Artificial Intelligence, or AI, is often spoken about in sweeping terms. It has the power to automate, to predict, to optimise and, more recently, to think. But the real test for India is whether AI can change the lives of the underserved and vulnerable communities, such as smallholder farmers, village artisans and school teachers, ASHA workers, and nano and micro entrepreneurs.
At the recent Pragati: AI for Impact convening co-hosted by Meta and The/Nudge Institute in Delhi, this question was central to the discussion. Could India direct its AI mission towards equity and inclusion or would it further increase existing divides? The answer lies in intention and design.
India has seen this happen before. The Green Revolution helped end famine, the White Revolution made the country a large milk producer, microfinance expanded rural credit and UPI democratised digital payments. None of these breakthroughs succeeded because of technology alone. Each of them worked because they were intentionally designed for affordability, accessibility and for large-scale social transformation.
AI should follow a similar path. Its success will be measured by whether it can deliver credit to a woman farmer in Satara, career guidance to a student who cannot afford personalised counseling or timely maternal health advice to a rural mother over a basic phone.
In his chat at Pragati, Abhishek Singh, Additional Secretary at the Ministry of Electronics & Information Technology - Govt of India, outlined seven pillars of India's AI mission: affordable compute, indigenous datasets, foundation models, applications, financing, skilling, and trust. The strategy is clear: focus on participation over possession. By subsidising access to GPUs, championing open datasets on AI Kosh and using linguistic platforms like Bhashini, India is building an inclusive digital infrastructure.
By lowering barriers to entry, the government is ensuring that startups, researchers and nonprofits—not just large corporations—can experiment, adapt and scale AI solutions. For a country as large and diverse as ours, distributed innovation is a path to inclusion and equity. These choices should be deliberate.
Openness is becoming a competitive advantage for India. India's support for indigenous open-source models like Sarvam and AI4Bharat indicates a move toward democratising access, lowering costs and accelerating adaptation.
Panelists in a discussion titled 'Transparency and Transformation - Open Source in the Age of AI' noted that open-source AI is not just about philanthropy but about strategy. This approach ensures that innovation is not limited by license fees or controlled by a few players. In a country where context is very important, openness is a way to design systems that reflect local realities.
A compelling proof came from real innovations such as Adalat AI, which is transforming legal documentation in 3,000+ courtrooms across eight states, providing instant transcription in multiple languages. Intelehealth equips frontline workers with AI-powered triage to improve access and accuracy of rural health care. iSTEM builds inclusive digital pathways for people with disabilities by offering personalised career guidance and accessible learning tools. And Farmers for Forests uses drones and AI to help smallholders participate in carbon markets, an income stream once out of reach. All of these, and other similar entities, have been designed for people who have historically been last in line for progress.
AI systems reflect their training data. If not addressed, they risk being fluent in English but not in local languages and precise in urban settings but less so in rural ones. India cannot afford such blind spots.
As a grassroots leader, Chetna Gala Sinha, the Founder of Mann Deshi Foundation, reminded us: "For a woman farmer, AI must mean credit, markets and weather alerts in her dialect and not abstract algorithms."
India's linguistic spread, patchy network, and device diversity can be laboratories and use cases for resilience. If AI can be designed to work here, it may be robust enough to work anywhere. In this sense, inclusivity is not just about fairness, but also about India building for global relevance.
What came out of the AI for Impact convening was not a technical roadmap, but a development blueprint. Shalini Kapoor, Senior Advisor, EkStep Foundation, described a 'DPI 2.0' vision where communities create local data economies, helping entrepreneurship in tier-3 and tier-4 towns. Sunil Abraham of Meta suggested that India's multilingual, open approach can shape a collaborative global AI ecosystem.
This aligns with India's past. UPI was transformative not because it was technically advanced, but because it was free and widespread. The White Revolution succeeded because it reorganised distribution, not just dairy science. Intentional design has always been a lever for transformation.
The future of AI in India depends on choices regarding openness, inclusivity, affordability and trust. It depends on whether society, government and the market can align around the principle that technology must serve the many, not just the few.
India has shown before that when intention meets scale, transformation follows. AI is the next proving ground. If done right, the last mile may not be the hardest part of the journey, but could be the starting point of a new kind of progress.
The article has been written by Jerold Pereira, Senior Director, The/Nudge Forum
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