Driving The Bouncy CX Journey

Driving customer experiences (CXs) on profitable, mutually satisfying journeys these days can be very much like driving on today’s roads.

There are greater demands to be at one’s destination quickly, comfortably, and with the least amount of cost and hassle. Getting there is being enabled by new technologies like AI and innovative methods.

But the CX pathways, like today’s roads, are often congested with bumps, potholes, delays, and detours. This has resulted in customers navigating often complicated, now AI-powered self-service systems and becoming stalled by long hold times to reach the right agents, who are also using AI tools.

To understand what is happening with CX and employee experience (EX), and how contact centers can make both journeys easier, we had a virtual conversation with our panel of leading supplier subject matter experts.

• Anna Convery, Chief Marketing Officer, Verint
• Dana Shalev, Senior Director, Product Marketing, GCO, NiCE
• Eric Williamson, Chief Marketing Officer, CallMiner

Hopefully, your center, customers, and agents will have many good trips together.

Q. What developments, issues, and trends in CX that you have heard from contact centers over the past 12 months will likely continue into the next 12 months? Did these change from your expectations?

The story of the last 12 months is that expectations moved faster than operations. In Verint’s “The State of Customer Experience 2026 report”:

• 42% of consumer respondents stated their service expectations rose over the prior year, up from 19% in 2024.
• 51% now say businesses fall short when they actually need help, the first time in five years of running this study we’ve seen a majority say this.

The unexpected part of this data was the cause. Businesses adopted AI at unprecedented speed, yet customers remained frustrated because most deployments stopped short of solving problems end-to-end. The data is clear that simply deploying AI is not a solution to better CX.

Over the next 12 months, I’d expect much harder questions about whether the AI initiatives are providing outcomes.

We also found that channel preference has settled around 70%/30% digital to phone, which indicates that consumers have not leaned into digital fully and abandoned the phone channel completely. I don’t expect that split to change into 2027.

Over the past 12 months, the conversation around AI in the contact center has shifted from experimentation to accountability. 18 months ago, corporate boards were asking their organizations to do something with AI.

This year, the boards are asking what those investments cost and what they returned, and many pilots haven’t survived that second question.

We expect that scrutiny to define the next 12 months as well. Here’s why:

First, headcount decisions are running ahead of the evidence. More than 80% of organizations expect to reduce agent headcount within 18 months, Gartner projects, also that half of companies that are cutting support staff because of AI will rehire those roles by 2027.

Second, the economics also require a more measured approach. Gartner’s January 2026 analysis projects generative AI cost per resolution passing $3 by 2030, potentially exceeding the cost of an offshore human agent.

Not every interaction needs a reasoning model. Matching the right technology to each interaction is where the economics are won.

We’re also seeing the formation of a hybrid workforce of human and AI agents. Employees increasingly see AI as a path to mastery, not replacement.

A NiCE survey highlights this trend with 84% wanting AI to reduce complexity and workload. As a result, leading organizations will start treating AI agents as a workforce, governed by the same focus as human agents on productivity, quality, resolution, and compliance.

Over the past 12 months, automation has been the dominant trend across CX and contact center organizations.

More teams are looking for ways to automate customer interactions, reduce manual work, and create more efficient customer journeys, which isn’t surprising at all. It aligns with where most of the industry expected CX to go as AI adoption accelerated.

What I think is evolving is the level of scrutiny around automation. The past year was largely about moving quickly and proving what was possible, but the next 12 months will be more about refinement.

“…organizations need evaluation programs that are more comprehensive, objective, and tied to business outcomes.”

Eric Williamson

Organizations will need to determine whether their automation initiatives are actually improving customer outcomes, reducing friction, and meeting customer expectations.

That also brings governance, compliance, and guardrails further to the forefront. As organizations rely more on AI and automation, they need to make sure those systems are secure, accurate, and aligned with both business goals and customer needs.

Q. How much of today’s customers’ satisfaction and loyalty, or conversely their dissatisfaction and churn, is based on the products or services they purchase versus their experiences purchasing and receiving service and support for them through the contact center?

