Driving AI Adoption - The Role of AI Managers as Boundary Spanners

As artificial intelligence (AI) transitions from an experimental technology to a strategic priority, organizations seek to align their AI initiatives with business objectives. However, AI adoption frequently fails due to a lack of internal expertise, unclear responsibilities and a lack of attention to organizational, regulatory and work design factors. In this context, we observe the emergence of dedicated AI roles driving AI adoption—referred to as AI Managers. Despite their emergence, the role of AI Managers remains under-researched. Through a qualitative interview study, we examine how AI Managers leverage boundary spanning mechanisms—boundary objects and boundary activities—to drive AI adoption. Our results indicate that the success of AI adoption depends on the alignment of these mechanisms to foster interdisciplinary collaboration, structured AI governance and business value creation. This study contributes by extending the boundary spanning theory to the AI context, highlighting the institutionalization of AI Managers as boundary spanners.