AI is often discussed in terms of chatbots, coding assistants, and workplace productivity. But a quieter transformation is taking shape across India, where AI is being used to tackle development challenges, from helping farmers cope with climate change to assisting teachers in rural classrooms, improving TB screening and making govt services available in multiple languages.
According to MeitY, India’s digital economy contributed Rs 31.6 lakh crore, or 11.7% of the GDP, in 2022-23. By 2030, that share is expected to approach one-fifth of the economy. Increasingly, govts, tech firms, startups, nonprofits and research bodies are working together to deploy AI where it can reach millions of people rather than a handful of users.
Education offers one of the clearest examples. Microsoft’s Shiksha Copilot, developed with the Shikshana Foundation using Azure OpenAI Service, is helping around 1,000 teachers prepare localised lesson plans for nearly 30,000 children across 750 govt schools in Karnataka.
What once took teachers hours can now be done in about 10 minutes. The programme is expected to expand to another 8,000 teachers across Karnataka and Telangana. The company will train 10 million Indians in AI by 2030 after achieving its earlier target of skilling 2 million people ahead of schedule.
Agriculture is another major beneficiary. Farmers increasingly face unpredictable rainfall, pest outbreaks, and rising temperatures, while agricultural extension services remain stretched. AI-powered advisory platforms help bridge that gap.
Farmer.Chat, developed by Digital Green, provides advice through voice, text, images and videos in local languages. It offers recommendations tailored to crops, soils, weather, and local conditions. The platform reportedly expanded from 15,000 to 250,000 Indian users within a year while costing less than Rs 100 per farmer annually. Digital Green says it has reduced the cost of introducing a new farming practice from Rs 3,500 to under Rs 100 by using AI-driven advisory systems.
Non-profit institute Wadhwani AI uses similar technologies at a larger systems level. Its AI tools are now reported to have reached more than 190 million people. The organisation says its solutions have helped prioritise over 35,000 villages for tuberculosis screening, supported nearly 2.7 lakh teachers, delivered personalised learning support to 7.9 million students and resolved more than 5 million agricultural grievances.
Healthcare is seeing practical applications. Startup Qure. ai has developed an AI system that analyses chest X-rays for tuberculosis. A Health Technology Assessment by the Indian Institute of Public Health Gandhinagar reportedly found the technology to be more costeffective than conventional screening pathways — significant in a nation that accounts for nearly 28% of the world’s new tuberculosis cases.And, language is a barrier AI is beginning to address. Platforms such as Bhashini aim to make govt services accessible in over 22 Indian languages. AI-powered translation is being used for grievance redressal, Aadhaar transliteration, railway enquiries and several citizen services, reducing dependence on English or Hindi and making digital governance more inclusive.
Climate resilience is becoming another area of rapid innovation. SatSure combines AI with satellite imagery to help banks assess agricultural loans, monitor climate risks, and evaluate farms. The company says its systems operate across more than 70,000 villages, reducing loan underwriting time while improving assessments for farmers who often lack formal credit histories.
India is also investing heavily in the infrastructure needed to support this ecosystem. Enterprise AI adoption is estimated at 87%, while the India AI Mission has expanded national computing capacity to 38,000 GPUs under an outlay exceeding Rs 10,300 crore. Nearly universal 5G coverage, digital public infrastructure such as AgriStack and a growing climate-tech startup ecosystem are providing the foundations for wider AI deployment.
The common thread across these initiatives is not the sophistication of the algorithms but their ability to solve everyday problems at scale.
