When AI Enters the Workplace, Who Faces Greater Risks? A Gendered Analysis

Gender inequality remains a persistent structural feature of the labour market, shaping women’s lifetime earnings and economic security. As artificial intelligence (AI) transforms organisational practices, there is growing concern that existing disparities may be unintentionally amplified through task automation, unequal access to upskilling opportunities, and differential returns obtained from technological change. In this paper, we examine how exposure to AI-driven innovation varies across male- and female-dominated occupations, with particular attention to differences across the skill and wage distribution. Using a novel dataset that links occupational characteristics to measures of AI exposure, we analyse how recent advances in Large Language Models (LLMs) and broader AI technologies are distributed across the labour market. Our findings show that, while AI exposure is generally concentrated in higher-skilled and higher-paid occupations for male-dominated occupations, female-dominated occupations display relatively uniform levels of exposure across both high-skilled, high-paid, and low-skilled, low-paid occupations.

Introduction. Inequalities in labour markets, such as occupational segregation (i.e., the unequal distribution of men and women across different occupations and industries)(European Institute for Gender Equality 2025; European Commission 2022), the under-representation of women in higher-paying and senior roles (Cook 2024), and the effects associated with motherhood and caring responsibilities (Office for National Statistics 2025), remain among the most persistent and structural forms of gender discrimination, shaping women’s lifetime earnings, career progression, and long-term economic security. Despite sustained policy attention, these disparities continue to characterise labour markets across the world. As AI increasingly transforms organisational practices, there is a growing risk that existing inequalities may be unintentionally reinforced.

Discussion / Conclusion. This paper investigates AI exposure across male- and female-dominated occupations, with particular attention to differences across the wage and skill distribution. Our findings suggest that while AI exposure in male-dominated occupations is more strongly concentrated in high-skilled and high-paid occupations, exposure within female-dominated occupations is distributed more evenly across both highskilled, high-paid and low-skilled, low-paid occupations. This distinction is important because lower-skilled and lower-paid workers are typically more vulnerable to labourmarket disruption due to factors such as weaker bargaining power, lower access to training and reskilling opportunities, and greater employment insecurity. Combined with existing evidence from the literature, our results suggest that women in these more vulnerable positions are disproportionately exposed to forms of AI associated with task automation and job restructuring, rather than productivity-enhancing augmentation. As a result, AI adoption may place women in lower-paid and lower-skilled occupations at greater risk of displacement, constrained wages and career progression. An additional contribution of this work is the distinction between different forms of AI.