The live polling provided directional insights, and the comparisons were revealing. In some cases, CX organizations appear to be moving in step with consumer expectations. In others, a meaningful gap remains between what customers value and where organizations focus their efforts. 

The comparisons build on the conversations at CRS, where leaders from YETI, Mercedes-Benz, Carrier Enterprise, and Cresta examined the Consumer Edition findings alongside audience polling. Read the full CX Livewire session recap for an overview of the discussion, the panelists, and the research behind the conversation.

CX Influence: The Voice Is There — But How Strong Is It? 

Consumers increasingly expect CX to have a meaningful voice inside the enterprise. In our Consumer Edition research, 90% say it is important for customer care and CX organizations to have influence within a company, while 86% believe CX should have moderate or extreme influence over the organization. 

The Scottsdale audience painted a broadly positive — but more nuanced — picture. Eighty-eight percent of respondents said their CX organization currently has at least moderate influence, but only 44% characterized that influence as significant or extreme. Another 12% said CX has little influence.  

The takeaway may not be that CX lacks a seat at the table, but that the strength of that seat varies considerably. Closing the gap means moving beyond reporting customer sentiment and using customer insight to influence upstream decisions in product, policy, technology, billing, operations, and strategy. The more directly CX leaders can connect customer friction to commercial and operational outcomes, the stronger that influence becomes. 

AI Use Cases: Leaders and Consumers Are Surprisingly Aligned 

Here, the comparison was striking. 

Consumers are most comfortable using AI for general inquiries and product or service information, both at 56%. Comfort drops as tasks become more consequential: 34% are comfortable using AI to correct an issue, 32% for purchasing or scheduling, and 31% for account updates or corrections.  

The live audience reported almost the exact same prioritization. 71% are using customer-facing AI for general inquiries and 69% for product information, followed by purchases or scheduling at 37%, issue correction at 36%, and account updates at 31%.  

That alignment is encouraging. Organizations appear to be starting where customers are most comfortable and where the risk of failure is typically lower. 

The next challenge is disciplined expansion. Rather than asking how many interactions AI can automate, leaders should ask where accuracy, data access, governance, escalation, and customer confidence are strong enough to support greater autonomy. The consumer data suggests there is an appetite for more — but brands need to earn permission through performance. 

Loyalty: Customers See the Connection More Clearly Than Brands Can Prove It 

This may have been the biggest gap of the session. 

In the consumer research, 90% say a positive customer care experience is likely to increase their loyalty, including 60% who say it is “Very Likely.”  

Yet when Scottsdale attendees were asked how effectively their organizations connect customer care performance to loyalty, retention, or customer value, only 47% said very or somewhat effectively. Nearly one-third (32%) said their organizations do so ineffectively.  

Although consumers appear convinced of the relationship, many organizations struggle to quantify it. 

Closing this gap requires connecting contact-center and experience data with actual customer behavior. Resolution, repeat contact, satisfaction, escalation, and effort become far more powerful when examined alongside retention, churn, purchase frequency, spend, or lifetime value. CX leaders do not necessarily need a perfect attribution model. Instead, they need enough evidence to move the conversation from “we believe great service creates loyalty” to “here is what happens to customer behavior when service succeeds or fails.” 

Agent Training: Perhaps the Most Interesting Disconnect 

The final poll produced the sharpest divergence. 

Consumers ranked clarity of speech and communication highest for agent training at 37%, followed by product and service knowledge at 21%, speed and efficiency at 18%, listening at 12%, and empathy at 9%.  

CX leaders in Scottsdale had very different priorities. Product and service knowledge led at 42%, followed by empathy at 25% and listening at 18%. Only 3% selected clarity of speech, while 9% chose speed and efficiency.  

Neither perspective is necessarily wrong. Leaders understand the operational realities their agents face, while consumers judge the experience from the other side of the interaction. But the contrast suggests training strategies may benefit from a stronger customer-feedback loop. 

Product knowledge and empathy matter. Consumers also appear to be saying something more fundamental: help me understand you, understand me, and move the interaction forward efficiently. Training agendas should therefore balance knowledge and empathy with communication clarity, comprehension, efficiency, and the ability to translate expertise into an answer the customer can readily understand. 

The Bigger Opportunity: Close the Loop Between What Customers Want and What Organizations Do 

Taken together, the four polls tell an encouraging story. CX leaders and consumers are not operating in completely different worlds. AI use-case priorities are remarkably aligned, and most organizations report meaningful CX influence. 

But the gaps matter. 

Consumers strongly link service to loyalty, with organizations still struggling to prove that link. While leaders prioritize knowledge and empathy for frontline development, consumers place greater emphasis on clear communication and efficient execution. 

That’s why comparing these perspectives can be valuable. Consumer research tells us what customers experience and prioritize. Practitioner insight tells us what organizations can do today. 

CX leaders’ opportunity lies in the space between the two. 

The key takeaway: Listen to both sides, identify the gaps, and use them to decide where the next investment, measurement, or operational change can create the greatest impact.

Check out the full CX Leaders Trends & Insights: Consumer Edition report now. 

The post When Consumer Expectations Meet CX Reality: Four Lessons from CRS Scottsdale appeared first on Execs In The Know.

Anticipatory Service in the Age of AI showed how luxury hospitality is raising the bar. Sedlak explained how the brand built its Customer Intelligence Center (CIC) to help advisors anticipate a guest’s needs before every call, while keeping every recommendation in human hands. 

Customer-First, Not Efficiency-First 

Sedlak opened with a word cloud familiar to anyone who has sat through a contact center AI pitch: reduction, savings, headcount, shrinkage, cost avoidance. The Ritz-Carlton Yacht Collection chose a different starting point: customer-first, not efficiency-first. 

The result is AI that works for agents, not instead of them. CIC doesn’t talk to guests, score agents, or make decisions. It listens at a scale no person can, then hands the patterns it finds to a human who decides what to do with them. 

Knowing the Guest Behind the Call 

The Ritz-Carlton Yacht Collection serves four distinct guest personas, and each needs something different to build trust: 

• The Returning Guest already trusts the brand and wants recognition and consistency. 
• The Luxury Upgrader is comparing The Ritz-Carlton Yacht Collection to what they know and needs proof of the upgrade. 
• The Experience-Led Traveler is moved by atmosphere and story, not spec sheets. 
• The Informed Evaluator needs clarity, documentation, and patience before committing. 

