Bridging India’s AI gender gap requires inclusive adoption, skill development, and safeguards to turn technology into an equitable opportunity for women
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As AI becomes increasingly embedded across workplaces and workflows, its implications for equity are only now being fully realised. This shift is unfolding amid a global gender gap in AI adoption, with women adopting AI tools at around 25 percent lower rates than men. In India, it interacts with an already entrenched 24 percent digital gender divide. Ahead of the AI Impact Summit, the policy challenge is not only to enable AI-led productivity gains but also to translate these into women’s employability and meaningful economic inclusion.
An inclusive AI agenda for women should therefore be assessed through labour-market outcomes across the employment lifecycle – from hiring and job matching, to working conditions, career progression and access to reskilling.
An inclusive AI agenda for women should therefore be assessed through labour-market outcomes across the employment lifecycle – from hiring and job matching, to working conditions, career progression and access to reskilling. In India, female labour force participation is rising—reaching 41.7 percent in 2023 after falling to 23.3 percent in 2017-18. However, women remain concentrated in informal and unpaid work. In this context, AI integration can represent a distributional shift: It can raise productivity and open pathways to higher-value work; however, it can also deepen constraints if skills, mobility, and job quality do not improve in parallel.
The benefits of any technological shift are rarely shared evenly, and the same holds for AI. As AI spreads across workplaces, it can both substitute routine tasks and augment workers in higher-value roles. Productivity gains are therefore expected to be larger in the latter segment. In a labour market already shaped by gender differentials, this transition is further skewed, with male professionals reporting approximately 46 percent higher rates of AI adoption than female professionals.
While the Periodic Labour Force Survey (PLFS) does not measure AI exposure directly, it provides a useful baseline to highlight where wage gradients are steep and where productivity-linked gains may concentrate. These distributional effects can be gauged through two indicators. First, inequality across skill tiers—captured by the skill premium, or the incremental earnings on moving up the skill ladder. Second, the gender wage gap, or the male-to-female earnings ratio, across the same segment. To capture this, skill baskets are constructed using NCO 2015 divisions—high-skill (Divisions 1-2), mid-skill (Divisions 3-4), and low-skill (Divisions 5-9).
In a labour market already shaped by gender differentials, this transition is further skewed, with male professionals reporting approximately 46 percent higher rates of AI adoption than female professionals.
Figure 1 highlights a sizeable occupation-based skill premium for both men and women. This suggests that AI-linked productivity gains may accrue disproportionately to workers positioned in higher-skill segments. Notably, the steeper gradient for women indicates a sharper divide between low- and high-skill earnings. Women in lower-skill roles may face a larger penalty, while women who reach high-skill work may see higher returns. In India, this matters because AI adoption is already accelerating in regular salaried service-sector work, including IT–BPM/ITeS and back-office functions (high- and mid-skill sectors) where a sizeable share of women are employed.
Figure 1: Skill premium (occupation-based) by gender
Source: Author’s Own, compiled using the Periodic Labour Force Survey PLFS (2023-24)
Figure 2 shows the gender wage gaps within skill segments. The gap widens as skill levels decline, reflecting lower earnings for women in low-skill occupations. Figure 3 captures the same disparity across casual labour and regular salaried employment, with larger gaps accruing to the former. The gig and platform work increasingly overlaps with casual and informal employment, and is also being shaped by algorithmic allocation and digital intermediation.
Beyond labour-market structure, enabling conditions also matter. Women’s ability to adapt depends on unpaid care burdens. India’s Time Use Survey 2024 highlights large gender differences in time spent on paid and unpaid work, reinforcing that inclusion requires enabling conditions beyond the workplace. In essence, both a steep earnings ladder across skills and persistent gender wage gaps indicate that AI should be treated as a distributional transition.
Figure 2: Gender Earnings gap by occupational skill bucket
Source: Author’s Own, compiled using PLFS (2023-24)
Figure 3: Gender earnings gap by worker type
Source: Author’s Own, compiled using PLFS (2023-24)
As the India AI Mission prioritises AI democratisation and inclusion, there is a need to move beyond intent to outcome-based measures. Three policy levers are particularly relevant. First, an AI Readiness Index can track women’s access to AI, adoption levels and on-the-job use across sectors. Second, inclusion will depend on whether women can effectively build AI-relevant skills. Since time poverty remains a binding constraint, skilling systems should be designed around women’s realities. This includes flexible learning formats, modular credentials, and care-enabling infrastructure, rather than assuming equal capacity to adapt.
State-level interventions show what delivery-grade inclusion can look like. Telangana’s WE Hub is building AI-linked capacity for women in technology and business. Partnerships with firms such as Google and Microsoft enable upskilling, training and mentorship. Similarly, the Additional Skill Acquisition Programme (ASAP) Kerala and ICT Academy’s elevateHER approach to skilling as workforce transition, pairing structured cohorts with employability linkages. National policy can replicate these levers by funding women-first pathways, tying skilling to enterprise outcomes, and embedding mentorship at scale.
As the India AI Mission prioritises AI democratisation and inclusion, there is a need to move beyond intent to outcome-based measures.
Third, alongside skill-based enablers, guardrails are essential to ensure that AI deployment does not deepen the existing gendered barriers to access and rights. The European Union (EU) AI Act adopts a risk-based framework, classifying AI systems used in education and employment as high-risk given their potential to shape opportunities and life outcomes. Embedding a similar risk-tier logic within the India AI Mission’s ‘Safe & Trusted AI' pillar can strengthen gender inclusion through enforceable compliance requirements.
Similarly, the Digital Personal Data Protection Act (DPDP) 2023 provides India’s baseline framework for personal data governance. It is pertinent to AI-enabled hiring and education systems, which increasingly rely on profiling and automated screening. However, data protection alone does not prevent gendered exclusion in practice, especially when models reproduce historical bias or rely on opaque ranking criteria. The recent Eightfold AI lawsuit underscores these risks. The DPDP safeguards should therefore be complemented with mandatory bias audits, gender-impact assessments, and accessible grievance redressal mechanisms in high-risk contexts such as employment and education.
Historical evidence suggests productivity gains are strongest when technology complements workers rather than simply replacing them. For India, AI can be a similar turning point—harnessing the other half of the demographic dividend, which remains constrained by unpaid care burdens and concentration in mid- to low-skill activities. If designed well, AI can help bridge persistent gaps in job quality, mobility, and wages for women. Leveraging AI for this transition is therefore not only a productivity imperative, but also a strategic pathway towards Viksit Bharat 2047.
Kumkum Mohata is a Research Assistant with the Centre for New Economic Diplomacy at the Observer Research Foundation.
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Kumkum Mohata is a Research Assistant with ORF’s Centre for New Economic Diplomacy. Her research interests lie in development economics, international trade, and macroeconomics, with ...
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