Women in AI Statistics (2025–2026)

Women remain significantly underrepresented across the artificial intelligence ecosystem — from the workforce and research labs to leadership roles and venture capital. While progress is being made, particularly in generative AI adoption, the gender gap in AI persists across nearly every measurable dimension.

This report compiles the most current statistics on women’s participation, challenges, and emerging opportunities in AI.

Women in AI: Key Statistics on the Gender Gap in Artificial Intelligence (2025–2026)

The AI gender gap is multi-layered: women constitute only 22–30% of the global AI workforce, hold fewer than 15% of senior executive AI roles, and author just 25% of AI research papers. Meanwhile, AI systems themselves reflect this imbalance — 44% of AI systems exhibit gender bias, and women face nearly three times the automation risk from AI compared to men.

However, the gender gap in generative AI adoption is closing rapidly, with Deloitte projecting US parity by the end of 2025. Record venture capital flows to female-founded AI startups, including a landmark $73.6 billion in 2025, signal shifting dynamics — though funding remains highly concentrated.

Global AI Workforce Representation

Women hold a minority share of AI jobs worldwide, with estimates ranging from 22% to 30% depending on the source and methodology.

• 22% of AI professionals globally are women, according to analysis of nearly 1.6 million AI professionals by the World Economic Forum and Interface EU.
• 30% of the AI workforce is female, per UN Women’s 2026 assessment, comparable to women’s overall representation in STEM fields.
• 26% of data science and AI employees are women across the world’s leading economies, with cloud computing (15%) and engineering (15%) showing even lower figures.
• In North America, women occupy 25% of AI roles, while in the EU the figure stands at 24%.
• A Statista/WEF analysis found that as of 2020, 14.2% of cloud computing workers and approximately 32% of data and AI workers were women.

Region/Metric	Women’s Share
Global AI workforce	22–30%
North America AI roles	25%
European Union AI roles	24%
Data science & AI (leading economies)	26%
Cloud computing	14–15%
Engineering	15%

Representation by Seniority Level

The gender gap widens at every step up the seniority ladder, creating what researchers call a “leaky pipeline” in AI careers.

• At the entry level, women comprise approximately 29% of AI workers.
• At the senior executive level, women occupy fewer than 15% of AI roles — nearly half the entry-level figure.
• Among 39 leading AI-focused organizations analyzed by Russell Reynolds, women hold only 30% of overall leadership roles and 10% of CEO and top technology roles.
• Only 22% of product, engineering, and science roles in AI companies are held by women. The study identified just four women CEOs and four women CTOs across these organizations.
• 19 of 39 AI company C-suites have fewer than 25% women, and 30 of 39 are less than one-third women.
• Women hold approximately 20.2% of CTO positions in mid-market tech firms.
• In India’s IT sector, women hold 23% of senior leadership roles as of 2024, up from 18.7% in 2023.

Women in AI Research & Academia

The research pipeline reveals an even starker gender imbalance, with women’s representation in AI research declining relative to their male counterparts over the past decade.

• Only 12% of leading AI researchers globally are women, according to 2025 data from the Stanford AI Index and World Economic Forum.
• Women hold just 16% of AI research roles, per UN Women.
• A Nesta study of over 1.5 million arXiv preprints found only 13.8% of AI research authors are women, with the proportion having stagnated since the 1990s.
• 75% of AI scientific publications are produced by all-male teams, based on analysis of 74,000+ AI-related papers across physics, mathematics, computer science, and engineering.
• At top AI research institutions, gender ratios remain low: only 11.3% of Google’s AI researchers on arXiv are women, along with 11.95% at Microsoft and 15.66% at IBM.

Generative AI Adoption Gap

One of the most dynamic areas in the women-in-AI landscape is the rapidly closing gender gap in generative AI usage.

• In 2023, women’s use of generative AI was roughly half that of men’s.
• By 2024, 33% of surveyed US women reported using or experimenting with GenAI, compared to 44% of men — a persistent but narrowing gap.
• The proportion of US women adopting GenAI tripled in one year, outpacing the 2.2x growth rate seen among men.
• Deloitte projects that women’s experimentation with and use of GenAI will equal or exceed that of men in the United States by the end of 2025.
• A Boston Consulting Group study found that 68% of women in tech use a GenAI tool at work more than once a week, compared with 66% of men. Senior women in technical functions lead their male counterparts by an average of 14 percentage points in GenAI adoption.
• Among daily AI users, 34% of women use AI daily compared to 43% of men.
• However, for every 100 men using GenAI tools, only 78 women do — after accounting for usage differences across demographics.
• Research from China shows that female professionals are adopting AI faster than male counterparts and report lower levels of anxiety around AI tools.