CIC helps advisors catch the personal details that make anticipatory service possible. On-screen examples included delayed honeymoons, milestone birthdays, retirements, and decades-long friendships. That kind of context turns a booking into a celebration. 

Coaching at Scale 

For leaders, CIC doubles as a coaching tool. They can ask questions in plain language, such as whether advisors missed mentioning a celebration or special occasion in the past 30 days. The answer comes back as an analysis across nearly 1,500 calls, showing where team members need support. 

The Question Every CX Leader Should Ask 

Tracy’s closing message was simple: technology alone does not create exceptional experiences. It never has. AI listens at a scale no person can, but the advisor still decides what matters. 

She left the room with a question worth bringing back to every AI roadmap: Is your AI strategy built to make your people faster, or to make them more perceptive? For organizations under pressure to automate, The Ritz-Carlton Yacht Collection offers a different model, one where AI raises the ceiling on service instead of just lowering the cost floor.   

Join us in Dallas 

Catch keynotes like this at the next CRS in Dallas, March 10–12, 2027, for more real-world stories from brands redefining customer experience. Register today and save your seat. 

The post Anticipatory Service in the Age of AI with The Ritz-Carlton Yacht Collection appeared first on Execs In The Know.

Customers don’t trust institutions to resolve issues without multiple handoffs. Employees don’t trust that transformation will improve their work rather than eliminate it. Boards don’t trust that AI will create advantage without reputational, regulatory, or security risk. And then generative AI arrived. 

The Paradox Every CX Leader Lives Under 

Deploy AI for efficiency while strengthening trust. Reduce expense while improving experience. Move with urgency while satisfying regulators.  

Contain cost-to-serve while raising the standard of care. None of these is an either/or choice, and that is what makes the job so hard. 

The Same Wrong Answer, Twice 

Alice made it real with a true story. A customer asked an AI assistant a simple question and got a confident, policy-backed answer. Two days later, unsure, she called. The associate checked the same knowledge base and confirmed it. She made a financial decision on it, then discovered the answer was wrong. 

The failure wasn’t the AI or the associate. It was the system. When both draw from the same source of truth, one wrong answer becomes two, delivered with the illusion of independence. 

Empathy Plus Precision 

The fix starts with a core equation: empathy plus precision, never one at the expense of the other. Without precision, empathy becomes theater, and customers feel understood but still misinformed. Without empathy, precision turns cold, and customers get the right answer and leave anyway. 

Alice then introduced the TERV model: four variables that determine whether AI earns trust or erodes it: Trust, Ease, Recovery, and Value. Her Trust Scorecard turns trust into something leaders can diagnose across transparency, accountability, fairness, security, and human agency. 

Builders, Breakers, and the J-Curve 

Trust builders include explaining what AI can and can’t do up front, designing fast escalation to a human, and involving frontline associates in design. Trust breakers include hiding automation behind a human-sounding voice and optimizing for handle time over customer confidence. 

The field lessons agreed. The biggest gains came from pairing automation with an always-available human path. The costliest missteps came from scaling pilots before building governance. And because AI adoption follows a J-curve, leaders must lead through the dip, not restructure during it. 

Myths to Let Go 

Gen AI doesn’t replace the frontline; it reshapes it. Deflection isn’t a strategy; resolution is. And governance doesn’t slow innovation; it’s what makes scale possible. 

The Choice 

Every leadership team now faces two paths: AI as a blunt cost-cutting instrument, or AI as a trust amplifier that elevates employees, strengthens relationships, and enables organizations to scale faster to increase revenue growth. As Alice put it, trust is not a feature you ship. It is a debt you pay, one human decision at a time. Technology doesn’t transform organizations; leaders do. 

Join us in Dallas 

Conversations like this are why CX leaders come to CRS. Join us at our next CRS in Dallas, March 10-12, 2027, to keep building trust into every layer of the customer experience. Save your seat today. 

The post The Trust Algorithm: What the CX Masterclass Taught Us About AI appeared first on Execs In The Know.

Moderated by Jim Von Seggern, Director of Research and Insights at Execs In The Know, the discussion brought research findings and practitioner perspectives together. As panelists interpreted the data through the lens of their own organizations, audience responses added another point of comparison, giving attendees an opportunity to benchmark their thinking against their peers. 

Four areas of research framed the conversation: CX influence, AI use cases, customer loyalty, and agent training. 

The Panelists 

Jim Von Seggern, Director of Research and Insights at Execs In The Know, moderated. He was joined by: 

• Stephanie Stafford, Senior Director, Customer Experience at YETI  
• Chad Anderson, Vendor Manager, Roadside and Customer Care, Customer Excellence at Mercedes-Benz 
• Michael Luyster, Director, Customer Experience at Carrier Enterprise 
• Antony Passemard, Vice President of Customer Strategy at Cresta 

What Consumers Expect from Customer Experience 

The panel explored four sets of consumer findings from the report, including shifts in sentiment from 2024 to 2026. The data provided a starting point for examining consumer expectations, while the panelists shared how they interpret those expectations and how they approach them within their organizations. 

The Influence of CX 

In 2026, 55% of consumers said it is very important for customer care and customer experience (CX) organizations to have influence within a company, up from 50% in 2025. Another 35% said it is somewhat important, bringing the total to 90%. Meanwhile, neutral responses dropped from 18% to 9%. 

These findings underscore how important consumers view CX’s role within a business. The discussion explored what that influence looks like in practice, including where CX has a voice in organizational decisions and where teams still need to demonstrate their value. 

AI for CX Use Cases 

Consumer comfort with AI varies by task. Fifty-six percent of consumers said they would use an AI-powered solution to make a general inquiry about an order or purchase, and the same percentage would use one to get product or service information. 

Comfort levels were lower for more complex or consequential interactions: 34% would use AI to correct an issue, 32% to make a purchase or schedule something, and 31% for an account update. 

The findings offered a basis for discussing how organizations approach AI adoption and where consumer expectations intersect with implementation decisions. Panelists brought their own organizational perspectives to the conversation, considering how these preferences shape AI’s role in customer service. 

Customer Loyalty 

The relationship between customer care and loyalty remains significant. Sixty percent of consumers said a positive customer care experience is very likely to increase their loyalty, up from 56% in 2025. Another 30% said it is somewhat likely to do so. 

For CX leaders, the findings raise an important business question: How can organizations translate the connection between customer care and loyalty into measurable outcomes? The discussion explored the importance of understanding that relationship and demonstrating its value across the business. 

