The introduction of generative artificial intelligence into the global workplace is poised to widen gender inequality in the labour market, with jobs dominated by women nearly twice as likely to face automation as those held primarily by men. This finding from the International Labour Organisation’s recent analysis places Nigeria and other developing economies on alert, as the technology that promises economic transformation may instead entrench the occupational segregation that already constrains women’s economic participation.
The ILO brief “GenAI, Occupational Segregation and Gender Equality in the World of Work” reveals a stark disparity. About 29 per cent of female-dominated occupations face exposure to generative AI, compared with 16 per cent of male-dominated jobs. The gap widens dramatically in roles facing the highest automation risk, where 16 per cent of women’s jobs fall into top exposure categories, against just three per cent for men. This is not accidental. It is the direct result of decades of occupational segregation that have concentrated women in clerical, administrative and business support roles, where tasks are routine and therefore more vulnerable to AI-driven automation.
Women globally remain heavily represented as secretaries, receptionists, payroll clerks and accounting assistants. These roles, typically characterised by repetitive processing, data entry and routine client management, are precisely the domains where generative AI systems like ChatGPT perform most effectively. Men, by contrast, are more concentrated in construction, manufacturing and manual trades, sectors that remain less susceptible to automation because they require physical presence, spatial reasoning and real-world problem solving that current AI cannot fully replicate.
The gender exposure to generative AI reflects a longer history of labour market segregation in Nigeria and across the African continent. Post-independence development policies and structural adjustment programmes created pathways that pushed women into lower-wage service and administrative sectors while reserving higher-skilled technical and leadership roles for men. Over decades, this created the current occupational geography, where women’s economic security rests disproportionately on roles that are now most threatened by technological displacement.
At the country level, the ILO analysis reveals an even more troubling pattern. Women were found to be more exposed to generative AI than men in 88 per cent of countries analysed. In several high-income economies, more than 40 per cent of women’s employment faces exposure to the technology, particularly where digital adoption has advanced rapidly. Developing economies with lower digital infrastructure face somewhat less immediate pressure, but as technology diffusion accelerates, the same exposure patterns will likely emerge.
The disparity cannot be dismissed as inevitable or technology-driven alone. Anam Butt, co-author of the ILO research, noted in the brief that generative AI “is not entering a neutral labour market”. She stressed that “social norms, unequal care responsibilities, and labour policies continue to shape who enters which occupations, leaving women concentrated in roles more exposed to automation”. This is a critical distinction. The problem is not the technology itself but the gendered labour structures that the technology will exploit.
Generative AI systems, like all machine learning models, are not inherently neutral. They replicate and often amplify the biases present in the data used to train them. As organisations implement AI-driven hiring systems, performance evaluation tools and customer service platforms, women risk facing compounded disadvantage in recruitment, pay assessment and access to opportunities. The impact intensifies for women facing intersecting forms of discrimination based on race, disability or migration status, where algorithmic bias compounds existing structural barriers.
The ILO report identifies three critical policy levers to prevent AI from reinforcing existing gender inequality. First, governments and employers must embed gender considerations into AI design and deployment from the outset, not as an afterthought. Second, access to science, technology, engineering and mathematics (STEM) education must expand significantly for women and girls, creating pathways into roles less vulnerable to automation. Third, labour market institutions, including trade unions, employment standards bodies and sectoral councils, must be strengthened to ensure that technological transitions are managed with social dialogue that protects workers rather than displacing them quietly.
Janine Berg, Senior Economist and Co-author of the ILO report, emphasised in the analysis that “the outcome is not predetermined”. She stated that “with the right policies, social dialogue, and gender-responsive design, we can prevent AI from reinforcing existing discrimination”. This framing matters. It positions technological change not as fate but as a policy choice. Countries that act now to reshape how AI is introduced into workplaces have time to shape a different outcome. Those that passively allow market forces to drive adoption will almost certainly see gender inequality deepen.
The ILO’s call to action extends to governments, employers and workers collectively. The organisation stresses that how generative AI is introduced at work will determine whether technological change supports productivity and job quality or becomes a mechanism for displacement and inequality. For Nigeria, where female labour force participation remains below regional averages and concentrated in vulnerable informal sectors, the stakes are particularly high. A technology transition managed without gender awareness could displace millions of women from the formal economy into deeper informality, reversing decades of incremental progress toward labour market inclusion.