STEM Education Pipeline

The gender gap in AI begins in the educational pipeline, where women remain underrepresented in computing and AI-related degree programs.

• Women account for just 35% of STEM graduates globally, with little improvement over the past decade, according to UNESCO.
• Only 21% of engineering degrees and 22% of computing degrees were awarded to women in 2023.
• In the UK, 23% of computer science enrollments in 2022/23 were female or non-binary, up from 19% five years earlier. At that rate, parity would take over 30 years.
• The gap in AI-specific degree conferrals between men and women is nearly three times wider than the gap in general STEM degrees, per Georgetown CSET analysis.
• Only one in four women with an IT degree in the EU took up digital occupations, compared to over one in two men.

Also Check: AI Voice Agents Market Size 2024–2034

AI Gender Bias Statistics

The underrepresentation of women in AI development has measurable consequences for the technology itself, as AI systems reflect and often amplify societal gender biases.

• 44% of AI systems exhibit gender bias, a direct consequence of homogeneous development teams.
• A UNESCO study found unequivocal evidence that Large Language Models (GPT-3.5, GPT-2, Llama 2) produce gender bias against women, with open-source models showing the most significant bias.
• Joy Buolamwini’s landmark “Gender Shades” study revealed facial recognition error rates of up to 34% for darker-skinned women versus just 0.8% for lighter-skinned men.
• In AI-powered hiring, a Brookings study found gender bias in 63% of tests: resumes with men’s names were favored 51.9% of the time, while women’s names were favored just 11.1%.
• When ChatGPT generated nearly 40,000 resumes, it assumed women were younger by 1.6 years, had more recent graduation dates, and less work experience compared to resumes with male names.
• In Belgium, 74% of recruiters now automate at least one hiring stage, yet only 12–17% have noticed biased outcomes in their AI tools.
• A separate experiment found that all five tested LLMs (GPT-3.5, GPT-4o, Gemini, Claude, Llama 3) systematically award higher scores to female candidates regardless of race — but this pro-female bias masks deeper intersectional issues, particularly disadvantaging Black male candidates.

AI’s Impact on Women’s Employment

AI-driven automation poses a disproportionate threat to women’s jobs due to occupational segregation patterns.

• Women are nearly three times more likely than men to work in jobs with the highest exposure to generative AI automation. In high-income countries, 9.6% of female employment falls into the highest-risk category, versus 3.5% for men.
• The UN Gender Snapshot 2025 estimates that approximately 28% of women’s jobs globally are at risk of automation by AI, compared with 21% of men’s jobs.
• Women dominate clerical and administrative roles — data entry, typists, customer service — that generative AI can most easily replicate.
• In India, roughly 80% of women work in the informal sector, with many in BPO roles vulnerable to automation. Without targeted reskilling, AI could reverse progress toward workplace parity.
• Emerging tech roles carry a 6% pay premium, yet women remain underrepresented in these positions, widening the gender pay gap. Women in tech earn approximately 20% less than men overall.
• A Wharton study found that only 16% of women in their sample worked with emerging technologies, compared to ~17% of men — a gap that persists even when controlling for qualifications.

Venture Capital & Female AI Founders

Record funding flows to female-founded AI startups mask a concentration problem, with a handful of companies driving the headline numbers.

• In 2025, startups with at least one female founder raised a record $73.6 billion, nearly double the $44.7 billion raised two years prior.
• Two-thirds of all US venture capital going to female-founded startups flowed into AI ventures.
• Nearly half of that AI funding went to just two companies: Anthropic (co-founded by Daniela Amodei) and Scale AI (co-founded by Lucy Guo), which together pulled in over $30 billion.
• Without Anthropic and Scale AI, female-founded companies would not have surpassed one-quarter of US deal value.
• All-female founding teams receive roughly 1–2% of total VC funding globally, a figure that has barely changed over the past five years.
• The number of deals involving female-founded enterprises has declined for four consecutive years since a 2021 peak, even as total funding amounts rose.
• 82% of decision-makers at US VC firms with assets exceeding $50 million are men.

