The global AI race today is being defined by scale. The United States and Europe are pouring billions into building massive frontier models that cost anywhere between $100 million and $300 million to train. These systems are powerful and impressive. But for India, following that path blindly may not just be difficult, it may be unnecessary.
India’s starting point is very different. Its economy runs on nearly 70 million micro, small, and medium enterprises. These businesses are not looking for cutting-edge AI that writes poetry or generates complex code. They are looking for simple, practical solutions that help them run better, save time, and grow.
For them, AI is not a technological race. It is a tool.
If India tries to replicate the Western playbook, it risks building AI that looks impressive on paper but struggles to create real impact on the ground. The more important question is not how to build the largest models, but how to build AI that actually works for India.
One of the biggest reasons this matters is language.
Much of today’s AI ecosystem is built around English and a few dominant global languages. Even when large models claim to support multiple languages, their understanding of regional languages is often shallow. India, however, does not operate in one or two languages. It lives and breathes in many.
From Hindi and Bengali to Tamil, Telugu, Kannada, and Marathi, everyday business and communication happen in local languages. A shop owner, a farmer, or a small logistics operator is far more comfortable interacting in their native language than in English.
This is where India needs a fundamentally different approach.
Instead of building one large, general-purpose system and expecting it to perform well across all contexts, India should invest in multiple smaller models tailored to Indic languages. These models do not have to be massive to be effective. In fact, being smaller and more focused can make them more accurate, faster, and easier to deploy.
For someone running a small business in a tier-2 or tier-3 city, an AI system that understands their language clearly is far more valuable than a sophisticated system that only partially understands them.
Language is only one part of the story. India’s diversity also shows up in how different sectors operate.
Take agriculture. A farmer does not need a global AI trained on internet-scale data. They need advice that reflects their local soil conditions, weather patterns, and crop cycles. And they need that advice in a language they understand. A localized AI model trained on Indian agricultural data can deliver exactly that.
The same applies to logistics, healthcare, and public services. India’s challenges are specific, and solutions need to reflect that reality. Smaller, domain-focused models can solve these problems far more effectively than large, generic systems.
Cost is another factor that cannot be ignored.
Frontier models require massive computing power and expensive infrastructure. This creates a barrier for startups and smaller companies, which are the real drivers of innovation in India. If access to AI remains expensive, its benefits will stay concentrated among a few large players.
A better approach is to build shared infrastructure. If startups and businesses can access computing resources when they need them, without heavy upfront investment, it can open the doors for much wider innovation. It allows more people to build, experiment, and solve real problems.
Open-source AI can play a big role here as well. When models and tools are openly available, developers can adapt them to local needs without starting from scratch or paying high costs. This not only speeds up innovation but also makes it more inclusive.
There is also a strategic advantage in choosing this path.
Trying to compete directly with global tech giants in building the largest models is a capital-intensive game. The returns are uncertain, and the competition is fierce. But focusing on practical, accessible, and localized AI gives India a chance to lead in a different way.
India can become a leader in AI that is actually used, not just admired.
This approach fits naturally with India’s broader goals. Technology here must reach beyond large enterprises and elite users. It should empower small businesses, farmers, teachers, and healthcare workers. Its success should be measured by how much it improves daily life, not just by benchmark scores.
The way forward is clear. India needs to invest in high-quality local datasets, support the development of Indic language models, and create infrastructure that lowers the cost of building and using AI.
Most importantly, it needs to resist the urge to follow a path that was designed for a very different context.
India does not need to copy the West to succeed in AI. Its diversity, its scale, and its constraints are not weaknesses. They are strengths that can shape a more inclusive and practical model of innovation.
In the end, the goal is not to build the biggest AI systems. It is to build systems that people can actually use.
For India, that means building AI that speaks its languages, understands its realities, and solves its problems.
Authored by Neelima Vobugari, CIO, AIEnsured