Research & Insights

Here I share research-based insights and practical perspectives on business development, project management, business transformation, AI adoption and organisational change.

New insights will be published as my current research develops.

Current Research

I'm currently completing my thesis exploring AI adoption, change management and organisational transformation, with a focus on what helps organisations turn AI initiatives into meaningful and sustainable business value.

Topics

The topic areas my research and consulting work focus on, with the sources behind each.

AI Adoption

How organisations move from AI experimentation to lasting, value-creating adoption.

Most organisations have already tried AI somewhere in the business. Few have made it stick. My research shows that the gap between piloting and scaling is rarely about the technology — it is about leadership, clear direction and a structured way of driving the change.

Sources

Change Management

The leadership habits and structures that make change hold after go-live.

AI adoption is fundamentally a change management question, not a technology question. Models such as ADKAR and Self-Determination Theory explain why people accept — or reject — a new way of working, and why participation and clear communication matter more than the tool itself.

Sources

Hiatt, J. M. (2006). ADKAR: A Model for Change in Business, Government and Our Community. Prosci Learning Center Publications.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
AI Training Case Studies (2025). What actually worked at 5 real companies.

AI Governance

Practical governance models that keep AI accountable without slowing the business down.

Governance is not paperwork for its own sake — it decides whether employees dare to use AI, and whether leadership can actually account for how AI is used. My research shows that responsibility today often lands disproportionately on IT, while leadership, HR and legal stand on the sidelines.

Sources

Psychological Safety

Why the ability to say what is not working is the single strongest predictor of a successful delivery.

Resistance to AI is rarely about the technology. It is about the fear of losing your role, your competence and your sense of control. Research on psychological safety and algorithmic anxiety shows that open, honest communication about the risks — not silence around them — is what actually builds trust.

Sources

Edmondson, A. C. (2019). The Fearless Organization: Creating psychological safety in the workplace for learning, innovation, and growth. Wiley.
Shekhar, A., & Saurombe, M. D. (2026). Algorithmic anxiety: AI, work, and the evolving psychological contract in digital discourse. Frontiers in Psychology, 17, 1745164.
RISE (2024). AI – hot eller möjlighet?

Human-Centred AI

Designing AI-supported work around the people who have to live with it — not around the technology.

AI should be understood as an assistant, not a replacement. It takes over the repetitive work and gives time back to what genuinely requires a human — but only if trust in the technology is built deliberately, through transparency and the ability to keep humans in control.

Sources