How Does AI Augment Entrepreneurial Opportunity Recognition: A Multiple Case Study from a Chinese Science Park

Abstract

 Despite growing scholarly interest in the intersection of artificial intelligence and entrepreneurship, the cognitive mechanisms through which AI tools shape entrepreneurial opportunity recognition remain empirically underexplored. This study addresses that gap through a qualitative multiple case study of eight early-stage ventures incubated at the Tsinghua University Science Park Yunnan Branch, a regional innovation hub in non-metropolitan China. Drawing on in-depth interviews, venture documents, and incubator records, and guided by cognitive load theory and the human-AI complementarity framework, we identify four themes that together describe how AI enters and reconfigures the opportunity recognition process. First, AI functions as cognitive scaffolding by reducing the extraneous informational load that crowds out deliberate judgment. Second, AI engagement transforms opportunity recognition from a moment of individual discovery into an iterative, dialogic construction process in which prompt quality serves as a critical mediating variable. Third, prior domain knowledge moderates the quality of human-AI engagement: founders with deeper expertise use AI critically and productively, while those with limited domain experience risk cognitive anchoring to AI-generated frames. Fourth, locally embedded tacit knowledge — knowledge of community dynamics, informal networks, and place-specific institutional logics — constitutes an irreducible human contribution that AI tools cannot replicate. These findings extend cognitive entrepreneurship theory into the human-AI era, specify the cognitive load mechanism through which AI augmentation operates, and theorize a form of human-AI boundary that is particularly salient in non-metropolitan and institutionally informal innovation contexts. Practical implications are drawn for entrepreneurs, incubator managers, and regional innovation policymakers.