Transforming the business model innovation process: how generative artificial intelligence shapes value creation, delivery, and capture

Purpose This study examines how Generative Artificial Intelligence (GenAI) may transform the Business Model Innovation (BMI) process and under which conditions it may reshape core Business Model (BM) mechanisms. Design/methodology/approach A systematic literature review of 131 peer-reviewed articles was conducted. Using the Gioia methodology in an abductive analysis, with the 4I-framework of BMI as a sensitizing lens, an integrative framework was developed to synthesize GenAI's role across BMI phases and BM mechanisms. Findings The findings show that GenAI supports all phases of the BMI process by enhancing opportunity recognition, ideation, experimentation, and implementation through the mechanisms of knowledge amplification, cognitive catalysis, and methodological scaffolding. Beyond process support, GenAI can reshape value creation through hyper-personalized and content-centric offerings, alter value delivery through hybrid human-AI service architectures, and extend value capture through efficiency gains and data-driven revenue logics. Whether such effects remain operational augmentation or amount to BM adaptation and transformation depends on their embedding in the BM architecture. At the same time, these effects remain conditioned by data governance, model opacity, ethical and regulatory risk, and workforce capabilities, suggesting GenAI's role in BMI is ambivalent: it may enable innovation while also creating new constraints for its realization. Research limitations/implications The review is conceptual and cross-sectoral. Future research should empirically test the proposed mechanisms and examine industry- and context-specific boundary conditions. Practical implications Firms should treat GenAI as a strategic lever for BMI and complement adoption with robust governance, human oversight, and workforce capability development. Originality/value The study offers a process-oriented synthesis of GenAI-enabled BMI and develops an integrative framework linking BMI phases, GenAI-enabled process mechanisms, and BM mechanisms while identifying key boundary conditions shaping GenAI-enabled BMI.