Tool fatigue is a real thing, especially in small teams. Why I recommend learning two tools properly, rather than dabbling in twelve, and how you can justify this to the supervisory board.
March 2026 · Noted by · reads in ~2 minutes
I regularly keep track of how many AI tools small teams have tried out over the last twelve months. The median is eleven. The median number of tools still actively in use is two.
That’s no surprise. It’s a pattern.
New tools are tempting. They solve a real problem during the demo. They fit well with the use case you have in mind at the time. And then comes everyday life.
In day-to-day life, it’s not the demo performance that counts. What counts is whether the team really understands the tool. Whether they know what to do if they make a mistake. Whether they can trust it.
Two tools. One tool for structured data and workflows. One tool for language and text. Leave the rest out, at least for the first six months.
That sounds simple. It is simple. The difficulty lies in justifying the decision to the supervisory board, which has just read that competitors are using fifteen different AI solutions.
Focus isn’t a weakness; it’s efficiency. Two tools that the team has truly mastered produce more value than twelve tools that nobody fully understands. This can be expressed in figures, and if you’d like, I’ll help you with that.
45 minutes, one screen shared, an honest assessment. Not a sales pitch, I show you where I see leverage and where I do not.