The job market is expanding for persons equipped with technical and scientific expertise and skills, particularly in areas such as artificial intelligence (AI), machine learning and other emerging technologies. The Ministry of Electronics and Information Technology claims, in a recent report, that there will be a market for approximately one million AI professionals in India by next year.
This increase in demand for skilled employment is, however, being outpaced by the increase in automation by AI in entry-level positions and continuously evolving job profiles, leading to an employment crisis. Anthropic’s CEO Dario Amodei recently warned that AI may eliminate 50 per cent of entry-level white collar jobs in the next five years. The adverse effects are likely to be unevenly distributed across demographic groups, affecting those already facing systemic barriers to workforce participation, such as women.
Automation and structured unemployment
Back in 2013, an Oxford study had indicated that nearly 47 per cent of US jobs were highly susceptible to being eliminated due to automation. As one of the earliest such studies on the future of employment, this report highlighted that there is an “inverse relationship” between the advancement of technologies and the creation of employment. Since the breakthrough of generative AI in 2023, these projections are slowly becoming reality. The World Economic Forum, in its Report on the Future of AI in 2025, predicts that about 22 per cent of jobs will be disrupted by as early as 2030, and about 40 per cent of the existing jobs will require an entirely new set of skills.
From this data, it is evident that while AI and innovation will enable the creation of new employment opportunities, they may also leave some people unequipped to take up the new jobs or continue in the existing jobs, making them prone to being unemployed cyclically. The consequence is the creation of a scenario of “structurally lower employment”. The situation is aggravated for women in the workforce, who have traditionally faced a high degree of exclusion from participation in the economic sphere.
Gendered exclusion in employment
International Labour Organisation projections indicate that there has been a consistent 30 per cent gap in labour force participation between men and women since the 1990s. Further, a high proportion of women continue to be employed at administrative or clerical levels across sectors, whereas the higher managerial level jobs are dominated by men. Further, a UNESCO study has revealed that women make up only 35 per cent of STEM graduates, with only 26 per cent working as AI or data science professionals. These numbers indicate a systematic exclusion of women from the workforce and technical education.
Historically, women were excluded from workforce participation due to the gendered division of labour. Under such division, the role of women is primarily relegated to the “private sphere”, involving tasks concentrated around domestic work, which kept them out of the economic sphere. Over time, while women’s participation in the public sphere has increased, the impact of initial structural disadvantages, coupled with persistent practices of division of work, has continued to hinder their professional advancement. As a result, a significant proportion of women remain concentrated in administrative and support roles, with limited representation in leadership or managerial positions. Women are also more prone to being forced outside of the workforce because of familial and societal pressures to prioritise child care and household responsibilities over gainful employment. A 2024 study of the Ministry of Statistics and Programme Implementation reveals that women in India spent 201 more minutes a day in unpaid domestic services for household members than their male counterparts.
In addition to the problem of gendered division of labour, women face exclusions from the job market due to deeply entrenched biases. These biases are often perpetuated through politics of essentialisation, which suggest that women are inherently weaker in areas such as science and mathematics. This is then used as a tool to justify their exclusion from STEM fields, to the extent that women are actively discouraged from taking up such jobs.
With the increase in automation and AI-driven job markets, this systematic exclusion of women creates three critical policy problems. First, the changing nature of jobs and job profiles increases the barrier to entry and re-entry of women in the workforce, leading to further exclusion from the workforce. The limited new jobs that are created through automation and innovation largely focus on fields such as AI, computer sciences and software engineering, where women already face a structural disadvantage.
Second, the concentration of women in low-skilled jobs and administrative positions makes them highly susceptible to facing full or partial automation and leads to a downward pressure on their wages. Third, the underrepresentation of women in science and technical fields increases the perpetuation of biases against their scientific skills.
The way forward
In order to address the threats posed by the female workforce, because of the accelerated shift to an AI-driven economy, there is a need for urgent legal and policy interventions. Targeted reskilling and education programmes are required to orient women towards the transitions to new jobs or new job profiles, given the existing disadvantages faced by them. As the government plans new curricula for scientific education, which is oriented towards promoting AI, it should ensure that the gendered stereotypes that are present in the current technological discourse are reduced and greater representation of women in science is promoted. Furthermore, policies on the development of AI and automation should be designed with an inclusive perspective. They should promote advancements not only in capital-intensive sectors but also in products and services that reduce the burden of unpaid domestic work. This approach will help support women’s participation in the paid labour market.
Additionally, to provide a safety net for women who may face temporary job losses or require time to acquire new skills, it is essential to consider measures that support income continuity and economic resilience. This may include basic income support for displaced workers and conditional cash transfers linked to undertaking upskilling programmes. Complementary to these efforts, financial instruments designed to promote long-term savings and asset accumulation for women, such as subsidised pension and savings plans, can enhance economic security and reduce dependency.
Furthermore, publicly funded childcare support, flexible work arrangements, and legal safeguards against discriminatory layoffs should be integrated into labour market policies to ensure that women are not disproportionately affected by technological disruption. These measures would not only address immediate vulnerabilities but also contribute to building a more inclusive and resilient future of work for women.
The writer is Senior Resident Fellow, Vidhi Centre for Legal Policy. Views expressed are personal
In order to address the threats posed by the female workforce, because of the accelerated shift to an AI-driven economy, there is a need for urgent legal and policy interventions. 
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