Artificial intelligence (AI) is increasingly used by entrepreneurs to support opportunity recognition, information search, customer analysis, content generation, and decision-making under uncertainty. However, the phenomenon of how women entrepreneurs perceive, adopt, and use AI in entrepreneurial decision-making remains insufficiently explained, particularly in relation to gendered constraints and uncertain entrepreneurial conditions. This conceptual paper develops an integrated framework to explain AI adoption in women’s entrepreneurial decision-making by combining the Technology Acceptance Model (TAM) and Effectuation Theory. TAM explains how perceived usefulness and perceived ease of use shape women entrepreneurs’ intention to adopt AI, while Effectuation Theory explains how AI is used in uncertain entrepreneurial contexts through means-driven action, affordable-loss experimentation, partnership building, and adaptive decision-making. The proposed framework suggests that AI anxiety, digital literacy, AI self-efficacy, training support, peer influence, resource constraints, and gendered entrepreneurial context influence AI adoption through TAM-based mechanisms and effectual use processes. This paper contributes to AI entrepreneurship and women entrepreneurship literature by linking technology acceptance with entrepreneurial action under uncertainty. It also provides propositions for future empirical testing.
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