2345: Enabling Individual Ambidexterity: The Impact of AI on Combining Dual Learning Orientations (19777)

How can firms achieve ambidexterity using Artificial Intelligence (AI)? While AI is becoming an increasingly important source of competitive advantage, our understanding of how this domain of technologies can be used to combine exploration and exploitation remains limited. To address this gap, this article develops the concept of AI-assisted individual ambidexterity. Searching for the antecedents of this capability, the article borrows and synthesizes concepts from knowledge-based theory, routine dynamics, problemistic search, paradox theory, and the broader Carnegie tradition. The theory’s main claim is that AI can be used to counteract tendencies associated with bounded rationality of individuals in those cases where mitigating factors are present, so that individual learning traps are avoided. However, while stimulating productivity and creativity in individual decision-makers, AI usage can itself also exacerbate learning traps as individuals try to engage in explorative and exploitative learning. This theory is supplemented by five research propositions, which are formalized in an integrated conceptual model for AI-assisted individual ambidexterity, ending the paper with a set of implications for researchers.

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