That makes real estate an especially interesting lens for the broader customer experience (CX) conversation.

For companies serving consumers through complex, high-consideration journeys, support can’t exist on the sidelines. Every search, question, handoff, and moment of friction is part of the customer experience and can either build confidence or erode it.

That’s the conversation Phoebe Schultz, Vice President of Customer Experience Operations at Zillow Group, will bring to the main stage at CRS Scottsdale, September 30–October 2, 2026, at The Phoenician.

Turning Support into Strategy

In her keynote, Turning Support into Strategy: Zillow’s Approach to Smarter Self-Service, Schultz will share how Zillow approached a challenge that will sound familiar to many CX leaders: a support ecosystem that had become fragmented over time.

Zillow Group had numerous separate help sites, four technology platforms, and 12 support silos, an infrastructure shaped in part by years of acquisitions. The systems supported customers, but the experience reflected the company’s internal structure rather than how customers moved through their journeys.

That distinction matters.

For CX leaders, fragmentation isn’t simply a technology problem. It can show up as customers repeating themselves, searching across multiple places for answers, being routed to the wrong resource, reaching a human when a simple digital answer would have solved the problem, or being pushed into self-service when what they really need is human help.

Zillow’s experience offers a compelling look at what happens when an organization starts treating support differently.

Rather than viewing consolidation as an exercise in reducing complexity, Zillow used it as an opportunity to rethink the role of support altogether: not simply as a cost center, but as a product, a source of customer insight, and a way to connect customers with the right help at the right moment.

What CX Leaders Can Learn

The lessons extend well beyond real estate. As customer expectations continue to rise, organizations across industries are asking similar questions: How much should we automate? Where does self-service genuinely improve the experience? How do we make it easier for customers to find what they need? And how do we preserve the human moments that matter?

Schultz will explore how Zillow is approaching those questions through four key ideas:

1. The org chart trap: Why support experiences can become organized around internal structures rather than customer journeys, and the strategic cost of that disconnect.
2. Help as a product: What changes when support infrastructure is designed with the same discipline and intentionality as any other customer-facing product.
3. Friction as a signal: How customer questions and support interactions can reveal problems in products, processes, and communications.
4. Human where it matters: How intelligent triage and digital self-service can help organizations scale while still making room for the human interactions that build trust and loyalty.

For CX executives, the bigger takeaway is simple: support is telling you something.

The questions customers ask, where they get stuck, and when they seek human assistance can reveal where the broader experience is working and where it isn’t. The opportunity is to listen to those signals and turn support from a reactive function into a strategic advantage.

Join your CX leadership peers at CRS Scottsdale to see Zillow’s keynote. Learn more and register for CRS Scottsdale today.

The post From Help Desk to CX Strategy: What Zillow is Rethinking About Self-Service appeared first on Execs In The Know.

For AMC Global Media, that experience is especially important because its audiences aren’t simply customers; they’re passionate fans with emotional connections to the stories, characters, and worlds they love.

In this Brand Spotlight, Nicholas Solomon, Vice President of Customer Service at AMC Global Media, shares how his team is combining AI, Voice of the Customer insights, and empathetic service to remove friction, strengthen loyalty, and keep customer support from getting in the way of the entertainment experience.

Removing friction at every moment that matters

In a crowded streaming market, simplicity and reliability are no longer differentiators; they’re expectations. Solomon says AMC Global Media focuses on making the experience work seamlessly across devices, platforms, and brands, without requiring customers to navigate complicated logins, entitlements, or support processes.

But seamless service isn’t just about efficiency. It’s also about empathy.

“When something goes wrong, a great customer experience is meeting customers where they are, resolving issues quickly, and making them feel heard,” Solomon explains.

That philosophy is particularly important when serving passionate fandoms. A billing question or technical issue may seem routine from an operational perspective, but for a viewer deeply invested in a show, even a small disruption can feel personal.

Using AI to remove friction, not the human touch

As AI continues to reshape customer service, AMC Global Media is taking an intentional approach. The goal isn’t to replace human agents, but to use technology where it can make the experience easier.

Automation and self-service can efficiently handle routine, high-volume needs, while agents remain available for situations that require judgment, empathy, or deeper problem-solving. Agent-assist technology also gives employees greater context, allowing them to focus on the interactions where human connection matters most.

