Author(s) :

Skyler Wood.

Volume/Issue :

Abstract :

Entrepreneurs increasingly adopt artificial intelligence (AI) tools during the earliest stage of venture creation—problem ideation. To clarify how AI reshapes this stage of opportunity identification, this study conducts a systematic scoping review of emerging literature. Searches were performed in Google Scholar and Scopus using Boolean queries combining “artificial intelligence,” “entrepreneurship,” “opportunity identification,” and “ideation.” After removing duplicates and screening records against predefined inclusion and exclusion criteria, 17 studies were included. Data were extracted using a structured coding framework capturing the entrepreneurial stage, AI’s functional role, reported benefits and risks, and boundary conditions. Findings show that AI expands entrepreneurs’ cognitive search space, accelerates pattern recognition, and supports reframing of problem domains, but also introduces risks of convergence, reduced contextual judgment, and overreliance. Synthesizing these patterns, the review develops the AI-Enhanced Opportunity Identification Framework: Human–AI Co-Evolutionary Extension, a refinement of a foundational opportunity identification model. The framework identifies three cross-stage AI roles—Inspirer, Analyst, and Organizer—and situates ideation within iterative human–AI collaboration shaped by institutional enablers such as AI-integrated incubators. This review contributes by mapping empirical evidence on AI’s influence on entrepreneurial ideation, clarifying mechanisms of human–AI co-creation, and outlining future research opportunities on judgment, novelty, and ecosystem-level effects.