Bonus Topic: Agent Training 

Consumers also shared where they believe companies should prioritize agent training. Clarity of speech and accent neutralization ranked first at 37%, up from 30% in 2025. Product and service knowledge followed at 21%, then speed and efficiency at 18%, listening at 12%, empathy at 9%, and company policies at 3%. 

These priorities gave panelists another point of comparison, inviting discussion about how consumer expectations align with organizational training priorities and how companies decide where to invest more. 

Key Takeaways 

Across all four topics, the research showed what consumers value, while the panel discussion explored how those expectations translate into organizational decisions. 

The findings point to several considerations for CX leaders: 

• CX influence matters. Nine in 10 consumers consider it at least somewhat important for customer care and CX organizations to have influence within a company. 
• AI adoption requires judgment. Consumer comfort is higher for routine inquiries than for issue resolution and other more consequential tasks. 
• Customer care has a measurable business connection. Consumers recognize the relationship between positive service experiences and loyalty, reinforcing the need to understand and demonstrate that impact. 
• Training priorities deserve continued attention. Consumers ranked clarity of communication and product knowledge highest on their list of agent training needs. 

The combination of research findings, panelists’ organizational perspectives, and live audience polling gave attendees multiple ways to consider the same questions: What do consumers expect, how do those expectations compare with the approaches CX organizations are taking, and where might there be opportunities to respond differently? 

The findings also raise a broader question: How closely do organizational priorities align with consumer expectations? In a companion analysis, When Consumer Expectations Meet CX Reality: Four Lessons from CRS Scottsdale, we compare the consumer research with live audience polling to identify where CX organizations are aligned with customer expectations and where gaps remain.

That exchange is central to the value of CX Livewire. Rather than treating research as isolated statistics, the session gave CX leaders space to interpret the findings, compare perspectives, and consider how consumer expectations should inform their decisions. 

You can download the 2026 CX Leaders Trends & Insights: Consumer Edition report here. 

See CX Livewire on the Mainstage in Dallas 

Missed it in Scottsdale? You’ll get another chance. CX Livewire is coming to the mainstage at CRS Dallas, March 10–12, 2027, with new consumer data, fresh reactions from CX leaders, and plenty of room for the audience to weigh in. 

Save the date and register for CRS Dallas today. 

The post YETI, Mercedes-Benz, Carrier Enterprise, and Cresta Examine the Gap Between Consumer Expectations and CX Delivery appeared first on Execs In The Know.

That question shaped the conversations last week at CRS Scottsdale, held Sept. 30–Oct. 2, where customer experience (CX) leaders tackled artificial intelligence (AI) adoption, customer trust, operational performance, and the future of CX. 

Under the theme Customer Assurance: Leading the Next CX Advantage, senior CX leaders came together to examine what it takes to build customer confidence in an increasingly automated world. 

Through keynotes, panel discussions, practitioner-led case studies, interactive workshops, tech forums, and candid peer discussions, one message emerged: The next CX advantage will depend on how well organizations connect technology, operational execution, and human judgment. 

Here are five takeaways from CRS Scottsdale that CX leaders can bring back to their organizations.

1. Customer assurance starts with leadership, not technology

Customer assurance goes beyond customer satisfaction scores or a successful service interaction. It reflects the confidence customers feel when a company communicates clearly, takes ownership, and delivers on its promises. 

In the panel discussion, Customer Assurance: A Leadership Decision, Not a Department, CX leaders explored how organizational policies, processes, technology, and accountability shape that confidence. 

The discussion reinforced an important distinction: customer trust cannot be delegated to a single department. It results from decisions made across the business, from how expectations are set to how employees respond when something goes wrong. 

For CX leaders, this means looking beyond individual touchpoints to identify where inconsistent processes, unclear ownership, and disconnected systems undermine the experience. The opportunity is to make customer assurance an enterprise priority, supported by clear measures and shared accountability.

2. AI must improve the customer journey, not just reduce costs

As organizations accelerate AI adoption, the question is shifting from what can be automated to what should be automated. 

This challenge was explored from multiple angles, including the tech forum Human + AI at Scale: The New Operating Model for Customer Experience and the workshop Designing Delightful Customer Experiences. These sessions challenged leaders to focus on the customer outcome first, then decide where AI, human expertise, or a combination can deliver it. 

The conversation extended beyond deployment to the operational foundations AI needs to work at scale: connected workflows, reliable knowledge, clear escalation paths, and governance over how work moves between people and technology. 

The takeaway for leaders is straightforward. Successful AI implementation requires more than selecting a capable solution. It requires thoughtful experience design, clear boundaries, and a plan to measure whether automation is improving the journey. 

As AI takes on more transactional work, human employees must be equipped to handle the complex, sensitive, and high-value interactions where judgment and empathy matter most.

3. Strong metrics don’t always tell the whole story

One of the biggest challenges facing CX leaders is determining whether their performance measures reflect what customers actually experience. 

In Green Metrics, Red Flags: Redefining Trust in the Era of AI, keynote speaker Julie Weingardt, Chief Operating Officer at Turo, examined the disconnect that can exist between positive dashboard results and underlying customer friction. 

That theme carried into COPC’s and Johnson Controls’ case study, When the Numbers Look Fine, but the Customer Doesn’t: Johnson Controls’ Search for the Lost Caller. The session explored how fragmented service operations and disconnected systems can create gaps across the customer journey, even when traditional satisfaction measures appear healthy. 

Together, these discussions highlighted the need to look beyond aggregate scores and examine the experience from the customer’s perspective. Are customers reaching resolution without unnecessary effort? Where are handoffs creating friction? What do unmeasured interactions reveal about the effectiveness of automation? 

For leaders, the opportunity is to connect customer feedback, interaction data, and journey analysis to identify the root causes behind performance results, then prioritize improvements based on customer and business impact.

4. Real-world results matter more than AI promises

Moving from experimentation to measurable impact was another recurring theme. The agenda featured sessions that showcased practitioner-led examples of organizations applying AI and automation to specific operational challenges. 

Travelzoo explored how Laivly helped the company use automation to absorb a global membership renewal surge without adding headcount. Princess Cruises and harpin AI’s case study examined how the organization evaluated 64 contact center improvement opportunities to identify where limited resources could make the greatest difference. 

Meanwhile, BJ’s Wholesale Club and ibex shared their approach to launching AI voice agents, including the decisions involved in bringing the technology into live member interactions. 

