Jyotsna Khandekar sits at the center of that work. As a Director of Product Management, she leads the customer-facing digital platforms and AI initiatives that serve millions of users, including Cisco Community and the systems that surface the right insights to the right customers at the right time. Over 17 years in technology, spanning go-to-market, partner commerce, and now customer experience, she’s built a rare, end-to-end view of how product, revenue, and customer relationships all connect.

We sat down with Jyotsna to talk about where AI is genuinely improving customer experience, what it actually takes to build trust in AI systems, and why she believes the next real competitive edge isn’t your product or your data; it’s how fast your organization can turn customer understanding into action.

Execs In The Know (EITK): AI is transforming customer experiences across industries. What opportunities excite you most, and where do you see organizations getting it wrong?

Jyotsna: What excites me most is the shift AI makes possible in customer experience — moving from reactive to proactive, predictive, and personalized. For most of our support history, we waited for a customer to hit a wall and come to us. AI lets us flip that: anticipate what a customer needs and meet them with the right help at the right moment, sometimes before they’ve even had to ask. That’s a fundamentally better experience, and we’re only at the beginning of it.

Where organizations get it wrong is in starting with technology rather than the customer. There’s a lot of pressure right now to “have an AI story,” which pushes teams to adopt AI first and then hunt for a problem to attach it to. The ones who get it right do the reverse; they start with a real customer problem and ask whether AI is genuinely the best way to solve it. The other trap is optimizing for internal efficiency at the customer’s expense, measuring success by what the company saves rather than the value the customer actually gains.

EITK: Trust has become a critical topic in the age of AI. What does “trustworthy AI” mean to you, and how should organizations build trust with customers while adopting new technologies?

Jyotsna: To me, trustworthy AI comes down to three things: visibility, governance, and safety.

Visibility means you can observe which model handled a request and what data and actions were involved, audit the decisions afterward, and explain the output with clear reasoning and sources. If you can’t see what the system did and why, you can’t trust it.

Governance means real human oversight, with clear conditions for when a person can step in and when they should.

And safety means guardrails: monitoring for abnormal behavior, setting stop conditions, and hardening against attacks. When you can account for all three, and put that visibility in the customer’s hands, the AI earns trust.

Working at Cisco shapes how I see this: trust in the AI era depends on the full stack working together — the network that carries it, the security that protects it, and the observability that lets you see what’s actually happening end-to-end. That’s how you build trust with customers, too: by showing them the system is observable, governed, and safe, not just telling them.

EITK: What’s one AI myth you’d like to debunk?

Jyotsna: One myth I’d debunk is that keeping a human in the loop is a weakness, a sign your AI isn’t good enough yet. I see it the opposite way: it’s a sign of maturity, because it means you understand the stakes. Autonomy without boundaries isn’t innovation, it’s risk.

The goal isn’t to remove people as fast as possible; it’s to know exactly where human judgment matters most and design for it.

EITK: What’s one CX metric you pay closest attention to?

Jyotsna: The one I watch closest is adoption depth — how much of what a customer already owns they’re actually using. I care about it because it’s a leading indicator, not a lagging one: a renewal tells you what has already happened, while adoption depth tells you, months ahead, whether a customer is getting value and is likely to stay. It’s also what my team directly works to move — sending customers the right insights through the right channels about what they’re using and what they’re not yet using, so we’re actively closing that gap. If someone’s using only a fraction of what they’ve invested in, that’s a signal to act now. And if they’re deeply adopted, satisfaction and renewal tend to take care of themselves.

EITK: What technology trend are people underestimating right now?

Jyotsna: This one isn’t about a specific model or chip; it’s about where competitive advantage actually comes from now.

For a long time, the moat was your product or your data. But AI has changed that: product ideas can be copied and rebuilt faster than ever, and data alone is everywhere. So in my view, the real moat now is context, and what I’d call contextual velocity. Context is how deeply you understand your users; contextual velocity is how fast that understanding flows through your organization so you can act on it and turn it into relevant experiences for customers. When anyone can copy the product, what differentiates you is the context you hold and how quickly you can put it to work. People are only starting to realize this, but it’s becoming the real differentiator.

This is the first installment of 5 Questions With, a new blog series in which we sit down with CX leaders from different brands to hear how they’re thinking about customer experience today. Keep an eye out for the next conversation.

The post 5 Questions with Cisco’s Director of Product Management appeared first on Execs In The Know.

