Bad Decisions, Only Faster

I tried to change it back through the app. No availability.

I called the clinic and spoke to a bot. It first offered me an even earlier appointment. When I explained that the test had to happen at the end of August, it suddenly found the original date and asked whether I really wanted to wait that long.

Yes. That was the whole point.

Another bot later called me to confirm the change. Three interactions, involving two bots and one app, simply to return to the correct starting position.

The automation appeared to work perfectly. The decision behind it did not.

Technology scales the rule it is given

The clinic may have been trying to fill an empty slot, improve asset utilisation or reduce waiting times. Each objective sounds reasonable in isolation.

But the process optimised one operational measure while ignoring the reason for the appointment date. It created avoidable work for the customer and, presumably, for the organisation too.

AI can make an existing process quicker, cheaper and more consistent. If the process is badly designed, however, it will also reproduce the wrong outcome more quickly, cheaply and consistently.

Efficiency is not the same as effectiveness.

Start with the basics

The experience reminded me of a fault-resolution transformation I led at ONO.

We did not begin with technology. We began by simplifying the existing telephone process and questioning the journey itself: what did the customer need, which decisions were necessary and how could we remove avoidable effort?

We then created a consistent fault-resolution process across channels, turned it into a digital self-service journey and connected it to network systems so diagnostics could support decision-making automatically.

The result was a faster digital journey that became customers’ preferred way to resolve faults—both in actual usage and in their stated preference. The same network integration also improved assisted resolution over the telephone.

This work formed part of a wider digital-care transformation. Online care became the company’s preferred service channel, handling 65% of customer interactions—more than the telephone. Digitalisation, self-service and contact deflection generated €2 million in annual savings, while company NPS improved by 20 points.

Technology mattered. But it worked because we had first understood and redesigned the process.

Find the need before the use case

AI gives us capabilities we could only imagine a few years ago. That makes it a powerful enabler—but still an enabler.

Instead of looking for AI use cases, start with a problem worth solving.

Otherwise, we are not transforming the business.

We are just making bad decisions faster.