By Sanjay Brahmawar, CEO, QAD|Redzone. Works with manufacturers to help frontline teams move faster and make better decisions.

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Spend enough time in manufacturing, and you'll hear concerns about AI coming for jobs. It's an understandable fear. Every wave of automation has raised the same question: What happens to the people doing the work?

If you walk a factory floor today, however, the reality looks very different from the headlines.

Manufacturers aren't dealing with excess labor. Rather, they're operating with too little of it. According to a 2024 study by The Manufacturing Institute and Deloitte, the U.S. may need up to 3.8 million additional manufacturing workers by 2033, with nearly half those roles at risk of going unfilled. Similar dynamics are playing out globally. The challenge is figuring out how to keep up with demand using the workforce you actually have.

That's the context AI is entering. At the same time, the work itself has changed. Manufacturing is more dynamic, more interconnected and far less predictable than it was even a decade ago. Supply chains shift mid-cycle. Demand moves faster than planning systems can absorb. Energy costs, regulation and geopolitical pressure all introduce variability that doesn't wait for a quarterly review.

Layer on top of that the explosion of data—every machine, process and transaction-generating signals—and the problem becomes clear: Information is overwhelming manufacturers. What I hear consistently from plant leaders is the need for fewer decisions competing for their attention and a faster way to act on the ones that matter.​

At its best, AI removes friction from the work. It helps prioritize what matters, filters out noise and supports faster decisions when conditions change. In environments where timing and consistency define performance, that kind of support can be the difference between staying on track and falling behind.​

The question of how to extend the capability of the workforce that already exists becomes more urgent when you look at how uneven adoption is. From my experience, I've found that in parts of Asia, manufacturers are embedding AI directly into day-to-day operations to increase output per worker and stabilize performance. In many Western markets, meanwhile, efforts are still stuck in pilots or analytics that rarely translate into action. Over time, that gap compounds. In a labor-constrained environment, productivity per person becomes a defining advantage.

This is also where earlier waves of digital transformation fell short. For years, the focus was on visibility—better dashboards, better reporting, better insight. That progress mattered, but it didn't fundamentally change how work was done. Seeing a problem faster isn't the same as solving it faster.

The next phase is about closing that gap.

On the factory floor, value is created in moments—adjusting a line, reallocating resources, preventing defects before they happen. If AI isn't present in those moments, it isn't changing outcomes. Too many initiatives still stop at recommendations. They produce insight, but they don't shape behavior in real time. Moving from systems of record to systems of action is where the real opportunity lies.

There's also a human side to this that doesn't get enough attention. Manufacturing's talent challenge isn't just about filling open roles. It's about making the work sustainable and attractive to the next generation. When constant firefighting and decision overload are defining jobs, retention becomes difficult, and recruiting becomes even harder. When technology reduces complexity and supports better decision making, the nature of the work improves. It becomes more manageable, more engaging and, ultimately, more scalable.

The idea that AI will take manufacturing jobs persists because it fits a familiar narrative, but I don't believe it reflects what's actually happening inside most operations. The more pressing reality is that the work is getting harder, the workforce isn't growing fast enough, and the systems supporting that work need to evolve.

AI is part of that evolution—not as a replacement for people but as a way to help them perform at a higher level. In manufacturing, execution has always been the differentiator. What's changed is the speed required to get it right and the systems needed to support it.

This isn't about replacing labor. It’s about amplifying it. The companies that understand that distinction will be the ones that pull ahead.​

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