That gap between what the data says and what customers actually feel is the defining challenge for customer experience (CX) leaders right now. And it is exactly what keynote speaker Julie Weingardt, Chief Operations Officer at Turo, is bringing to the mainstage at CRS Scottsdale.

There is a quiet crisis happening inside organizations that, by every measurable standard, appear to be doing everything right. Operational benchmarks are met, and satisfaction scores hold steady. And yet trust, the thing that actually drives repeat business and long-term loyalty, is eroding in ways the traditional dashboard was never built to detect.

Weingardt has a name for this problem: dashboard blindness. It is what happens when organizations confuse hitting their numbers with understanding their customers. When the metrics are green, but the friction is real, the disconnect does not stay invisible for long. It shows up in social feeds, in churn, and eventually, in revenue.

The Signal in the Silence

One of the most important insights Weingardt will unpack is what unrated interactions reveal. An interaction that goes unscored is not a neutral data point. It is a gap, and those gaps have consequences that quietly compound over time.

As automation becomes a larger part of the customer journey, the moments where customers disengage without providing feedback deserve far more attention than most organizations give them. The absence of a response is itself a response.

The Pressure Every CX Leader Feels Right Now

There is no avoiding the conversation about artificial intelligence (AI). The pressure to reduce costs through automation is real, it is coming from the top, and it is not going away. But Weingardt will challenge CX leaders to think carefully about what gets lost when the pendulum swings too far.

Human empathy, speed in the right context, and the ability to read nuance are not luxuries. They are operational assets with a measurable impact on customer trust. The question is not whether to adopt AI. The question is how to evaluate the trade-offs honestly, using frameworks that account for what a cost-savings analysis alone will never show you.

This is the cost-trust paradox, and navigating it well is quickly becoming one of the most important skills a CX leader can develop.

From Satisfaction to Advocacy

The session is ultimately a call to rethink the entire leadership model around customer loyalty. Satisfaction, as a goal, is reactive by nature. You wait for the interaction to happen, measure how it went, and adjust. Advocacy requires something different: a proactive, systematic approach to how organizations listen, what they measure, and how they scale the kind of experiences that build loyalty over time.

That shift is harder than it sounds, especially inside organizations that have spent years optimizing for the metrics they know how to measure. But it is the work that separates companies’ customers return to from companies customers tolerate.

Why This Conversation Matters Now

The timing could not be more relevant. As AI capabilities expand and budget pressures intensify, the temptation to optimize for efficiency at the expense of connection has never been stronger. Weingardt brings a practitioner’s perspective to a conversation the industry needs to have with honesty and urgency.

Whether you lead CX for retail, a financial institution, healthcare, or anywhere in between, the dynamics she is describing are not unique to Turo. They are the defining tensions of the moment.

Hear Weingardt’s full keynote at CRS Scottsdale. Learn more and register to attend.

The post Why Trust is a CX Metric That Matters appeared first on Execs In The Know.

These shifts were at the center of our recent Outsourcing Briefing, held on May 7, 2026, at Microsoft Headquarters in Redmond. The one-day executive gathering brought together CX leaders for candid conversations about the realities of outsourcing in an AI-driven world, from workforce transformation and operational governance to trust, performance, and the evolving role of strategic partnerships.

Here are some of the biggest themes and takeaways that emerged throughout the day.

Turnover isn’t just a BPO problem; it’s a shared cost

Nearly every leader in the room is wrestling with attrition and has started building it into the contract.

One approach: instead of chasing a raw turnover number, track time to proficiency: how long it actually takes an agent to become fully productive on your specific product, and tie incentives to retaining people past that point. For complex products, that number can be well over a year. Once a partner’s team clears that threshold, they gain access to higher-volume or growth opportunities during peak season. It reframes retention as a shared investment rather than a penalty clause.

Another leader described a simpler version: rather than tracking 10 KPIs, pick 2 or 3 that actually matter, and cap how much a partner’s top-performing quartile can shrink in a given month. Fewer metrics, tighter guardrails, and less noise.

The consensus: if turnover isn’t written into your service level agreements (SLAs) yet, it probably should be because even when a partner backfills a seat for free, you’re still paying for it in longer handle times and lost institutional knowledge.

