Generative AI and the Entrepreneurial Process: A Conceptual Model of Opportunity Recognition, Resource Substitution and Venture Legitimacy in Resource-Constrained Economies
DOI:
https://doi.org/10.68854/29dv8843Keywords:
Entrepreneurship, Generative artificial intelligence, Opportunity recognition, Bricolage, Resource constraints, Venture legitimacy, MSMEs, Nigeria, AfricaAbstract
Firms are moving artificial intelligence (AI) from operational automation into the strategic layer, where decisions are rare, consequential and made under ambiguity rather than measurable risk. Recent evidence shows that large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors, yet combinations of humans and AI often underperform the better of the two working alone, and AI assistance can homogenise outputs and erode human skill. The literature lacks a principled basis for deciding which strategic decisions can be delegated to AI, which should only be augmented by it and which must remain with human judgement. This conceptual study addresses that gap. It combines an integrative review with typological theorising. The population was peer-reviewed journal research on AI in managerial and strategic decisions; a purposive sample of 44 articles published between 2023 and 2026 was retained after screening against explicit inclusion and exclusion criteria and verification against DOI and publisher records. The articles were coded for decision task, AI role, outcome and boundary conditions, and analysed abductively to identify the task properties that explain when human–AI decision-making succeeds or fails. The analysis identifies three properties: the stability of the environment that generated the data, the verifiability of the output before action and the reversibility of the consequence. Crossing them yields a delegation map in which delegation fits only decisions that are stable and reversible, augmentation takes four distinct forms, and judgement retention is required where instability and irreversibility coincide. Eight propositions specify that the three properties are complementary; that augmentation improves strategy without homogenising it when AI is used divergently before humans select; that human-first sequencing, structured dissent and skill maintenance protect retained judgement; and that common adoption of the same systems drives competitive parity and correlated strategic error. The paper recommends that boards allocate decision rights by decision rather than by technology, that executives sequence human and AI inputs deliberately, and that management education teach when not to rely on AI.
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