That balance is already producing results. As AMC Global Media reduced friction across high-volume customer journeys, CSAT increased from 69% to 82%, while NPS doubled.

Turning customer service into a source of insight

For Solomon, the contact center has a role that extends far beyond issue resolution. It serves as both a safety net for customers and a feedback loop for the organization.

Customer interactions reveal where customers are struggling, where expectations aren’t being met, and which problems keep coming up. AMC Global Media uses those insights across product, engineering, marketing, and other teams to inform improvements and reduce repeat pain points.

The result is a more proactive approach to CX, one where customer service doesn’t simply respond to problems but helps prevent them.

Building loyalty by making customers feel heard

Ultimately, AMC Global Media’s approach comes down to a simple idea: great CX should protect the customer’s relationship with the content and the brand.

Technology can make service faster. Data can make it smarter. But empathy is what makes the experience meaningful.

As Solomon puts it, “Solving the immediate issue is expected. Building trust is the differentiator.”

For CX leaders, that’s a powerful reminder: operational excellence and memorable experiences don’t have to compete. When organizations invest in their people, use technology thoughtfully, and genuinely listen to customers, they can create experiences that do more than resolve issues; they build trust that keeps customers coming back.

Want to hear more from Nicholas on how AMC Global Media is turning customer service into a driver of trust and loyalty? Read the full Brand Spotlight in the latest issue of CX Insight magazine. 

The post How AMC Global Media is Building Loyalty Through Seamless Service appeared first on Execs In The Know.

The fastest way to separate AI hype from opportunity is to learn from organizations already putting it into practice. 

The session focused on measurable outcomes: how FOX, Everlane, Amazon Ring, and FuturHealth are deploying conversational AI agents, voice AI, proactive engagement strategies, and AI-powered workforce planning tools across use cases in streaming media, retail, telehealth, and consumer technology.  

Common threads ran throughout the discussions: the importance of grounding AI initiatives in real customer data, the need for continuous iteration after launch, the shift in how success is measured (moving beyond simple containment metrics to sentiment and implied satisfaction), and the reality that human oversight becomes more important, not less, as AI systems mature.  

Our partner presenters from Sierra, Kustomer, Vapi, and Assembled offered concrete frameworks, results, and lessons learned for CX and operations leaders looking to move from experimentation to measurable impact. 

Here’s a recap of the virtual event. 

Sierra + FOX: Building and launching an AI agent in 60 days 

FOX partnered with Sierra to build its AI customer service agent, timed to coincide with the launch of its Fox One subscription product. The AI wasn’t just answering FAQs; it was resetting passwords, updating billing information, and processing cancellations on customers’ behalf. That flexibility proved critical when contact volume spiked 430% in a single week during a major global sporting event.  

FOX’s philosophy: treat AI and human agents as one unified team, holding both to the same QA bar. The payoff has been a retention rate above 80%, driven by continuous iteration rather than a “set it and forget it” mindset. As FOX’s presenter put it, “It is never perfect, and the key to success is to ensure that we are constantly looking to improve.” 

Kustomer + Everlane: Getting ahead of the customer entirely 

Kustomer introduced a four-level maturity model for proactive AI (responsive, predictive, preventative, and orchestrated), arguing that each step up requires progressively richer customer context. To bring it to life, Kustomer walked through its work with Everlane, the sustainable apparel brand, which noticed that customers who returned their first purchase were far less likely to buy again. Everlane used that single signal (a customer’s first purchase) to trigger a simple automated outreach checking in on fit and sizing, reducing avoidable returns without a heavy data lift. The bigger lesson for CX leaders: you don’t need a perfect system to start. “Export the data, download it, drop it in an AI tool with a good prompt. That’s a perfectly valid way to explore a signal,” Kustomer’s presenter noted. 

Vapi + Amazon Ring: Solving the trust problem in voice AI 

Vapi’s team was candid about the uphill battle voice AI faces: “The last 20 years of CX have really broken trust for customers… it truly is until they experience it, they don’t believe it.” Their headline case study was Amazon Ring, which had evaluated roughly 40 voice AI vendors without success before finding Vapi’s platform flexible enough to meet its strict requirements around telephony integration and model choice. Ring went live in about two weeks and posted striking results: a 50% drop in average call time, a 70% cost reduction, and CSAT scores that matched or beat those of human agents. 

