“AI is coming for your job”. Experts have told us about AI’s impact on jobs, but now we know that it will not affect everyone equally. AI will hit women first and hardest. India, with its unique mix of a booming AI economy and a stubbornly low female workforce participation rate, is not ready for what that means. The AI boom doesn’t necessarily mean millions of women will lose their jobs overnight. The evidence so far suggests that AI is more likely to change jobs than eliminate them entirely. But if governments, employers and workers fail to prepare, women could end up bearing the biggest burden of that transition. And that is exactly where India faces a challenge.

AI impact on jobs: The research is in

The International Labour Organization and Poland’s National Research Institute found that women-dominated occupations are almost twice as likely to be affected by generative AI as male-dominated ones. Female-dominated occupations face significantly greater exposure to GenAI, with 29 per cent at risk compared with 16 per cent of male-dominated occupations. The same research found that in high-income countries, women are nearly three times more likely than men to lose their jobs due to generative AI, and globally, 4.7 per cent of women’s jobs fall into the highest-risk category, compared with 2.4 per cent for men. The reason isn’t abstract or difficult to understand. Women disproportionately occupy routine, repetitive, process-driven roles such as data entry, clerical support, customer service, and back-office processing that large language models can replicate exceptionally well.

It’s not only about losing jobs

It’s also about who gets to build the technology in the first place. Research from Revelio Labs and Interface, which analysed data on nearly 1.6 million AI professionals worldwide, found that women account for just 22 per cent of global AI talent and occupy fewer than 14 per cent of senior executive positions. Fewer women building the systems means fewer people in the room asking who those systems might leave behind, which is its own quiet, compounding risk.

Even when women do use AI tools, they seem to get less credit for it. A Harvard Business School study found that female engineers who used AI to generate code were rated nine per cent less competent than their male counterparts, even when the outputs were the same. A separate meta-analysis covering 1,43,008 individuals across 25 countries found women had 22 per cent lower odds of using generative AI than men, often because, as Harvard’s Working Knowledge reports, women worry about the ethics of using these tools, or fear being judged harshly at work for relying on them. It’s a lose-lose bind: don’t use AI, and risk falling behind on a skill that’s fast becoming essential; use it, and risk being seen as less capable regardless.

The World Economic Forum’s Global Gender Gap Report 2025 sums up the scale of the wider problem rather starkly: it will take another 123 years to reach global gender parity, even as women are missing out on jobs in the burgeoning AI sector and are more likely to lose their jobs to generative AI.

What is AI doing to Indian women?

Here’s where it gets personal for us. India doesn’t have the luxury of treating this as someone else’s problem, because two Indian realities collide directly with this global trend.

First, India’s female labour force participation rate is already among the lowest for a major economy. World Bank data puts India’s female labour force participation at 32.8 per cent in 2024, against a world average of 51.13 per cent across 176 countries.

Second, India’s IT-BPM sector, historically one of the biggest formal employers of urban Indian women, is exactly the kind of sector AI is disrupting fastest. Nasscom data from the sector’s Gender Diversity report found that IT-BPM employed 1.3 million women, or 34 per cent of its workforce, with women making up 51 per cent of entry-level hiring, making it India’s second-largest private-sector employer. This was the sector that, for an entire generation, gave middle-class Indian women their first taste of a monthly salary.

That’s precisely the layer AI is eating into first. Women in this sector are mostly concentrated in entry-level jobs, which are the most at risk from AI automation. This loss of opportunities could mean women are pushed toward early marriage or traditional gendered roles instead. Families that once stretched their budgets to fund a daughter’s engineering or BCA degree may simply stop doing so if the jobs look uncertain.

Skilled, but not in the right way

Here’s the harder layer to this. India actually produces plenty of qualified women; the problem is where they end up. India’s Economic Survey 2025-26 itself flags that women aged 25 and above with advanced degrees make up just 2.9 per cent of the employed female workforce across rural and urban India.

Mitali Nikore, economist and founder of Nikore Associates, put her finger on the structural issue in a conversation with Indiastat. She said that Indian women often work in low-productivity, low-wage roles such as agricultural labour, handicrafts, and nursing. This occupational segregation makes them vulnerable to mechanisation. AI doesn’t discriminate by degree; it discriminates by task. And Indian women, however qualified, are disproportionately doing the tasks it automates first.

There’s also a quieter, less-discussed piece of this puzzle: unpaid care work. Data shows the average Indian woman spends close to 289 minutes a day on unpaid domestic work, more than three times the 88 minutes logged by men. Even if a reskilling programme exists on paper, a woman juggling a full-time job, a stressful commute, and the bulk of housework rarely has the spare hours to sit through an evening upskilling course. Access to opportunity isn’t just about whether a course exists; it’s about whether life leaves room to take it.

So, what would “being ready” actually look like?

Nobody credible is arguing that India should turn its back on the AI economy. The argument is narrower and more practical. Readiness means noticing, in advance, who bears the cost of transition, rather than discovering it after layoffs.

That means employers actually auditing which roles and whose roles are first in line for automation. It means designing reskilling programmes around the real-time constraints of women’s lives, rather than their theoretical availability. It means enforcing labour protections instead of exempting them in the name of “ease of doing business.” And it means bringing more women into the rooms where people design and deploy AI systems. So they can prevent the technology from inheriting and automating the same old blind spots on a much larger scale. As ILO senior economist Janine Berg noted in the organisation’s own release, AI’s effect on women’s jobs isn’t set in stone. The right policies and gender-responsive design can stop it from simply reinforcing the discrimination that exists.

But will anyone notice who gets disrupted first and act before it’s too late?

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