Ask any CIO in Indian manufacturing where the money is going, and the answer comes quickly: sensors, digital twins, predictive maintenance platforms, AI-driven quality systems. The investment case writes itself. Indian manufacturers are now spending at 1.6 times the global rate on industrial technology — a bet on leapfrogging straight into an AI-led production era, skipping the incremental steps other markets took over the past decade.
It’s an aggressive number, and on paper, it looks like conviction. But spend alone doesn’t build intelligence. It builds capacity — and capacity is only as useful as the data flowing through it. That’s where the story gets less flattering: 60% of Indian manufacturers say their biggest internal obstacle isn’t technology adoption at all. It’s their inability to capture, interpret, and actually use the data they already have — compared with 37% of manufacturers globally, according to Rockwell Automation’s 2026 State of Smart Manufacturing Report, which surveyed more than 1,500 manufacturers across 17 leading manufacturing countries.
Put those two numbers side by side and a pattern emerges. India isn’t struggling to invest in AI. It’s struggling to feed it. And according to Ankur Pancholi, Head of Software & Control at Rockwell Automation India, that gap between spend and readiness is quietly becoming the defining risk of the country’s manufacturing transformation — one that no amount of additional budget can solve on its own.
A Fragmented Foundation
India’s data challenge, Pancholi explains, stems from decades of technology layered on top of technology without a unifying architecture. Legacy operational technology (OT) systems, enterprise resource planning (ERP) platforms, manufacturing execution systems (MES), and plant-level controls have historically been built and bought in isolation — and it shows.
“Data is often not contextualized, not trusted, and rarely actionable. This prevents AI and automation from scaling beyond pilots,” says he. The fix, he argues, isn’t simply collecting more information — it’s building a unified industrial data fabric that integrates IT and OT, contextualizes machine and workflow data, and enforces governance for accuracy and security.
Rockwell’s own approach to this problem, its Connected Enterprise framework, centers on secure, interoperable environments where data moves freely between the plant floor and the enterprise, feeding digital twins, simulation, and analytics that turn raw signals into predictive insight. Zero-trust cybersecurity, he adds, has to be built in from the start rather than bolted on later, and none of it works without a workforce trained to actually interpret and act on what the data is telling them.
The Cost of Unreliable Pipelines
The stakes of getting this wrong are already visible. AI is augmenting an estimated 41% of manufacturing operations in India today, but Pancholi says a meaningful share of that potential value is being lost simply because the pipelines feeding those AI systems aren’t standardized. Globally, only 43% of the data organisations collect is ever put to effective use — and India’s gap, he notes, runs wider still.
When information stays fragmented across programmable logic controllers, MES, ERP, and plant-level systems, only a fraction of it becomes usable intelligence. The result shows up in familiar ways: slower decision cycles, AI models that underperform their promise, and missed gains in quality control, predictive maintenance, supply chain optimisation, and energy management. Instead of enabling the self-optimising, autonomous systems manufacturers are chasing, AI ends up constrained by the same inconsistent inputs that have long frustrated plant managers.
Building Toward 2030
The trajectory ahead is steep. Industry projections point to AI-augmented operations climbing from 47% in 2027 to 61% by 2030 — a jump Pancholi says will hinge less on algorithms and more on whether manufacturers get the fundamentals right, starting now.
“The trajectory to 61% AI augmentation depends less on algorithms and more on whether manufacturers can standardize data, secure systems, and prepare people,” says Pancholi.
On the infrastructure side, that means the same unified data fabric, open standards for interoperability, and zero-trust IT/OT security that underpin near-term AI performance. But Pancholi is equally emphatic about the organisational side of the equation: reskilling programs, AI literacy at the leadership level, and governance frameworks that clearly define data ownership, accuracy, and compliance.
Just as important, he says, is the shift from isolated pilot projects to enterprise-wide execution — planning for scale from day one rather than retrofitting it later.
Spending Ahead of Capacity
That brings the conversation back to the 1.6x figure. Is outspending the rest of the world on industrial technology a sign of competitive urgency, or a warning sign that investment is outrunning the organisation’s ability to absorb it? Pancholi sees it as both.
The urgency, he says, is legitimate: supply chain fragility, workforce shortages, and rising cybersecurity risk all demand accelerated investment, and India’s push to position itself as an AI-led manufacturing powerhouse reflects real competitive pressure. But the risk lives in absorptive capacity. If spending outpaces an organisation’s ability to integrate, standardize, and operationalize new technology, investments can stall at the pilot stage or fail to deliver a return — and the same 60% data bottleneck that limits AI today is the clearest evidence that budget alone won’t solve the problem.
The Gap CIOs Don’t Say Out Loud
Publicly, India’s manufacturing sector is often framed as a “future-ready powerhouse.” Privately, Pancholi says, CIOs are grappling with a much less polished reality: data pipelines that remain fragmented, governance frameworks that are still immature, and workforce readiness that varies widely from plant to plant.
Many facilities still run on legacy OT systems that don’t integrate cleanly with IT, leaving data collected but never contextualized — a gap that quietly undermines AI’s effectiveness and slows every scaling effort behind it. Layer on the absorptive-capacity question and a workforce transformation that hasn’t kept pace with the speed of technology adoption, and the picture that emerges is one of a sector racing ahead on investment while its foundations are still being poured.
The Path Forward
For Pancholi, the message to Indian manufacturing leaders is consistent across every question: the technology and the ambition are not in short supply. What’s missing is the unglamorous, foundational work of making data trustworthy, secure, and usable at scale — and building the people and governance structures to act on it. Manufacturers who close that gap, he suggests, won’t just catch up to the AI-augmented future everyone is promising. They’ll be the ones defining it.