The role of artificial intelligence in entrepreneurial decision-making under uncertainty: a corporate entrepreneurship perspective

Purpose This study investigates how corporate entrepreneurs utilize artificial intelligence (AI) to make decisions under different types of uncertainty, examining AI’s role in facilitating transparency and supporting decision-making under complex conditions. Design/methodology/approach Data were gathered from 39 semi-structured interviews with corporate entrepreneurs across industries to understand how AI assists them in coping with different types of uncertainty. A flexible pattern matching approach (FPMA) was used to analyze the data, integrating both theoretical perspectives and novel empirical insights. Findings The findings reveal a critical insight: while AI can enhance transparency and improve decision-making under high uncertainty, it may paradoxically increase uncertainty in tasks of lower complexity due to AI’s remaining error susceptibility for such tasks. This suggests that while AI is a powerful tool for addressing complex challenges, it may introduce new uncertainties in more predictable contexts. Research limitations/implications Besides advancing conventional entrepreneurial uncertainty theory, this research extends the theories of entrepreneurial effectuation and causation by examining AI’s distinct role in helping (corporate) entrepreneurs make decisions under uncertainty. Our findings suggest that entrepreneurs need a balanced approach, adopting both causal and effectual strategies when utilizing AI. Practically, these insights can guide corporate managers and policymakers in developing strategies for effective AI integration that address varying uncertainties. Originality/value By focusing on how AI supports entrepreneurial decision-making under uncertainty, this study provides a novel perspective on AI’s impact across different uncertainty types, offering a framework to optimize AI’s potential while mitigating its limitations to entrepreneurial decision-making.