Some companies are quietly rethinking the phone call

One of the more candid moments came from a leader whose team eliminated voice support almost entirely, pushing customers to a self-service portal to reduce inefficient back-and-forth. It worked until it didn’t. For a subset of high-value customers doing complex annual transactions, the lack of a phone option became a genuine trust problem, and the team is now building a more targeted (and possibly paid) phone experience back in.

The lesson wasn’t “don’t cut voice” or “always keep voice”; it was that channel strategy needs to be segmented by customer, not applied as a blanket policy.

Onshore vs. offshore

More than one attendee pushed back hard on the old mental model of “our team” versus “the BPO.” Their approach: every location, including domestic work-from-home teams, gets a site code and is managed to the same standard. No special treatment for the U.S. team, no lower bar for an offshore site. Leaders who’ve made this shift report that it kills the “us vs. them” dynamic that quietly undermines many outsourcing relationships, and in some cases, offshore teams have gone on to outperform the domestic team once expectations are equalized.

Transparency reinforced this. Several leaders described sharing performance data openly across partners by letting one BPO see how another is performing on the same account as a surprisingly effective way to build trust and drive improvement, rather than a competitive risk.

Vetting a partner properly

This was maybe the most tactical, immediately useful advice of the day: when you visit a potential BPO site, don’t just talk to leadership. Ask to speak with frontline agents without their managers present.

The same skepticism applies to the numbers behind the business. A few leaders said they’ve started looping in finance and audit teams to vet a partner’s financial health directly, not just data security, because a BPO that looks great in a pitch deck can still be late paying its own vendors. With consolidation picking up across the outsourcing industry, that diligence is becoming more important, not less.

Regional consideration is getting more nuanced

Geography came up constantly, but not as a straightforward cost conversation, more as a question of fit. The Philippines remains a major hub and was described as a safe, proven choice for a reason, but several leaders pushed back on treating it as a default. A few flagged longer ramp times for complex, less-scripted products, and there was real debate about whether the market’s famously warm, accommodating service style is always the right match. Some customer bases respond better to a more direct, no-frills tone, and that mismatch can show up in satisfaction scores in ways that are easy to miss if you’re only looking at cost and language proficiency.

The most notable shift was geographic curiosity: multiple leaders are actively expanding into or exploring Morocco, Nigeria, Kenya, Ethiopia, and South Africa, citing strong English proficiency and labor quality alongside open questions about political stability and operational risk that the group agreed are worth investigating firsthand. Nobody in the room described these markets as fully proven yet; the tone was less “we’ve cracked it” and more “we’re piloting carefully and comparing notes with peers who are doing the same,” which is exactly the kind of ground-truth intelligence that’s hard to get anywhere except a room like this one.

Underneath both points was a broader shift in how leaders are evaluating location altogether: less “where is labor cheapest” and more “where can we find the right combination of skill, tone, stability, and scalability for this specific line of business.” A single global outsourcing strategy is giving way to a more deliberately blended footprint, matched to the complexity and sensitivity of the work being handled.

Outcome-based pricing has momentum

There’s a real appetite for moving away from pure productive-hour pricing toward outcome- or value-based models. But leaders running businesses with many product lines and transaction types described the practical challenge honestly: it’s hard to set fair, meaningful targets when your business has dozens of different call types, each with its own effort and value profile.

Nobody in the room claimed to have fully solved this, but the direction of travel was clear.

AI is the backdrop to every one of these conversations

AI didn’t dominate the discussion, but it ran underneath all of it as the thing that will keep pulling the “mundane, repetitive” work out of outsourced teams over time, and as a source of new tension (agents optimizing for first-contact resolution sometimes over-extend calls trying to preempt every possible follow-up question).

Those in attendance don’t think outsourcing is going away because of AI. If anything, the room’s read was the opposite: outsourcing is likely to grow as a strategic lever over the next few years, not shrink, with AI reshaping what gets outsourced rather than whether it does.

As customer expectations, AI capabilities, and outsourcing strategies continue to evolve, ongoing peer dialogue has never been more valuable.

Want in on the next conversation?

Keep an eye on our leadership events page and join us at an upcoming Outsourcing Briefing in Spring 2027 to connect with fellow CX leaders, exchange practical insights, and explore the strategies shaping the future of customer experience outsourcing. Whether you’re reevaluating existing partnerships or building your next-generation operating model, these discussions are designed to help you lead with greater confidence and clarity.

The post What We Learned About CX Outsourcing at Microsoft HQ appeared first on Execs In The Know.