These examples illustrated a common principle: technology investments need a defined business problem, clear measures of success, and a practical path to execution. 

For CX leaders facing competing priorities and constrained budgets, the lesson is to focus on outcomes rather than adoption alone. The most useful AI strategy is one that connects implementation decisions to measurable improvements in customer experience, operational performance, or both.

5. The future of CX depends on how leaders prepare their people

Technology may be changing the work, but leadership remains central to how organizations navigate that change. 

In Mental Athlete: Leadership in a Dysregulated World, keynote speaker Todd Sale, Senior Vice President of Customer Experience at Corpay, challenged leaders to examine how their own responses to pressure affect their teams. Other sessions explored how AI is changing frontline responsibilities, workforce planning, training, and performance management. 

As transactional work becomes increasingly automated, employees will need different skills, stronger judgment and the confidence to navigate more complex customer situations. That transition requires more than communicating a technology roadmap. Leaders must build trust, create room for experimentation, and support employees as they adapt. 

The organizations best positioned for the future will treat workforce readiness as a core part of transformation, not an afterthought. 

Continue the Conversation in Dallas 

CRS Scottsdale brought CX leaders together around a shared challenge: building customer confidence while navigating rapid technological and operational change. 

At Execs In The Know, we believe the most valuable insights come from leaders learning from leaders. That means exchanging practical experiences, challenging assumptions, and learning from what is working, what isn’t, and what comes next. 

The conversation continues March 10–12, 2027, in Dallas at The Ritz-Carlton Dallas, Las Colinas. Join us for The Discipline of CX Reinvention, where senior CX leaders will explore how to turn bold ideas into practical strategies that move the business forward. 

Learn more and register today. 

The post What CX Leaders Need to Know About Trust, AI, and the Future of Customer Experience appeared first on Execs In The Know.

By summer, that cluster has turned into a trend. More customers are canceling over billing confusion, agents have started using a workaround script they wrote themselves, and finance is asking why churn went up in a segment that had looked healthy all year.

The team caught this early. They talked about it openly, and everyone agreed it mattered. Months later, nothing had changed.

If that sounds familiar, you’re in good company. Most organizations have more customer feedback than they know what to do with, and better tools to analyze it than ever. The trouble usually comes down to two things. Insights often aren’t specific enough for anyone to act on. And even when they are, nobody clearly owns turning them into a decision, and then into something customers actually notice.

Both problems tend to show up in the same four places.

Where insights go to die

1. The report

Most insights show up as observations, something like “Billing-related contacts rose 40% month over month.” That’s true, and it’s easy to agree with. It just doesn’t give anyone enough to act on. Which customers? Confused about what, exactly? Is it one issue, or five different issues sharing a label?

Compare that with “Customers who switched to annual plans are contacting us about their first prorated invoice, mostly within two days of the charge, and about one in ten of them mentions canceling.” Now the product team knows where to look, and someone can estimate what it’s worth to fix. The first version gets a nod. The second gets a follow-up question, which is a much better sign.

Volume doesn’t help. In a deck with fifteen findings that all look equally important, the one that matters most gets lost, and readers fairly assume they’re all optional.

2. The meeting

Oddly, the meeting is where an insight is most likely to stall, even though it’s where it gets the most attention. The room agrees, someone suggests digging in, and then everyone moves on to the next agenda item.

Agreement feels like progress. But unless the insight leaves with a named owner and a date, it’s been filed. Cross-functional meetings make this worse, because when CX, product and operations all care about the same problem, it’s reasonable for each of them to assume one of the others has it.

3. The backlog

Say the insight does get assigned. Now it’s sitting in a queue next to roadmap commitments, tech debt and revenue projects, and most of those came with a business case. Customer insights usually don’t. They describe the problem the way customers experience it, while the backlog gets ranked on cost, revenue and effort.

This is where vague insights get hurt the most. An insight that can’t say how many customers are affected, how fast it’s growing or what it’s tied to has nothing to put on the scale. So when someone asks what it costs to wait, it has no answer, and it loses. That’s rarely because the issue matters less. It’s just harder to compare.

4. The follow-up

This is the one that’s easiest to miss. The fix ships, the ticket closes, and no one goes back to see whether the customer signal moved. Are billing contacts down? Did those cancellations recover? A lot of teams can’t say.

The damage goes past the original issue. Frontline teams who flag problems and never hear what happened eventually stop flagging them. Over time that early-warning system gets quiet, and a quiet system is easy to mistake for a healthy one. The dashboard stays green while customers drift.

How to keep an insight alive

None of this gets solved with more data. It gets solved with sharper insights and a few habits that teams actually keep.

Start with the insight itself. Before it can have an owner, it has to be specific enough to hand to one. A good test is whether someone could start working on it tomorrow without asking a clarifying question. That usually means it names who’s affected, what exactly is going wrong, how big it is and whether it’s growing. If it can’t do that yet, it isn’t ready for the meeting.

Own it

Every insight that comes out of a review needs one person’s name on it, not a team’s. You can’t hold “product” accountable, but you can follow up with the product lead who owns billing.

It also helps to write the insight as a request. Instead of “Billing contacts rose 40%,” try “We recommend clarifying the prorated charge on first annual invoices, and we’d like a decision from the billing product lead by the 15th.” That version is much harder to nod at and forget.

Be clear about who owns what, too. CX usually owns the signal, but another team usually owns the fix. Naming both makes the handoff obvious.

A quick gut check: if you asked the room a week later who was responsible, would everyone give the same name?

Rank it

This is where CX earns credibility with partner teams, or loses it.

• Bring fewer insights. Three that get decisions will do more than fifteen that get nods. Deciding what to leave out is part of the process.
• Speak the other team’s language. Product weighs roadmap trade-offs, finance looks at revenue and cost, and operations cares about volume and handle time. Put the cost of waiting in their terms.
• Show where it’s heading. A small issue that’s growing fast usually deserves more attention than a big one that’s holding steady, and a trend line makes that case better than a single number.
• Split quick fixes from bigger ones. Some insights need a policy change or a new help article. Others need a spot on the roadmap. Handle them separately so the easy wins don’t get stuck behind the long debates.

Close it

Closing the loop means checking that the change worked, and then telling the people who raised it.

