KPIs), many organizations assess:

• Repeat contact rates
• Escalation patterns
• Sentiment trend shifts
• Customer trust indicators
• Brand perception following automation changes

Empathy, in this sense, becomes embedded in both experience architecture and performance strategy.

Transparency and Trust in AI-Driven CX

As virtual agents become more conversational and emotionally responsive, questions about transparency have become more critical. Customers increasingly encounter AI systems that resemble human interaction in tone and pacing. While this can improve usability, it also introduces new considerations for trust.

Many organizations are now establishing principles around:

• Clearly identifying AI-powered interactions
• Explaining handoff paths to human support
• Defining guardrails for emotionally sensitive use cases
• Ensuring customers retain access to live assistance when needed

These practices are not uniform across industries, but they reflect a broader recognition that transparency itself is a component of respectful, human-centered experience design.

Organizational Alignment Matters as Much as Technology

Technology alone does not determine whether a customer experience feels empathetic. Organizational incentives, policies, and performance metrics play a defining role.

In environments where speed, cost containment, and deflection dominate success measures, even well-designed AI systems may inadvertently deprioritize emotional quality. Conversely, organizations that align automation strategy with customer trust objectives often approach AI deployment differently, using automation to absorb transactional strain while reserving emotionally complex interactions for human teams.

This alignment extends across:

• Workforce planning
• Agent training
• Vendor selection
• Experience design
• Leadership priorities

Empathy in modern CX extends beyond how conversations sound to how decisions are structured behind the scenes.

Blended Experience Models

What’s emerging across many contact center environments is neither a fully automated future nor a return to human-only service models. Instead, CX leaders are building blended experience ecosystems in which:

• AI manages scale, speed, and consistency
• Humans manage judgment, nuance, and emotional complexity
• Data flows unify the experience across channels
• Design decisions shape where empathy is delivered artificially, humanly, or in combination

This blended model acknowledges both the practical advantages of automation and the enduring importance of human connection in moments of friction, vulnerability, and decision-making.

Empathy in the Next Chapter of CX

As AI becomes deeply woven into customer experience operations, empathy is not disappearing; it is being redistributed across digital and human touchpoints. The question for CX leaders is no longer whether empathy matters in an automated environment, but how it is defined, supported, and delivered at scale. The answers will differ by industry, customer base, and risk profile. Some organizations will lean heavily into virtual engagement. Others will preserve high-touch human service as a brand differentiator. Most will continue refining hybrid approaches as technologies and customer expectations evolve.

What remains consistent is this: trust, understanding, and emotional resonance still shape how customers evaluate their experiences. In an ecosystem increasingly powered by algorithms and automation, how those human elements are designed into the system will continue to influence loyalty, reputation, and long-term value.

Continue the Conversation at Customer Response Summit (CRS)

The questions surrounding AI, empathy, and the future of human-centered customer experience don’t have simple answers, and they’re best explored together.

Join CX leaders, practitioners, and technology innovators at CRS, February 25–27, 2025, at The Ritz-Carlton, Amelia Island. Through mainstage keynotes and panels, case studies, and peer-led breakout discussions, CRS offers a space to examine how empathy, automation, and performance intersect in today’s contact centers. We invite you to be part of the dialogue shaping what comes next.

Learn more and register today.

The post The New Architecture of Empathy in Customer Experience appeared first on Execs In The Know.

The call center industry is at a crossroads today, with tech seemingly overshadowing the people factor. Customer experience (CX) leaders and organizations are confronted with finding the right balance between a people-focused culture vs. tech overreach. While technology continues to drive impressive transformation across the CX landscape, there’s a growing risk: in our rush to innovate, we’re losing sight of what built this industry—people.

Today, the call center and business process outsourcing (BPO) industry is a $300 billion marketplace, projected to reach $500 billion by 2030, according to Grandview Research. The broader adoption of outsourcing, combined with innovative technology, has fueled the explosive growth of BPO within the customer experience ecosystem. The CX community has smartly embraced innovations that drive customer excellence. But lately, it seems tech is overshadowing the fundamentals of “call center 101.”

Let’s dive into this a bit deeper.

Tech Can’t Solve Everything

Since call centers were founded, we have been, we are, and we will always be a people-centric industry. Technology enables us to be more efficient and analytical. However, overreliance on tech is causing a “disturbance in the force.” In my interactions with leaders across our industry, there is a consensus that we are overwhelmed by technology, tech talk, and tech everything, so much so that we may be losing touch with the basics of daily blocking and tackling, most importantly, the people factor.

I am not an AI hater, an AI denier, or anti-tech. Like many of you, I welcome tech innovations, as they can enhance customer experience, agent performance, and much more. Much like the early days of the internet, AI is omnipresent and permanent. My concern isn’t about AI replacing humans in call centers. I’m more concerned that tech hysteria may distract organizations from the fundamentals required to effectuate CX in call center operations.

