With AI Changing Jobs Fast, Are Women Being Left Behind In The Race For Skills?

The year 2025 saw artificial intelligence (AI) not just making headlines but disrupting many industries that once prided themselves on creativity, originality, and individuality. In 2026, AI is set to

transform how human workers are relied upon, making upskilling one of the most critical routes to sustenance, growth, and even success.

But in this technological pyramid, women could face a bigger challenge.

Emerging research flags a widening AI upskilling gap. The problem is structural, not individual. Women, who shoulder a disproportionate share of unpaid care work and face persistent gaps in education and digital access, have far less time and opportunity to build the skills the AI-driven economy now demands.

Let’s looks at why AI upskilling is creating a gender divide, what Indian women need to watch out for, how AI is being used in companies, and what can be done to bridge the gap.

Why Is The AI Upskilling Different From Earlier Skill Gaps?

“The gender-skills gap in AI is largely driven by uneven access to early and applied exposure. While interest in AI learning is growing, women remain underrepresented in hands-on, real-world AI roles. In India, AI skill penetration among men is estimated at 2.78 compared to 1.65 for women, pointing to an access and usage gap rather than a capability gap. Many women enter roles that do not actively use AI tools, which limits skill development over time. The issue is not whether women can work with AI, but whether workplaces are designed to give them sustained access to it,” said Maaz Ansari, Co-founder and CRO, Oriserve — a deep-tech company specialising in generative AI.

For many women in India, this constant upskilling clashes with reality. Data consistently shows that women spend significantly more time than men on unpaid household labour and caregiving. This “time poverty” limits their ability to enrol in courses, experiment with new tools, or pursue informal learning that often happens after work hours.

“Indian women spend roughly 10 hours less per week on self-development than men, largely due to disproportionate unpaid care work (childcare, eldercare). In an industry that moves as fast as AI, this lack of ‘learning time’ is a massive disadvantage,” says Sonica Aron, Founder and Managing Partner, Marching Sheep — an HR advisory firm.

The result is not a lack of interest in AI, but a lack of access to the conditions required to engage with it meaningfully. Unlike earlier skill gaps that could be bridged through formal education alone, AI demands sustained engagement — something unevenly distributed along gender lines.

“Men often have more informal ‘tinker time’ with new tech. Structural barriers in the workplace mean women are frequently tasked with high-volume execution, leaving little room for the experimentation required to master AI,” she adds.

How AI Is Reshaping Work And Reward

AI is no longer confined to specialist roles in technology companies. It is becoming embedded across sectors, from finance and consulting to healthcare, logistics, education, and even public administration. Tasks involving data analysis, forecasting, content generation, customer engagement, and decision support are increasingly augmented by AI tools.

This shift is creating a new divide in the labour market. Workers who can effectively use AI tools are seeing productivity gains, better visibility, and faster career progression. Those who cannot are increasingly confined to routine or support roles that offer limited growth and are more vulnerable to automation.

“AI adoption often benefits employees already working in tech-forward roles with access to data, tools, and decision-making authority. Women are underrepresented in these roles, particularly at senior levels. Industry data shows that while men already outnumber women in AI and GenAI roles by over 40%, this gap widens to more than 60% at leadership levels. This limits who gets hands-on exposure to AI in real business settings, especially where strategic decisions are made,” points out Ansari.

Crucially, AI does not eliminate jobs evenly. It changes what is valued within jobs. Fluency with AI tools is fast becoming a baseline expectation rather than a specialised advantage. In such an environment, even small differences in access to training can snowball into significant disparities over time.

Why Women’s Participation In AI Lags Attitudes And Access

Studies across countries show that women tend to adopt AI tools more cautiously than men. This is often interpreted as reluctance or scepticism, but the reasons are more complex. When job security is fragile, experimentation with new technologies carries a higher perceived risk. Mistakes can have reputational or professional costs that women, already navigating bias, may feel more acutely.

There is also a confidence gap shaped by years of underrepresentation in technical fields. AI tools are frequently designed, marketed, and discussed in male-dominated spaces, reinforcing the idea that they are “not for everyone.” Without supportive environments, many women hesitate to integrate AI into their work, even when it could benefit them.

These behavioural patterns do not arise in isolation. They reflect structural inequalities in education, mentorship, and workplace culture — all of which are pronounced in the Indian context.