Anna Convery:

CX is carrying more of the loyalty weight than product now. Two numbers from our research illustrate this: 79% say they’d switch to a competitor after one bad experience and 80% say they’d buy again and recommend that brand after a great one. That’s a thin margin on either side of a single interaction.

Product quality is a baseline differentiator, but not the only one. CX is one of the most critical differentiators. However, most customers can’t accurately evaluate a product against the alternatives they didn’t buy.

Everyone can evaluate what happened when they called for help. Service is the part they experience over and over, meaning it’s the piece they can describe to someone else afterward. Which is why I’d encourage leaders to stop reviewing service as a cost line.

A failed resolution isn’t a handling expense. It’s a retention test the brand just took.

Dana Shalev:

Product problems are now nearly universal. 77% of U.S. consumers experienced a product or service problem in the past year, according to a study by Customer Care Measurement & Consulting (CCMC) and Arizona State University.

When three in four customers encounter a problem, the recovery is what they judge. CCMC and Arizona State estimate that more than $596 billion in revenue is at risk from poor complaint handling.

Not surprisingly, the American Customer Satisfaction Institute (ACSI)’s Q1 2026 data shows satisfaction falling and complaints at record levels, even as retention rose and profits soared.

“…the conversation around AI in the contact center has shifted from experimentation to accountability.” 

Dana Shalev

It’s tempting to conclude that service is fine. It isn’t. Unhappy customers are staying because leaving is hard, as markets have grown more concentrated and switching costs have risen.

Service is part of what the customer bought, not separate from it. Pricing and functionality are hard to tell apart across competing brands today, but brands can still differentiate on CX.

When companies align their brand promise with the experiences they deliver, they can unlock up to 3.5x revenue growth, according to Forrester’s “Global Total Experience Score Rankings, 2025,” and significantly boost customer loyalty.

Eric Williamson:

Product quality and CX are intrinsically linked, and in many cases they compound each other.

• A poor product experience can be frustrating, but customers are often willing to give a brand another chance if the issue is resolved quickly, fairly, and with minimal effort.
• The reverse is also true. What might have been a manageable issue becomes a broader reflection of the brand itself, making customers far more likely to churn, leave negative feedback, or take their business elsewhere.

The organizations that perform best are the ones that look at the entire customer journey. It’s not enough to fix product issues without improving service, and it’s not enough to improve service if recurring product issues remain unresolved.

With 60% of consumers leaving a brand after just one or two poor experiences, and approximately 82% switching to a competitor after multiple negative interactions, the stakes are high. Good experiences reinforce loyalty while bad experiences reinforce dissatisfaction.

Q. Are you seeing any changes in or pressure to change quality assurance (QA) and quality monitoring (QM), including customer feedback methods?

Anna Convery:

There’s enormous survey fatigue in the market and customers expect quality to be addressed in real time, not weeks later.

Quality programs are now moving beyond whether an agent said the right thing to whether the right action actually happened.

Historically, QA focused heavily on language, process adherence, and scorecards, which can miss the issue mattering most to the customer.

For example, an agent may tell a customer, “I’ve processed your refund for $153,” but accidentally enter $135. A traditional quality program may not catch that if the conversation sounded right but an automated answer validation can.

As a result, quality isn’t just reviewing the conversation after the fact, but checking whether the customer’s issue was handled correctly. Speed matters too.

With manual evaluations, even serious issues may not surface until days or weeks later, when the customer has already felt the impact or a compliance issue has already happened.

But automated quality gives teams a way to catch critical errors much closer to the moment they occur, so they can step in before the problem becomes harder to fix.

Dana Shalev:

All three of these pressures are real.

Formal feedback is drying up. Retently’s study of more than 25 million eCommerce survey invitations found email response rates fell from 4.09% in Q1 2025 to 2.50% in Q4, even as survey volume nearly doubled. Surveying harder produces less signal, but the opinions still exist.

Sampling was always a limited view. Enterprise centers processing millions of interactions monthly still review under 5%, according to the Retently email response survey.

Moving to full coverage takes more than a bigger scorecard. The objective shifts from grading a sample to understanding every conversation.

AI agents should be held to the same outcome standard as human agents, but measured with a different instrument.

AI agents can be tested at a scale no human process allows, running thousands of simulated conversations before a real customer is involved, then continuously monitoring live interactions for accuracy and compliance.