At the core, tech can’t run an effective agent focus group. Tech can’t create the right culture in your organization. Tech can’t coach/counsel a call center agent who needs mentoring and direction. Nor can tech teach leadership skills and groom today’s agents to become tomorrow’s management rockstars. Tech can’t solve every staffing or workforce management problem, a client solutions crisis, or any other issue that requires critical thinking, intuition, experience, and instinct. Tech is a tool to enhance, not replace, human connection.

The Lost Art of Communication

As our industry’s reliance on technology continues, are today’s call centers becoming a reflection of society? We complain that technology is slowly diminishing the art of human connection and communication. That our kids are raised in a culture where interpersonal skills are secondary to tech-talk nomenclature and social media idioms that dominate our language today.

Are we so over-focused on technology that we’re losing the art of communication with our agents, leaders, and peers? I hear complaints about this from every corner of our industry. We banter and joke among ourselves that “we don’t talk anymore.” However, this issue seems to be more real now. Organizations are hyper-focused on becoming tech leaders and latching onto the latest AI because they’re afraid of losing market share. This is understandable, but you must extend the same passion to your people, who are still the heart and soul of your operation.

Are we effectively communicating with people in our organizations? Are we fostering open dialogue and measuring sentiment? Are we taking feedback to drive organizational success and cultural synergy? Or are we hyper-focused on AI, automation, and other technologies as our foremost priority over people?

The future is uncertain, but you can manage the controllables today. Losing touch with your people will put you at a greater disadvantage than losing the technology race vs. your competitors.

And no tech solution can fix a broken culture.

Leading With Tech – Forgetting the Basics

This new wave of tech hysteria seems existential for many organizations, which fear they will become surplus to requirements due to AI. Maybe, or maybe not. We don’t know the answer, and it is too early to tell. Again, let’s put this issue on the back burner for now and wait and see how things shape up in the CX industry in the years to come.

My chief concern is that too many BPOs are trying to become technology companies like “Microsoft” and “Google,” leading with tech solutions first rather than talking about how they will deliver on quality, culture, reduce attrition, manage performance, manage a crisis, etc. As I mentioned earlier, the basics of “call center 101.”

An abundance of CX leaders have expressed their displeasure with BPOs that overlook the client’s stated needs and overload sales pitches with technical solutions and jargon. Many BPOs present themselves as focused on call center 101, then bait and switch clients into tech sales pitches. Again, I’m not a tech hater. However, the fact is that not every CX leader or brand is interested in purchasing your tech stack or prioritizing AI over people. Tech is among the many priorities that CX leaders are dealing with, not always “the” priority.

Numerous brands and CX leaders seek BPOs that focus on the essence of effective call center management, driven by, supported by, and augmented by effective technology—not the other way around.

Tech vs. Culture Growth

How many AI sales pitches do you receive daily? At some CX conferences, sessions are overwhelmingly focused on technology-related solutions. What happened to peer-to-peer knowledge sharing on other fundamental aspects of effective CX? Like how to conduct effective agent and leadership training. Or how to create a partnership with your BPO, or manage client expectations successfully, or create the right culture.

I enjoy listening to leaders share their journey—this is inspiring. I remember when BPOs and clients alike would invite motivational speakers to give “TED”-like talks to rally and motivate their teams. Are we still doing this? Many of today’s CX leaders, like yours truly, were coached and mentored by our predecessors. They taught us how call centers function at a molecular level and how to lead effectively. We learned to appreciate the essentials of people management, processes, and, of course, technology.

The best BPOs prioritize culture and employee sentiment above all else. They understand that successful CX relies on productive agents, experienced leadership, and innovative tech solutions. The best tech stack in the world cannot create an inspiring culture that motivates people, retains employees, and produces exceptional results. And the best tech stack can never replace effective frontline leadership who are trained, skilled, and ready to take on the most complex CX challenges.

What Do Your Fellow CX Leaders Think?

We asked a few of our fellow CX leaders to share their thoughts, and here is some fascinating insight:

“AI right now is great at handling most routine tasks, basic questions, simple transactions, and standard requests. But that means human agents are now dealing with the more complex, emotionally charged, or high-stakes interactions. So, while AI boosts efficiency, it also raises the bar for people on the front lines. That is why it is more important than ever to invest in agent training and development—because human skills like empathy, problem-solving, and adaptability are becoming even more valuable. Even as AI technology improves, this dynamic will continue and likely become even more pronounced. “

John Parente | Director, Contact Center Operations, Stanley Steemer

“In the rush to implement AI technology in contact centers, it’s vital not to lose sight of what truly sets exceptional service apart: the human element. While automation can enhance efficiency, it is emotional intelligence, genuine human interaction, and the thoughtful development of skilled agents that build lasting relationships, foster client trust, and establish BPOs as indispensable business partners. True innovation lies in blending smart technology with the irreplaceable power of human empathy and connection.”

