For the past two decades, we have put the word “smart” in front of almost everything in industry. Smart factories. Smart plants. Smart manufacturing. Smart machines. Smart supply chains. Smart maintenance. Smart automation. Smart cities. Smart airports. You get the point. Everything is smart!

But were they really smart? What does smart really mean?

These questions might sound provocative, especially after billions of dollars of investment in Industry 4.0, IoT, machine learning, advanced analytics, automation, and digital transformation.

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There is no question that industrial operations have become more connected, more automated, and much more data rich. But perhaps we have been too generous with the word “smart.” Artificial intelligence also is forcing us to revisit its meaning.

We confused connectivity with intelligence

The first generation of the smart factory was fundamentally about connectivity. Sensors generated data. Machines communicated with systems. IoT platforms collected enormous amounts of operational information. Companies gained visibility into processes that previously operated largely in the dark.

This was a major advancement. And that provided real economic value.

Then analytics became more sophisticated. Instead of simply knowing what had happened, manufacturers could increasingly predict what might happen. Machine learning helped identify anomalies, anticipate equipment failures, optimize schedules, improve quality, and detect patterns invisible to human operators.

Then came greater automation. Machines could execute predefined actions automatically. Workflows could trigger other workflows. Decisions that once required human intervention could increasingly be codified into business rules.

We called all this “smart.”

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But connectivity is not necessarily intelligence. Automation is not necessarily intelligence, either. A highly automated system can execute the same predefined process thousands of times with extraordinary precision without understanding why it is doing it.

Perhaps much of what we called smart was actually sophisticated or advanced automation.

AI raises the standard

Generative AI and agentic AI fundamentally raise the expectations for what industrial intelligence might mean. The distinction is important.

A connected system can tell us what is happening. A predictive system can tell us what might happen. An automated system can execute a predefined response.

An intelligent system should potentially be able to interpret a situation, understand context, evaluate alternatives, reason across competing objectives, determine an appropriate course of action, execute that action, and learn from the outcome.

That is a very different standard. And a greater level of complexity to handle.

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The evolution of industrial intelligence might therefore be described as a progression from connected to predictive, from predictive to adaptive, from adaptive to agentic, and eventually from agentic to increasingly autonomous.

The important transition is not simply from less automation to more automation. It is the transition from executing instructions to participating in decisions. That changes the meaning of "smart."

A dashboard is not ‘smart’

Industrial companies have invested enormous amounts of money creating visibility. We have control towers, dashboards, digital twins, alerts, predictive models, and sophisticated visualization tools. Yet many organizations still depend on humans to connect the dots. Some of these humans still sit in old-fashioned control rooms!

A dashboard identifies a problem. Someone interprets it. Another system contains additional information. Someone reconciles the two. A meeting occurs. Alternatives are discussed. A decision is made. Someone else enters that decision into another system.

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The technology surrounding the decision may be incredibly sophisticated while the decision process itself remains fragmented and manual. This is why AI represents something more significant than another analytics technology. AI has the potential to move industrial technology from information architecture toward decision architecture.

The question is no longer simply, “What can the machine tell me?” Increasingly, the question becomes, “What can the machine help us decide?”

‘Smart’ should mean sense, make sense, act, reason, and transform

Perhaps we need a higher standard for using the word smart.

A truly smart industrial system should be able to sense what is happening across its environment. It should make sense of those signals by adding context rather than simply collecting data. It should be able to act when action is appropriate. It should reason across alternatives, constraints, risks, and consequences. And, ultimately, it should transform by learning from outcomes and improving the way work gets done.

This does not mean removing humans from industrial operations. Quite the opposite.

The smartest industrial environments may combine human judgment, machine intelligence, automation, and data in ways that allow each to do what it does best. The objective should not be a factory without people. The objective should be a factory capable of making better decisions.

From operational intelligence to economic intelligence

There is one more dimension that deserves attention. A factory can be operationally optimized and still make poor economic decisions.

Imagine an intelligent system identifying an opportunity to increase the throughput of a production line. Technically, increasing throughput might appear optimal. But what if the additional production creates excess inventory? What if energy costs are temporarily high? What if accelerating production increases maintenance requirements? What if customers simply do not need the additional output?

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The technically optimal decision might actually destroy value. In that situation, the smartest decision might be to do nothing. This is where the definition of smart becomes much more interesting.

Industrial intelligence should eventually move beyond optimizing machines and processes toward understanding the economic consequences of decisions. Smart systems will need to consider cost, capacity, customer value, revenue, margin, risk, and opportunity simultaneously.

The ultimate measure of industrial intelligence is not whether a plant can optimize itself. It is whether it knows what is worth optimizing.

It’s time to raise the bar

Industry 4.0 gave us extraordinary capabilities. IoT connected the industrial world. Analytics helped us understand it. Machine learning helped us predict it. Automation helped us execute faster and more consistently.

AI introduces something different. It introduces the possibility of reasoning and action at scale. That means we should become much more demanding about what deserves to be called smart.

The next smart factory will not simply generate more data, deploy more sensors, automate more processes, or create more dashboards. It will increasingly understand context, evaluate choices, coordinate actions, learn from outcomes, and connect operational decisions to economic value.

Maybe the next industrial revolution is not about making machines smarter. Maybe it’s about finally becoming precise about what we mean by “smart.”