Why Are Women Underrepresented in AI? The Barriers Behind the Numbers
Women remain underrepresented in AI jobs and leadership. Here are the workforce, skills, hiring and adoption barriers that help explain why.
By Amara Okafor, Women in AI Editorial Fellow ยท 27 August 2026
Women are underrepresented in artificial intelligence at several different stages at once.
They are less represented in AI talent pools, less represented among new AI hires and much less represented in senior AI leadership. Research also suggests women are less likely to use some generative AI tools at work, which could create another gap in experience and productivity as AI becomes part of everyday professional life.
There is no single explanation. The problem sits across education, hiring, career progression, workplace access and the design of the technology sector itself.
The gap starts before leadership
LinkedIn reported in August 2026 that women accounted for 26% of US AI hires in 2025. Stanford's 2026 AI Index, using LinkedIn data, reports women at 30.5% of global AI talent with AI engineering skills.
These are different populations, but both show a substantial imbalance before the conversation even reaches executive leadership.
At the C-suite level the gap is wider. LinkedIn found that women held only 13% of C-suite AI leadership roles at AI companies across 27 countries.
This means the sector is not dealing with one "leaky pipeline" moment. Representation drops across multiple stages.
Traditional hiring can narrow the pool unnecessarily
AI is a young and fast-moving profession. Many current roles did not exist in their present form several years ago, yet employers can still hire as though there is one correct career path into them.
A requirement for exact previous titles, a particular degree or a narrowly defined technology stack can filter out candidates who have the underlying skills but a less conventional background.
LinkedIn's skills-based hiring research suggests that changing this approach could improve women's representation in AI talent pools in several countries.
That matters because women are often more represented in adjacent disciplines that AI teams increasingly need: product, operations, policy, risk, healthcare, finance, education and customer-facing functions.
A broader skills lens does not lower the bar. It changes what evidence is accepted when deciding who can meet it.
Access to AI tools is another emerging divide
The OECD has highlighted a gender gap in the use of generative AI tools. Its 2024 policy brief cited research in which female workers were 20 percentage points less likely than male workers in the same occupation to say they had used ChatGPT.
The reasons are likely more complicated than confidence or willingness to experiment.
People use AI more when their employer gives them access, when their manager encourages experimentation, when they work in roles where AI tools are useful, and when they have enough time and support to learn new workflows.
If women are concentrated in jobs with less access to AI tools or less organisational support for experimentation, an adoption gap can develop even when interest is high.
That matters for careers. Employees who use AI frequently are more likely to build practical judgement about what it can and cannot do. Over time, that experience becomes a form of professional capital.
Leadership pipelines compound earlier gaps
Leadership is not created at the moment someone applies for a C-suite role. It is built through years of access to high-value projects, visible responsibility, sponsorship, promotion and ownership of commercially important work.
If women are less likely to be hired into AI roles at the beginning, and then less likely to be placed on the most strategic AI programmes, the leadership imbalance becomes predictable.
Companies therefore need to look beyond the headline proportion of women in a technology organisation. Who leads model deployment? Who owns AI budgets? Who presents to the board? Who gets promoted after a successful AI programme? Who is recognised publicly as the expert?
Those questions reveal whether representation reaches power and influence.
Networks matter in fast-changing industries
Established professions have relatively clear routes into seniority. AI is still inventing many of its job titles, team structures and standards.
That makes professional networks unusually important.
People hear about emerging roles through peers. Founders meet investors through trusted introductions. Researchers find collaborators through conferences. Executives are invited onto advisory boards because they are visible in the right communities.
When women are less represented in those networks, they can miss opportunities before a formal recruitment process even begins.
Communities focused on Women in AI can help by creating denser connections between practitioners, leaders, researchers, founders and investors rather than treating networking as a soft side benefit.
Role models change what a career in AI looks like
Representation is also informational.
If the public face of AI is dominated by male founders and researchers, people can underestimate how many different routes exist into the field. Visible women in engineering, governance, product, investment and leadership make those pathways easier to imagine and easier to navigate.
That is particularly important because the AI workforce is broader than the people training frontier models. The industry needs engineers, but it also needs experts in data, security, product, ethics, regulation, change management, design and industry-specific implementation.
What employers can do
There is no single diversity initiative that fixes the problem. A more serious approach includes several changes at once:
Hire around demonstrated skills where possible. Review who gets access to high-value AI programmes. Give employees practical access to AI tools and training. Measure promotion and leadership pipelines, not only entry-level hiring. Build sponsorship as well as mentoring. Make senior women in AI visible internally and externally. Ensure governance and product decisions include varied professional and demographic perspectives.
The business case is not simply that AI teams should look more representative. AI systems are increasingly making or informing decisions that affect entire populations. The people deciding how those systems are built, tested and deployed should not come from an unnecessarily narrow slice of society.
The gap is solvable, but not automatically
AI is still early enough that its institutions, career ladders and professional norms are being formed now.
That creates a risk that existing technology inequalities become embedded in a much larger economic system. It also creates an opportunity to design better routes into the field while those structures are still flexible.
The data shows a real gender gap. It does not show that the gap is inevitable.