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

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

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

What Actually Frustrates Customers

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

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

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

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

What Transparency Looks Like

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

They’re doing a few things deliberately:

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

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

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

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

Some Interactions Shouldn’t Be Handled by AI

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

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

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

Are You Solving for the Wrong Problem?

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

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

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

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

Join our mailing list to receive event invites.

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

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

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

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

Turning AI into Operational Impact

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

The same research underscores the tension:

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

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

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

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

From AI Possibility to AI Proof

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

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

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

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

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

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

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

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

Consumers Still Favor Live Agents, but the Gap Is Closing

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

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

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

Performance Over Personality

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

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

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

Complexity Changes Everything

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

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

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

What Live Agents Need to Get Better At

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

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

The Bigger Picture for CX Leaders

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

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

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

Download the Full Report →

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

Uber decided to do something different.

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

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

The Three Measuring Levers 

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

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

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

The Lessons Were Hard-Won

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

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

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

What’s Next for Digital Experience at Global Scale

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

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

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

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

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

But something else is happening at the same time, and it deserves equal attention: the interactions that reach human agents are becoming more complex, more emotionally charged, and more consequential than ever before.

That’s an opportunity. And the brands that recognize it are pulling ahead.

The Shift Nobody Is Talking About Loudly Enough

When AI handles the easy stuff, what’s left for human agents isn’t the leftovers; it’s the moments that define the brand. Service recovery. High-stakes exceptions. Customers who are frustrated, confused, or vulnerable. These are the interactions where trust is either deepened or permanently lost.

Research backs this up. According to Qualtrics, when a customer issue is resolved positively, that customer is likely to demonstrate greater loyalty over time than someone who never encountered a problem. This is the Service Recovery Paradox, and it only works when a human agent has the empathy, judgment, and autonomy to execute it.

Scripts don’t get you there. Rigid escalation paths don’t get you there. Empowered agents do.

The 18-Point Lesson from Southwest Airlines

At CRS Amelia Island, Kevin McDorman, VP of Customer Care at Southwest Airlines, made a point that reframed the entire conversation around CX transformation: the frontline isn’t where strategy gets executed; it is the strategy.

His argument wasn’t anti-technology. It was pro-human, with intention. As automation absorbs more transactional contacts, the human role must be elevated with equal deliberateness. That means moving from rigid scripts to guardrails that give agents room to exercise judgment. It means investing in soft skills (active listening, empathy, problem-solving) with the same rigor as any technology deployment.

Because customers don’t experience transformation through a roadmap. They experience it in the moment they need help.

What AI Can Genuinely Do for Your Agents

Here’s where it gets interesting: AI isn’t just replacing agent work; it’s also making agents better at their jobs. Simulation-based onboarding tools are helping new agents handle complex scenarios before they encounter them in real time. AI co-pilots are supporting agents in real time with knowledge retrieval, sentiment analysis, and next-best-action guidance. Auto-summarization is reducing after-call work so agents can focus on the customer in front of them.

The companies winning this transition aren’t choosing between AI and humans. They’re designing operating models where each does what it does best and where the intersection between them is intentional, not accidental.

Looking Ahead to 2030

The contact center of 2030 looks meaningfully different from today’s. Agent profiles will shift, and compensation structures will evolve. Hiring, onboarding, and career pathing will be redesigned around a new contact mix that demands higher emotional intelligence and sharper judgment.

The organizations that start building toward that now, investing in frontline empowerment as infrastructure, not afterthought, will have a structural advantage that’s hard to replicate later.

The question isn’t whether to automate. It’s whether you’re elevating your people at the same pace.

Read the full article in the April issue of CX Insight magazine →

The post Why Human Agents Are Your Most Important CX Asset appeared first on Execs In The Know.

Artificial intelligence (AI) is absorbing routine interactions at an unprecedented scale. Diversity, equity, and inclusion (DEI) commitments are being publicly tested. Frontline agents are navigating more complexity than ever before. And as customer expectations continue to rise, organizations are being forced to reckon with a more fundamental question, not just how to serve customers faster, but how to serve them better, more inclusively, and with the kind of human judgment and empathy that no technology can replicate at scale.

Our April issue of CX Insight magazine brings together research, practitioner perspectives, and real-world case studies to examine the forces reshaping CX leadership and how organizations can stay ahead of them.

