Abstract
Recent years have seen a surge in research on artificial intelligence (AI)-driven business model innovation (BMI), reflecting its profound impact across industries. However, the field's current state remains fragmented due to varied conceptual lenses and units of analysis. Existing literature predominantly emphasizes the technological aspects of AI implementation in business models (BMs), treating BMI as a byproduct. Additionally, there is a lack of coherent understanding regarding the scope of BMI propelled by AI. To address these gaps, our study systematically reviews 180 articles, offering two key contributions: (1) a structured analysis of evolving research dimensions in AI-driven BMI, differentiating between static and dynamic views of BMI, and (2) a framework presenting distinct research perspectives on AI-driven BMI, each addressing specific managerial focuses. This synthesis facilitates a comprehensive understanding of the field, enabling the identification of research gaps and proposing future avenues for advancing knowledge on the management of AI-driven BMI.