As generative AI transforms the workplace, women face a disproportionate risk of automation—revealing deep inequalities in who builds, controls, and benefits from artificial intelligence.
The future of work is being written in code, and women are getting a raw deal. A comprehensive analysis by the International Labour Organization and Poland’s National Research Institute reveals a stark reality: women are nearly three times more likely than men to work in jobs with high exposure to generative AI automation. In high-income countries, 9.6% of female employment sits in the highest-risk category compared to just 3.5% for men—a disparity that becomes even more pronounced when you consider that 25% of global employment faces some level of GenAI exposure [1].
This isn’t just about jobs disappearing—it’s about power, representation, and who gets to shape the AI revolution. The data exposes a troubling paradox: while artificial intelligence promises to democratize access to information and capabilities, it’s actually amplifying existing gender inequalities in unprecedented ways.
34% vs 11%: The Global Exposure Divide
The ILO study reveals a striking geographic pattern in AI exposure. High-income countries show 34% GenAI exposure compared to just 11% in low-income countries [1]. But this disparity masks a more complex reality—while developing nations face lower immediate automation risk, they also have fewer resources to manage workforce transitions when change inevitably arrives.
The research methodology was exhaustive: analyzing 29,753 tasks across Poland’s 6-digit occupational classification system, surveying 1,640 workers who evaluated 2,861 tasks, generating 52,558 data points, and validating findings through international experts reviewing 608 tasks and AI predictions from large language models. Poland was selected because it represents a middle ground between high-income advanced economies and emerging economies, making it an ideal context for assessing task automation potential. [1].
9.6% vs 3.5%: The Gender Automation Gap
The most striking finding centers on occupational segregation. In high-income countries, 9.6% of female employment falls into “Gradient 4″—the highest automation exposure category—compared to just 3.5% for men [1]. This nearly three-fold difference stems from women’s concentration in clerical and administrative roles that generative AI can most easily replicate.
Women dominate positions like data entry clerks, typists, and customer service representatives—precisely the routine cognitive work that large language models excel at automating [1]. Meanwhile, men cluster in technical trades and manual labor roles classified as “Gradient 1” (low exposure, high variability), performing physical tasks that require dexterity and real-world problem-solving that current AI systems can’t match [1].
The research framework categorizes occupations across four gradients [1]:
- Gradient 4 (Highest exposure, low variability): Clerical roles with high automation potential
- Gradient 3 (Significant exposure, high variability): Moderate automation with task variability (financial analysts)
- Gradient 2 (Moderate exposure, high variability): Partial automation (sales representatives)
- Gradient 1 (Low exposure, high variability): Minimal automation (manual labor)
30% Representation, 44% Bias: The Development Gap
Beyond employment risks, women represent just 30% of the AI workforce globally, with even lower representation in STEM and ICT leadership positions [3]. This underrepresentation has measurable consequences: 44% of AI systems exhibit gender bias, a direct result of homogeneous development teams that lack diverse perspectives [3].
The digital divide compounds these problems. In developing countries, only 20% of women have internet access—a stark barrier in a world where AI capabilities are becoming increasingly central to economic participation [3]. This digital exclusion creates a cascading effect that locks women out of the AI economy entirely.
Corporate Concentration and Control
The concentration of AI development within a handful of major corporations presents another layer of concern. When AI research and deployment decisions are made by organizations with limited gender diversity, the resulting systems inevitably reflect those biases [3]. This corporate concentration means that gender considerations are often treated as secondary concerns rather than fundamental design principles.
The result is AI systems that not only fail to serve women’s needs but actively disadvantage them. From hiring algorithms that discriminate against female candidates to healthcare AI that performs poorly on women’s health issues, the consequences of male-dominated AI development are already visible across multiple sectors.
The Skills Gap Challenge
The ILO research, which incorporated data from across ISCO-08 1-digit occupational groups, reveals that women in automation-prone occupations often lack access to the technical training needed to transition to AI-adjacent roles [1]. The study’s analysis shows that women cluster in the most vulnerable categories regardless of economic development level, though the disparity manifests differently across regions.
This skills gap is compounded by systemic barriers that have long plagued women in STEM fields: discrimination, unconscious bias, and persistent gender pay gaps [5]. These factors create a talent pipeline problem that starts in education and continues throughout careers, making it difficult for women to gain the technical expertise needed to shape AI development.
Policy Solutions: A Multi-Pronged Approach
Addressing the AI gender gap requires coordinated intervention at multiple levels.
