Four focus areas. One common starting point: the business problem.
Technology in my work is always a means — never an end in itself. What I do begins with a clear diagnosis of what truly drives a company.
Strategic Technology Decisions
Technological developments — from AI to data platforms to automation — are creating increasing decision pressure in executive suites. The question is rarely whether technology is relevant. The question is which technology, when, to what extent — and what that means for the business model.
Many companies answer this question reactively: driven by competitive pressure, external system vendors, or internal technology enthusiasts. This leads to investments without strategic grounding.
- Strategic assessment of technological developments — independent and without vested interest in any particular solution
- Developing a clear picture of which technological options are relevant for the specific business model
- Decision preparation: which investments can be justified when — and which cannot
- Prioritizing technology initiatives with limited resources
Technology-Driven Transformation
Technological transformation rarely fails because of the technology. It fails because organization, leadership, and strategy are not considered together. New systems are introduced, but decision pathways, responsibilities, and ways of working remain unchanged.
The result: substantial investments without lasting effect. Successful transformation requires that technology, organization, and strategy are developed together — not sequentially.
- Diagnosis of the current state: where do technology, organization, and strategy stand — and where are they diverging?
- Developing a realistic target picture and an actionable transformation approach
- Supporting operational embedding — not just conception
- Designing leadership and governance structures that carry change
Technology M&A and Acquisition Strategy
Digital capabilities cannot always be built organically — sometimes acquisition is the strategically right decision. But technology acquisitions are complex: evaluating digital assets requires different knowledge than classical M&A processes, and integration frequently fails not because of systems, but because of people, culture, and processes.
- Developing a clear build-or-buy strategy: when does acquisition make sense — and when does it not?
- Identifying and evaluating acquisition targets from a combined strategy and technology perspective
- Supporting M&A processes from target identification to signing
- Developing a realistic integration approach for digital assets and technology teams
Human-AI Decision Architecture
Artificial intelligence changes not only processes — it changes how organizations make decisions. Algorithms take on recommendations, forecasts, sometimes decisions themselves. This creates new leadership questions that have so far rarely been answered in a structured way: Who is responsible when an AI-supported decision is wrong? How do leadership teams retain judgment when they increasingly rely on algorithmic recommendations? How do you prevent AI from leading to worse rather than better decisions?
These questions are not theoretical future scenarios — they arise today in companies that seriously deploy AI.
- Analysis of existing decision processes and identification of AI deployment potential
- Developing clear accountability structures for AI-supported decisions
- Designing governance frameworks that meaningfully connect humans and machines
- Supporting leadership teams in the organizational integration of AI systems