How AI Impacts Entrepreneurs’ Psychology: Self-Efficacy, Technostress, Dependency, and Work Engagement (A Literature Review)

The rise of artificial intelligence (AI) presents entrepreneurs with unique yet significant psychological challenges and opportunities that remain underexplored. This literature review examines the impact of AI adoption on entrepreneurs’ psychological well-being, performance, and work engagement, addressing the question: What are the psychological effects of AI adoption on entrepreneurs? It builds on existing psychological theories regarding entrepreneurship and the rise of new technologies,, such as technostress and burnout, while narrowing the focus to the emergent field of AI and its impact on founders. Given that scholars have expressed that negative founder mental health correlates with poor business performance, examining how AI affects the psychological states of entrepreneurs is essential for the flourishing of both individuals and their startups. Using a narrative synthesis of 39 studies, this paper categorizes findings into four key themes: Self-Efficacy, Mastery, and Confidence; AI Dependency and Automation Bias; Technostress, Burnout, and Anxiety; and Work Engagement. Findings indicate that AI tools can enhance entrepreneurs’ self-efficacy, confidence, and decision-making by lowering barriers to entry and reducing cognitive load (e.g., streamlining tasks and providing data-driven insights), particularly for diverse and underrepresented founders. At the same time, over-reliance on AI fosters dependency, automation bias, and diminished critical thinking, while both under- and over-use of AI correlate with heightened technostress, anxiety, and burnout. The relationship between AI adoption and work engagement appears two-fold: moderate use enhances productivity and focus, while excessive or minimal use undermines satisfaction and resilience. These findings underscore that entrepreneurs’ psychological outcomes depend on how they navigate AI’s capabilities and limitations, highlighting the need for balanced adoption strategies. Future research should examine longitudinal effects, individual differences, and targeted interventions to mitigate adverse outcomes and support founders’ well-being in an AI-driven entrepreneurial landscape.