1. Social Dialogue and Workforce Training
Governments, employers, and workers’ organizations must collaborate to enhance job quality and address disparities in AI-exposed sectors [2]. This includes:
- Gender-sensitive upskilling programs targeting women in high-exposure occupations [1]
- Reskilling initiatives specifically designed for clerical and administrative workers [1]
- Industry-specific training programs that bridge digital skills gaps [1]
2. Regulatory Frameworks for Ethical AI
Establishing regulations that promote fairness and transparency can reduce gender bias in AI systems [4]:
- Mandatory bias testing for AI systems used in hiring, lending, and healthcare
- Transparency requirements for algorithmic decision-making processes
- Public procurement policies that incentivize inclusive AI development
3. Inclusive AI Development Standards
Creating requirements for diverse development teams and gender expertise integration [3]:
- Funding incentives for AI research teams with gender diversity
- Mandatory inclusion of gender experts in AI development processes [3]
- Standards for inclusive design practices in AI system development
4. Global Governance and Multi-Stakeholder Approaches
Developing international frameworks to address AI bias on a global scale [3]:
- Cross-border cooperation on AI ethics and gender equality standards
- Knowledge sharing platforms for best practices in inclusive AI
- Support for developing countries to build inclusive AI ecosystems
5. Educational System Reform
Restructuring education to prepare women for AI-adjacent careers [3]:
- Interdisciplinary programs combining technical skills with domain expertise
- Early intervention programs to encourage girls in STEM fields
- Adult education programs focused on digital literacy and AI literacy
Rethinking the Automation Narrative
The conventional wisdom suggests that AI will eliminate routine jobs while creating new opportunities for creative and strategic work. But this framing obscures the gendered nature of “routine” work. Many jobs categorized as automatable—customer service, administrative support, content moderation—require emotional intelligence, cultural sensitivity, and interpersonal skills that current AI systems struggle to replicate effectively.
The question isn’t whether AI can technically perform these tasks, but whether organizations will choose to automate them and what the quality implications might be. A customer service chatbot might handle basic inquiries, but human representatives excel at managing complex emotional situations and building customer relationships.
The Innovation Imperative
Perhaps most importantly, the AI gender gap represents a massive missed opportunity for innovation. Research consistently shows that diverse teams produce better outcomes, identify more potential problems, and create more robust solutions. In a field as consequential as artificial intelligence, excluding half the population from development and decision-making isn’t just unfair—it’s strategically shortsighted.
The companies and countries that succeed in building inclusive AI development ecosystems will likely produce superior technologies. They’ll create systems that work better for broader populations, identify market opportunities that homogeneous teams miss, and avoid costly bias-related failures that can damage both reputation and bottom lines.
Implementation Challenges and Opportunities
The ILO research methodology, validated through multiple approaches including expert assessments and AI predictions, demonstrates that the gender gap in AI exposure is both measurable and addressable [1]. However, implementation faces several challenges:
Industry-Specific Dynamics: More research is needed on how specific industries beyond clerical work are impacted by AI-related gender disparities [1, 2]. The current analysis provides a foundation, but sector-specific interventions require deeper investigation.
Employment Impact Analysis: Detailed studies on actual job displacement versus transformation are necessary to refine policy interventions beyond general strategies like “social dialogue” or “digital skills enhancement” [5].
Leadership and Innovation Gaps: Specific interventions for improving female leadership presence in AI development remain underdeveloped, despite their critical importance for innovation outcomes [5].
The Path Forward: From Risk to Opportunity
The AI revolution is still in its early stages, which means there’s time to course-correct. But the window for intervention is narrowing as AI systems become more entrenched in economic and social systems [1]. The choices made today about AI development, deployment, and governance will shape gender equality for generations.
The goal isn’t to slow down AI development or limit its capabilities, but to ensure that its benefits are distributed equitably and its risks don’t fall disproportionately on already marginalized groups [1]. This requires acknowledging that AI isn’t gender-neutral—it’s shaped by the biases, priorities, and blind spots of its creators [3].
Key implementation priorities include:
- Immediate Action: Implementing bias testing and transparency requirements for AI systems [4]
- Medium-term Goals: Restructuring education systems and workforce training programs [3]
- Long-term Vision: Building inclusive AI governance frameworks and development ecosystems [3]
Looking Ahead: Power, Participation, and Progress
As artificial intelligence reshapes the global economy, the question isn’t whether women will be affected—it’s whether they’ll have agency in determining how. The AI gender gap is ultimately about power: who gets to define the problems AI solves, who benefits from its capabilities, and who bears the costs of its disruptions.
The future of work is being written now, and women deserve a voice in that conversation. Not as an afterthought or a diversity checkbox, but as equal partners in building the AI-powered world we’re all going to inhabit.
The data is clear: women face 9.6% high-risk exposure compared to 3.5% for men, represent only 30% of AI workers despite 44% of AI systems showing gender bias, and have just 20% internet access in developing countries [1, 3]. But these statistics also represent opportunities for intervention, innovation, and inclusive growth.
The stakes couldn’t be higher. Get this wrong, and AI becomes another tool for perpetuating inequality. Get it right, and it could be the catalyst for creating a more equitable future of work. The choice is ours—but time is running out.
References
[1] ILO & NASK. (2025). Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO Working Paper.
[2] ILO. (2024). One in Four Jobs at Risk of Being Transformed by GenAI, New ILO–NASK Global Index Shows. International Labour Organization.
[3] UN Women. (2024). Artificial Intelligence and Gender Equality. UN Women Explainer.
[4] MDPI. (2024). AI and Gender Bias in Healthcare. Journal of AI Ethics 6(1).
[5] Interface. (2024). AI Gender Gap: Employment and Innovation Challenges. Interface Publications.