Questions that matter
at the intersection of
technology and organization.

The development and deployment of AI is not merely a technical challenge — it is a profoundly social one. I examine questions of AI ethics, accountability, and governance, as well as the ways AI systems reconfigure power relations, agency, and trust within and between organizations.

The field of AI ethics has produced a wealth of principles. What it has produced far less of is practical governance: the structures, roles, and processes that make ethical AI real inside organizations. My work focuses on this gap — between principle and practice, between aspiration and institution.

I am particularly interested in how AI shifts accountability structures: who is responsible when an algorithmic system causes harm? How do organizations build meaningful human oversight into systems that operate at machine speed? These are not only technical questions — they are deeply organizational and social ones.

How do organizations become genuinely data- and AI-driven? Not in the brochure sense, but in the day-to-day reality of how decisions are made, how teams are structured, and how leadership operates?

The literature on digital transformation tends to overstate the technical and understate the human. In practice, most transformations fail not because of the technology, but because of culture: the values, habits, and power structures that shape how an organization actually works.

I study this phenomenon particularly in the German Mittelstand — the mid-sized, often family-owned businesses that form the backbone of the German economy. These organizations face distinct challenges: finite resources, deep organizational traditions, and the pressure to modernize without losing what makes them strong. My work seeks to understand what transformation actually looks like in this context, and what leadership strategies prove genuinely effective.

Microteaming is an original framework I developed for building high-impact Data & AI teams in organizations that cannot, or should not, build large data departments.

The dominant model of data organization — large centralized teams with dozens of specialists — is not accessible to most companies, and not appropriate for many of those that could afford it. The alternative is not to abandon ambition, but to redesign the organizational unit. A well-designed microteam of three to five people, operating with genuine autonomy and a clear outcome mandate, can outperform a department of thirty.

The Microteaming framework addresses the design questions that determine whether this is possible: how to select and combine capabilities, how to structure decision rights, how to connect a small team to organizational leadership, and how to measure impact in ways that preserve the team's ability to take calculated risks.

I have documented the framework in a white paper and continue to develop it through practice and dialogue with practitioners.

Artificial intelligence is changing what it means to lead, to learn, and to work. The transformations underway are not merely about efficiency or automation — they raise fundamental questions about professional expertise, organizational authority, and the nature of knowledge itself.

I explore how professional roles are evolving as AI takes on tasks previously considered the exclusive domain of human expertise: analysis, synthesis, recommendation, and increasingly, judgment. This is not only an economic question — it is a question about identity, meaning, and the social structures through which professional communities maintain standards and confer status.

For leaders, the challenge is particularly acute. The skills that made a manager effective in the pre-AI era — mastery of domain knowledge, capacity for information synthesis, speed of decision — are precisely those most subject to augmentation or replacement. What remains irreducibly human, and how should leadership be conceived and developed in light of this? These are the questions I pursue in this strand of my work.