Artificial intelligence and business model: a bibliometric analysis of strategic and entrepreneurial trends

Artificial intelligence (AI) is increasingly reshaping business models (BMs), yet the literature remains conceptually fragmented. This study provides a bibliometric synthesis of 189 articles indexed in the Web of Science, combining co-citation and bibliographic coupling to map both the intellectual foundations and current research fronts of AI-driven BM research. The analysis identifies five core conceptual pillars, centred on dynamic capabilities, digital transformation, ecosystem strategy, and entrepreneurial orientation, and six emerging thematic clusters, including AI commercialisation, sustainable entrepreneurship, generative AI applications, and ecosystem governance. Findings show that AI becomes strategically transformative not when adopted as a stand-alone tool, but when embedded across the business model architecture, reshaping value creation, coordination mechanisms, and capture logics. The review clarifies the mechanisms through which AI acts as an “architect” of business models and highlights boundary conditions related to data access, platform dependence, regulatory constraints, and capability asymmetries. By integrating science mapping with structured qualitative content coding, the study advances theory by linking AI-enabled business model change to capability renewal and entrepreneurial scaling dynamics. Managerially, it identifies hybrid intelligence, governance design, and AI-readiness as critical levers for competitive advantage. The article concludes with a research agenda that moves the field beyond adoption narratives toward mechanism-based and context-sensitive explanations of AI-driven business model innovation.