India Is Building More Factories. AI Agents Could Help Run Them Better.

Indian manufacturing ambition has largely been told as a story about capacity. More factories. More investment. More electronics. More components. More domestic production. A bigger role in global supply chains. But building manufacturing capacity is only half the challenge.

The harder part is making those factories run better. As our country  pushes to become a more significant global manufacturing hub, competitiveness will increasingly depend not just on how much the country can produce, but on how efficiently its factories can respond to breakdowns, quality issues, supply disruptions and changing production demands. This is where AI agents could become more prominent than the usual AI conversation.

Not because factories need another dashboard. Because they may need fewer decisions waiting for someone to manually connect the dots.

The factory already has data. The problem is getting it to work together.

Walk into a modern manufacturing operation and there is no shortage of information. Machines generate performance data. Maintenance systems record failures. Production software tracks output. Quality teams collect inspection data. ERP platforms hold inventory and procurement information.

The challenge is that these systems rarely tell one coherent story. A production manager may know output has dropped. A maintenance engineer may know a machine has been behaving unusually. The quality team may have noticed an increase in minor defects. But connecting those events often requires people to investigate across multiple systems, speak to different teams and reconstruct what actually happened.

By then, the problem may already have become expensive. This is one of the less glamorous but more consequential problems in manufacturing: decision latency. The delay between something happening and an organisation understanding what it means and then deciding what to do about it. India’s next manufacturing advantage may depend increasingly on reducing that gap.

AI agents are interesting because they can move beyond the dashboard

Traditional industrial software is good at recording what happened.AI agents could help manufacturers work out what should happen next. That distinction matters.A conventional analytics system might alert a plant manager that a machine’s downtime has increased.

An AI agent could potentially investigate the maintenance history, compare recent operating conditions, identify similar incidents, assess the likely impact on production and prepare the next action. That could mean recommending an inspection. Flagging a potential spare-parts issue. Escalating a quality risk. Or preparing a maintenance task for human approval. The point is not that AI suddenly replaces the engineer.

The point is that the engineer no longer has to spend the first hour gathering information. That is where the idea becomes particularly relevant to manufacturing. The next generation of industrial AI is increasingly being imagined as an orchestration layer, connecting data, intelligence and action across production, maintenance, inventory and other operational functions. India’s own advanced manufacturing roadmap explicitly identifies a move towards intelligent and agentic ecosystems capable of coordinating decision-making across industrial operations.

We may have more to gain than it appears

For a highly automated factory in Germany, Japan or South Korea, AI agents may represent the next stage of a decades-long digital transformation. India’s  situation is different. Its manufacturing economy is remarkably diverse. At one end are sophisticated automotive, electronics and pharmaceutical operations with increasingly advanced digital infrastructure.

At the other are thousands of smaller manufacturers operating with a mixture of modern machinery, legacy equipment, spreadsheets, ERP systems and deeply experience-driven decision-making. That complexity is often seen as a disadvantage for AI adoption.

But it could also create a significant opportunity. It does not mean that every factory should become a fully autonomous “Factory of the Future” overnight. It needs practical ways to make existing operations more intelligent. An AI agent that helps a maintenance team identify recurring equipment problems could be valuable even in a factory that is far from fully automated.

A quality agent that connects inspection data with production conditions could help reduce rework. A planning agent could identify potential production bottlenecks before they affect delivery schedules. A supply-chain agent could flag risks that would otherwise only become visible when material shortages begin affecting the shop floor.

This is particularly relevant for the MSME manufacturing ecosystem. The question is not whether every small manufacturer will suddenly build an in-house AI team. It is whether AI tools can increasingly become accessible enough to act as an operational multiplier for businesses that cannot afford large digital transformation programmes.

That challenge is now firmly on our policy agenda. The government has explicitly focused on advancing AI readiness and adoption among manufacturing MSMEs, while broader AI initiatives are increasingly being positioned around productivity, competitiveness and access rather than technology for technology’s sake.

The real AI opportunity may be augmentation, not autonomy

There is a risk that the AI-agent conversation becomes distracted by the idea of autonomous factories. Factories that run themselves. Machines making decisions. Software coordinating everything. That is an appealing future. It is also not where most Indian manufacturers need to start.

The more immediate opportunity lies in augmentation. Manufacturing organisations depend heavily on experienced people who understand how operations actually work. The technician who recognises that a machine sounds wrong. The supervisor who knows why a particular production line regularly misses targets. The engineer who can identify a problem by looking at a combination of seemingly unrelated signals. That expertise is valuable but it does not scale easily.

AI agents could help manufacturers capture more of the information surrounding those decisions and make it available across the organisation. Not by pretending that software suddenly possesses decades of industrial judgement. But by reducing the amount of routine investigation required before experienced people can apply that judgement.

That could become increasingly important as manufacturing faces pressure to operate with leaner teams while also becoming more technologically complex. The most successful model may therefore not be AI versus the factory workforce. It may be AI handling more of the information and coordination work, while people focus on the decisions that actually require expertise.

Should avoid creating another generation of AI pilots

There is one major risk. AI in manufacturing could become another industry of demonstrations. A predictive maintenance pilot here. A quality AI project there. A chatbot for plant data somewhere else. Impressive presentations. Limited operational change.

The real test will be whether AI agents can move beyond isolated use cases and become part of everyday workflows. That will depend on problems that have little to do with the sophistication of the AI model itself. Data quality. System interoperability. Cybersecurity. Clear operating boundaries. And, perhaps most importantly, trust. A factory manager does not need an AI agent that confidently produces an answer.

They need one that can reliably explain what information it used, understand the constraints of the operation and know when a decision should remain with a human. India’s own manufacturing and AI discussions increasingly recognise these underlying constraints. 

NITI Aayog’s work on AI and manufacturing highlights the potential for significant productivity gains, while also identifying fragmented data, infrastructure gaps and shortages of cross-skilled talent as barriers to scaling AI across the industrial economy.

We are building manufacturing capacity. The next challenge is operational capability.

India’s manufacturing ambitions are becoming bigger and more complex. The country is competing for a larger role in electronics, automotive, semiconductors, engineering and other global value chains. That means Indian manufacturers will increasingly be judged not simply on their ability to produce at scale, but on consistency, quality, speed and resilience.

AI could help close that gap. NITI Aayog has estimated that AI-led productivity and efficiency improvements could create an additional $85-100 billion opportunity for Indian manufacturing by 2035, with gains expected through areas including process efficiency, predictive maintenance, quality control and improved throughput.

AI agents will not solve every manufacturing problem. They cannot compensate for poor processes. They cannot magically fix unreliable data. And they should not be given control over critical decisions simply because the technology can automate them. But they could change something more fundamental.

They could reduce the distance between information and action. And that may be where their greatest value lies. We have spent years building the physical infrastructure of its manufacturing future. The next competitive layer may be less visible. It may sit between the machines, the data and the people running the factory. Not another dashboard. Not another chatbot.

But a new operational layer that helps manufacturing recognise problems earlier, understand them faster and act before delay turns into loss. India is building more factories. AI agents could help it run them better.