A CX Leaders Guide to Embracing AI

These are all fair questions. In fact, they’re healthy ones. A responsible leader should ask them. 

But here’s a better question: What happens if we wait too long to act while our competitors move forward? 

The so-called “safe approach” is to keep going without change. But that might just be a trap into a dead-end. So many former giants have failed or shrunk at a time when technology changes in their industry. For example, think of Kodak. They were once the dominant force in film. But Kodak failed to start the transition to digital as fast as others, and it led to their bankruptcy. The risk is that this could become us, if we aren’t careful. 

The reality is that AI in customer experience is not a far-off or unproven future. It’s already here. In CBA, we’ve seen companies use intelligent routing, predictive analytics, virtual agents, and so much more. AI is truly reshaping both the tools’ agents use and the way customers interact with a brand. From large call centers to mid-sized contact centers to small customer service teams, many are finding ways to make AI work for them. 

That’s why we believe in the “fail fast, fail safe” approach. Not because we view failure as the goal. No, but because we can see that waiting for the “perfect” plan might mean missing the window to build real momentum. You don’t need to go big to start strong. What you (and all of us) need is a culture and strategy that allows for small steps forward. 

What does that look like in practice? It starts with a shift in mindset. 

One of the biggest roadblocks to AI adoption in CX is perfectionism. And I’ve been there, so I know what it is like. Worry drives us to wait to deploy anything new until it’s airtight, fully integrated, and risk-free. 

But what we are doing when we hesitate in this way? We are making perfect the enemy of the good. What do I mean? That AI doesn’t need to be perfect to be useful. It just needs to improve things a little bit from where they are now. 

Consider this: Some of the most impactful AI wins may not come from massive system overhauls. Instead, they can come from small, imperfect experiments. For example, an agent using GIDR.ai to get the answer to a customer’s question. Or a LivePerson bot handling a customer return. Small add-on tools or processes that add value, but don’t require major changes. 

These might not be flawless launches either. But they can be valuable. 

Ask: Where could AI help us improve just by 1%? And that’s where to start. 

That might sound like a small gain. But small gains add up. Over time, they can lead to real change. But more importantly, this approach is lower risk and smart. And it allows CX teams to build confidence without putting quality service on the line. And like regular exercise builds muscle, each small project builds experience and skill for the next one. 

This is where the “fail fast, fail safe” mindset really comes into play. It’s not about moving recklessly. It’s about creating an environment where trying, learning, and adjusting are part of the process. And when we can frame early AI adoption as a learning journey, not a final exam, it really gets momentum started. 

So, what is it? What’s the one thing that your team can try, safely, that would help them get moving? If the answer feels small, you are on the right track. 

It’s easy to get swept up in the features. I’ve done it. 

But here’s the hard truth: a great product doesn’t guarantee a great outcome.

Falling in love with a product and pushing it on our teams won’t make our customer service better on its own. That’s why this second mindset shift is so important for CX leaders that want to embrace AI. We must stop thinking in terms of products and how we can use them. Instead, we must start thinking in terms of purpose. 

It really takes starting with the problem first and not thinking about a product or solution. Then we can identify the right solution to that problem. It sounds simple. And if I am honest, we all think we are doing this already. But it is harder than it appears. 

We get trapped by excitement over a product or trend and then try to make it fit. We must let that go and force ourselves to work from the problem. It’s not about having AI or forcing AI. It’s about picking a sensible use for it when we already see where it can be used. That connects it back to real, measurable outcomes. 

By changing to focus on outcome-driven questions, you will be more likely to choose a tool that works in your context. It will also help avoid expensive dead-ends that look good on paper but fall short in practice. 

This approach also aligns with something we’ve seen. AI doesn’t need to be revolutionary to be meaningful. The best innovations are often the ones that improve everyday tasks. And small, purpose-led upgrades add up fast. 

When the purpose is clear, it also helps with buy-in from executives, agents, and even customers. All of these are much more skeptical of AI now than just a year ago. By showing the purpose, that can get buy-in. 

So, ask: What do we want to accomplish for our customers? And is that something AI can help us do better in some way? The answer might lead you somewhere unexpected. And that’s exactly the point. 

It’s true that the customer support industry has been built on stability. But the world is changing quickly. What are you going to do to keep up and prepare for the future? 

Embracing AI doesn’t mean you need a major project. It might not involve extensive cost. You don’t even have to chase the latest trends. The most effective course is to start small, start quickly, fail fast but safely, and learn from the results. Small successes add up over time. And knowledge and experience will enable you to thrive. 

I know it sounds risky. Personally, I’ve always leaned towards stability instead of trend chasing. I haven’t always jumped into something just because everyone else is. But over time, I’ve come to see AI, not as a threat, but as a powerful tool. One that can help us do better work, deliver improved service, and solve real problems in smarter ways. 

That’s why the moment to get started isn’t months from now. It’s today. 

Start with a pilot test. Start with one use case. Start with one team. But start. 

So, what will your first step be?