Industrial AI: What It Is and Why It’s Now (Finally) Transforming the Factory Floor

Industrial AI: What It Is and Why It’s Now (Finally) Transforming the Factory Floor

When you think of software innovation, the factory floor isn’t the first place that leaps to mind. Factories are noisy, no-nonsense, unglamorous places. But looks can be deceiving. Over the past few decades, the factory floor has evolved from a space full of dumb machines to a network of high-tech assets.

A lot of change has happened in the last 50 years or so. But that change is now set to shift into overdrive thanks to the rise of industrial AI.

So what is Industrial AI, exactly? In this piece, we’ll take a deep dive into it: what it is, where it came from, why it has taken so long to mature, and why it’ll make the factory of three years from now look and work so differently from anything that came before.

And this shift isn’t theoretical—it’s already well underway. Industrial AI is already a $54 billion market, accounting for more than a quarter of the roughly $200 billion industrial software market, and it’s still in its early innings.

Not all smart factories are intelligent

Before we go any further, let’s define a few terms. Or, rather, redefine them. The industrial tech space is full of jargon that sounds interchangeable – terms like Industry 4.0, IIoT and OT analytics. Sales decks and speakers at conferences throw them around as if they all mean the same thing. But they don’t, not really.

Industry 4.0 is the broad agenda of transformation, including digitization and new operating models for factories. IIoT (the Industrial Internet of Things) is the sensors, gateways and protocols that generate a stream of raw data. OT analytics is the dashboards, threshold alerts and reports from SCADA and similar systems.

Industrial AI is different altogether. It’s the intelligence layer that sits above the data and below the operator, enabling operators to make software-augmented decisions that are optimized and automated, unlike the judgment calls made by the operators of yesteryear.

A factory might have those other pieces described above. But if it doesn’t have the intelligence layer, it doesn’t have Industrial AI. The difference is vital, because that’s where the real market opportunity is.

Years in the making

Industrial AI is now getting its moment in the sun but what appears to be overnight success has actually been taking shape for over the past several decades and across four overlapping eras.

The first era was digitization. Sensors got cheaper, which made it easy for factories to collect massive amounts of data from the floor. But, once collected, the data was siloed, poorly labeled and largely inaccessible. It was nice to have, but there was really no way to put all that data to use. 

The second era saw the rise of edge computing. Affordable processors arrived and moved processing closer to physical assets. Narrow AI use cases also emerged, such as computer vision for quality inspection and the detection of unusual vibration patterns that signal a machine is about to fail.

Next came generative AI. Industrial engineers started using large language models to parse technical manuals and auto-generate work orders. The LLMs were not built for an industrial context per se but they could be adapted for a variety of industrial use cases.

This brings us to the era we’re in now, which is verticalization. Rather than adapt general-purpose AI to specialized needs, industrial companies are implementing AI designed specifically for the factory floor. This has led to systems that can improve factories in all sorts of ways. They can see when a piece of equipment isn’t running right, explain how to fix it and order replacement parts. They can make adjustments to machines to optimize their operation. They can spot a defective product on an assembly line before it goes out the door. They can schedule energy use to cut power costs. The possibilities go on and on.

Why it’s taken so long to get here

Industrial AI took so long to arrive because it had to. There was a great deal that had to work flawlessly before it could be fully deployed. Like latency. A CRM system that’s running a little slow can be an annoyance. But a delayed alert in a chemical plant with toxic gas leaking out? That can mean the difference between life and death. Many industrial environments also run on air-gapped networks. This means any software they use has to be fast and reliable, even on a factory floor with no internet or cloud connectivity whatsoever.

Then there’s the trust factor. A model that aces every benchmark will still fail in the real world if the operators who are supposed to act on its recommendations don’t trust it. Industrial workers have spent years and decades learning all the ins, outs and quirks of their equipment. Many are still reluctant to hand over decisions to a black box, so the AI has to prove its work before it can do its work.

The stars are finally aligning

All that said, Industrial AI has been theoretically possible for years. So, what has changed to make companies embrace it now? Four things are happening at once and, together, they’re creating an opportunity that didn’t exist before.

First, industrial data is in better shape. Industrial companies have spent years cleaning up their act, so to speak. They’re unifying data that used to live in completely separate systems and making it actionable.

Second, edge computing and the cost of running AI locally, right on the factory floor without sending data to the cloud, has dropped dramatically. This makes AI viable for a much wider range of factories, not just those with deep pockets.

Third, generative AI has given industrial workers genuinely useful AI tools for the first time. Engineers can ask questions of a technical manual the same way they’d ask Google a question.

And finally, the pressure to find efficiencies has never been greater. Supply chain chaos, energy costs and a shortage of skilled workers are forcing industrial companies to find answers that only software can deliver.

Summary

Industrial AI is the missing intelligence layer turning connected factories into truly autonomous ones. After decades of groundwork, it’s finally moving from promise to deployment. 

Stay tuned for the second part of this series, in which we’ll look deeper at the technical details around Industrial AI, the data challenges that most vendors are glossing over and where the investment dollars are headed.