Looking for inspiration on where AI can create the biggest impact?
When we started implementing AI across our group, I had dozens of use cases in mind. AI can be applied almost everywhere. And that's exactly the problem.
After going through several dead ends, experiments, and countless iterations, I realized one important thing.
The goal isn't to implement AI everywhere.
The goal is to identify one or two areas that have the greatest impact on your company's economics — and go all in on them. That's how you maximize the impact, build trust across the team, and move the company miles ahead.
So our question wasn't:
"What can AI do?"
It was:
"How can AI move our company forward the most?"
For us, the answer was obvious
Churn.
Churn is one of the most important metrics in our business.
Every percentage point of improvement has a significant impact on LTV, profitability, and ultimately the entire economics of the company — especially when you're acquiring thousands or even tens of thousands of new customers every month.
So we asked ourselves one simple question:
How can AI maximize the way we work with churn?
The company BEFORE AI
The typical scenario. A weekly management meeting. A report. The numbers. A discussion.
If everything followed the expected trend, we moved on. If a problem appeared, we started solving it.
The problem? By then, it was often already too late.
Reviewing churn once a week — or even once a month — is simply too slow today. On top of that, you need to get the right people into the same room, spend time reviewing the data, and interpret everything correctly.
We knew there had to be a better way.
The company AFTER AI
An absolute game changer.
We built a system that monitors churn in real time, understands the context of the business, identifies weaknesses across the company, and immediately alerts us whenever something unusual happens.
Today, AI:
- compares current performance with historical trends and company goals while understanding business context (for example, it knows how many customers we acquired a month ago and what level of churn should therefore be expected),
- monitors anomalies and unusual changes — if something starts moving in the wrong direction, we know within minutes,
- immediately alerts us when product quality, logistics, or any other recurring issue starts appearing in customer feedback,
- identifies the main reasons customers leave and provides management with a very specific list of areas that need improvement.
And the highest level?
Imagine being able to evaluate every single retention call.
A transcript alone is already an incredibly powerful AI tool. But the real magic starts when you add emotion, tone of voice, and the ability to understand what's happening between the lines.
The real reason the customer is leaving. What frustrated them. What they appreciated. And what could have convinced them to stay.
It's like having a personal conversation with every customer — even if you have thousands of them.
Until recently, this was almost unimaginable. Today, it's not only possible. It's real.
The biggest benefit?
Surprisingly, it's not productivity itself — even though that's probably the biggest measurable outcome in the long run.
It's peace of mind.
As a CEO, I have dramatically more confidence that one of the company's most important metrics is truly under control. I know what's happening. I'm using a system that understands the business in context, monitors everything in real time, and gives me fast, accurate, and trustworthy feedback.
If something starts going wrong, I know immediately.
But there's something even more important.
AI doesn't just help us monitor churn. It helps us improve the entire company.
Every customer who leaves gives us valuable feedback about our product, our service, and our processes. Without exaggeration, you could say that the real evaluation of a company happens at churn.
What did we learn?
The biggest lesson wasn't about AI. It was about priorities.
Choose one area that has the greatest impact on your business. For us, it was churn. Invest maximum energy into it and take it all the way.
There are several benefits. When you fully commit to one problem, you'll think through every detail, refine the solution, demonstrate the impact to the team, and show everyone what AI is actually capable of.
That first successful implementation completely changes how people across the company think about AI.
Bonus: how we approached it
If I had to do it again today, I'd follow exactly the same process.
The first step is always the same: prepare your data infrastructure (AI Readiness).
Next, move your existing reporting into AI and validate its accuracy. We achieved 99.98% accuracy compared to our historical reports, which is more than enough to build on.
Then define simple use cases. Really simple ones. Stick to the K.I.S. principle — Keep It Simple.
Only after that should you start adding additional intelligence layers.
In my opinion, that's a much better approach than launching ten different AI projects at once, finishing none of them, demotivating the team, and never realizing the real value.
The first successful AI project changes the way the entire company thinks about AI.
Thank me later. 😀