• Decide what “fixed” looks like before anything ships. Pick the signal you expect to move, whether that’s contact volume, sentiment, repeat contacts or cancellations. If you can’t name it, you won’t be able to tell whether it worked.
• Put the check-in on the calendar. A look back at 30 and 90 days covers most issues. Schedule it when the decision is made, or it probably won’t happen.
• Tell the frontline what happened. Agents and analysts who hear back keep flagging problems. It costs almost nothing and keeps the signal coming.
• Close it out or reopen it on purpose. If the numbers moved, say so and retire the insight. If they didn’t, reopen it with what you learned.

Making the loop run at scale

All of this gets harder as feedback volume grows. Getting from a broad label like “billing confusion” to the root cause, spotting which issues are picking up speed and confirming that a fix actually worked are close to impossible to do by hand across thousands of conversations a week. This is where technology earns its place. It makes ownership, prioritization and follow-through something a team can keep up week after week, instead of something that depends on one person staying late.

People still make the calls, like which trade-off to accept, who owns the fix and when an issue is really closed. Better signal means they can make those calls sooner and with more confidence.

What this means for CX leaders

As AI takes on more of the routine contacts, the people in CX are increasingly the ones who make sense of what customers are going through and push the rest of the business to respond. That changes the job. It’s less about reporting what happened and more about making sure something happens next.

It changes how trust works, too. Customers never see your insights deck. What they see is whether the confusing thing got fixed, and whether they had to contact you twice to find out. When an insight stalls internally, customers feel it as a broken promise.

Where to start

When insights keep stalling, it’s tempting to add more, like another dashboard or another report. But most teams can already see the problem in broad strokes. What’s missing is enough detail to act on it, and someone who owns what happens next.

So start small. Make each insight specific enough that someone could act on it tomorrow, put a name on it and don’t call it done until customers can tell the difference. The teams that do this well don’t necessarily know more about their customers than anyone else. They just act on more of what they know.

 Guest post written by Christina Wiesendanger Stewart, Senior Product Marketing Manager, Unwrap

The post Why Customer Insights Die Before Anyone Acts on Them appeared first on Execs In The Know.

As artificial intelligence (AI) changes how customer service is delivered, the skills organizations need are changing with it. The opportunity is not simply to make agents better at using technology. It is to build a workforce that understands how people, processes, data, and technology work together to produce a business outcome.

That is a different kind of upskilling.

From Using Technology to Orchestrating It

The contact center of the future will not be defined by how many AI tools an organization deploys. It will be defined by how effectively people can orchestrate those capabilities.

AI may summarize an interaction, recommend a response, identify a customer’s intent, surface relevant information, automate a transaction, or determine that a situation requires human intervention. But someone still has to understand where those capabilities fit into the broader customer journey and business process.

That creates a growing need for people who can connect the dots.

They need to understand the technology, but they also need to understand the operation around it. They need to recognize when an automated process creates friction, where a handoff breaks down, what information is missing, and how a change in one part of the customer journey affects another.

In that environment, the workforce becomes an orchestration layer between the customer, the business, and the technology.

The Rise of the AI Builder

This does not mean every contact center employee needs to become a software engineer. It does mean organizations will increasingly need people who can help build and shape AI-enabled capabilities.

Consider an employee who understands the most common reasons customers contact the organization. That person may be able to identify opportunities to improve an AI workflow, refine the information being surfaced to agents, redesign an escalation path, or identify a process that should be automated altogether.

That is valuable expertise.

The people closest to the customer often have a practical understanding of where technology succeeds and where it creates more work. Upskilling those employees to help design and improve capabilities can turn frontline knowledge into an organizational asset.

The emerging role is not simply an “agent who uses AI.” It is closer to an AI-enabled problem solver: someone who understands the customer need, the business objective, the available technology, and the process required to bring them together.

Business Context Becomes a Critical Skill

One of the biggest challenges in workforce development is helping employees understand not just what they are being asked to do, but why it matters to the business.

If an employee understands only the task, they can follow a process. If they understand the desired outcome, they can make better decisions when the process, technology, or customer situation changes.

That distinction matters when AI enters the picture.

An employee who understands that the business is trying to reduce customer effort may evaluate an automation differently from someone whose only objective is to increase containment. An employee who understands the importance of customer retention may recognize that a seemingly efficient automated interaction is creating risk for a high-value customer.

The more technology takes over individual tasks, the more important that broader context becomes.

CX leaders therefore have an opportunity to connect workforce development much more directly to business strategy. Employees should understand the outcomes the organization is trying to achieve, the customer problems that matter most, and the measures used to determine whether the strategy is working.

That context gives people a framework for making decisions rather than simply executing instructions.

Build Capabilities, Not Just Training Programs

This also changes how leaders should think about program management. Upskilling cannot be a series of disconnected courses launched whenever a new technology arrives. It needs to be managed as an ongoing capability-building effort.

That means mapping the skills required across the operating model: customer communication, process knowledge, data literacy, AI fluency, problem-solving, workflow design, change management, and business acumen. It means identifying which capabilities belong with frontline employees, which require specialized roles, and where technology can augment both.

It also means creating mechanisms for continuous learning.

When a new AI capability is introduced, the question should not simply be, “How do we train employees to use it?” Leaders should ask, “What new decisions will employees need to make? What processes will change? What knowledge will they need? What should they be able to improve themselves?”

That is a much more durable approach to workforce transformation.

The Workforce is Part of the Technology Strategy

For CX leaders, the larger opportunity is to stop treating workforce strategy and technology strategy as separate conversations.

They are increasingly the same conversation.

Organizations are building new technology capabilities, but their value depends on whether people can integrate them into how work actually gets done. The workforce needs enough technical fluency to work alongside AI, enough operational knowledge to connect systems and processes, and enough business understanding to know what outcome all of that work is supposed to produce.

The goal is not to train people to keep up with technology. It is to build people who can help shape what the technology becomes.

That shift, from training employees to building organizational capability, may ultimately be one of the most important elements of the AI transformation taking place across CX.

The post Upskilling the CX Workforce: Building the People Who Can Orchestrate What Comes Next appeared first on Execs In The Know.

The challenge is not a lack of data. Most organizations have more customer data than they know what to do with.

The more consequential question is whether the measures being used are actually reinforcing the experience the organization intends to create. CX metrics do more than describe performance. They shape it.

The measures an organization reviews most consistently influence where leaders invest, which problems receive attention, how teams prioritize their work, which technologies get scaled, and what employees understand to be important. Over time, the measurement system becomes an organizational blueprint.

