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The Uneven Impact of Generative AI on Entrepreneurial Performance

Author

Listed:
  • Otis, Nicholas G.
  • Clarke, Rowan Philip
  • Delecourt, Solene
  • Holtz, David

    (University of California, Berkeley)

  • Koning, Rembrand

    (Harvard Business School)

Abstract

There is a growing belief that scalable and low-cost AI assistance can improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it hard to generalize from recent studies showing that generative AI improves performance on well-defined writing tasks. In our five-month field experiment with 640 Kenyan entrepreneurs, we assessed the impact of AI-generated advice on small business revenues and profits. Participants were randomly assigned to a control group that received a standard business guide or to a treatment group that received a GPT-4 powered AI business mentor via WhatsApp. While we find no average treatment effect, this is because the causal effect of generative AI access varied with the baseline business performance of the entrepreneur: high performers benefited by just over 20% from AI advice, whereas low performers did roughly 10% worse with AI assistance. Exploratory analysis of the WhatsApp interaction logs shows that both groups sought the AI mentor’s advice, but that low performers did worse because they sought help on much more challenging business tasks. These findings highlight how the tasks selected by firms and entrepreneurs for AI assistance fundamentally shape who will benefit from generative AI.

Suggested Citation

  • Otis, Nicholas G. & Clarke, Rowan Philip & Delecourt, Solene & Holtz, David & Koning, Rembrand, 2023. "The Uneven Impact of Generative AI on Entrepreneurial Performance," OSF Preprints hdjpk, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:hdjpk
    DOI: 10.31219/osf.io/hdjpk
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    Cited by:

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    2. Manuel Menkhoff, 2025. "Belief Updating and AI Adoption: Experimental Evidence from Firms," CESifo Working Paper Series 12291, CESifo.
    3. Otis, Nicholas G. & Cranney, Katelyn & Delecourt, Solene & Koning, Rembrand, 2024. "Global Evidence on Gender Gaps and Generative AI," OSF Preprints h6a7c, Center for Open Science.
    4. Alhorr, Layane, 2024. "Virtual Windows Through Glass Walls? Digitalization for Low-Mobility Female Entrepreneurs," Policy Research Working Paper Series 10803, The World Bank.
    5. Kiran Tomlinson & Sonia Jaffe & Will Wang & Scott Counts & Siddharth Suri, 2025. "Working with AI: Measuring the Applicability of Generative AI to Occupations," Papers 2507.07935, arXiv.org, revised Dec 2025.
    6. Jason P Davis & Jian Bai Li, 2024. "Early Adoption of Generative AI by Global Business Leaders: Insights from an INSEAD Alumni Survey," Papers 2404.04543, arXiv.org.
    7. Jacob Schaal, 2025. "A theory-based AI automation exposure index: Applying Moravec's Paradox to the US labor market," Papers 2510.13369, arXiv.org.
    8. Eleanor Wiske Dillon & Sonia Jaffe & Nicole Immorlica & Christopher T. Stanton, 2025. "Shifting Work Patterns with Generative AI," Papers 2504.11436, arXiv.org, revised Nov 2025.

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