The Missing Middle: India's AI Talent Gap

India is in the midst of an AI renaissance. From government-backed models like Sarvam and BharatGPT to a wave of generative AI startups and AI-powered enterprise tools, the energy is unmistakable. Conferences are buzzing with optimism, investors are taking notes, and policymakers are lining up their AI roadmaps.

But under this enthusiastic surface lies a quieter, far more structural gap, one that could hold back India’s AI ambitions in the long run.

Between Research And Digital Literacy Lies A Talent Gap

Much of the current conversation swings between two extremes. On one end, we celebrate our leading AI researchers, the IITs, the top-tier scientists, and the model builders. On the other hand, there is a push to improve basic digital literacy across the broader workforce. Both are essential, but between these two ends lies a crucial layer that’s often missing from the discussion, the applied AI workforce.

Who Makes Up This ‘Missing Middle’?

This ‘missing middle’ is made up of people who aren’t deep-learning researchers, but who know enough to take AI from lab to field. These are the AI product managers, ML engineers, data translators, prompt engineers, operations leads, ethics consultants, and domain experts who understand both business problems and how to solve them using AI.

They don’t necessarily build models from scratch, but they are fluent in building with models. They are the ones who can translate between what a founder wants, what the market needs, and what a model can realistically deliver.

A Top-Heavy Ecosystem Is Hindering Deployment

In global markets like the US or Europe, this middle layer has matured alongside the AI ecosystem. As research advanced, so did deployment capabilities.

But India’s AI ecosystem is growing in a very different pattern; it is top-heavy on research and excitement, but alarmingly light on deployment capability. And that’s becoming a bottleneck.

Startups Need Builders, Not Just Believers

Startups, especially, are facing the brunt of this gap. With limited hiring budgets and high expectations, they need team members who can implement AI tools quickly, iterate fast, and integrate them into actual workflows. But the supply of such talent is thin.

What ends up happening is that AI becomes an afterthought, either bolted onto a product as a buzzword or abandoned entirely after a failed proof of concept. Demos are built that impress investors but don’t scale. In some cases, founders themselves become de facto AI leads, which slows everything down.

The Risk Of A Growing Execution Deficit

It’s not just about startups. India’s vision of ‘AI for All’, including small businesses, government services, and social sector applications, rests on the assumption that someone will be there to translate that vision into reality.

But if we don’t build this middle layer fast, we will see a widening chasm between ambition and execution. AI will remain something that’s either done to us by large tech companies or out of reach for the majority.

Skilling For Scale: A Call To Action

What we need now is a serious focus on vocationalising AI. Not everyone needs a PhD to work in this space. We need bootcamps that teach deployment, certificate programs that build specific job-ready skills, and micro-learning modules designed for engineers, analysts, product leaders, and business managers.

Government skilling initiatives, corporate learning teams, and even edtech players can step into this space meaningfully.

Why India’s AI Future Depends On This Layer

The future of India’s AI story hinges on this missing middle. Not the top 1% of researchers building foundation models, nor the bottom 60% who are still acquiring basic digital fluency, but the 30–40% in between who will actually operationalise AI across sectors. This group will define whether AI becomes a scalable solution or remains stuck in pilot purgatory.

They will decide if India’s AI moment becomes a movement or fizzles out as hype. And yet, they remain largely invisible in public discourse, media narratives, and policy conversations. They won’t make headlines like the models, moonshots, or breakthroughs do.

But without them, those breakthroughs won’t translate into real-world impact. They are the ones who will quietly integrate AI into healthcare systems, manufacturing floors, classrooms, governance tools, and small businesses. If India wants to truly lead in AI, not just in theory, but in execution, then this is the layer in which we must urgently invest.

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