Why India’s AI startups need more women in science and stronger risk thinking

India’s AI startups are scaling at unprecedented speed, but velocity without structured risk thinking can undermine long-term trust, regulatory acceptance, and investor confidence. In this high-growth phase, the competitive edge will not come from compute power alone, but from cognitive diversity and disciplined AI governance embedded early in model development.

Key aspects of the Union Budget 2026 highlight India’s economic objectives related to the development of artificial intelligence (AI). Expanding investment in AI labs, research ecosystems and digital skilling, a coordinated push to position India among global AI leaders. 

For AI startups, these public investments reduce entry barriers and accelerate innovation cycles, but they also increase expectations around responsible deployment.

The recent India–AI Summit culminated in the New Delhi Declaration on AI Impact, endorsed by 88 countries and the EU, to promote “collaborative, trusted, resilient, and efficient” AI. 

This milestone demonstrates India’s growing influence in shaping global AI regulatory frameworks. The success of the summit, including participation by global leaders from technology, generative AI development, and AI business solutions, reinforces India’s potential to be a leader in AI. Moreover, India’s participation in Pax Silica, a US-led initiative for critical minerals and AI supply chains, further highlights international collaboration. For startups building generative AI models or AI-enabled platforms, this global alignment signals that governance standards will increasingly influence market access and cross-border expansion.

A report suggests AI is projected to contribute nearly $1.7 trillion to India’s economy by 2035, with roughly 89% of new technology startups across fintech, healthcare, manufacturing, and enterprise software already integrating AI into their offerings. The India AI Mission has allocated Rs 10,372 crore to strengthen national capabilities, including the integration of over 38,000 GPUs into centralised computing infrastructure and the development of 12 indigenous foundation models.

These investments signal India’s dual role as both a developer and a facilitator in the emerging AI ecosystem. However, as foundational model capabilities expand, the responsibility for safe, unbiased, and explainable deployment increasingly shifts to startups building applications on top of this infrastructure.

Risk and regulatory scrutiny will likely intensify while firms compete to lead generative AI model development. As India moves toward a principle-based AI governance framework with bodies such as the AI Governance Group and the AI Safety Institute, global measures such as the EU AI Act underscore that responsible AI is a market expectation. Trust in AI will be a key differentiator—87% of Indian organisations are using AI, with only a quarter operating at scale.

For early-stage ventures, trust can become a decisive factor in securing enterprise clients, regulatory approvals, and long-term capital.

The summit emphasised ethical AI practices, including transparency, fair access, and user privacy. A balanced regulatory framework is needed to maximise AI’s benefits while mitigating risks and addressing concerns about bias, privacy, and societal impact.

Cognitive diversity and structured risk governance can play a key role in shaping AI 

Research shows that cognitively diverse teams challenge design choices more effectively, identify risks earlier, and are less prone to groupthink. Demographically diverse AI development teams, including gender diversity, can help mitigate the risk of unintended bias in the outputs of AI models used by Indian banks for credit scoring, fraud detection, and AML. 

Building strong AI model governance frameworks can yield practical risk-control questions that test assumptions, e.g., Who could be harmed by this decision? What assumptions does the data embed? How will model output be evaluated and explained to regulators, customers, or the board? 

Why expanding gender diversity matters in AI development in India

A representative from UN Women noted during the India-AI Summit that an imbalance in the representation of women in AI design, development, and testing may result in an AI “design gap”, where missing or underrepresented information in training data creates systemic model bias. For startups operating in sensitive sectors such as lending, insurance, health diagnostics, or employment platforms, such bias is not merely a technical flaw—it is a regulatory and reputational risk.

For Indian startups and AI labs, the strong and growing pipeline of women in STEM is a strategic advantage. Expanding women's participation in AI development strengthens model development, validation, and governance processes, resulting in trustworthy AI systems that ensure resilience and global competitiveness. 

Demand for responsible AI literacy is growing

As organisations scale AI use, the need for technical knowledge of AI models, their risks, and data and model governance processes is growing. Industry bodies, academic institutions, and professional associations have developed frameworks and educational offerings in response that support knowledge and upskilling to drive responsible AI that builds trust. 

From AI talent acquisition to ensuring responsible AI at scale

The global race to lead in AI development will be won with talent, investment, and a disciplined mindset. As a host to more than 1,800 Global Capability Centres, according to reports, including over 500 focused on AI, India is ranked in the top four countries globally in AI talent acquisition in the Stanford AI Index, and is the second-largest contributor to AI projects on GitHub. Significant public and private investment commitments announced at the summit reflect growing confidence in India’s AI ecosystem.

Yet for AI startups, the real differentiator will lie in how effectively they combine technical depth with structured risk governance and inclusive scientific talent. Organisations that recognise the importance of cognitive and gender diversity, with expanded participation by women—not as a downstream HR initiative, but as an enhancement to AI model development —will create responsible, trustworthy models that regulators accept, customers trust, and markets reward.

In the next phase of India’s AI growth story, the winners will not simply be those who build the fastest models—but those who build the most resilient and responsibly governed ones.

(Michael Sell is Senior Vice President, Global Association of Risk Professionals (GARP))

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)