This study undertakes a systematic and a bibliometric review of the literature on artificial intelligence as a cognitive extension within the domain of entrepreneurship, with particular emphasis on how computational systems interact with and enhance key dimensions of entrepreneurial cognition. Employing co-citation, co-occurrence, and thematic network analyses, this review maps the intellectual evolution of research that links AI capabilities to opportunity recognition, analytical reasoning, and early-stage decision making. Prior scholarship suggests that generative and predictive models broaden the scope of entrepreneurs' informational searches, enabling the discernment of patterns that typically elude unaided human judgment. Studies on problem-solving underscore the role of language-based and reasoning-oriented systems in fostering structured ideation and refining cognitive strategies. Concurrently, research in decision support highlights the utility of machine learning in forecasting, facilitating more systematic evaluations under conditions of uncertainty. Drawing insights from these intersecting literatures, this review delineates methodological imperatives for investigating AI-augmented cognition, including considerations of model interpretability, alignment between task and technological affordances, and the appraisal of human–AI reasoning dynamics. The resulting conceptual framework positions AI not as a replacement for entrepreneurial judgment but as an auxiliary cognitive apparatus that reshapes how entrepreneurs acquire information, construct alternative courses of action, and appraise nascent opportunities.