Conversations on women and AI often begin with representation and, in many cases, end there. As UNESCO notes in “Cracking the Code: Girls’ and Women’s Education in STEM,” only 28 percent of the world’s researchers are women. This gap is shaped by “discrimination, biases, social norms and expectations”, pointing to structural barriers beyond participation.
While increasing participation is important, a more fundamental question is whether women have equal access to the AI ecosystem. Digital access including reliable internet, computing devices, cloud infrastructure, and exposure to digital tools, are crucial to AI education and building careers.
Without these, “AI readiness” remains aspirational rather than real.
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In many cases, inequality begins at home. In households with limited resources, access to devices such as laptops is often prioritised for male children, while daughters use them only when available. This has been observed in India and other developing countries.
The Mobile Gender Gap Report 2025 by GSMA highlights that women in South Asian countries, including India, are significantly less likely than men to own and use mobile internet. According to the report, of the 885 million women who remain unconnected across low and middle income countries, around 60 percent live in South Asia and Sub-Saharan Africa.
The report also points to barriers such as social norms and discrimination that prevent women from gaining equal access to digital technologies.
The problem becomes even more pronounced beyond urban India. While women in cities and semi-urban regions are increasingly engaging with AI education and careers, many in rural areas remain excluded. Data from the International Telecommunication Union (ITU) highlights persistent disparities in internet access and usage across regions, with women and rural populations facing significant barriers to digital inclusion.
Besides women, lack of access affects children too. For instance, during the pandemic, UNICEF reported that a large number of children couldn’t access remote learning due to limited availability of devices and internet connectivity in low-income households.
These early disparities, though subtle, shape confidence, exposure, and long-term career pathways.
But things seem to be improving. There are encouraging examples such as women in Jharkhand participating in data annotation and digital work for AI systems.
Such opportunities, though, are not yet widespread. If AI is to be truly inclusive, access must extend beyond metropolitan centres and into underserved geographies.
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Skills matter, but foundations matter more
Once access is addressed, the next step is capability building. Educational institutions in the country are rapidly introducing areas such as Generative AI, large language models, prompt engineering, AI DevOps, and multimodal systems.
However, technical exposure alone is not enough. The ability to use tools must be complemented by the ability to question them. AI systems can generate powerful outputs, but they can also produce inaccuracies, biases, or hallucinations. Navigating this requires strong foundational thinking.
For example, lawyers in the United States were sanctioned after submitting a legal brief containing fictitious case citations generated by an AI tool . In India too, similar cases have attracted the ire of judges of the Supreme Court.
Research and industry analysis highlight that such “hallucinations” remain a persistent risk in professional settings, requiring users to verify outputs rather than rely on them blindly.
Thus, future readiness relies on critical and analytical thinking, problem-solving, and adaptability, including the ability to question outputs, validate reliability, and apply AI to real-world challenges.
Many professionals now work with technologies that did not exist during their training. The World Economic Forum has also noted that nearly a quarter of all jobs are expected to change over the next five years. For example, engineers trained before cloud computing or generative AI are transitioning into new roles as technology reshapes job requirements. Their adaptability stems from strong foundations in reasoning, curiosity, and learning agility rather than prior technical knowledge.
This highlights an important distinction in education: schools must prioritise thinking and learning skills, while universities can focus on technical depth and specialisation. Without this balance, we risk producing graduates who can operate tools but struggle to think independently.
Inclusion must extend to leadership
Another important dimension is how we define inclusion.
Data suggests that women’s participation in AI education and early-career roles such as data analytics, product management and entry-level AI engineering is gradually increasing.
In India, women now account for 31 percent of new AI roles — up from 26 percent the previous year — and constitute around 35 percent of entry-level positions and 36 percent of the overall tech workforce. However, this progress is not reflected in leadership: women occupy only 23 percent of leadership roles and 17 percent of C-suite positions, highlighting a persistent “leaky pipeline” from entry-level to senior roles.
Even more critical is the question of whether women are able to shape how systems are designed, deployed, and governed. If their presence goes beyond representation, it directly impacts the quality, fairness, and inclusivity of AI.
Evidence from organisational diversity shows that gender-balanced teams are better at considering multiple perspectives, identifying biases, and making sound decisions. In AI, diverse leadership can help ensure tools are developed and deployed to serve a wider range of users and reduce unintended harms.
AI is often described as a transformative force. But it’s true potential will depend on who gets to participate in shaping it.
Creating a more inclusive AI ecosystem is not only a matter of fairness, but also essential for building systems that are representative, responsible, and relevant to the societies they serve.
Kiran Khatter is a Professor at BML Munjal University with over 18 years of experience across academia and industry. Her research interests include AI, urban sensing, fuzzy theory, and decision-making systems. She actively contributes to global academic and policy discussions as an invited speaker, panelist, and session chair at international conferences and forums on artificial intelligence, education, and emerging technologies. She has also worked on developing entrepreneurial ecosystems in collaboration with international institutions. This post appeared first on 360.