The Uneven Impact of Generative Artificial Intelligence on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya

• 成果类型: Article; Early Access
• 署名作者: Otis, Nicholas G.; Clarke, Rowan; Delecourt, Solene; Holtz, David; Koning, Rembrand
• 署名单位: University of California System; University of California Berkeley; Harvard University; Columbia University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.06909
• 发表日期: 2026
• 关键词: Artificial intelligence; BUSINESS PERFORMANCE; entrepreneurship; entrepreneurial strategy; field experiment

摘要

Scalable and low-cost artificial intelligence (AI) assistance has the potential to improve firm decision making and economic performance, particularly in emerging markets. However, running a business involves a wide range of open-ended problems, making it unclear whether and how recent advances in AI can help business owners around the world make better decisions. In a field experiment with Kenyan entrepreneurs, we evaluated the impact of AI advice on small business revenues and profits by randomizing access to a GPT-4-powered AI business assistant. Although we are unable to reject the null hypothesis of no average treatment effect on firm revenues and profits, we find that the effect for entrepreneurs who were low performing at baseline is over 0.20-standarddeviations lower than for initial high performers. Subsample analyses show that low performers did nearly 10% worse because of the AI assistant, whereas high performers may have benefited by over 15%. This differential impact does not appear to result from differences in the questions posed to the AI or the advice that it provided but rather, from the advice that entrepreneurs chose to implement. More broadly, these results show that generative AI is already capable of impacting real-world business performance-although in uneven and sometimes unexpected ways.