AI Transformation Accelerator
The future is visible.
The path is not.
I help organizations move from AI hype and uncertainty to practical action by combining technological realism, human psychology, and research on Human-AI Trust.
Clarity for leaders. Trust for people. Movement for organizations.
The movement problem
AI is moving fast. Organizations need to move wisely.
The question is no longer whether AI matters. The real challenge is moving from pressure, pilots, and possibility toward practical action that people can trust and use.
AI-readiness – reality before roadmap
Cut through hype, fear, and vague ambition. Clarify what AI can realistically do, where it creates value, and where it introduces risk or distraction.
Human-readiness – trust before adoption
AI adoption is not only a tooling problem. People need the right mental models, confidence, judgment, and boundaries to use AI well.
Change-readiness – action before perfection
Transformation starts when people change how they decide, work, learn, and collaborate. Turn uncertainty into clear next steps and sustained movement.
The path I built
A bridge built across disciplines.
My perspective comes from moving across industrial engineering, business psychology, consulting, research, teaching, and AI management.
AI management and transformation
AI strategy, AI products, implementation, user-centric design, and organizational adoption in an enterprise environment.
Consulting, process digitization, and project leadership
Process optimization, digitization, partner management, and the translation of new technologies into practical business value.
Human factors, AI in work systems, and teaching
Research and teaching around intelligent technology, AI in production, digital business processes, and Human-AI collaboration.
Industrial engineering and business psychology
Combining systems, processes, and value creation with how people trust, resist, adopt, decide, and grow with technology.
The perspective on AI reality
Thinking in public.
Three essays on agentic AI, mental models, and the conditions for trust in real work.
Why AI Agents Don’t Belong in the Org Chart
Agentic AI needs a better map than hierarchy alone — one that connects people, process, technology, and accountability.
Calibrate Your Mental Model for the Work with GenAI
Better mental models help leaders and teams make more realistic decisions about generative AI.
Why the AI Operating Model Is Not Enough
The most dangerous question in many AI transformations sounds highly professional at first: What AI Operating Model do we need?
The science behind the action
Trust needs evidence.
Research helps separate polished confidence from real understanding. It shows where trust breaks, where adoption fails, and where better mental models are needed.
Human Trust in Otherware
Trust in AI has multiple drivers. Trust must be designed into interaction, governance, and operating routines.
Effects of AI Explanations on Trust and Reliance
Explanations can increase reliance without creating real understanding. Trust calibration becomes a design responsibility.
Mental Models in the Age of GenAI
People need better mental models of what kind of system they are working with to avoid both overtrust and underuse.
Selected collaborations
Advisory, talks & collaboration
For leaders and teams who want structure, perspective, and movement when the path is not yet obvious.
Executive advisory
Guidance for leaders who need clarity, grounded perspectives, and a realistic path through risk and opportunity.
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Keynotes & talks
Sharp and research-based impulses on AI transformation, human-AI collaboration, and agentic AI.
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Team impulses
Interactive sessions for teams that need shared language, practical understanding, and actionable next steps.
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Project sparring
Focused support for AI initiatives, pilots, or concepts that need clearer framing, sharper use cases, stronger adoption, and realistic expectation management.
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