How will AI reshape customer service management and customer expectations? It’s a complicated question, and one that many customer service leaders are struggling to answer.
To get a better idea, I interviewed 35-year customer experience (CX) veteran Eric Burton. He shared what he’s learned about CX, humans, and AI over his career, which started in a contact center and progressed to him leading corporate contact centers and CX organizations at Time Warner Cable and Comcast.
The following conversation has been edited for clarity and length.
When you're focused on customer experience, it's never the same job twice. People change. Expectations change. The technology changes. Looking back over 35 years in the business, it's honestly as exciting today—maybe more so—than it was in those early years.
Because people have always been at the center of it—building teams internally, supporting customers externally—it never really felt like a CX job. It felt like a people job. And since we're all customers ourselves, that connection was never hard to find.
What has changed is the bar for what easy looks like. Airline apps are a good example. What used to require standing in a long line or waiting on hold to rebook a flight or get a new boarding pass now takes 30 seconds on your phone.
Because so many companies have made everyday interactions frictionless, customers now expect that everywhere. Whether it's a human or a machine on the other end, customers know it's possible. So when it's not, they have very little patience. And the bar is only going up.
I used my AI app of choice three times today. If I can get an answer to almost any question at my fingertips, what's my expectation when I reach out to a company?
The skills required 30 to 35 years ago are actually what's needed now. When I started as an agent, you got trained, you got thrown in, and you had to figure things out. All the knowledge lived in your head. It wasn't scripted.
When someone needs to talk to a person, it's because self-service can't handle it. That means your agents need to think, navigate messy systems, and demonstrate real empathy in real time. The companies that win will be the ones that recognize this and invest accordingly.
Customers can tolerate a complicated problem that takes time to fix as long as the rep on the other end sounds like they know what they're doing and makes you feel like they care.
One of the biggest missed opportunities I've seen is companies failing to think about their technology stack outside the contact center as part of the same ecosystem. Decisions get made in silos.
The contact center buys a chatbot, the stores do their own thing, and field service does something else. A chatbot sitting on top of all that fragmentation is never going to work well, because fully answering a customer's question often requires access to all those underlying systems.
The real driver behind a lot of chatbot adoption was cost reduction, not customer experience or resolution. If we can deflect an interaction, we save money. And when deflection becomes the goal, you end up deliberately creating friction.
The good news is that technology today is dramatically better. I'm genuinely optimistic that AI can resolve a much larger share of interactions on its own. When we look back three to five years from now, the impact on contact centers will be more meaningful than anything we've seen before. But the same trap is still there.
If you reduce contact volume by half, do you reinvest those savings in upskilling and empowering your people, or do you just take it to the bottom line? The companies that reinvest will be the ones that finally close the empathy gap.
I've been in more conversations over the last couple of years about data quality, underlying systems, and knowledge content than in the previous decade. And the reason is AI. When you want to give AI access to your platforms, you must confront what's in those systems and whether you can trust them.
Many companies weren’t motivated to clean all of this up when it was human agents dealing with the mess. I think a lot of organizations are going to assume AI will just work on top of whatever foundation they have.
For example, let’s talk about managing cases for a minute. Most service teams have many different ticketing systems across the organization that they’re using. When you add up all the cases sitting across all those platforms, the volume of fallout is staggering. It’s best to consolidate onto one platform, and then the visibility alone becomes eye-opening.
Yet technology consolidation is the easy part. Changing the policies, processes, and the way cases actually get handled—that's where it gets hard. And no amount of AI is going to fix that.
Before doing anything else, make sure you genuinely understand what your customers need from your specific business—not a generic framework, but what the real drivers are for your customers. Then don't overcomplicate things.
Even if your mandate is limited to the contact center, don't treat it that way. Look across the whole organization. More often than not, when something goes wrong in a contact center, it's because the problem requires reaching into other parts of the business where nobody's connected the dots.
Another thing: Get serious about your fallout, the number of cases that don’t get resolved. Know what fallout you have, where it's going, who's touching it, and how often it actually gets resolved. Then ask what policies, processes, or upstream technology changes could prevent it from happening in the first place. In my experience, when companies focus on it, the opportunity is enormous.
I haven't mentioned AI yet. Once you understand your business—what customers need, where your fallout is, what drives satisfaction—then you ask how AI can help.
The companies that have always done CX well—focused on the basics, sweated the details, done the small things right—are going to get dramatically more out of AI than the ones that haven't.
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