Women hold only 22% of all AI roles
Globally, women hold just 22% of AI roles. Women are 20% less likely than men to directly engage with generative, a new report from US-based National Girls Collaborative Project. Women earn half of science and engineering degrees, but only 26% in engineering and 23% in computer science. Women represent 35% of the STEM workforce, despite being 48% of the total workforce.
“Research shows that exposure to relatable women STEM role models can shift perceptions of who participates in STEM and increase girls’ sense of belonging and identification with STEM careers. Programs that connect learning to real world impact and community benefit are especially effective, aligning with girls’ strong desire to help others”, the Project report says.
“The increase in women’s participation in the STEM workforce includes all racial and ethnic groups, but Latina, Black, and Native American women represent less than 10% of the STEM workforce overall.
A report from consultancy McKinsey shows that the share of women graduating from European tech degree programs has seen a small uptick since 2022 rising to 33% of bachelor’s degrees and 39% of PhD graduates. But women in the tech workforce has fallen by 20 percentage points with the share of women in tech roles falling to 19%.
“Companies that take a holistic approach to embedding gender-inclusive strategies and tangible actions at the team, individual, and organizational levels can address the AI talent shortage and the diversity gap simultaneously.”
Just 13% in tech management roles are women and only 8% in senior management roles (such as director and C-level) are women, according to McKinsey data.
“The steepest losses occur early: Between entry-level and first managerial roles, women’s representation falls by seven to 18 percentage points, depending on job family.”
“These early losses compound the gender gap at the leadership level. Even in AI, data, and analytics—the only job family showing entry-level growth—representation drops by 16 percentage points between entry and C-level, with a particularly sharp drop at midcareer leadership transitions.”
“In the AI era, this attrition is especially concerning. Leadership roles in data, product, and engineering increasingly shape how AI systems are built and governed. When women exit early from these pathways—and from the sector altogether—the result is a narrowing of perspectives at precisely the levels at which bias, accountability, and societal impact must be addressed.”
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