April A. Sealy | Executive Vice President, Elevate

“I look at AI as a set of tools that improves both customer and employee satisfaction by driving excellence in execution. AI is a piece of the picture, not a replacement for what agents do well. We need to help our agents gain proficiency faster while providing training experiences that cement great CX practices. Through high levels of achievement, agents gain more job satisfaction, and end customers are better supported, driving higher-end results. AI is a piece of the picture, not a replacement for what is done in great ways by agents.”

Mike Nessler | Executive Chairman, Support Services Group (SSG)

“BPOs must remain focused on what built their business– investing in their people, coaching them, and building a strong culture to best support their teams and clients.  In the rush to differentiate themselves through expanded services (software, analytics, AI-driven agent assist, etc.), BPOs cannot afford to take their eye off the ball.  Having a high level of engagement with their teams allows them to provide the best possible service.”

Pablo Pareja | Director of Call Center Vendor Management, Cox Communications

Summary

In the immortal words of Depeche Mode and their 1984 song “People are People”—truer words were never spoken. This sentiment should be the theme song for BPOs and the CX ecosystem because, no matter what technological innovations emerge, people drive technology, and people are required to perform the fundamentals for call centers to succeed.

I can’t tell you how many outsourcing contracts BPOs lose because they no longer do the basics right. Many are leapfrogging into tech so quickly that they’re forgetting the foundational requirements to win and retain their clients in this highly competitive industry, which is poised for exciting growth.

The future of CX belongs to those who can harness the best of both worlds—cutting-edge technology and deeply human leadership. Let’s not trade one for the other but elevate both together. Technology will continue to evolve, but our true competitive advantage will always stem from how well we support, lead, and invest in people. Let’s never forget culture isn’t created by code—it’s built through connection.

The Execs In The Know Expert Network

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About Nick

Nick’s professional journey started humbly in 1986 as a call center agent in the very early days of the industry. What started as a “call center job” ignited his passion for people and led to a lifelong career in CX and outsourcing. Nick helped pioneer the business process outsourcing (BPO) industry as we know it today, initially in operations and ultimately as an executive leader.

Nick’s thought leadership and vision led to the creation of CustomerServ, which is a natural progression of his devotion to world-class customer service. Founded in 2006, CustomerServ is the BPO industry’s authentic outsourcing ecosystem, crystallizing the rapidly changing landscape, to help corporate brands outsource smarter with the right BPO partners.

Nick is a thought leader, author, BPO market expert, relationship builder, mentor, impact sourcing leader, and advocate for responsible business practices. His vision has led to over 100,000 new call center jobs worldwide and nearly $3.5 billion in successful outsourcing contracts.

Nick was born in Tanzania, emigrated to the USA in 1974, and his first language was Swahili. Nick is a graduate of City University of New York with a bachelor’s in business administration and a minor in art history. He’s a WWII buff, a vintage collector, a soccer fanatic, and an 80s music nut. Nick resides in Houston, TX, and he channels his passion for life into his family, community, and advocacy.

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For years, customer satisfaction (CSAT) scores have served as a compass for service teams. But most measurement models still focus on human-assisted channels: calls, chats, or emails managed by agents. AI interactions often sit outside that framework — not out of neglect, but because traditional metrics haven’t kept pace with automation.

The challenge is clear: as AI becomes the voice of your brand, your satisfaction measurement must evolve, too.

Rethinking What “Good” Looks Like

Early AI success was defined by efficiency: shorter handle times, higher containment rates, and lower costs. Those remain valuable, but efficiency alone doesn’t equal satisfaction. A chatbot can resolve an issue quickly and still leave a customer feeling unheard or frustrated.

Today’s leaders are broadening their lens. Some are layering sentiment data and post-AI surveys into their analytics. Others use predictive tools like expected NPS (xNPS) or customer success scores to estimate satisfaction in real time. The goal isn’t more data; it’s better context.

Legacy metrics were built for a human-centric era. Measuring AI means expanding the definition of quality to include emotion, effort, and trust. You can’t optimize what you’re not measuring, and you can’t measure AI the same way you measure people.

Why It Matters More Than Ever

In 2023, customers were curious about AI. By 2025, they’re discerning. They know when they’re interacting with a bot, and they have expectations. The first impression your AI makes is the first impression your brand makes. When satisfaction isn’t tracked or understood, missteps can happen fast.

AI’s reputation depends on listening, learning, and iterating with the same rigor you apply to human teams. Our 2025 CX Leaders Trends & Insights: Consumer Edition report, created in partnership with Transcom, revealed that while 78% of consumers now use self-help tools, these rank lowest in satisfaction across all channels, a widening “Experience Gap” between what customers expect and what they receive.

Bridging the Gap

Measuring AI satisfaction isn’t just a tech initiative; it’s a leadership one. It demands collaboration across CX, operations, and technology teams to define success, build new metrics, and ensure back-office systems support the experience AI promises upfront.

Because at the end of the day, customer satisfaction isn’t about checking a box. It’s about proving you care. And that applies whether the “agent” is human or machine.

Want to explore how leading brands are redefining measurement for the AI era? Read the full article Are You Measuring CSAT for AI? in the latest issue of CX Insight magazine.

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