What you measure is what you’re building for.

That is an increasingly important consideration as CX leaders navigate the tension between experience, efficiency, and the rapid adoption of artificial intelligence (AI).

The CX Metrics We Choose Become Management Priorities

Every organization has competing objectives. Customers want experiences that are easy, responsive, and effective. Employees need the tools and capacity to deliver them. Finance wants sustainable economics. Operations wants consistency and productivity. Technology teams are looking for opportunities to automate and scale.

Metrics help leaders reconcile those competing demands, but they can also unintentionally tilt the organization toward one objective at another’s expense.

Consider a contact center where average handle time has historically been one of the most closely watched measures. There is nothing inherently wrong with wanting interactions to be efficient. The problem emerges when efficiency becomes the dominant definition of performance.

A representative who resolves a complicated issue in eight minutes may be viewed as performing better than one who takes 15 minutes, even if the longer interaction prevents a repeat contact, eliminates an escalation, and leaves the customer confident that the issue is actually resolved.

The organization has not necessarily decided that resolution matters less than speed. It has simply created a measurement environment in which speed is easier to see, compare, and manage.

The same dynamic is emerging in AI.

Containment, automation rates, deflection, and cost reduction are relatively straightforward to quantify. But if those measures become the primary evidence of success, organizations can inadvertently build AI programs around reducing human involvement rather than improving the customer outcome.

That distinction matters.

A customer who never reaches an employee because an AI system successfully resolved the issue represents a very different outcome from a customer who never reaches an employee because the system made it difficult.

From a dashboard perspective, both can look like successful containment. From the customer’s perspective, they are not remotely the same experience.

The Danger of Optimizing the Metric Instead of the Outcome

This is where mature CX measurement becomes less about selecting individual metrics and more about understanding relationships between them. A metric is rarely meaningful on its own. A reduction in contact volume could indicate that customers are using self-service successfully. It could also indicate that customers have stopped trying to get help.

A reduction in handle time could reflect better employee tools and more efficient processes. It could also reflect interactions ending before the underlying problem has been resolved. An increase in chatbot adoption could signal that customers appreciate a convenient digital channel. It could also reflect a company aggressively steering customers toward automation.

The number itself does not tell us which story is true. Context does.

That means CX leaders increasingly need to look at measures as a system rather than a collection of independent KPIs. Customer outcomes, operational performance, employee experience, and business results should inform one another.

For example, a contact center focused on improving efficiency might track handle time alongside first-contact resolution, repeat contacts, transfers, customer effort, escalation rates, and cost to serve. The goal isn’t to create a larger dashboard. It is to understand whether efficiency comes from better performance or is simply shifted elsewhere in the customer journey.

The same principle applies to digital experiences and AI.

Containment becomes more meaningful when viewed alongside successful resolution. Automation becomes more meaningful when considered alongside customer effort and trust. Speed becomes more meaningful when paired with accuracy and clarity.

The goal is not to eliminate the operational metric. It is to prevent the operational metric from becoming a substitute for the outcome.

What Gets Measured Gets Funded

CX organizations also face financial consequences based on how they measure performance.

Executives naturally allocate resources to areas with a visible problem, a measurable opportunity, or a credible business case. If an organization can demonstrate that a particular intervention improves a metric that leadership already values, it becomes easier to justify additional investment.

That makes measurement a strategic lever.

If an organization consistently measures the cost of service but has limited visibility into the value of customer retention, for example, investment decisions may naturally favor efficiency initiatives. If it can show how experience improvements influence retention, revenue, risk, or customer lifetime value, the conversation changes.

This is one reason CX leaders have spent years connecting experience measures with broader business outcomes.

The objective is not to turn every customer interaction into a financial calculation. It is to make the relationship between customer experience and enterprise performance more visible.

The measures leaders choose signal to the rest of the organization what kind of CX investment it will value.

Some of the Most Important Outcomes Are the Hardest to Measure

The other challenge is that some of the experiences organizations most want to create don’t translate easily into traditional operational metrics.

Trust is difficult to reduce to a single number. So is confidence and the feeling that a company understands what a customer needs and will do what it says it will do. Yet these outcomes matter more than ever, especially as customers interact with automated systems, AI-generated content, and increasingly complex digital journeys.

An outcome being difficult to measure does not make it less important. In some cases, it makes it more important to find meaningful ways to understand it.

This may mean combining quantitative measures with qualitative customer feedback, behavioral data, journey analysis, employee insight, and targeted research. It may also mean resisting the temptation to force every aspect of CX into a single composite score simply because a single number is easier to report.

Precision is not the same thing as simplicity. A dashboard can be beautifully simple and still tell the wrong story.

The Question CX Leaders Could Be Asking

The next evolution of CX measurement is unlikely to come from finding one perfect KPI. It will come from becoming more deliberate about what the organization’s measurement system is designed to produce.

Before adding another metric to the dashboard, leaders should consider what behavior that metric will encourage if it becomes a major organizational priority.

If we optimize for this number, what will our employees do differently? What decisions will our technology teams make? Where will investment flow? What trade-offs will leaders make?

And, ultimately, what will customers experience as a result?

Those questions move measurement beyond reporting and into strategy.

They also create an important discipline for CX leaders: measure the outcomes you want to create, not simply the activities that are easiest to count.

Operational measures will always matter. Financial constraints, productivity expectations, service-level requirements, and efficiency opportunities will always exist. The answer is not to choose customer experience over business performance. Mature CX organizations understand that the two are connected.

The opportunity is to build a measurement architecture that makes those connections visible. Because every metric signals what matters. Every KPI creates an incentive. Every dashboard directs attention. And every repeated measurement, over time, helps determine what an organization becomes better at building.

What you measure is what you’re building for. The question for CX leaders is whether the experience being built is the one the organization actually intends to create.

The post What You Measure is What You’re Building For appeared first on Execs In The Know.

For years, CX leaders have focused on what AI can take on: which contacts can be automated, which journeys can become self-service and where technology can improve efficiency. But as those capabilities move from pilots into the standard operating model, there is a bigger question: What happens if the AI strategy actually works?

Predictable contacts disappear. Proactive service resolves more issues before customers ever need to reach out. What increasingly reaches the agent is the customer whose self-service journey failed, the exception the workflow cannot resolve, the emotionally charged situation, or the problem spanning multiple systems and departments.

