As generative AI transforms the business landscape, a concerning trend demands immediate attention from executives and policymakers alike. In the recent Harvard Business School (HBS) working paper, “Global Evidence on Gender Gaps and Generative AI,” authors Nicholas G. Otis, PhD candidate at the Berkeley Haas School of Business; Solène Delecourt, Assistant Professor at the Berkeley Haas School of Business and Affiliated Researcher at the Laboratory for Innovation Science (LISH) at Harvard; Katelyn Cranney, PhD student at Stanford University; and Rembrand Koning, Associate Professor of Business Administration at HBS and Principal Investigator at the Digital Data Design (D^3) Institute at Harvard Tech for All Lab, describe a significant gender gap in the adoption and use of generative AI tools worldwide. This disparity threatens to exacerbate existing inequalities and risks limiting the potential benefits of this revolutionary technology across various sectors and industries.
Key Insight: A Universal Gender Gap in AI Adoption
“To estimate the extent of the gender gap in generative AI use, we first identified every publicly available study that has surveyed people about generative AI use along with their gender […] [Surveys show] a remarkably consistent pattern in generative AI use: men are more likely to adopt generative AI tools than women in all but one survey.” [1]
Otis and his colleagues uncovered a pervasive gender gap in generative AI adoption. Their comprehensive analysis, drawing from 18 diverse studies among more than 140,000 individuals worldwide, showed that women are approximately 20% less likely than men to directly engage with generative AI technology. This gap was not confined to specific industries, geographic locations, or occupations, but appeared to be a universal phenomenon.
Key Insight: Persistence of the Gap Despite Equal Access
“[F]indings show, that even when efforts to increase participation by equalizing access are in place, women are still less likely to use generative AI than men.” [2]
The researchers demonstrated that simply providing equal access to generative AI tools is not sufficient to bridge the gender gap. Their findings suggest that deeper, more complex factors are at play, potentially rooted in cultural, social, or institutional barriers. For example, in a study conducted in Kenya where access to ChatGPT was equalized, women were still about 13.1% less likely to adopt the technology compared to men.
Key Insight: Implications for AI Development and Effectiveness
“As generative AI systems are still in their formative stages, the under-representation of women may result in early biases in the user data these tools learn from, resulting in self-reinforcing gender disparities.” [3]
Otis and his team warned of a potential feedback loop where the current gender gap in AI usage could lead to biased AI systems that further discourage women’s participation. This cycle threatens to perpetuate and even amplify existing gender inequalities. The researchers discovered that women accounted for just 42% of the approximately 200 million average monthly users who visited the ChatGPT website worldwide between November 2022 and May 2024. In smartphone app usage, the gap widens further, with women estimated to make up only around 27.2% of total ChatGPT application downloads.
Key Insight: Multifaceted Roots of the Gender Gap
“[B]ecause women tend to work in different types of firms, jobs, and occupations than men, they may be less exposed to this new technology. Such differences are often further reinforced by the gendered differences in women’s personal and professional networks, further limiting diffusion and learning.” [4]
The working paper identified several potential factors contributing to the gender gap in AI adoption, including differences in workplace exposure, variations in personal and professional networks, and potential disparities in confidence and persistence when using new technologies. Research shows that women consistently say they are less familiar with and knowledgeable about generative AI tools than men. The team found that in the tech industry, junior women significantly lag behind men in generative AI use in both technical and non-technical functions, indicating that even in technology-focused environments, the gap persists.
Why This Matters
For business leaders and policymakers, understanding and addressing the gender gap in generative AI adoption is crucial. It represents a significant untapped potential in workforce productivity and innovation. As generative AI becomes increasingly integral to various business processes, ensuring equal participation across genders will be vital for maintaining competitiveness and fostering diverse perspectives in problem-solving and decision-making.
Moreover, the self-reinforcing nature of this gap poses a serious threat to gender equality in the workplace and beyond. If left unaddressed, it could lead to a widening skills gap, further entrenching gender disparities in high-growth, high-paying sectors of the economy. For executives, this translates to a pressing need to implement targeted strategies that provide equal access to AI tools and address the underlying factors that discourage women from engaging with these technologies.
References
[1] Nicholas G. Otis, Solène Delecourt, Katelyn Cranney, and Rembrand Koning, “Global Evidence on Gender Gaps and Generative AI”, Harvard Business School Working Paper No. 25-023, (2024): 30, 3.
[2] Otis et al., “Global Evidence on Gender Gaps and Generative AI”, 5.
[3] Otis et al., “Global Evidence on Gender Gaps and Generative AI”, 5.
[4] Otis et al., “Global Evidence on Gender Gaps and Generative AI”, 2.
Meet the Authors
Nicholas G. Otis is a PhD candidate at the Berkeley Haas School of Business, researching the societal and economic effects of generative AI and how it can help underserved people, places, and organizations. He earned his BA in Sociology and MA in Social Statistics from McGill University in Montreal.
Solène Delecourt is an Assistant Professor at the Berkeley Haas School of Business and Affiliated Researcher at the Laboratory for Innovation Science (LISH) at Harvard. Her studies focus on inequality in business performance and factors that create variation in company profits. She holds a master’s degree in Economics and Public Policy from Sciences Po Paris and École Polytechnique. She earned her PhD at the Stanford Graduate School of Business.
Katelyn Cranney is a PhD student in economics at Stanford University. Her interests include labor, behavioral, and experimental economics and technology adoption, innovation, gender, entrepreneurship, and productivity. Formerly a research assistant at Harvard Business School working with Rembrand Koning and Solène Delecourt, she earned her BS in Economics from Brigham Young University.
Rembrand Koning is an Associate Professor of Business Administration at Harvard Business School. He is the co-director, co-founder, and a Principal Investigator in the Tech for All Lab at D^3 at Harvard, studying how entrepreneurs can accelerate and shift the rate and direction of science, technology, and AI to benefit humanity. He earned his PhD in Business from the Stanford Graduate School of Business and his BS in Mathematics and BA in Statistics from the University of Chicago.