Artificial intelligence and entrepreneurial opportunity recognition: a judgement-centred conceptual framework

Purpose The study aims to reconceptualise entrepreneurial opportunity recognition (EOR) in artificial intelligence (AI) enabled innovation contexts by clarifying the distinct yet interdependent roles of AI and human judgement. It addresses growing theoretical ambiguity regarding whether AI substitutes for, augments, or fundamentally transforms entrepreneurial cognition. Design/methodology/approach This study adopts a conceptual theory-building approach grounded in EOR theory and the entrepreneurial judgement perspective. Through a diagnostic comparison of dominant opportunity recognition theories and an analytical decomposition of cognitive functions, it develops a framework that conceptualises EOR as a process of distributed cognition governed by human judgement. Findings This study proposes that AI reshapes EOR by externalising analytical cognition through mechanisms of signal amplification, cognitive offloading and temporal compression. However, opportunity recognition remains a judgement-bound process involving contextual interpretation, evaluative assessment and responsibility-bearing commitment. The framework demonstrates that AI intensifies, rather than diminishes, the importance of judgement by expanding the volume and velocity of analytically plausible opportunities. Unlike perspectives that distribute cognition across human actors, the proposed model theorises the externalisation of analytical processing to algorithmic systems while preserving human agency and accountability. Practical implications The framework cautions organisations against treating AI as a substitute for entrepreneurial decision-making. Instead, it underscores the importance of designing AI-enabled innovation systems that preserve evaluative governance, accountability and ethical responsibility. Originality/value This paper advances EOR theory by explicitly theorising AI as a cognitive augmentation mechanism rather than a decision maker. By reframing opportunity recognition as distributed cognition governed by human judgement, it provides an integrative and theoretically precise foundation for research on AI and innovation.