Assembled + FuturHealth: AI for what happens behind the scenes 

FuturHealth, a telehealth company that grew patient volume nearly 1,000% in its first year with a single operations leader and no dedicated data science team, partnered with Assembled for workforce planning. By connecting Assembled’s data platform to a general AI assistant, FutureHealth’s team could ask questions in plain language instead of writing code. Tasks that used to take days now happen in a single session, and FutureHealth proposed a simple adoption framework any operations leader could use: Ask, Act, Automate. Start by asking questions, then move to approval-based actions, and finally automate the repetitive work. 

In Summary 

Across all four use cases, a few themes kept surfacing. First, none of these companies started with a “cool AI idea,” they started with data on what customers actually needed. Second, launch is just the beginning; every case study described ongoing iteration, rather than a finished product. And maybe most importantly, the metrics are changing. Presenters repeatedly cautioned against leaning on old-school containment or deflection numbers alone, pointing instead to sentiment analysis and “implied CSAT” as better indicators of whether AI is actually helping, since, as FOX’s presenter noted, “folks who leave CSAT leave it when they’re negative. It’s just like an app review.” 

The overarching message: AI in CX is operational and measurable, and when grounded in real customer data and paired with human oversight, it delivers results companies can actually point to. For CX and operations leaders still in the experimentation phase, the advice from every presenter was the same: start small, start now, and let the data lead the way. 

Ready to see these AI use cases in action? 

The full session, including live demos, deeper dives into each case study, and audience Q&A, is available to watch on demand. Whether you’re just starting to explore AI in your CX operations or looking to benchmark against what these leaders have already built, this recording is worth the 90 minutes. 

Watch the full session on demand → 

The post How Four Companies Turned AI Into Measurable CX Results appeared first on Execs In The Know.

That distinction is important for every business partner. Organizations are still investing in AI, but they’re becoming far more selective about where those dollars go and what evidence they expect before approving another initiative. 

Our latest research, The ROI Imperative: Building the Business Case for Exceptional CX, found that 65% of CX leaders expect AI and automation to receive the largest share of new CX investment over the next 12 to 24 months, while 75% believe those investments have the greatest potential to deliver ROI. At the same time, 86% say demonstrating ROI is now extremely or very important when proposing any new CX initiative. 

Those numbers tell an interesting story. AI budgets aren’t disappearing. They’re becoming harder to earn. Buyers are investing in AI that solves measurable business problems. The organizations moving forward with AI aren’t buying platforms because they’re innovative. They’re investing in use cases tied directly to operational outcomes. 

The strongest business cases connect AI to metrics executives already care about: 

• Lower cost per contact 
• Faster resolution times 
• Reduced handle time 
• Higher retention 
• Revenue protection 
• Better operational consistency 

One example highlighted in the research came from Michael Kors, where agent-facing AI was implemented to improve decisions around lost eCommerce orders. Rather than automatically issuing refunds, AI helps agents identify replacement inventory, recognize repeat claims, and resolve issues faster. The result wasn’t simply a better customer experience. It reduced unnecessary refund costs while protecting customer lifetime value. 

That’s the pattern appearing across many organizations. The most compelling AI investments solve an expensive operational problem first, while improving customer experience as a result. 

Process Improvement Still Beats Shiny Technology 

One of the more surprising findings in the research should give every technology provider pause. When asked which investments have historically produced the strongest ROI, process improvement ranked ahead of AI and automation. Process improvement was selected by 58% of respondents, compared with 54% for AI and automation. 

That doesn’t mean AI is losing momentum. It means buyers increasingly understand that technology rarely fixes broken processes by itself. Many organizations are looking for partners who can identify friction, redesign workflows, and then determine where AI belongs, not the other way around. 

If your conversation begins with product capabilities instead of business problems, you might already be behind. 

Where Skepticism is Growing 

The research also highlights where CX leaders remain cautious. Many organizations believe AI can deliver value. Fewer believe every implementation will. 

