New Delhi: India is carving out a unique, decentralised, and inclusion-driven path for artificial intelligence (AI) adoption, according to the latest Economic Survey. Unlike the capital-intensive, top-down AI models prevalent in many advanced economies, India’s AI ecosystem is increasingly being shaped from the ground up—by local innovators, start-ups, municipal bodies, and community institutions addressing immediate, context-specific challenges.
The Survey notes that India’s AI journey is problem-driven and application-led, spanning critical sectors such as healthcare, agriculture, education, urban governance, environmental management, and disaster preparedness. This bottom-up model, it argues, allows India to leverage its late-mover advantage while avoiding costly global path dependencies.
AI Tackling Grassroots Challenges Across Sectors
The Economic Survey highlights multiple examples where AI is already delivering tangible benefits at the grassroots level, particularly in areas where traditional systems have struggled to reach scale or efficiency.
In healthcare, non-invasive AI-enabled thermal imaging tools deployed in southern India are enabling early breast cancer screening in low-resource settings. Similarly, low-cost AI-assisted oral cancer screening devices in eastern India are improving early detection rates through primary health centres and outreach camps, helping bridge critical gaps in preventive care.
Urban and environmental applications are also gaining traction. In Bengaluru, AI-based water management systems are being used to monitor consumption patterns and detect leakages in real time, improving efficiency in urban water distribution. In the Himalayan region, indigenous sensor networks integrated with machine learning models are providing real-time landslide alerts, significantly strengthening disaster preparedness in ecologically fragile areas.
AI Boosting Agriculture and Education Outcomes
In the agriculture sector, AI-enabled networks have improved market access, price discovery, and logistics for nearly 1.8 million farmers across 12 states. These systems are helping farmers make informed decisions, reduce inefficiencies, and better integrate with markets.
In education, municipal-level pilots are demonstrating the potential of AI analytics to improve learning outcomes and governance. One such initiative in Pimpri-Chinchwad, covering 18 classrooms across three schools, has shown improvements in student engagement, teacher accountability, and school-level supervision by tracking classroom-level learning outcomes.
Language, Inclusion, and the Rise of Frugal AI
A key theme of the Survey is the role of language and accessibility in making AI inclusive. Initiatives such as Bhashini, led by the Ministry of Electronics and Information Technology, and AI4Bharat at IIT Madras are identified as major enablers of inclusive AI adoption.
By focusing on voice-first systems, Indian languages, low-cost devices, and low-bandwidth environments, these programmes are extending digital and AI-enabled services to populations historically excluded from text-heavy platforms. The Survey underscores that such frugal AI solutions align technological scale with social inclusion.
Scaling Innovation Through the National AI Mission
While bottom-up adoption is gaining momentum, the Survey stresses that scaling these innovations will require structured institutional support under the National AI Mission. This includes identifying successful grassroots applications and providing shared infrastructure, standards, governance frameworks, and funding—without stifling local creativity.
A strong emphasis is placed on open-source and open-weight AI models, where India already enjoys a comparative advantage. The country hosts one of the world’s largest and fastest-growing open-source developer communities, contributing actively to global codebases. Unifying these efforts under the IndiaAI Mission, the Survey argues, could reduce dependence on foreign proprietary systems, lower entry barriers for domestic developers, and accelerate innovation at relatively low cost.
AI as a Public Good: The ‘AI-OS’ Vision
The Economic Survey proposes an ambitious ‘AI Operating System (AI-OS)’ approach, drawing parallels with national digital public infrastructure successes such as UPI and Aadhaar. Under this model, the sovereign would act as a stakeholder to help transform AI into a public good.
Key elements of this vision include pooling data centre capacity into shared cloud infrastructure, expanding access to anonymised and machine-readable datasets, and coordinating open-source AI development through common platforms. The Survey also suggests creating a government-hosted, community-curated central code repository under the IndiaAI Mission to fast-track experimentation and collaboration, while embedding national priorities into AI development.
Balancing Productivity, Jobs, and Safety
The Survey notes that global AI adoption has accelerated rapidly, with 88 per cent of firms worldwide using AI in at least one business function in 2025, although usage remains concentrated in high-income economies.
For labour-abundant countries like India, rapid AI deployment presents a policy tension between productivity gains and employment absorption, particularly in white-collar services. The Survey argues that the key challenge is not whether to adopt AI, but how to pace its diffusion to prioritise labour augmentation over labour displacement.
India’s bottom-up, application-specific AI model—focused on smaller, efficient systems tailored to defined use cases—is seen as a way to spread value creation across sectors while keeping entry barriers low.
Infrastructure Constraints and Geostrategic Risks
AI expansion, the Survey cautions, is closely tied to physical infrastructure, especially energy- and water-intensive data centres. Issues of grid stability and resource constraints must be addressed alongside AI growth.
The Survey also highlights AI’s growing geostrategic importance, warning that excessive reliance on foreign AI systems could expose India to geopolitical vulnerabilities, similar to dependencies in critical minerals or semiconductors.
While India ranks among global leaders in AI research output and talent, it faces constraints in access to advanced compute and large-scale model training, reinforcing the case for an application-led rather than frontier-model-focused strategy.
Towards a Deliberate and Trusted AI Future
Positioning AI as a strategic priority, the Economic Survey calls for stronger coordination, transparent governance, and the establishment of a robust AI Safety Institute to manage emerging risks. Transparency in safety evaluations and public disclosure of findings are identified as essential to building trust as AI becomes embedded in critical sectors.
The Survey concludes that India’s late-mover advantage offers a unique opportunity to shape an AI ecosystem that is inclusive, resilient, employment-oriented, and strategically autonomous, transforming grassroots innovation into a national strength in the AI era.
