The current literature on Generative Artificial Intelligence (i.e. Gen AI) often presents AI as a silver bullet to the limitations of humans’ information processing capabilities, efficiency and data quality improvements as well as data-driven decision-making. Yet it remains unclear how, when and under what circumstances such technologies foster the proposition, creation, delivery and capture of value in entrepreneurial firms. The chapter examined what role Gen AI plays in the creation of innovative business models (i.e. business model innovation) and which Gen AI strategies are germane to each stage of the business model innovation process. Drawing on the resource dependence theory, strategic management constructs (i.e. strategic alignment, strategic core and periphery) and a systematic review of literature, the study explored how entrepreneurial firms are appropriating Gen AI to develop novel business models, thereby engage in business model innovation. We demonstrate that at different stages in the business model innovation, different generative AI strategies employed span prompt engineering and prompt-based communication (at value proposition stage), parametric designs and machine learning models (value generation), robotic process automation for process mining (value delivery) and virtual assistant-based communication (value capture). We also demonstrate that while some Gen AI strategies have wider application transcending one stage of business model innovation, others are ideal for specific phases of this process. Design engineering, gaming and creative content development are more germane for value generation stage; service automation, service and business process enhancements are more attuned to value delivery while product or service reconfigurations align with the value capture stages. The study could have excluded some research studies in other databases, published in languages other than English, and outside the time horizon of this investigation. The findings have some implications for AI-based information generation architecture of the business innovation process, spanning increasing transparency of data gathering processes, improving data quality, enhancing integrity and efficiency. Other implications include reconciliation of ethical concerns of AI (e.g. biases, hacking, privacy invasion) and the need to regulate AI operations in business process innovation. In the author’s view, this is one of the pioneer studies to examine the contribution of Gen AI on business model innovation and innovation processes, drawing on strategic management concepts (strategic alignment, strategic core and strategic periphery) and a systematic literature review covering entrepreneurial firms.