Several themes surfaced repeatedly: 

• Difficulty measuring business impact 
• Internal adoption challenges 
• Revenue expectations that don’t materialize 
• Weak post-implementation measurement 
• Generic ROI assumptions that don’t reflect their business 

In fact, internal adoption and revenue growth were the areas where CX investments most frequently fell short of expectations. The skepticism isn’t about AI itself. It’s about whether vendors can demonstrate that this implementation will deliver measurable outcomes within their organization. 

What CX Leaders Are Asking Business Partners to Do Differently 

One of the clearest messages from the research wasn’t directed at AI. It was directed at vendors. 

CX leaders repeatedly asked for: 

• Organization-specific ROI models instead of generic calculators 
• Benchmarks based on similar operating environments 
• Pilot programs using their own data 
• Practical implementation roadmaps 
• Continued measurement after deployment 

One respondent summarized it well: “Use our own data to make the ROI calculations ‘real’ and not hypothetical.” Another noted that the best vendors arrive with the business case already built, not simply a product demonstration. 

That should reshape how business partners think about the sales process. Today’s buyers increasingly expect strategic advisors who can help secure internal funding, not just solution providers. 

The Opportunity for Business Partners 

The AI market isn’t slowing down. It’s the buying process that is becoming more disciplined. The organizations winning investment are those that can clearly answer four questions before implementation begins: 

• What business problems are we solving? 

• How will success be measured? 

• What operational metrics will improve? 

• What financial outcome should we expect? 

The business partners who can help answer those questions will stand out in an increasingly crowded AI market. Because in today’s CX environment, buyers aren’t looking for the most impressive AI demonstration. They’re looking for the investment they can defend in the boardroom. 

If these findings reflect the conversations you’re having with customers, there’s value in staying connected with the practitioners shaping those conversations. Execs In The Know’s research, events, and leadership community, business partners gain direct access to the CX leaders defining tomorrow’s investment priorities.  

To learn more, connect with Scott Moberly, Vice President of Partner Advocacy, at Scott@execsintheknow.com or download our 2026 Media Kits to discover upcoming partnership opportunities. 

The post AI Investments CX Leaders Are Actually Making (and Where Skepticism is Growing) appeared first on Execs In The Know.

AI agents are now answering questions, completing transactions, making decisions, and transferring customers into live queues, but performance management and governance of AI agents in active production haven’t followed at the same pace. In some contact centers, AI agents have replaced live agents completely, handling sensitive customer interactions without training against high-performer behaviors or scoring against quality standards already used across your live teams. AI agent ROI originally promised to enterprises and BPOs looked stronger than they ended up being, with 83% of CFOs reporting that less than half of their AI investments have generated a measurable financial lift and 72% of customer service and support technology purchases last year ending in high or moderate buyer’s regret.  

Customer support agent headcount landed  the same way, with Goldman Sachs estimating AI agent representative costs at roughly $92 per day against roughly $90 for a human agent, while customer service jobs haven’t disappeared, in fact they’ve grown through restructuring with job postings outperforming the broader job market since August 2025. 

Customer service has been treating AI agents as a substitution question, which tasks to pull from live agents and hand to automation for greater capacity and ROI, instead of as a hybrid  workforce to manage. Contact centers that replaced live capacity in favor of AI agents have learned the hard way there are hidden costs to removing human reasoning, judgment, empathy, and service recovery from customer interactions and handing them to agentic AI without governance or rigorous performance management systems in place.  

Some enterprises have gone as far as scaling their hiring of customer support reps back up after deciding agentic AI wasn’t ready, but the real issue underneath the readiness is the systems to manage AI agent performance alongside live teams weren’t in place before the rollout. 

Hybrid workforces are the reality for customer service and support, with AI agents and live agents shaping the same outcomes through different behaviors, failure patterns, and corrective paths. Real ROI from agentic AI workforces comes from pairing the speed and scale AI brings to repeatable service volume with managing your digital and live workforce under the same unified performance management system. Some enterprises and BPOs are reading early misses as failure or a reason to pause, but investment is moving the other way, with AI spending in customer service and support up 38% and 13% of the average service and support budget now going to AI initiatives. The imperative isn’t pausing AI Agent rollout, it’s knowing the root of why AI agents, left ungoverned or managed like just another rep on the roster, turn expected ROI into unexpected costs. 

