Artificial intelligence (AI) is increasingly transforming the entrepreneurial process by changing how entrepreneurs search for information, identify opportunities, develop products, make decisions, configure resources, and create value. The emergence of generative artificial intelligence (GenAI) has accelerated this transformation by providing entrepreneurs with accessible capabilities for knowledge generation, content creation, ideation, prediction, experimentation, and intelligent assistance. Despite the rapid expansion of research on AI and entrepreneurship, the theoretical foundations explaining how AI capabilities are converted into entrepreneurial outcomes remain fragmented. Existing studies have examined technology adoption, opportunity recognition, innovation, business-model transformation, entrepreneurial decision-making, and digital entrepreneurship, but these streams have rarely been integrated into a unified theoretical explanation. This conceptual paper addresses this gap by developing a dynamic capability framework of AI-enabled entrepreneurship. Drawing on Dynamic Capability Theory, Entrepreneurial Opportunity Theory, Resource Orchestration Theory, and the emerging perspective of Responsible AI, the paper conceptualises AI capability as a technology-enabled resource that contributes to entrepreneurial sensing, seizing, and transformation. The framework proposes that AI capabilities influence entrepreneurial opportunity recognition through enhanced information processing, knowledge recombination, experimentation, and decision support. These entrepreneurial capabilities subsequently facilitate product, process, marketing, and business-model innovation, which can contribute to sustainable value creation. However, the framework argues that the relationship is not technologically deterministic. AI literacy, human–AI collaboration, ethical governance, digital infrastructure, and entrepreneurial expertise condition the conversion of AI resources into entrepreneurial outcomes. Six theoretical propositions are developed to guide future research. The paper contributes to entrepreneurship theory by moving beyond an adoption-centred view of AI and positioning AI as part of an evolving entrepreneurial capability system in which human judgement and machine intelligence interact. The framework provides a foundation for future empirical research and offers implications for entrepreneurs, educators, business-support institutions, and policymakers.

Keywords: Artificial intelligence; generative AI; entrepreneurship; dynamic capabilities; entrepreneurial opportunity recognition; entrepreneurial innovation; responsible AI; sustainable value creation; human–AI collaboration.