How India uses AI to empower the next billion users

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How India uses AI to empower the next billion users

India’s AI-for-public-good trajectory reveals a pattern of pragmatic, use-case-driven development that is capitalising on the country’s growing innovation ecosystem and tech-adaptive populace.

India is increasingly positioning itself as a global ‘use-case capital’, deploying Artificial Intelligence (AI) solutions for the public good, where technological innovation is closely linked to governance priorities and social development goals. This direction is evident in the Prime Minister Narendra Modi’s meeting with leaders of Indian AI startups ahead of the India AI Impact Summit 2026 in February, where he emphasised that India should not simply replicate the technological pathways of developed economies but instead develop AI systems rooted in its own knowledge traditions, social realities, and public policy needs, while promoting “indigenous content and regional languages.” Such an approach reflects a broader vision in which AI is seen not merely as a driver of economic growth, but as a tool to address large-scale societal challenges.

The prominence given to AI start-ups and their solutions at the AI Impact Summit 2026 reflects this shift towards socially oriented innovation. Initiatives such as ‘India’s AI Impact Startups’ map over 110 start-ups and non-profits developing AI applications across healthcare, agriculture, climate action, and financial inclusion, among others. The emergence of voice-based and vernacular AI interfaces, alongside investments in Indian foundation models, indicates a growing focus on accessibility and population-scale impact. The flagship initiatives at the summit—YuvaAI: Global Youth Challenge, AI by HER Challenge, and the AI for ALL: Global Impact Challenge—encourage youth participation, women-led innovation, and scalable AI solutions, reflecting a widening AI ecosystem aimed at inclusive technological development. As such, India-led policy thinking is increasingly framing AI in terms of social empowerment. The New Delhi Declaration 2026 emphasises expanding access to knowledge, services, and opportunities through AI, while encouraging collaboration and the exchange of scalable practices. This reflects an emerging consensus that AI deployment should enhance participation in economic and social life, particularly for underserved communities.

India’s broader AI landscape provides a promising foundation for this ambition. With nearly 1.8 lakh start-ups and about 89 percent of new ones reportedly integrating AI into their products or services, India has rapidly emerged as a major AI adoption hub. The country scores 2.45 out of 4 on enterprise AI readiness, with 87 percent of enterprises using AI solutions across sectors such as banking, healthcare, manufacturing, and retail. With AI projected to contribute up to US$ 1.7 trillion to India’s economy by 2035, the key question is no longer whether India will use AI, but how this expanding ecosystem is shaping new models of AI deployment for the public good.

Use-Case Driven AI for Public Good in India

Indian start-ups are now actively building foundational AI systems within domestic compute and data ecosystems. Sarvam AI has introduced large-scale foundation models, including a 105-billion-parameter model trained and hosted on India-based data centre infrastructure, alongside multilingual speech, document, and enterprise AI agent layers. Its partnerships with state governments to build AI-optimised data centres further demonstrate efforts to develop indigenous compute capacity—an important element of technological sovereignty. Similarly, BharatGen, a government-backed initiative, has launched Param2, a multimodal foundation model supporting 22 Indian languages and designed as public digital infrastructure accessible to government services and research institutions. Other firms, such as Tech Mahindra and Gnani.ai, are developing domain-specific and multilingual AI systems focused on education and voice-based public interfaces, reinforcing India’s emphasis on locally relevant AI ecosystems.

This technological direction aligns with India’s policy vision articulated in the National Strategy for Artificial Intelligence (2018), which identified healthcare, agriculture, education, smart mobility, and infrastructure as priority sectors. From the outset, the strategy framed AI not merely as a commercial technology but as a tool for social development and public service delivery, shaping subsequent investments in sector-specific AI applications.

Healthcare illustrates how this public-good direction is faring in practice. Indian firms are building specialised medical AI systems grounded in domestic datasets and clinical needs. Start-ups such as JiviAI’s ‘MedX’ have developed medical LLMs designed for clinical decision support, while Eka Care’s ‘Eka-Scribe’ provides multilingual AI documentation tools compliant with Ayushman Bharat Digital Mission standards, reducing administrative burdens on doctors. Public-facing platforms such as Fractal Analytics’s ‘Vaidya.ai’ further expand access by offering multilingual health information and report evaluation, helping reduce informational barriers in India’s access-oriented healthcare system. Additionally, indigenous AI-powered surgical technologies reflect a growing ambition to integrate AI into advanced medical hardware.