The DEI Performance Gap Is Widening

The business case for inclusive customer experience has never been stronger, or more urgent. Yet a critical gap persists. CX leaders rate their own accessibility and inclusion efforts 18 points higher than consumers do. That disconnect does not resolve itself. It shows up in churn, in negative sentiment, and in the quiet erosion of loyalty over time. This article examines what closing that gap actually requires operationally, structurally, and strategically.

The Frontline is Not a Support Function

Kevin McDorman, Vice President of Customer Care at Southwest Airlines, made the case plainly at CRS Amelia Island: brands will not differentiate by how much they automate. They will differentiate by how well they equip their people for the moments automation cannot handle. The shift from rigid scripts to empowering guardrails is not a cultural preference; it is a strategic imperative. This article explores what that shift looks like in practice and why organizations that get it right will find it difficult to replicate.

Brand Spotlight: How Grubhub Maintains Service Reliability at Global Scale

Behind every seamless food delivery experience is an operational infrastructure most customers never see. In this issue’s Brand Spotlight, Tanisha Parker, Associate Director at Grubhub, pulls back the curtain on how one of the world’s most recognized food delivery platforms manages global BPO partnerships, workforce strategy, and service consistency across a vast and dynamic ecosystem of couriers, merchants, and diners. Her perspective on eliminating operational friction, without compromising customer value, is one of the most practically useful conversations on this issue.

Case Study: How Uber Is Closing the Customer Feedback Gap with AI

Traditional customer satisfaction surveys capture only a fraction of the experience. Uber decided that was not good enough. In this issue’s case study, Anindya Sundar Das, Senior Director and Head of Global Digital Experience at Uber, details how Uber’s Global Digital Experience team deployed an AI engine that infers customer satisfaction across every single support interaction. It was structured around three core metrics: Resolution, Effort, and Sentiment. The result is a real-time quality signal that enables proactive improvement at a global scale and fundamentally changes how Uber understands and responds to the experience it is delivering.

KIA Spotlight: Balancing AI and Human Connection at Carrier Enterprise

In a service environment where HVAC contractors rely on fast, accurate support to keep their businesses running, the balance between technology and human connection is not an abstract leadership question; it is an operational reality. Michael Luyster, Director of Customer Experience at Carrier Enterprise and a KIA Champion, shares how Carrier Enterprise has built a hybrid service model grounded in customer choice, operational consistency, and trust. His conviction that AI should augment human capability rather than replace it offers a model worth studying across industries.

The Growing Impact of Women in CX Leadership

CX is one of the most cross-functional, human-centered disciplines in business, and women are leading it with measurable impact. The feature on women in CX leadership examines why the pipeline still narrows too early, what structural barriers remain, and why advancing women into senior leadership roles is one of the most strategically sound decisions a CX organization can make. Contributing their perspectives to this important conversation are Lisa Oswald of Travelzoo, Maureen Barnett of Fanatics, Jessica Patel of Belk, and Dima Cichi of Microsoft: four leaders who are actively shaping what modern CX leadership looks like and what it can become.

When AI Improves Metrics but Not Outcomes

Organizations across industries are deploying AI to reduce costs and increase efficiency. By conventional measures, it appears to be working. Automation rates are rising, and handling times are falling. Containment continues to improve, yet retention remains flat. Customer experience remains inconsistent, and business outcomes remain unchanged.

Cortney Jonas Burnos, Vice President of AI & Digital at Transcom, offers a sharp diagnosis: the problem is not the technology. It is the misalignment between how customer experience is measured and where it actually creates value. As AI absorbs the most repeatable interactions, what remains for human agents is more complex, more consequential, and more emotionally demanding — and most organizations have not restructured around that reality.

The Trillion-Dollar Cost That Never Appears on the P&L

According to Qualtrics XM Institute, businesses worldwide risk losing $3.8 trillion annually due to poor customer experiences. More than half of negative interactions lead customers to reduce or stop their spending entirely. Yet this cost rarely surfaces on a standard profit and loss statement. It disappears into churn metrics, missing renewals, and referrals that were never made. This article from Procedureflow provides the framework CX leaders need to translate experience quality into financial risk and bring that conversation into the boardroom where it belongs.

The question this issue keeps returning to is this: what does it actually take to lead well in this moment?

The answer, across every article and every conversation, is the same. It begins with people. The organizations that understand this and build accordingly are the ones that will emerge from this period of disruption with something the competition will find very difficult to replicate: the trust of the people they serve.

Read and download the full April 2026 issue of CX Insight magazine.

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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. 

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