The standard answer is that agents will handle the harder contacts. That answer is too small. The agent organization cannot simply become whatever is left after AI has automated the rest.

We’re already seeing signals of what that means in practice.

A team of agents in the Philippines supporting a communications client had been trained on the customer experience surrounding an in-home field technician visit. But most had never lived or worked in the market they supported, so their understanding of that experience was limited to classroom training.

Field technician visits were filmed and documented, critical moments were mapped, and agents experienced the journey through virtual reality. Within days, first-contact resolution increased from 22% to 65%.

The technology made the experience possible, but the lesson wasn’t really about VR. The agents didn’t need more information. They needed context. They needed to understand what happened beyond their part of the customer journey so they could make better decisions within it.

This is a small example of a much bigger shift now underway in CX. Context, judgment, and the ability to take ownership when the expected path breaks becomes more important.

The question is no longer simply how technology can make agents better at the jobs we’ve designed for them. It’s whether we’re designing the right jobs, and the right operating model for what comes next.

That starts with five moves:

• Map the work AI leaves behind. Understand which interactions will still require an agent and how that work is changing.
• Define agent ownership before headcount. Decide what agents should own before determining how many are needed.
• Build the talent profile around the work. Hire and develop for the skills the new workload actually demands.
• Train for where predictability ends. Prepare agents to navigate exceptions, ambiguity and moments that require judgment.
• Measure and invest for the model you’re building. Align KPIs, technology and investment to the future operating model, not the one automation is making obsolete.

Start with what AI leaves behind 

Most AI dashboards tell leaders what the technology handled: containment, automation, resolution, adoption and cost. Those numbers matter, but they only tell half the workforce strategy. The other half is what AI consistently hands back to the agents.

When AI resolves predictable interactions, what remains becomes more concentrated around exceptions, ambiguity and failed journeys. That work deserves the same rigor organizations have applied to automation: where does the standard process break, what judgment is required, and which outcomes require human intervention?

The answers can change how performance should be interpreted. If AI disproportionately removes short, predictable contacts, average handle time can rise because the average agent interaction is harder. Escalation patterns can change. Performance variance can widen as judgment matters more than adherence. Some KPIs can appear to deteriorate precisely because the AI strategy is working.

Traditional metrics do not suddenly become obsolete. However, their context has changed. Your automation rate tells you what AI took away. It doesn’t tell you what it left behind. Build a residual-work map alongside the automation roadmap: the exceptions, failed journeys, cross-functional issues, judgment calls and recovery moments that remain. Use it to forecast the skills, capacity and authority the remaining work will require.

The automation roadmap tells you where technology is going. The residual-work map tells you what your workforce needs to become.

Agentic AI raises the stakes further. If ownership is unclear, data is fragmented or the underlying process is broken, giving AI more autonomy won’t fix the operation; it can accelerate its weaknesses. The question isn’t simply how agentic the technology can become, but whether the operation around it is ready for that autonomy.

Redesign ownership before you redesign headcount

Once the remaining work is visible, the natural question is capacity: How many agents will we need? But capacity should not be the first question. Ownership should be: What areas are most important to prioritize agent ownership?

If an AI strategy reduces contacts while the workforce strategy simply reduces headcount, the organization risks automating the old operating model without designing the next one.

Consider the traditional tier structure. Tier 1 handles common issues, while increasingly complicated problems move through specialized levels of support. Moving agents higher up that ladder as AI absorbs Tier 1 work still assumes agent work should be organized around contacts, queues, and escalations.

AI gives us a reason to question the ladder itself. When technology can identify intent, retrieve information, complete actions and resolve predictable needs, agents can be designed around ownership of the outcomes that require judgement.

A contact has a beginning and an end. An outcome can cross channels, systems, departments, and multiple interactions. A contact can be transferred. Ownership stays accountable to the result. Managing a contact asks how efficiently the interaction was handled. Owning an outcome asks whether the customer’s actual need was resolved.

But ownership without authority is just another label. If an agent cannot deviate from a workflow, make a judgment call or coordinate across the functions required to resolve an issue, they do not really own the outcome. They own the next step.

Before redesigning capacity, run an ownership test against the highest-value work AI leaves behind: Who owns the outcome? Do they have the authority to resolve it? Can they cross the organizational boundaries required to do so? Define ownership first, align authority to it, then design capacity around the work.

Build tomorrow’s talent profile from tomorrow’s work

Changing ownership changes what high performance looks like. Contact centers have spent decades creating repeatability: know the process, find the answer, follow the workflow, be consistent, and reduce unnecessary time. We spent years teaching agents to behave more like machines. Now we finally have machines that are exceptionally good at being machines.

When information can be surfaced instantly, knowledge retention becomes less differentiating than knowing how to apply it. When predictable workflows can be automated, speed through a defined process becomes less valuable than knowing what to do when the process no longer fits.

As routine tasks are automated, agent performance shifts towards investigation, judgment, ownership, recovery, and contextual decision-making. That means today’s highest performers will not automatically be tomorrow’s most valuable talent.

The signals are already visible in agents who investigate rather than immediately escalate, recognize when the prescribed answer does not fit, connect information across systems, recover a relationship after something goes wrong, and take ownership when the process stops providing an obvious next step.

Even the language we use needs to catch up. Cortney Jonas Burnos, VP of AI & Digital Solutions at Transcom, has questioned whether “agent” still accurately reflects the role, describing today’s frontline professionals as “high empathy, high skill CX experts working alongside AI.” The language matters because it shapes the roles we design, the capabilities we reward, and the talent we recruit.

Build tomorrow’s talent profile from the emerging work, not yesterday’s top-performer scorecard. Identify the capabilities that the work requires, find the agents already demonstrating them and compare those behaviors with what current recruiting profiles, scorecards, coaching, and career paths reward. The gaps tell you where the talent strategy needs to change.

Train for where predictability ends.

The field technician example illustrates the difference: better performance didn’t come from giving agents more information. It came from giving them the context to apply what they already knew.

That becomes increasingly more important as AI absorbs more of the predictable path. Agents enter where predictability ends. They need experience applying knowledge when information is incomplete, the prescribed answer does not fit, an AI recommendation needs to be challenged or the customer’s problem crosses the boundaries of the workflow.

The lesson is not simply to use more technology in training. It is to change what agents are being prepared for. Train the exception, not just the process. Use simulation to expose agents to incomplete information, failed recommendations, emotional situations, competing priorities, and cross-functional problems before the customer becomes the test.

