Why AI for good demands population scale: India’s Blueprint

By Raj Koneru, CEO and Founder, Kore.ai

The defining challenge of AI is not intelligence. It is reach. Technologies that shape societies succeed when they accommodate diversity by default – different languages, economic realities, and institutional constraints. As AI begins to influence everyday decisions and services, the countries that know how to design for heterogeneity will define what responsible, scalable AI looks like. Without reaching the masses, AI remains as just another impressive technology and not societal infrastructure.

The India AI Impact Summit arrives as this transition from boardroom to communities becomes not just possible, but provable. India has already demonstrated operational excellence at unprecedented scale. UPI processed 228 billion transactions in 2025, averaging 700 million daily, bringing hundreds of millions into formal banking, a feat that redefined digital payments globally. The Mahakumbh coordinated 400 million people with seamless management of crowd movement, safety, and logistics, an operational achievement unmatched anywhere.

The capability that made this possible i.e., designing for extreme diversity while operating at massive scale, is precisely what AI deployment demands. At 1.4 billion people, 22 official languages, and quality infrastructure, India has both the conditions and the proven track record to show how AI scales to serve the majority. What works across this entire range works globally.

India’s Unique Position for Scalable AI

India operates across the full spectrum of infrastructure and economic conditions, where major metros, emerging Tier-2 cities, and rural districts all exist within the same ecosystem. This diversity isn’t a deployment obstacle to work around; it’s what drives AI solutions to be genuinely inclusive from the start.

An agricultural AI tool can’t scale if it only serves large commercial operations while ignoring smallholder farmers. Healthcare diagnostics won’t reach meaningful impact if they require advanced hospital infrastructure. Financial services built on traditional banking assumptions will exclude the hundreds of millions who need them most. At India’s scale, building only for the well-connected isn’t just incomplete, it’s non-viable.

This necessity is what makes India the proving ground for genuinely scalable AI. Solutions validated here demonstrate that AI can expand opportunity at population scale, establishing the blueprint for deployment globally.

The Convergence Enabling Scale

Several factors are converging to make scalable AI deployment possible in India. Language barriers are dissolving as multilingual large language models can now communicate across India’s 22 official languages and hundreds of dialects. Farmers and health workers can now interact with AI systems in their native languages, transforming what was once a fundamental barrier into an inclusion layer that democratizes access.

The economics have shifted dramatically as well. AI infrastructure costs that once limited innovation to well-funded institutions are falling rapidly. Compute access through the IndiaAI Mission is available at rates under INR 100 per hour, less than half the global average. This democratization of resources means innovation can emerge from where problems actually exist, not just from capital-rich centres.

Government initiatives like Viksit Bharat and the IndiaAI Mission are creating the policy foundation and infrastructure support needed for deployment at scale. Equally important is India’s thriving startup ecosystem and deep engineering talent, with funding in GenAI startups growing 30 percent year-over-year crossing a billion.

At the same time, a new generation of engineers is emerging with AI-native thinking, trained through curriculum integration and hands-on experience with real-world deployments. When capital, talent, policy support, and falling infrastructure costs align toward grassroots challenges in healthcare, agriculture, and financial inclusion, the conditions for meaningful impact emerge.

Building Scalable AI: For India, From India, For the World

The real measure of AI isn’t technological sophistication but whether it creates access where none existed. A farmer hours from the nearest bank branch can now check loan status, resolve payment issues, and get answers to banking questions in their own language through AI systems. Community health workers in remote areas gain diagnostic support and direct connections to specialists. Students in understaffed districts engage with personalized learning platforms that adapt to their needs.

These examples represent something more than incremental improvements. They’re fundamental expansions of who gets served.

What makes these solutions effective is that they’re built in India, by teams who understand these constraints because they live them. At our company, building AI platforms for large enterprises and governments serving hundreds of millions globally, we have seen how designing for intermittent connectivity, multiple languages, and resource constraints from day one creates something fundamentally different.

As these systems mature, they’re evolving beyond simple interactions to become truly agentic, capable of understanding context, navigating complex workflows, and taking autonomous action to solve problems. At India’s scale and diversity, this evolution becomes necessary, not optional.

When you architect for India’s reality, you build systems that are resilient, inclusive, and efficient by necessity.