Reframing Innovation in the Age of Artificial Intelligence: A Human–AI Innovation Dynamics Model

Authors

DOI:

https://doi.org/10.66203/manexia.02203

Keywords:

human–AI collaboration, innovation dynamics, generative artificial intelligence, co-creation, dynamic capability, distributed creativity

Abstract

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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Published

04-05-2026

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Vol. 2 No. 2 (2026): Human–AI Value Systems

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Conceptual Papers

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Copyright (c) 2026 Dianti Eka Aprilia (Author)

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