AI Agent Authority Expanded Faster Than AI Agent Management 

Customer service automation started with IVRs routing calls and chatbots answering scripted questions, expanding into voice bots and AI agents that verify customers, access account data, and transfer complex issues into live queues. Interactions have moved deeper into AI automation while performance management has centered on live agents, with chatbots, voice bots, and AI agents siloed from performance KPIs, coaching, and quality assurance. 

With increased AI automation we’ve seen an increase in customer journey data silos, without a unified performance management system connecting the customer conversation, scoring AI agents against quality standards used for your live team, or showing whether automation reduced customer effort or moved unresolved issues farther down the queue. 

Agentic AI has taken on interactions once reserved for experienced live agents, including verification, account changes, billing questions, troubleshooting, complaints, and service recovery, expanding AI agent authority faster than its management. For enterprise brands early to the rollout, trial and error resulted in loss of customer trust, compliance exposure, and brand risk. 

AI Agent Vendors Score Their Own Performance 

Enterprises and BPOs deploying AI agents in most cases are working through siloed vendors, where defining and managing performance remains disconnected from the rest of the contact center. AI agent vendors define the metrics, keep conversation data inside their reporting, and return containment, completion, latency, and transfer rates, while CSAT, FCR, compliance, and live-agent performance sit inside your QA, survey, and CRM systems. 

Siloed performance management scores your workforce two different ways, live agents held to QA reviews, coaching plans, and quality standards, while AI agents currently answer primarily to an undefined rubric isolated from your live teams. Vendor reporting may mark a transfer successful, but if the customer handoff wasn’t tracked, monitored, or managed, your live agents have to carry the repeats, giving an unreliable view of performance or the prescriptive actions to fix it. Was it an AI agent failure, or a failure in live agent coachable behaviors? The AI agent’s action splits from the customer outcome it helped create, the inevitable result of agentic AI scaled without governance and oversight. 

Expansion of AI agent deployment has multiplied dashboards and definitions of success, increasing the burden on CX leaders managing multiple versions of performance across one customer conversation. 

One Customer Journey Needs One Performance Standard 

Your customers experience one conversation, whether an AI agent opens it, a live agent finishes it, or the interaction moves between them. Currently performance measurement of your AI agents stops at the handoff or is loosely patched together, leaving ambiguity in quality and compliance results. When verification fails, account details repeat, answers conflict, or context dies in transfer, failures surface in customer outcomes attached to neither workforce. 

Split performance data creates the wrong corrective action, a live agent coached for longer handle time or failed resolution after inheriting a broken handoff, while the AI response that caused the failure receives no prompt change, knowledge update, or regression test. Even where agentic AI vendors score their own agents through Auto QA, a separate rubric and separate calibration keep those scores disconnected from your QA findings and live-agent performance, leaving no way to tell which side of your workforce needs the fix, or how either side moves overall CX outcomes. Customer intelligence ends up cut off from the rich data insights required to power the right actions for QA teams, CX leaders, and executives.

One performance standard needs to follow the full customer journey, scoring AI agents and live agents through calibrated behavior drivers while tracking the handoff between them. A unified performance management system makes that standard enforceable, managing both workforces from one data layer where AI conversations and live interactions stay inseparable, routing live-agent findings to coaching and AI-agent failures to fixes, and showing through shared performance data whether either action improved the outcome. In sectors like healthcare and financial services, deploying a digital workforce outside the management your live teams work under leaves critical gaps in quality and compliance monitoring, alongside patient care, member services, and NPS outcomes at stake in each conversation. Customer harm, regulatory exposure, and financial penalties still belong to the business, no matter which side of your workforce created them. 

AI Agent Management Is Where ROI Lives 

AI agents aren’t replacing human agents in customer service yet, and the ROI story behind the rollout leans on a myth, that value comes from interaction volume AI agents handle and repetitive tasks they lift off live teams. Volume and automation are real, but the illusion that AI agents will one day be ‘good enough’ to replace live teams sits a long way out, and the brands that acted on it, Klarna the loudest example, have rolled the decision back and rehired the agents they had planned to replace, absorbing losses the business case never counted, in brand reputation and workforce engagement. Who wants to work for a company actively trying to replace its agents with AI? 

