How to implement AI in customer experience: A people-first approach to adoption

In this guest post, Mike Matoush from Career Certified explains why implementing AI in customer experience starts with mindset and change management, not technology.

Artificial intelligence is moving quickly in the customer experience industry. Most companies recognize the opportunity, but many are still figuring out how to translate that into meaningful results. It’s easy to get caught up in the technology and hard to decide what tools to use, what processes to automate, or how quickly to deploy.

In our experience, implementing AI in customer experience isn’t primarily a technology challenge but a people and leadership challenge.

Start with a customer-focused AI strategy.

About a year ago, as we were planning for the year ahead, we decided to invest in AI. Not cautiously explore it on the side, but really commit time and resources to understanding where it could take us. What’s important is how we approached that decision.

We didn’t begin by identifying a specific problem to solve, start with a shortlist of tools, or a target for cost reduction. Instead, we anchored our approach in our core values—customer focus and continuous improvement. That meant we were willing to invest in learning before we had all the answers.

Many companies take the opposite approach. They start with efficiency: how can we reduce handle time, automate workflows, and save costs? Those are valid outcomes, and in many cases, you’ll achieve them with AI. But when they become the primary driver, they can limit the impact. For us, the goal was to improve the customer experience.

Make change management the foundation of AI adoption.

If there’s one area that determines whether AI adoption succeeds or fails, it’s change management. AI can be intimidating, especially for frontline teams. When people hear about automation, they often assume it means replacement. If you don’t address that head-on, resistance is inevitable.

One of the things we were very intentional about was what we didn’t do. We didn’t mandate tools from the top down. We didn’t force new systems on our teams solely for efficiency. Instead, we focused on creating an environment where people could explore. We encouraged our teams, starting at the agent level, to learn about AI tools, try them out, and think about how they could make their own jobs easier. That shift in framing is important as it moves the conversation from “this is something being done to you” to “this is something that can help you.”

As a leadership team, we also made a clear commitment early on: we would not use AI to replace individuals. Our focus was on scaling our capacity and improving the experience we deliver, not reducing headcount. And this level of transparency matters; if you want adoption, you have to build trust first.

Turn early adopters into AI champions.

Another key part of our approach was leaning on early adopters. In any company, there are people who are naturally more curious about new tools and ways of working. We identified those individuals early and gave them space to explore. When they shared their experiences, the message was simple: less admin work, more time, and greater focus on the customer.

That message resonates in a way that top-down communication often doesn’t. When peers see tangible benefits from people they trust, it builds momentum organically. Over time, those early adopters became advocates, and that advocacy helped drive broader adoption across the team.

If you’re thinking about implementing AI, this is one of the most effective levers you have. Adoption doesn’t scale through mandates; it scales through credibility.

Design for reality: AI won’t be perfect from day one.

A common misconception is that AI needs to be fully mature before you can use it in your operations. In reality, you’re going to learn most of what you need to know after you start.

We saw that firsthand with our virtual assistant. Early on, it became clear that it wasn’t ready to handle more complex, information-based questions because many of our interactions require a level of discussion and nuance that the assistant couldn’t fully support. Rather than forcing it to work in scenarios where it wasn’t effective, we adjusted.

We made sure it was easy for customers to connect with a human agent at any point. That path needed to be seamless—no friction, no frustration. At the same time, we continued to monitor performance, looking at resolution rates and customer satisfaction. The key is being willing to acknowledge gaps and adapt quickly. AI is not a “set it and forget it” solution. It requires iteration, testing, and continuous improvement.

Measure success through customer experience, not just efficiency.

As AI becomes more embedded in customer support, there are plenty of metrics you can look at, such as handle time, automation rates, or cost savings. Those all have value, but they don’t tell the full story.

For us, the most important indicator is customer satisfaction. If we see a decline in CSAT, that’s a clear signal that something isn’t working, regardless of what other metrics are saying. On the other hand, if we’re maintaining or improving satisfaction while increasing efficiency, we know we’re on the right track.

We also look at resolution rates and service levels, but they’re supporting indicators. The balance here is important. It’s easy to over-optimize for efficiency and lose sight of the experience. AI gives you powerful tools to scale, but it also raises customer expectations. People are increasingly comfortable interacting with AI, but they still expect high-quality outcomes—that’s why measurement must remain grounded in the customer perspective.

The future of AI in customer experience requires a balance.

There’s no question that AI is now a standard part of support. Customers are more open to it than they were even a few years ago. In many cases, they prefer quick, automated interactions for straightforward needs. At the same time, when issues are more complex, they expect easy access to human support.

That’s where the balance comes in. We must think carefully about where automation adds value and where human interaction is still essential. And that balance isn’t static; it will continue to evolve as both technology and customer expectations change. From our perspective, the key is to keep listening. Look at your data, but also pay attention to qualitative feedback.

Implementing AI in CX is a leadership challenge.

At the end of the day, implementing AI in customer experience comes down to how you lead the change. The technology is important, but it’s not the differentiator. What matters is how you introduce it, how you position it, and how you support your teams through the transition.

The most value from AI will come from those who:

• Start with a clear, customer-focused strategy.

• Invest in change management and team buy-in.

• Measure success through customer outcomes.

• Stay flexible and continue to iterate.

AI has the potential to transform customer experience, but it doesn’t replace the fundamentals; it builds on them. When you get the foundation right—your people, your mindset, your approach—AI becomes a powerful tool to amplify what you’re already doing well.