Why Indian Women Are Especially Vulnerable

India’s gender digital divide remains stark. Women are less likely than men to own smartphones, access the internet regularly, or possess advanced digital skills. Rural women and those from lower-income households face compounded barriers, including poor connectivity, limited training opportunities, and social norms that deprioritise their education.

Women are also underrepresented in STEM education and high-skill technology roles. Only Women account for just 28% of the global STEM workforce. Women represent only 18% of engineering and 20% of computer science professionals. In the US, women held about 23-24% of tech positions at the end of 2023, per Women Tech Network. This limits their exposure to AI not just as users, but as creators and decision-makers. As AI becomes embedded across industries, this lack of representation risks reproducing itself, with tools designed and deployed without diverse perspectives.

Labour market data adds another layer of vulnerability. Women’s labour force participation in India, while showing recent improvement to around 32-41% (PLFS 2023-24/2026 reports), historically remains low compared to the global average and other emerging economies. And many are employed in sectors that are more susceptible to automation. With the unpaid care responsibilities, this creates a structural bottleneck at precisely the moment when continuous learning has become essential.

“Women in India face additional constraints as AI reshapes work. A large share of prime-age women remains outside the formal workforce due to caregiving responsibilities, which reduces the time and flexibility available for continuous reskilling. Women also spend significantly more time on unpaid work, making it harder to keep pace with rapidly changing skill requirements. When combined with lower representation in digital and AI-linked roles, this increases the risk of women being excluded as AI adoption accelerates across industries,” said Ansari.

Why Women Need To Be Cautious Of AI Transition

If current trends continue, the AI transition could entrench existing inequalities rather than disrupt them. Women may find themselves crowded into lower-paying roles with limited mobility, while high-growth, AI-intensive jobs remain male-dominated.

This has implications beyond individual careers. Wage gaps could widen as AI-driven productivity gains accrue disproportionately to men. India’s talent pool would effectively shrink, undermining its competitiveness in global digital markets. At a societal level, the promise of AI as a driver of inclusive growth would ring hollow.

For younger women entering the workforce, the consequences could be long-lasting. Early career disadvantages tend to compound over time, shaping lifetime earnings, leadership representation, and economic security.

“AI is shifting work away from routine tasks towards roles that require judgment, problem-solving, and collaboration with AI systems. Employees are no longer expected to build AI models, but they are expected to understand how to use AI tools effectively. Skills like adaptability, decision-making, and AI literacy are becoming critical across functions. While women now account for roughly one-third of technology roles in India, representation drops sharply in AI-led leadership positions, making workplace design and access to learning essential as AI becomes part of everyday decision-making,” cautions Ansari.

What Companies Can Do To Bridge The Divide

Addressing the AI gender gap requires moving beyond general calls for “more women in tech.” The barriers are structural, and so must be the solutions.

Skill development programmes need to recognise time poverty. Flexible, modular learning formats that allow women to acquire AI-related skills in small, manageable units are essential. Online and hybrid models can help, but only if paired with reliable access to devices, connectivity, and digital support.

“Companies need to embed AI learning into everyday roles instead of treating it as a separate training initiative. This means giving employees access to AI tools, linking learning to real workflows, and redesigning roles so more people can work alongside AI systems. Inclusive hiring and mentorship matter, but practical exposure matters more. Companies that get this right will not only reduce inequality, they will also build stronger, more productive teams that can compete in an AI-first economy,” explains Ansari.

Aron highlights HR should “proactively audit” roles, which are being automated. “If 80% of automated roles are female-held, the company should have a mandatory ‘lateral move’ and reskilling programme for those employees.”

Ensure women are not just “users” but hold design and oversight committees for AI deployment to prevent coded-in gender bias, she adds.

Public investment matters too. Expanding digital infrastructure and adult education programmes, particularly in rural and underserved areas, is foundational. Without this, AI inclusion will remain concentrated among already-privileged groups.

AI strategies need explicit gender inclusion goals, backed by funding and accountability. Skill programmes should track participation and outcomes by gender, not as an afterthought but as a core metric of success. Public-private partnerships can help scale training while ensuring it reaches those who need it most.

With thoughtful policy design, investment in inclusive skill-building, and changes in workplace culture, India can ensure that the AI era expands opportunity rather than concentrates it.