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Controls Engineers as Industrial AI Leaders

Summarize the Content of the Blog

The Misconception of AI in Manufacturing

So I have been sitting with a thought lately, and the more I work through it, the more it lands. When you talk to people about AI in manufacturing, the conversation almost always ends up in one of two places. Either it is a data science problem, or it is an IT problem. And honestly, I do not think it is either.

The Role of Domain Experts in Data Capture

The way I think about it is this. A successful AI infrastructure for analytics has to come from good data. And good data does not just appear. To have clean data you have to enable the domain experts, your Industrial Engineers and Process Engineers, the people who actually know what is happening in the process, to capture the right signals. They are the only ones who know what the right data even is. The thing is, that is not really what domain experts are set up to do. Some of them could probably figure it out, but standing up the infrastructure and keeping it reliable long term is a different job entirely.

‍Why Controls Engineers Are Uniquely Positioned

Reliable, long term, plant floor grade infrastructure that connects machines, signals, and people, that is what controls engineers do every day. So if I had to put money on who leads AI in manufacturing, it would be the Automation and Controls Engineers. The ones that live on the plant floor and work with these systems all the time. They are positioned for it, I just don't think the industry has realized it yet.

Scaling Skills to Agent Interconnections

Now here is where it gets interesting. The skill set does not stay on the plant floor. Once individuals start having their own personal agents, the plant manager, the domain experts, the factory worker, every one of those agents is going to need to talk to the others. Someone has to design that interconnection, and it has to be reliable in the same way a control loop has to be reliable. That is the same muscle, just applied at a bigger scope. So the controls engineer who is wiring up sensors and PLCs today is the same person who is going to be wiring up agent to agent communication tomorrow.

Redefining "Training" for AI Agents

The thing that throws people is the word "training." When most people hear "train an agent," they jump to fine tuning, model weights, GPUs. That is not what is happening here. Training in this context means giving the agent purpose, goals, routines, responsibilities, and reporting structures. It is design work. It is the same work a controls engineer already does when they program a system to do something specific. The vocabulary is different, the muscle is identical.

Clear Division: Builders vs. Question Askers

There is one more piece of this that I think most people miss. The controls engineer does not know the question. The driver of the agent, the plant manager, the domain experts, the factory worker, that is the person who knows the question. The controls engineer builds the infrastructure that lets those questions be asked, and lets the answers come back. That separation matters. People will have agents, and controls engineers enable those questions to be asked and answered. It is a clean division of labor and it is one most organizations have not figured out yet

The Missing Enablement for Controls Engineers

Here is the catch. Controls engineers are not doing this yet. I haven't seen it. It is a natural transition for them, but they need to be enabled to step into it. They need architectural patterns, they need deployment patterns, and they need a toolset that actually fits the way plant infrastructure works. Without that, the opportunity drifts to whoever picks it up first, and that is going to be a worse outcome for everyone

Call to Action for Plant Leaders

So if you are a plant manager or a plant engineer reading this, my honest take is that the people who are going to do this for you are already on your plant floor. You probably haven't been thinking about them this way. If you want to talk through what the architecture actually looks like, reach out. Happy to walk through it.

Frequently Asked Questions (FAQs)

AI in manufacturing isn't just a data science or IT problem—it's about reliable plant-floor infrastructure led by domain experts like controls engineers, who ensure clean data capture from machines and processes.

Domain experts (industrial and process engineers) know the right signals to capture for good data, but they need controls engineers to build and maintain reliable, long-term infrastructure for it.

Controls engineers already design reliable plant-floor systems connecting machines, signals, and people daily—the same skills needed for scalable AI agents and interconnections.

Their expertise in wiring sensors and PLCs translates to designing reliable agent-to-agent communication, just at a larger scope, ensuring stability like a control loop.

It's not about fine-tuning models or GPUs—it's design work: assigning purpose, goals, routines, responsibilities, and reporting, similar to programming control systems.

Controls engineers are "builders" who create the infrastructure; plant managers, domain experts, and workers are "question askers" who define needs and interpret answers.

They lack enablement: architectural patterns, deployment tools, and plant-grade toolsets tailored to factory infrastructure, so the opportunity defaults to others.

Recognize your on-floor controls engineers as AI leaders, provide them with the right patterns and tools, and reach out for architecture walkthroughs to get started.

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