It’s hard to imagine now, but back then, service desks still relied on phones, pens, and paper to dispatch technicians. I still remember the resistance from management and the works council against a pilot program using devices to share service data and addresses digitally. They were convinced the technicians wouldn't be able to—or want to—use them.

But to our surprise, we noticed a lot of traffic to and from these devices on the test server in the evenings after dinner. It turned out that when the technicians brought their devices home, their children loved them. "Dad, that's such a cool gadget! Show me how it works." And so, the technicians started using them at home.

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While technology used to enter people's homes through the workplace, it’s now the other way around. Many households are now more technologically advanced than many workplaces. Everyone has a smartphone, streams movies, and uses apps to communicate, navigate, and pay. At home, almost no one can function without advanced IT solutions, so it should be exactly the same in the workplace.

There is huge potential for productivity gains here. Not just by making processes more efficient, but by making smart use of all the data being generated. This can give companies a strategic advantage. If we accept that Netflix decides what we watch at home, why wouldn't we use company data to predict what will happen in a few weeks or months?

On a personal level, AI is now fully woven into our daily lives. Think of the assistants used in Google searches, navigation apps, and the algorithms behind Netflix, Spotify, and TikTok. And who hasn't created a funny picture of themselves or someone they know using ChatGPT by now?

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AI is about processing massive amounts of data at incredible speeds. Where we once had to wait hours or even overnight for a data report, AI now makes connections and draws conclusions in split seconds. But beware of hallucinations: AI is fast, but it is not (yet) intelligent.

To use AI reliably in a business context, both the solution and the data must be well understood. The same rule applies here as it does to classic data warehouse (DWH) and business intelligence (BI) applications: garbage in, garbage out.

Technical capabilities continue to evolve at breakneck speed. On the surface, it seems incredibly easy to use, but truly understanding it requires increasingly specialized knowledge. And to ask the right questions, it is becoming more important to clearly define a company's objectives and strategy, which in turn can be influenced by the technology itself.

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