Addressing challenges of AI implementation in healthcare through business model innovation: insights from collaboration between healthcare professionals and AI experts

Purpose Artificial intelligence (AI) is increasingly integrated into healthcare, but technological capabilities alone are insufficient for successful implementation. This study examines the implementation challenges that arise in collaboration between AI experts and healthcare professionals (HCPs) and explores how these challenges can be addressed through business model innovation by changing the content, structure and governance of the business model activity system. Design/methodology/approach Drawing on the NASSS (non-adoption, abandonment, scale-up, spread and sustainability) framework, a qualitative approach was adopted using semi-structured interviews with four healthcare professionals and five AI professionals in Finland. The findings were then used to define challenge areas and changes in the activity system for business model innovation. Findings The study identifies implementation challenges related to demand-side value, the scope of AI tool use, trust in AI and stakeholder interaction. Interpreting these findings through the activity-system perspective shows how changes in the content, structure and governance of business model activities can address these challenges by improving collaboration between AI experts and HCPs throughout AI development and implementation. Originality/value This study contributes to the business model literature by linking AI implementation challenges to the activity-system perspective of business model innovation. It further contributes by demonstrating how changes in the content, structure and governance of business model activities can practically address these challenges through improved collaboration between AI experts and healthcare professionals.