In agriculture, AI deployments are even more explicitly oriented toward the public good due to state involvement and scale. Initiatives such as the AI-enabled Kisan-eMitra chatbot under PM-KISAN, which handles millions of farmer grievances in multiple Indian languages, demonstrate how LLMs and Natural Language Processing (NLP) are being embedded in grievance redressal and welfare delivery systems to reduce bureaucratic delays. AI-based weather forecasting, combining open models such as Google’s NeuralGCM and the European Centre for Medium-Range Weather Forecasts (ECMWF)’s AI systems, has enabled the government to send early monsoon advisories to nearly 38 million farmers, materially influencing planting decisions and incomes. Domain-specific models such as BharatGen’s ‘AgriParam’ further highlight efforts to build India-centric foundational models trained on local data and agrarian contexts, delivering contextual farmer advisories, policy information, and research insights, among others, in response to agricultural queries.

Such application-layer successes are reinforced by institutional efforts under the IndiaAI Mission, including public compute, datasets via AIKosh, language infrastructure through Bhashini, and sectoral Centres of Excellence. Taken together, India’s AI-for-public-good trajectory reveals a pattern of pragmatic, use-case-driven development that is capitalising on the country’s growing innovation ecosystem and tech-adaptive populace.

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India’s AI Readiness Gap: Adoption, Infrastructure, and Capability

The Indian AI landscape today is characterised by strong technological capabilities, a commitment to responsible innovation, and growing structural readiness. India has the highest skill penetration level in the world. According to the Stanford AI Index Report 2025, India ranks among the top countries globally in AI skill penetration, with 47 percent of Indian enterprises already having several Generative AI use cases live, while another 23 percent are in pilot stages, indicating strong experimentation. Surveys show that 76 percent of Indian business leaders believe GenAI will have a significant impact, and a majority feel operationally ready to deploy it.

However, countries with high rates of AI diffusion tend to have a larger share of internet users or near-complete internet penetration. In countries such as the United States (US), the UAE, and Singapore, AI is diffusing rapidly due to high internet penetration—something India has yet to achieve. India’s AI infrastructure is still maturing, leaving its level of AI adoption behind that of leading AI-first societies. Countries such as the UAE and Singapore have achieved significantly higher AI usage penetration among their working populations, exceeding 60 percent, despite having far smaller populations. Norway, too, despite its smaller demographic scale, shows higher proportional adoption. The United States also benefits from near-universal internet access, mature digital infrastructure, and high mobile internet usage, enabling AI tools to diffuse faster and more evenly. India’s challenge, therefore, is not demand or openness to AI, but incomplete digital inclusion and uneven institutional capacity. At present, only about 44 percent of India’s internet users—approximately 429 million people—interact with AI-enabled features, while roughly 970 million have not yet done so.

In terms of AI-led industrial transformation, some major conglomerates have begun investing heavily in AI development and deployment, but over 95 percent of organisations still allocate less than 20 percent of their IT budgets to AI, creating a mismatch between ambition and execution. Specialised talent availability is another major constraint. Despite having the largest pool of AI learners globally, India ranks low in applied AI proficiency, resulting in a significant skills gap that directly affects the design, deployment, and governance of public-sector AI systems.

Thus, to more effectively realise its ‘use-case capital’ ambitions, India could move towards deeper public AI capacity building. Sustained investments in computing infrastructure for public compute and open foundational models trained on Indian data need to be actualised, while reducing reliance on external platforms. While India’s domestic GPU manufacturing plans are still at a relatively nascent stage, its current approach of making GPUs and compute infrastructure available at highly subsidised rates is proving transformational.

Finally, robust and enforceable institutional frameworks for AI governance, such as the recently released AI Governance Guidelines, should set the template for institutional norm-setting and implementation. With transparency and periodic evaluation, AI systems can be successfully deployed for welfare and public services, making them more trusted, accountable, and inclusive.

Debajyoti Chakravarty is a Research Assistant with the Centre for Digital Societies at the Observer Research Foundation.

This article was originally published on the Observer Research Foundation website.

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