Most of what I've written recently has been about where the software and logic processing hardware in our industry is headed. Software-defined automation, software engineering concepts like object-oriented programming and DevOps applied to industrial automation, and virtual PLCs running in IT-managed stacks.

More of what used to be determined by hardware will be handled in software, and controls workflows will increasingly resemble the ones software teams have used for years. It's tempting to read that as controls engineers needing to become software engineers, or completely obsolete, but the core controls engineering skill set isn't going anywhere. If anything, it's about to become more valuable.

Modern tools reduce the cost of producing logic, but they don't solve the harder part of the work, which is understanding a complex machine or automated process well enough to decide what that logic should do. They don't lower the cost of being wrong about the physical process, which has real implications for safety, business operations, and maintenance risk. When writing machine logic becomes faster, the bottleneck shifts toward engineering judgement.

Knowing the process before the program

Every control system represents something physical, and the machine behaves the way it behaves whether a model captures it or not. Functional specs, simulations, and digital twins help define a machine before it's built or changed, and they catch major problems early.

But even a good model has its limits, and often custom machinery is iterated upon as it is being built. It can’t fully capture the behavior of equipment that’s been running in production for years, with normal wear, changing materials, and the small workarounds people develop to keep it moving.

That gap is where a large part of controls engineering lives, filled by experience and a personal Swiss Army knife of mechanical, electrical, software, and IT / networking knowledge. An engineer who has spent time with their hands on equipment knows when a recurring fault points to a mechanical problem rather than a software one, and which adjustments will get a line running versus which will create a larger problem downstream. 

Operators and maintenance teams hold a lot of that knowledge too, and bringing their voices to the automation retrofit or redesign is a critical part of the job. A controls engineer can turn that experience into better sequencing, more useful diagnostics, recovery behavior that works, and human-machine interaction that is understandable by operators with a variety of backgrounds. 

Logic written against physical constraints

The difference between PLC logic that compiles and logic that runs safely and efficiently almost always comes down to constraints imposed by the equipment and its interaction with the process, environment, or other systems. Those details drive sequencing, interlocks, alarm handling, and the machine software architecture itself, all of which are core controls engineering skills.

Machine logic can be well-written and still be wrong for the machine if it doesn’t account for what can actually happen in the physical system, including the unexpected or intermittent cases that could cause significant downtime or loss of a product that costs thousands of dollars. Normal operation is comparatively easy to specify and easy to generate code for. 

Handling everything around it is the difference between automation that operations teams trust and automation they want to rip out, because they can never count on it running without constant calls for support.

It's fair to ask whether a computer could learn this instead, and over a long enough horizon that may well be an evolution of capability we see. But learning a process requires examples of it, and the conditions we most need to handle correctly are by definition the ones with the fewest examples. 

There are few public examples of code for a new machine, or for a process built around a manufacturer's internal know-how. For now, someone still has to decide how equipment should behave in situations it has never been in.

System-level thinking and good communication are the durable skills

What ties all of this together is the ability to think at the system level and communicate your conclusions. Controls engineers hold the process, the equipment, the logic, the timing, the operators, and the failure modes in their head at once, and understand how a change to one affects the others. Getting useful feedback from an operator who has run a piece of equipment for ten years, or getting up to speed on an unfamiliar process quickly, is as much a part of the job as the logic itself.

A weekend retrofit with two technicians requires clear scopes and good communication. It also requires someone to come back through the work, verify what was installed, and see how the equipment behaves once it is running. Leading a controls group across a plant is the same basic work at a larger scale. Directing an AI agent to generate logic is not so different.

You still have to give it enough context to do something useful, review what it produces, and decide whether the result makes sense for the machine. The tool changes who does the typing, but it doesn't change who has to understand the machine.