Country-Level Comparisons

The women-in-AI gender gap varies considerably across nations, with some surprising leaders and laggards.

Country/Region	Key Statistic
Saudi Arabia	World leader in women’s AI engagement (female-to-male ratio exceeds 1.0)
Latvia, Finland	Over 40% female AI representation — highest in EU
Italy (Milan)	30.7% female AI professionals — leads European AI hubs
Germany	~20.3% female AI workforce — among lowest in EU despite strong overall gender equity
India	AI skill penetration ratio: men 1.9x women
United States	AI skill penetration ratio: men 1.7x women
Portugal	Near gender parity in general workforce, but 51% AI gender gap
Frankfurt, Germany	Just 19% female AI talent — lowest among European AI hubs

Saudi Arabia’s position as the global leader in women’s AI engagement reflects targeted national programs under Vision 2030 and is supported by Stanford’s 2025 AI Index Report.

In contrast, countries like Portugal and Estonia, which have achieved near gender equity in their general workforce, show dramatic AI-sector imbalances of up to 51%, underscoring that general labor market progress does not automatically translate into AI workforce equity.

Also Check: Adaptive AI Market Size (2024 to 2034)

Notable Women Leading in AI

Fei-Fei Li speaking at the AI for Good event in 2017.
Several women are playing pivotal roles in shaping the AI industry, from technical research to corporate leadership and AI ethics advocacy.

• Fei-Fei Li — Often called the “godmother of AI,” Li created ImageNet, co-directs Stanford’s Human-Centered AI Institute, and founded World Labs (raised $230 million). She also co-founded AI4ALL to increase underrepresented groups’ participation in AI.
• Daniela Amodei — Co-founder and President of Anthropic, which has reached a $183 billion valuation. Her leadership in AI safety has positioned Anthropic as a key player in responsible AI development.
• Mira Murati — Former CTO of OpenAI who led the development of ChatGPT and DALL-E. In 2025, she secured a record-breaking $2 billion seed round for her AI startup Thinking Machines Lab, the largest seed funding in history.
• Joy Buolamwini — Founder of the Algorithmic Justice League, whose “Gender Shades” research exposed racial and gender bias in commercial facial recognition systems.
• Timnit Gebru — Co-authored the landmark paper “On the Dangers of Stochastic Parrots” and founded the Distributed AI Research Institute (DAIR) to pursue independent, community-rooted AI research.
• Lisa Su — CEO of AMD who steered the company into the AI chip market with the MI300X accelerator, transforming AMD into a major competitor in AI hardware.

Closing the Gap: Key Barriers and Opportunities

The persistent gender gap in AI stems from structural, cultural, and systemic factors — but there are also clear levers for change.

Barriers:

• Women are 25% less likely than men to have basic digital skills and four times less likely to have advanced programming skills globally.
• Structural constraints including relocation demands, inflexible hours, and uneven caregiving responsibilities limit women’s access to high-value AI roles.
• A confidence gap persists: a 2025 study found an 18-percentage-point gap in AI skill confidence, with young women reporting lower confidence (56%) versus young men (74%).
• In developing countries, only 20% of women have internet access, creating a cascading barrier to AI economy participation.
• Only half of the 68% of countries with STEM education policies specifically target girls and women.

Opportunities:

• Companies with equitable gender diversity on boards and in C-suites report on average 10% better financial performance.
• Women in AI are more likely to consider values like safety, accountability, and human autonomy as extremely significant — by 4–5 percentage points more than male counterparts.
• The rapid closure of the GenAI adoption gap suggests that with the right access and trust-building, women can match or exceed men in AI tool usage within a few years.
• AI itself offers opportunities to accelerate women’s inclusion by reducing hiring bias, providing personalized skill development at scale, and enabling flexible work arrangements.

Conclusion

The data paints a complex picture: women are underrepresented in AI across the workforce, research, leadership, and funding — yet the trajectory is shifting. GenAI adoption rates are converging, record venture capital is flowing to female AI founders (even if concentrated), and awareness of AI bias is driving accountability measures.

Closing the AI gender gap requires simultaneous action on education, workplace structures, funding ecosystems, and the AI systems themselves. As UN Women, the World Economic Forum, and leading researchers have emphasized, the stakes extend beyond equity — who builds AI determines whose values it reflects.