Remember, if an agent serves your customers, its quality is your responsibility, human or not.

Eric Williamson:

There is definitely pressure to evolve QA, QM, and customer feedback methods as the traditional approach is no longer enough for how contact centers operate today.

Traditional QA programs often evaluate only a very small percentage of interactions, which creates an incomplete and sometimes unfair view of agent performance.

As more interactions move across digital, voice, self-service, and AI-driven channels, organizations need evaluation programs that are more comprehensive, objective, and tied to business outcomes.

Resistance to formal feedback often comes down to how it is introduced:

• If agents feel like technology is being used to watch over their shoulders, adoption will be difficult.
• But when organizations include agents in the rollout and position QA as a way to make feedback more objective, fair, and useful for career development, agents are more likely to see the value.

Additionally, although AI agents should be evaluated with the same level of rigor as human agents, they should not necessarily be judged against the same criteria.

An AI agent may need to be measured on containment, escalation accuracy, brand integrity, and how well it handles questions it should not answer.

However, human agents, especially those who are handling complex escalations, should be measured on empathy, resolution, accountability, and whether the customer’s issue was truly solved.

Scorecards are still valuable, but only when they reflect what quality actually means for the business, the customer, and the agent.

The issue is not scorecarding itself. The problem lies with poorly designed scorecards that measure activity without accurately assessing performance, miss key areas of the CX, or push agents toward scripted behaviors instead of effective, human conversations.

When scorecards are optimized around the right outcomes, they become more than just a QA tool. They become a way to deliver objective feedback, personalize coaching, and help agents understand how to improve in ways that directly support better CX.

Q. What are your recommendations to contact centers to help them provide high quality CX, including and enabled by EX?

Anna Convery:

Organizations should decide what outcome they’re asking AI to provide, then measure that one thing. From our research, 61% of consumers now say they’d rather talk to a human, and that number went up this year.

This isn’t a verdict on AI, but on automation that gets three-quarters of the way there. So put self-service where it can finish the job end to end and put AI behind the agent everywhere else.

“…customers expect quality to be addressed in real time, not weeks later.” 

Anna Convery

EX isn’t a separate consideration; it’s part of how the AI strategy works. Agents are telling us the work is getting harder: 94% expect AI to change their role within three years, and 61% expect more complex, technical work.

Contact centers can’t ask people to take on harder judgment calls while leaving the busywork on their desk.

The companies that are doing this well get knowledge to the agents in the moment and automate manual processes end-to-end. They use the time they recover for coaching instead of handle time targets, then feed what they learn back upstream.

Dana Shalev:

First, start with your interaction data. Pull the conversations with high satisfaction, no callbacks, and short handle time. That’s the answer your best agent already gave, and it becomes the foundation for everything you automate.

Second, match the tool to the job. Routine questions like order status call for a deterministic answer, delivered quickly and at low cost. Reserve reasoning models for the complex, frequent, multi-intent conversations where they earn their cost.

Third, build compliance rigor in from the start, not at user acceptance testing. In a regulated context, one hallucination is one too many, and that constraint should shape the architecture from day one.

On EX, be honest about the math. Automating routine work leaves agents with queues of difficult, high-stakes, often emotional conversations.

Gartner found nearly 80% of organizations plan to move agents into new roles and 84% are adding skills to agent profiles. That harder queue deserves to be paid, staffed, and coached for.

Eric Williamson:

Listen to what customers are actually telling you. Organizations cannot deliver high-quality CX if they do not understand what customers want, where they are experiencing friction, and what changes would make their experiences better.

Customer insights should be used to drive real operational improvements, not just measure performance after the fact.

Recognize that CX and EX are directly linked. Agents who are supported, coached, and equipped to do their jobs well are more likely to deliver strong CXs. That becomes even more important as automation shifts more complex interactions to human agents.

Use AI and automation to remove unnecessary burden from employees. Automating tasks like call summaries, after-call work, dispositioning, and other repetitive administrative work gives agents more time and energy to focus on customers.

The goal should not be automation for its own sake. The goal should be better outcomes for customers, employees, and the business.

This article appears in November 2026.