India’s artificial intelligence economy is taking shape alongside a gender gap that remains among the country’s deepest structural inequalities. The evidence cited in the World Economic Forum’s Global Gender Gap Index 2026 and the warnings from gender and development experts point to a central risk: women may contribute labour to AI systems while remaining underrepresented in the roles, decisions and opportunities that determine how those systems affect society.
That risk is not limited to the technology sector. AI is entering recruitment, healthcare, education, banking and public-service delivery, areas in which automated decisions can influence access to jobs, benefits, credit, treatment and essential services. If the people designing and governing these systems do not reflect the communities affected by them, existing exclusions can be reproduced through digital infrastructure rather than removed by it.
## India’s gender gap creates the starting disadvantage
India ranked 131st in the World Economic Forum’s 2026 Global Gender Gap Index. Except for Pakistan, it ranked below Bangladesh, Nepal, Bhutan and Sri Lanka among countries in its neighbourhood, according to the report cited in the source material. The WEF estimated that complete global gender parity remained 120 years away, while Southern Asia could take 149 years at its current pace.
The report did not provide an individual estimate for India, but the country’s parity score of 64.5 per cent was equal to the South Asian score and below the global average of 69.2 per cent. The figures indicate that India is entering the AI transition with a substantial pre-existing gap in economic participation, opportunity and decision-making.
Poonam Muttreja, Executive Director of the Population Foundation of India, said India had made important commitments to women-led development, but a significant distance remained between policy ambition and lived reality. India ranked 143rd out of 145 economies for economic participation and opportunity, she said.
The contrast between educational progress and economic power is particularly important. Muttreja said progress in girls’ education had not yet translated into comparable gains in economic participation, leadership roles or decision-making authority. That means a larger number of women may be entering the education pipeline without gaining equivalent access to higher-paying jobs, technology leadership or institutional influence.
## AI gender inequality is also a jobs question
The WEF evidence cited in the report shows how AI could reinforce the occupational patterns that already disadvantage women. Women account for nearly half of data annotators but only around one-fifth of AI developers and deployment engineers. The distinction is consequential: women are participating in the production of AI-related labour, but are less represented in the technical and managerial roles that shape the systems being built.
Silja Baller, the WEF’s Director of Economic Inclusion, said women were more highly represented in roles exposed to disruption and less represented in jobs where AI was expected to augment workers’ capabilities. She warned that the growing AI economy appeared likely to reproduce existing patterns of sectoral and occupational segregation, with women underrepresented in core technical roles and concentrated in lower-paid supporting positions.
This creates a two-level inequality. At the workplace level, women may face greater exposure to disruption while having fewer opportunities to move into the roles created or strengthened by AI. At the institutional level, their limited presence in development, deployment and leadership can reduce the diversity of experience considered when systems are designed.
Dr Seema Bhaskaran, a gender expert at Transform Rural India, said women needed to be involved in the design and development of AI rather than being limited to the roles of beneficiaries or victims of its misuse. Her intervention shifts the question from whether women will receive the benefits of AI to whether they will have power over the systems that distribute those benefits.
## The Indian technology pipeline remains uneven
Bhaskaran said women account for 40-43 per cent of STEM graduates in India, but their underrepresentation in AI follows a similar pattern. The gap between participation in education and presence in the AI workforce suggests that the problem is not simply a shortage of qualified women.
It also points to the institutional pathways between education and employment. Training, recruitment, workplace conditions, career progression and leadership access all influence whether women move from academic qualifications into technical and decision-making positions. The source material does not establish which of these factors contributes most in different parts of India, but it does show that educational participation alone has not delivered parity in economic opportunity.
The consequences may be especially uneven across regions and communities. Muttreja noted that India is a union of states at very different stages of human development and that progress towards gender parity would inevitably vary. Some states, she said, were likely to achieve parity sooner than others.
