Background
A designer's route into practical AI
I have worked in design and media for roughly 30 years, with agency phases early on and around 15 years of freelance practice across the last decades. Over that time I have worked on around 300 projects for roughly 30 clients, mainly individuals, startups, and cultural organizations.
I came to AI through design, systems thinking, and long years of independent work. That turns out to be a useful route in. It makes me pay attention not only to what a tool can do, but to how it changes a workflow, a team dynamic, and a person's confidence inside the work.
Useful context
- Berlin-based
- ~30 years in design and media
- ~300 projects across ~30 clients
- Works alone or within a small team
Why this matters
AI adoption is not only a technical question
My design background trained me to look at journeys, interfaces, friction, review logic, and what makes a system usable enough that people actually stay with it instead of quietly inventing workarounds three days later.
That is one reason I work well with small and midsize organizations. I understand the work around the work, not just the polished slide deck at the end.
I have spent most of my working life self-employed. That means I am used to the practical realities around the work: winning trust, caring for clients well, prioritizing limited time, and making systems useful under real constraints.
Useful AI work lives exactly there. Not in theater, not in inflated promises, but in clearer ownership, better judgment, and systems people can continue to use once the initial excitement wears off.
Why AI
Curiosity seemed more productive than despair
When AI began reshaping creative and knowledge work, I chose curiosity over distance. I studied the tools closely, used them in my own process, and built enough prototypes to understand both their power and their instability.
That is why Human in the Middle focuses on consultation, training, and prototyping rather than automation theater. The point is not maximum AI. The point is better work, clearer ownership, and stronger judgment.
I prototype through AI-assisted coding and bring a long-standing working knowledge of code and server environments from years of collaboration and personal interest. Enough to be useful, careful, and productively skeptical.
What people usually bring to me
- A team that needs a clearer entry point into AI.
- A workflow that has become too tedious or too scattered.
- Training that should be serious without turning sterile.
- A prototype that needs judgment as much as speed.