Achieving ROI from your AI investments needs to consider the management of your AI agents. With agentic AI investment set to increase, only 3% of service and support leaders reporting no plans to deploy, pilot, or explore it in the next two years, and AI agent costs yet to reach parity with live teams, performance from both sides has to keep pace with the spend. Unified AI agent management software like AmplifAI is purpose-built for holistic management of both your live teams and AI agents on one platform. By unifying structured and unstructured data sources across CCaaS, CRM, AI agents, surveys, and flat files into one AI-ready data layer, AmplifAI powers automated quality management, performance KPIs, and role-based insights turned into next best actions. AmplifAI sees who your highest performers are, where your middle 60 live, and where low performers need development, providing insights and prescriptive actions for team leaders, CX, and QA teams, while managing your AI agents against the same proven behaviors. 

The 97% of enterprises and BPOs adding more AI agents to their digital workforce don’t need to relearn what early rollbacks already proved: performance management belongs in place before rollout, not after rollback. Deeper returns are landing where AI agents and live agents run under one unified performance management system; both workforces are scored on shared standards, coached and corrected on their own terms, and measured on whether the customer experience improved. 

Rather than replacing your live team, AI agents have joined your workforce; managing both sides under one unified performance management system is where lasting ROI lives.  

To see what your AI agents and live teams look like managed under one unified platform for CX and performance management, speak to a CX leader at AmplifAI. 

Contributed blog post written by Sean Minter, CEO/Founder, AmplifAI 

The post AI Agent ROI Comes from Unified Performance Management Across the Hybrid Workforce appeared first on Execs In The Know.

PHOENIX, ARIZONA, US, July 14, 2026 — As organizations race to integrate AI into the customer journey, customer experience (CX) leaders are grappling with a new challenge: maintaining trust while accelerating innovation. New research from Execs In The Know’s 2026 CX Leaders Trends & Insights: Corporate Edition finds that two-thirds (67%) of contact centers now use some form of AI — yet leaders are shifting their focus from cost savings to outcomes, increasingly measuring success by resolution, customer trust, and abandonment rather than deflection alone.

To address that challenge, Execs In The Know today announced the speaker lineup for Customer Response Summit (CRS) Scottsdale under the theme, Customer Assurance: Leading the Next CX Advantage, a premier gathering of CX leaders from the world’s top corporate brands, taking place September 30–October 2, 2026, at The Phoenician in Scottsdale, Arizona.

The event will feature keynote presentations and executive-led discussions focused on responsible AI adoption, leadership resilience, customer trust, and the strategies brands are using to create assurance at every stage of the customer journey.

Featured Keynote Speakers

CRS Scottsdale will feature four main stage keynotes from executives who are redefining what CX leadership looks like.

• Julio Lopez, Vice President of Sales Enablement, Global Reservations at The Ritz-Carlton Yacht Collection, will deliver a keynote highlighting leadership lessons and customer experience strategies from one of the world’s premier luxury hospitality brands.
• Todd Sale, Senior Vice President, Customer Experience at Corpay will explore why emotional regulation, self-awareness, and inner resilience have become critical leadership skills for guiding teams through uncertainty, change, and the rapid impact of AI on the workplace.
• Phoebe Schultz, Vice President, Customer Experience Operations at Zillow, will share how the company transformed a fragmented support ecosystem into a unified, AI-enabled self-service experience that reduces customer friction, strengthens trust, and delivers more personalized support at scale.
• Julie Weingardt, Chief Operations Officer at Turo, will address the growing gap between customer satisfaction metrics and customer reality, highlighting why trust has become the defining factor in long-term loyalty.

Featured Panel Speakers

In addition to the four keynote speakers, the conference will feature main stage panel discussions from CX leaders at some of the world’s most recognized brands, including: Travelzoo, Microsoft, Cox, Intuit, Priceline, YETI, and more.

These speakers will join a broader roster of CX executives sharing candid insights of building Customer Assurance on AI through the agent’s eyes, channel strategy, the contact center workforce, measuring what matters in CX, leadership wellness, and operationalizing customer feedback.

