AI and the Common Indian: How Artificial Intelligence Is Quietly Solving Problems You Didn’t Know It Could

The public conversation about artificial intelligence in India is dominated by two poles: breathless optimism about becoming an AI superpower, and anxious concern about job displacement. Both miss the quieter, more immediate story — the ways AI is already touching the lives of ordinary Indians in ways they neither recognise as “AI” nor explicitly asked for.

Consider a cotton farmer in rural Maharashtra who began receiving alerts on his basic Android phone — text messages in his own language warning him of elevated pest risk based on weather patterns, satellite soil moisture readings, and pest migration data from nearby farms. He may not know the alerts are generated by a machine learning model built by an agritech startup. He knows only that his pesticide costs dropped meaningfully that season.

The Three Frontiers of AI for Common Indians

Healthcare: AI diagnostic tools are enabling a form of triage at the last mile that India’s overburdened public health system cannot provide through human specialists alone. AI-powered tuberculosis screening, which analyses chest X-rays with high sensitivity, has identified substantial numbers of additional cases in pilot districts that might otherwise have been missed by overstretched radiologists. For a disease that kills hundreds of thousands of Indians annually, this is not a marginal improvement.

Financial Inclusion: Alternative credit scoring using AI — drawing on UPI transaction history, utility payment records, and mobile usage patterns — is extending formal credit to first-time borrowers who lack any conventional credit history. Fintech lenders using these models have disbursed substantial sums in small-ticket loans to customers who had no prior credit bureau score, with default rates, after initial teething problems, settling at levels broadly comparable to traditional priority sector lending.

Government Services: Multilingual government chatbots, powered by natural language processing trained across numerous Indian languages, now handle millions of citizen queries monthly. More significantly, AI-assisted document processing at motor vehicle registration offices in several states has meaningfully reduced the time required to issue a driving licence — with a measurable reduction in intermediary “agent” costs that applicants previously had to pay to navigate the bureaucracy.

The Equity Problem We Are Not Discussing

AI’s benefits in India are not equally distributed, and the current policy framework does not adequately address this. The most sophisticated AI tools — for healthcare, financial planning, legal assistance — are being built for and deployed in urban, English-speaking markets first. The vernacular AI stack lags significantly behind, despite genuine progress. When a first-generation smartphone user in a less digitally connected state asks a question in a regional language, the AI model responding to them is typically significantly less capable than one responding to an English-speaking user in a major city.

India has a National AI Strategy. What it currently lacks is the execution architecture for AI equity — the deliberate policy choices that would close the vernacular and rural access gap rather than leaving it to market forces alone.

Closing this gap requires deliberate policy: mandatory vernacular capability requirements in government AI procurement, public datasets specifically built for low-resource Indian languages, and targeted subsidies for AI deployment in healthcare and agriculture at the district level, where the need is often greatest and the commercial incentive for private deployment is weakest.