That variation matters for an AI economy because digital access and opportunity are not distributed evenly. Muttreja called for affordable training, digital access and career opportunities beyond metropolitan centres. Without that wider reach, the geography of AI employment could mirror existing disparities between metropolitan and non-metropolitan India, even as AI systems themselves are deployed across public services.
## AI in public services raises an accountability problem
The strongest public-interest concern is not only who gets hired into the AI economy, but how automated systems affect people who may never work in technology. AI is already being considered or used in fields including recruitment, healthcare, education, banking and public-service delivery, according to the source material.
Muttreja warned that biased algorithms could reinforce existing discrimination, particularly against women from poorer, rural and marginalised communities. In these settings, a system can influence access to an opportunity or service without the affected person having visibility into the data, criteria or process behind the decision.
The issue is therefore connected to governance. A digital system may be presented as efficient or impartial, but its outcomes depend on the data used to develop it, the assumptions embedded in its design and the mechanisms available to challenge an adverse decision. The experts quoted in the report called for AI systems used in public services to be tested for gender bias and subjected to accountability requirements.
This is a significant shift in how public-service technology must be understood. Digital delivery is not only a question of faster processing or wider reach. It is also a question of whether automated systems recognise the different barriers faced by women across income groups, locations and social circumstances. The source does not provide examples of specific Indian algorithms or government systems that have already produced such outcomes, so the scale of current harm remains unestablished. It does, however, identify the sectors in which the risk could become consequential.
## The policy challenge extends beyond skills
The expert recommendations cited in the report combine skills, employment and institutional safeguards. Muttreja called for more girls and women to receive advanced digital and AI skills, pathways into higher-paying technology jobs and leadership positions, and testing of public-service AI systems for gender bias. She also identified women’s economic independence, safety, childcare, reproductive autonomy and equal opportunities as areas requiring greater investment.
That broader framing is important because a narrow focus on coding or technical education would not address the full problem. Women’s participation in the AI economy depends not only on whether they can acquire skills, but also on whether they can remain in the workforce, advance into leadership and access opportunities outside major metropolitan centres.
The division between policy ambition and implementation is visible in the evidence presented. India has made commitments to women-led development, yet its ranking for economic participation and opportunity remains low. It has a substantial share of women among STEM graduates, yet women account for only around one-fifth of the AI developer and deployment-engineering roles cited by the WEF report. These gaps show why representation must be measured at multiple stages rather than through education figures alone.
The responsibility is also distributed across institutions. Employers shape recruitment and career progression. Technology developers determine how systems are designed and tested. Public authorities decide where AI is used in service delivery and what accountability requirements apply. Training institutions influence who can enter the sector. The supplied evidence does not set out a single national framework assigning these responsibilities, but it makes clear that no one intervention can close the gap.
## The larger urban question is who controls digital infrastructure
For cities, the AI gender gap is becoming a built-environment and governance issue because urban systems increasingly depend on digital decisions. Recruitment platforms, banking systems, healthcare access, education services and public-service delivery all mediate everyday urban life. The question is not only whether cities adopt AI, but whose needs are represented when these systems are designed and administered.
Metropolitan centres may attract a disproportionate share of advanced jobs and training, while women in smaller cities, rural areas and marginalised communities encounter AI mainly as users of systems they did not help create. That divide could turn digital infrastructure into another layer of unequal access unless training, employment and accountability extend beyond the largest urban hubs.
The evidence confirms that women are already participating in the AI economy, but not equally across its value chain. It also confirms that India’s wider gender gap gives the technology transition a difficult starting point. What remains uncertain is how quickly individual states will progress, which sectors will see the greatest disruption and whether public-service AI systems will adopt effective gender-bias testing and accountability.
The next phase of India’s AI economy will therefore be measured not only by adoption or technical capability. It will also be judged by whether women move from being contributors in lower-paid supporting roles to decision-makers in the systems that increasingly shape work, services and opportunity.
