cat README
Innovation management, meet AI
My master's is in Sustainability and Innovation Management. It turns out to be exactly the toolkit companies need right now, because AI is not an IT project. It changes products, processes and who does what.
1. Innovations that use AI
Most companies bolt AI onto what they already do. Innovation management asks a different question: what is possible now that was not possible before? I help teams search for that systematically, from the idea to a small prototype you can test, with clear criteria for when to stop.
2. Organisations that can absorb AI
Tools change faster than org charts. Who decides what AI may do? Who owns which data? Which roles change when routine work gets cheap? Only 10 % of Austrian companies have guidelines for AI at all (Statistik Austria, IKT-Einsatz in Unternehmen 2025). I help design structures that let people experiment safely and scale what works.
3. People at the front end, not the tail end
People who use AI well get a lot more done. People who do not fall behind, often without noticing. The difference is rarely talent. It is practice, good examples and permission to try. That is where teaching comes in: ~/teaching.
grep --tag innovation ~/questions
Questions in this area
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How do our innovations make use of AI, in the product and in the way we innovate?
We look at your innovation process as a whole, from where ideas come from to how they get tested, and find the steps where AI changes what is possible, not just what is faster.
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How do we move our employees from the tail end of AI to the front end?
With practice on their own tasks, good examples and explicit permission to try. Tool training alone does not do it.
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Are we using AI, or are we getting value from it? And how would we know?
We define what value means for one concrete process, measure where you are today and compare. Most companies skip the baseline and then cannot tell.
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How do we run AI experiments without derailing the core business?
Small, time-boxed pilots with a clear stop criterion, run next to the core process rather than inside it until they have proven themselves.