Generative Artificial Intelligence and Entrepreneurial Opportunity Recognition in Emerging Economies: A Conceptual Framework

Objective: Entrepreneurial opportunity recognition, as the central core of the entrepreneurial process, plays a fundamental role in the formation, continuity, and growth of new ventures. A substantial body of classical and contemporary entrepreneurship literature considers this process as the result of the interaction among prior knowledge, entrepreneurial alertness, social networks, and individual cognitive processes. However, this stream of research implicitly assumes the existence of a certain level of information transparency, access to reliable data, and the presence of efficient intermediary institutions; such that the entrepreneur’s main challenge is not the lack of information, but its interpretation. In contrast, emerging economies face different conditions, where chronic shortages of reliable data, dispersed market signals, weak regulatory institutions, and extensive institutional voids impose serious constraints on the process of opportunity recognition. In such environments, formal information is either unavailable or of insufficient quality, and as a result, information asymmetry is structurally reproduced. The consequence of this situation is the emergence of a condition of “limited observability,” in which many potential opportunities are essentially not detectable. This, in turn, leads entrepreneurial activities to be reduced to limited, short-term, and arbitrage-based opportunities, and diminishes the possibility of identifying innovative and scalable opportunities. In this context, the emergence of generative artificial intelligence offers a new perspective for rethinking these limitations. Unlike previous technologies that relied on structured data, this technology is capable of generating new insights from incomplete and heterogeneous data. However, the question remains as to how these capabilities redefine the cognitive boundaries of opportunity recognition in data-scarce environments.

Method: This study adopts a conceptual and theory-building approach and, by drawing on the logic of theory synthesis and abductive reasoning, integrates insights from the literature on entrepreneurial opportunity recognition, institutional voids, digital entrepreneurship, and recent studies on artificial intelligence. Within this framework, an attempt is made to recombine key concepts from these domains to provide an integrated explanation of the role of generative artificial intelligence in reshaping the opportunity recognition process. The central argument of the paper is that generative artificial intelligence can be conceptualized as a distinct form of “cognitive augmentation” that enhances entrepreneurs’ ability to observe, interpret, and recombine market signals under conditions of information scarcity.

Findings: The findings of this study, presented in the form of a conceptual framework, indicate that the impact of generative artificial intelligence on opportunity recognition is realized through three main mechanisms. The first is “data augmentation,” which refers to the ability to combine and integrate dispersed market signals to create actionable representations. The second is “market insight generation,” which is achieved through identifying hidden patterns and transforming heterogeneous data into meaningful narratives about emerging market trends. The third is “pattern recognition and recombination,” which enhances entrepreneurial imagination and enables the transition from limited opportunities to innovative and scalable ones. At the same time, the findings indicate that this process is not always error-free, and in some cases, outputs generated by artificial intelligence systems may lead to misinterpretations or even the formation of false opportunities.

Originality: This study extends opportunity recognition theory by introducing the concept of “limited observability” as a fundamental constraint in emerging economies and conceptualizes generative artificial intelligence as a mechanism for reducing this limitation. In addition, two variables—“the intensity of institutional voids” and “entrepreneurs’ digital literacy”—are introduced as key moderating factors that influence the extent and manner of this technology’s impact. The framework also demonstrates that alongside its enhancing capabilities, risks such as automation bias and overreliance on algorithmic outputs can affect the quality of opportunity recognition. From this perspective, the present study contributes to the digital entrepreneurship literature by highlighting the role of generative technologies in data-scarce and institutionally weak contexts.

Conclusion: Overall, the findings of this study suggest that generative artificial intelligence can transform the opportunity recognition process from a limited, experience-based activity into a more systematic and insight-driven process, provided that its use is accompanied by human judgment and critical thinking. Accordingly, from a policy perspective, investment in digital literacy development, improvement of data infrastructures, and the design of flexible regulatory frameworks play a decisive role in the effective utilization of this technology for the advancement of entrepreneurship.