Do you value stability and consistency? I do. And stability can truly be a strength. After all, the contact center industry has long been built on consistency. Things like scripts, processes, metrics, and SLAs have made customer service reliable. But could it be that this strength may also become a weakness?
The truth is that we are facing momentous changes in customer service. AI has upended the status quo. We don’t want to chase hype, yet no CX leader can afford to fall behind either.
So, what’s the solution? What does a smart approach to AI really look like?
The secret is knowing that you don’t need to overhaul everything to start using AI in a meaningful way. You just need to start. That’s the beginning of Embracing AI. But it requires some mindset shifts:
- We must be willing to fail, instead of seeking perfection. Each failure is a learning opportunity, if kept small in scope. The key is to fail fast to learn the lessons and succeed later.
- We must be willing to focus more on outcomes than on tools. This keeps us stable in the most critical area, how we help our customers. But it makes us flexible and agile when it comes to how we do this.
How can we do this as CX leaders? That’s exactly what this guide to embracing AI is all about. Before we consider these mindset shifts and how to accomplish them, let’s consider why now is the right time.
Why Standing Still is the Biggest Risk
- What if the AI doesn’t work as expected?
- What if it disrupts the flow my team has spent years perfecting?
- What if we spend the money, invest the time, and see no clear return?
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.
Of course, hesitation remains. I understand that. In fact, I’ve written before about the fears that keep teams from implementing AI. Fear of cost. Fear of complexity. Fear of losing the human touch. But at CBA we also get to see firsthand that the real risk isn’t failure, but rather inertia.
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.
Mindset Shift #1: Embrace Imperfection and Be Willing to Fail
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.
The key is in being willing to get moving with small and fast to deploy projects. Even if they don’t improve things, such projects are still immensely valuable. Why? Because they give you something precious: a learning loop.
- What worked?
- What didn’t?
- What surprised us?
- What should we try next?
Many successes were born out of learning that came from failures. Flight and the airplane. The electric light bulb. Navigation. And so much more. These teach us that learning, even from failure, is a key step to sustained success.
What Does Mean in Practice?
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.
Mindset Shift #2: Change Focus from Product to Purpose
It’s easy to get swept up in the features. I’ve done it.
Natural language processing, predictive analytics, voice biometrics, AI routing, real-time translation, Agentic AI, and on and on. We’ve written about them. And at CBA we offer them. And the hype train mentions them over and over. The list of capabilities in modern CX tools is impressive.
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.
- What specific challenge are we trying to solve?
- Whose experience will this improve? Our customers? Our agents? Or both?
- How will we measure success, beyond implementation?
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.
What Does Mean in Practice?
- How does it handle language variations?
- How fast does it respond?
- Does it integrate with our existing tools?
- Can this reduce average wait time for customers?
- Can it free up our live agents to focus on higher-value conversations?
- Will it reduce burnout or give agents time to upskill?
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.
Tip: At CBA, we have experience with a wide variety of AI solutions. As a systems integrator, our strength is in helping you answer these outcome-driven questions and then find the right product for your needs. We try to help our clients clarify their goals before we suggest a solution.
What Will Be Your First Step?
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.
- Embrace imperfection and be willing to fail. Don’t let perfect become the enemy of the good.
- Focus on purpose instead of on products. Let your outcome drive your AI tool choices, not the other way around.
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.
And when you start, don’t go it alone. CBA is here as a partner at your side. Whether its exploring tools like GIDR.ai or full contact center suites like Bright Pattern, or just getting guidance on how to experiment safely, CBA is here to help you move forward with confidence.
So, what will your first step be?