There is something counterintuitive about that. One of AI’s most valuable roles in the future of CX will be helping humans strengthen the capabilities that matter most when automation reaches its limits.

Measuring the work you are creating, not the work that is disappearing 

Changing the role without changing the scorecard leaves agents doing tomorrow’s work while being evaluated against yesterday’s job.

Leaders increasingly need two views of performance. The first measures what AI handles: automation, containment, adoption, resolution, and efficiency. The second measures what agents now own: complex resolution, recovery, judgment, ownership, and customer outcomes. Looking at one without the other creates an incomplete picture.

The investment strategy needs to evolve too. The hidden cost of a successful AI strategy may be everything an organization failed to redesign around it. Funding technology without investing in the data integrity, governance, systems integration, training, role design, and frontline authority it depends on can create an AI-enabled version of yesterday’s operating model.

The workforce plan and the AI plan can no longer be separate exercises. Every successful automation decision changes the work agents receive, which changes the capabilities, authority, training, and measures the organization needs around them.

Customer connection deserves the same precision. Customers do not need an agent inserted into every journey simply to prove one is available. When AI can resolve a need accurately, immediately, and with less effort, that is a better experience. But access to an agent still matters when the situation requires judgment, recovery, trust, meaningful value or risk. Fewer agent interactions make those moments more consequential, not less.

As Cortney Jonas Burnos puts it, “We spent years designing contact centers around managing routine contacts. AI now handles those routine interactions effortlessly. The real opportunity is empowering people with true authority and full ownership over meaningful outcomes.”

For years, organizations have asked what AI can do inside the contact center. The more consequential question for the next three to five years is what the contact center must become because of it.

The organizations that stop at automation will have a more efficient version of the past. The ones that redesign the work around it will build what comes next.

Continue the conversation 

The AI conversation is moving fast. The operating model around it needs to move just as deliberately.

Transcom’s AI, but Make It Human series on Leading Voices goes beyond the AI hype to explore what this shift means in practice, from the changing role of frontline professionals to where AI creates real value and where judgment, empathy and expertise still matter most.

Explore AI, but Make It Human on Transcom Leading Voices

Guest post written by Transcom.

The post The Work AI Leaves Behind: Designing the Next Generation of CX appeared first on Execs In The Know.

That shift matters.

For the past several years, CX leaders have been surrounded by possibilities. Generative AI could transform service. Voice AI could reshape the contact center. Predictive models could enable more proactive engagement. Automation could change how work gets done.

But possibility is no longer enough. As AI investment moves from experimentation toward implementation, leaders are being asked to make harder decisions: Which use cases deserve investment? Where can AI create measurable value? What needs to remain human? How should success be measured? And what happens when an AI deployment doesn’t deliver the expected results?

The answers are increasingly being found in the details of actual deployments.

What Happens After the Pilot?

One challenge facing CX leaders is that much of the conversation around AI remains conceptual. Industry reports can identify trends, and technology providers can demonstrate what their platforms can do, but neither necessarily answers the questions leaders face when putting AI into production.

What problem were you actually trying to solve? Why was AI the right approach? What changed after implementation? Did customers notice a difference? Did employees? Did the business? And perhaps most importantly, what did you learn that changed the next decision?

These questions matter even more as organizations evaluate AI across different parts of the customer experience. A voice AI deployment, for example, may be evaluated through containment, transfer rates, resolution, and customer sentiment. An employee-facing application may be measured through productivity, quality, or time saved. Proactive AI may require an entirely different definition of value.

No single AI metric tells the whole story. The real work for CX leaders is connecting the use case to the business problem, defining what meaningful improvement looks like, and building the measurement discipline to determine whether the technology actually delivered it.

Use Cases Can Reveal More Than One AI Success Story

Looking at AI through individual use cases can also help leaders make better decisions about their own roadmaps.

Customer-facing AI may reveal where automation can improve access without creating additional friction. Voice AI can demonstrate how conversational technology is changing the economics and experience of the contact center. Proactive engagement can show what becomes possible when organizations use AI to anticipate customer needs rather than simply respond to them.

Workforce applications offer another perspective, particularly as AI begins changing the nature of the work itself. The value of examining these applications together is not to identify one universal model for AI adoption. It is to see how different organizations are approaching the same fundamental challenge: turning AI capability into measurable CX and business impact.

The differences are often as instructive as the similarities.

Some organizations may be focused on efficiency. Others may be pursuing revenue, experience improvement, employee enablement, or greater scalability. Some use cases may begin with a narrowly defined problem and expand. Others may reveal limitations that lead the organization to change course. Those distinctions matter when building an AI roadmap because the path from experimentation to scale rarely looks the same across organizations.

What Leaders Should Be Looking for in an AI Use Case

As more AI deployments move into production, the most useful examples are not necessarily the ones with the biggest claims. They provide enough detail to understand the decisions behind the result.

A useful use case should answer four questions:

1. What problem was worth solving?
AI should start with a meaningful customer, employee, or business problem, rather than the technology itself.

2. What did the organization actually deploy?
Understanding the application, workflow, and role AI plays provides important context for determining whether the approach could translate to another environment.

3. How was impact measured?
Outcomes should extend beyond an AI adoption metric. Leaders need to understand what changed for customers, employees, and the business.

4. What did the organization learn?
Implementation rarely follows the original plan perfectly. The adjustments, challenges, and lessons can be just as valuable as the final outcome.

That last question may ultimately be the most important. The organizations moving AI forward are not simply proving that a technology works. They are learning where it works, where it doesn’t, and how to make better decisions with each deployment.

Moving From AI Curiosity to Discipline

The next phase of AI in CX will require more than identifying exciting applications. It will require discipline around prioritization, implementation, governance, measurement, and scale. That means CX leaders need fewer hypothetical conversations and more visibility into what is happening in the real world.

What are organizations deploying today? What results are they seeing? Where are they encountering friction? And what are they changing as they learn?

Those questions can turn someone else’s AI deployment into a useful input for your own roadmap.

On October 29, Execs In The Know will bring CX AI use cases together for a 90-minute rapid-fire virtual session focused on real deployments, results, and lessons learned, with live Q&A following each use case. For CX leaders evaluating where AI can create meaningful impact, the goal isn’t to leave with someone else’s roadmap. It’s to leave with a clearer view of the decisions, measurements, and lessons that can inform your own.

Learn more and save your seat for the virtual event.

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