AI Adoption in SMEs – Empowered by higher education institutions

Artificial intelligence (AI) holds great promise by improving processes, uncovering optimization potentials, and enhancing decision-making. Adopting AI can thereby boost companies' competitiveness. However, especially small and medium-sized enterprises (SMEs) face significant challenges in AI adoption.
This research explores how SMEs can be supported in their AI adoption by higher education institutions (HEIs). The dissertation is structured around four studies: the first study reviews the extant literature on AI adoption in companies and develops a conceptual AI adoption framework. The second study explores the relevant capabilities SMEs need to adopt AI through a research world café. The third study focuses on the potential of capability development for AI adoption through university-business collaborations using exploratory interviews. The last study examines the impact of AI knowledge spillover from HEIs on SME AI adoption probability and intensity applying a Heckman selection model on a cross-sectional dataset of German SMEs and HEIs.
Through these four interlinked studies, this dissertation provides three key findings on AI adoption in SMEs. First, AI adoption does not depend solely on technological factors, but also on environmental, organizational, managerial, and individual factors. Second, SMEs require distinct capabilities for AI adoption, such as an open-minded and error-tolerant organizational culture, managerial communication and commitment, and employees with a deep understanding of organizational structures and processes. Third, SMEs can be supported by higher education institutions in their AI adoption, both actively through collaborations and passively through knowledge spillover, particularly in the early stages.