“Customer Assurance has become one of the most important strategic priorities facing CX leaders today,” said Susan McDaniel, COO and Co-Founder of Execs In The Know. “AI may change how we serve customers, but it can never replace the trust that earns their loyalty. CRS Scottsdale is designed to help leaders build the trust, confidence, and operational excellence required to thrive in this environment.”

About Customer Response Summit (CRS)

For more than 15 years, CRS has brought together senior customer experience leaders from many of the world’s most recognized brands to share ideas, tackle challenges, and explore what’s next in CX. Designed exclusively for corporate practitioners, CRS delivers real-world insights through practitioner-led keynotes, interactive discussions, peer-to-peer learning, and curated networking opportunities.

Attendees gain access to candid conversations with fellow leaders, discover innovative solutions shaping the future of customer experience, and build meaningful relationships that extend well beyond the event. Past attendees have represented organizations including Google, Grubhub, Microsoft, MLB, T-Mobile, American Airlines, Staples, Ulta Beauty, Uber, Michael Kors, YETI, Walmart, Target, Visa, Peloton, The Home Depot, and many others.

Learn more about the agenda details, speaker information, venue, and registration, visit: https://execsintheknow.com/events/customer-response-summit-scottsdale-2026/

About Execs In The Know  

Execs In The Know brings together customer experience (CX) leaders from across industries in an effort to advance the conversation and set a new agenda for delivering amazing experiences for consumers. As a global community of the brightest minds in CX, Execs In The Know provides opportunities to learn, share, network, and engage to innovate. Operating under the motto, “Leaders Learning from Leaders,” Execs In The Know facilitates many opportunities for community engagement, such as its bi-annual national Customer Response Summit and private, online community, Know It All “KIA.” There are also exclusive, laser-focused engagements like industry briefings and executive roundtables. Execs In The Know also guides and informs the industry with a rich tapestry of CX-related content that includes CX Insight magazine, industry research, webinars, blogs, and much more. For more information, visit Execs In The Know’s website.

The post Trust in the Age of AI: Customer Response Summit Scottsdale to Explore the Future of Customer Assurance appeared first on Execs In The Know.

But as artificial intelligence (AI) continues to evolve, the conversation is shifting. The question is no longer just how organizations can use data to personalize experiences; it is how they can use technology to better understand customers and support the people who deliver those experiences.

How can AI help organizations move from knowing customer preferences to understanding the motivations behind them? And how can companies use AI in a way that enhances the human connections at the center of exceptional service?

A Luxury Hospitality Perspective on AI-Powered Service

In luxury hospitality, where every detail and personal connection matters, this opportunity takes on even greater importance. The challenge is not simply applying technology to make interactions faster or more efficient. It is exploring how AI can help teams create experiences that feel thoughtful, personal, and considered.

That is the conversation Tracy Sedlak, Director, Contact Center Operations & Analytics at The Ritz-Carlton Yacht Collection, will explore during her keynote at CRS Scottsdale.

In today’s AI discussions, much of the focus centers on automation, efficiency, and cost reduction. The Ritz-Carlton Yacht Collection is approaching AI through a customer-first lens, exploring how the technology can help uncover richer insights, recognize individual preferences, and support employees in delivering more personalized experiences.

During her keynote, Tracy will share how her team is using AI to better understand guest personas, identify motivations and expectations earlier in the customer journey, and equip advisors with the intelligence needed to deliver the anticipatory service that defines The Ritz-Carlton Yacht Collection.

As organizations across industries continue evaluating how AI fits into their CX strategies, the conversation around meaningful implementation becomes increasingly important. Technology alone does not create exceptional experiences. The value comes from how organizations apply intelligence to better understand the people they serve and empower employees with the information they need.

What opportunities could emerge if organizations had a deeper understanding of customer expectations before each interaction begins? How can CX leaders balance the possibilities of AI with the importance of thoughtful, human-centered service?

If your organization is exploring what’s next for AI in CX, this keynote will offer a compelling perspective on where the conversation is headed.

Join us at CRS Scottsdale, September 30–October 2, to hear Tracy share how The Ritz-Carlton Yacht Collection is reimagining CX through anticipatory service.

Learn more, explore the full agenda, and register today.

The post How Luxury Hospitality is Raising the Bar for Anticipatory Service appeared first on Execs In The Know.

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.

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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.

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