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From my experience: general AI training rarely changes habits. You need to connect the „training“ with workflows and tasks from daily work to see the benefits and see how AI works. A better first step is to look at how the team works today. In this article, I explai…

Many companies add AI agents to processes that were designed for people, and then wonder why the results are unreliable and the process is no faster than before. In my new article, I explain the five causes behind this and show five building blocks that make agents deli…

I often hear this comment and challenge when I speak with leaders in my AI Practice Hub: We have more AI tools and faster output, but the work has not become easier.
This is the gap is real: AI has made individual tasks faster, but the way the work is organised has st…

AI results depend on three decisions that only the leadership team can make. A company can have tools, training and pilots in place and still see little value if these decisions stay open. In this article ,I walk through all the steps to put these decisions to work.

Look closely at most AI strategy documents, and you will find the same ingredients: the same tool rollouts and licences, a handful of pilots, some prompt training for staff, maybe a governance policy. This is all useful, but none of it is a strategy. This article shows …

In almost every AI project I work on, the focus sits on the technology decision. What gets far less attention is what happens afterwards: whether the organisation actually uses it, who is responsible for it, and whether leadership stays involved once the first excitemen…

Ask people whether AI is saving time, and you will usually get a confident yes. But if you look behind the scenes, you often get another picture, and it gets more complicated. This is one of the most underestimated problems in AI adoption. AI adoption that increases wor…

The real challenge is maintaining a balance between governance and speed. It’s designing governance that supports daily work, provides clarity, and accelerates safe adoption. This is exactly what I break down in this article.

The shift for better AI output happens when teams stop asking, “How can we phrase this better?” and start asking,  “What does the system need in order to perform reliably in our workflow?” That question marks the move from prompting to context engineering.

What is often missing is the AI operational layer in organisations: the habits, workflows, ownership structures, and validation routines that turn occasional AI use into reliable AI use. Without this layer, AI remains something people do when they remember, not somethin…