Organizational Change and Artificial Intelligence
Digitalization and human-AI collaboration are not arriving from outside international business — they are reshaping it from within, transforming how leaders, teams, and organizations navigate decisions, coordination, and trust across borders. Our research shows that organizational change is most difficult when it disrupts identity and belonging — and that resistance reflects these concerns rather than defiance. Leaders who recognize this, and who build structures for collective ownership rather than enforcing compliance, are the ones who turn transformation into lasting organizational capability. These insights are captured in our 2025 Insight Report — Collective Agency in Leading Change — produced in collaboration with EHL Hospitality Business School and HEG Arc, drawing on practitioner reflections across Europe, North America, and Asia Pacific.
Read the full report here
Our expertise is organized around three interconnected areas:
AI and Organizational Transformation
Our work examines how the growing integration of artificial intelligence reshapes work, organizational roles, and decision-making processes, particularly in complex and international environments. We view AI adoption not simply as a technological development, but as a transformation that influences how individuals and groups understand their roles, contributions, and future within organizations. While AI is often discussed in terms of automation and task substitution, our research highlights that AI-driven change also generates new roles and novel forms of human–AI collaboration, expanding opportunities for human judgment, creativity, and problem-solving alongside new challenges that organizations must actively navigate.
We show that the critical question is not whether organizations adopt AI, but under what conditions AI use supports human agency, shared understanding, and effective collaboration — rather than simply standardizing decisions and reducing human judgment. Our research demonstrates that organizational context, leadership practices, and cultural environments are what determine which outcome prevails.
For more insights, see the work of Dr. Evangelos Syrigos and Prof. Anna Lupina-Wegener
People and AI under life threatening changes
When organizations operate under pressure — in crisis, conflict, or rapid transformation — the conditions under which people collaborate, make decisions, and maintain trust become critical. Our research extends into these high-stakes environments, examining how AI can support collective sensemaking and coordinated action across organizational and cultural boundaries.
One example of this work in practice is the SHAPE project — Shelter-based Hubs for Adaptive Partnerships and Engagement. Drawing on frontline insights from the Ukrainian context, SHAPE investigates how people collaborate across organizational and cultural boundaries in high-stakes environments, and how AI can support collective sensemaking and decision-making under stress. The project reimagines adaptive spaces for working and learning during crisis — integrating physical infrastructure, digital technologies, and a human-centered approach that promotes psychological safety and coordinated action. It exemplifies our broader commitment to research that is both academically rigorous and directly relevant to the organizational challenges that leaders face in today's world.
For more insights, see the work of Dr. Magdalena Zabicka-Wlodarczyk
Digital Transformation and Managerial Cognition?
Digital transformation reshapes managerial decision-making not merely by introducing new technologies but by fundamentally altering how actors perceive, interpret, and respond to their environments. In digitally mediated contexts such as platform markets, decision realities are increasingly structured by data abundance, algorithmic intermediation, and continuous performance feedback, which modify causal beliefs, perceived control, and attention allocation.
Rather than operating under conditions of information scarcity and stable competitive rules, managers face opaque algorithmic systems, granular and noisy feedback signals, and evolving choice architectures that require ongoing cognitive adaptation. As a result, heterogeneity in outcomes under digital transformation arises not only from differences in technological assets or capabilities but from variation in managerial cognition — specifically, how decision-makers interpret algorithmic outputs, attribute performance changes, update mental models, and balance human judgment with automated or AI-driven recommendations.
For more insights, see the work of Dr. Evangelos Syrigos