<![CDATA[The rapid advancement of artificial intelligence, particularly generative AI, is fundamentally transforming how innovation is conceived and executed within organizations, yet existing research remains limited by human-centric, linear, and static conceptualizations of innovation processes. This study addresses this gap by developing a conceptual framework that explains how human–AI collaboration reshapes the dynamics of innovation. Adopting a theory synthesis approach, the study integrates insights from innovation theory, knowledge-based perspectives, hybrid intelligence, and co-creation literature to construct the Human–AI Innovation Dynamics Model. The model conceptualizes innovation as an interaction-based, iterative, and co-adaptive process driven by continuous exchanges between human cognition and AI-generated outputs. It further identifies key mechanisms, including iterative co-creation and iteration depth, as well as moderating and boundary conditions that influence innovation outcomes. The study contributes to the literature by reconceptualizing AI as a co-creative agent, extending dynamic capability theory toward interaction-based systems, and advancing the notion of distributed creativity. The framework provides a foundation for future empirical research and offers strategic implications for organizations navigating AI-enabled innovation environments.]]>
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