How Will AI Impact Technical Roles in Industry?

The real disruption isn't AI replacing workers — it's that one professional with strong fundamentals and effective AI fluency may do the work of two or three who don't.

Deep expertise and strong fundamentals will be increasingly important. Those who lack deep understanding of concepts, rely on procedural step-by-step knowledge alone, and who also lack the ability to use AI tools effectively face the greatest risk of being left behind.

The debate around AI and jobs has become more polarized than productive. Alarmists predict mass unemployment. Nay-sayers insist it's all hype. Both miss the point.

When I was young my mother worked at a GM plant in Oklahoma City. I still recall listening as she and her colleagues complained bitterly about the evil robots being installed on the floor back in the late 70's. Those "evil robots" turned out to be PLC control systems — ironically, the same systems I have largely built a career working on and around. Did those evil robots eliminate all the workers and jobs? Some people admittedly had to adapt, but the gains in efficiency and quality spawned entirely new fields and countless new jobs and even whole careers (like mine). The same pattern has repeated with every major technology shift.

Most voices dominating the AI conversation are either selling it or afraid of it. The actual threat to individual I&E professionals isn't being replaced directly by an AI bot — it's being outpaced by a colleague who has strong fundamentals and knows how to use AI as a precision tool. That person solves problems faster, documents better, learns more quickly, and in many cases quietly handles work that used to require multiple people.

That's not a prediction. It's already happening.

From Skeptic to Convert — With Eyes Open

My early exposure to AI and the frequent mistakes and hallucinations it made had me convinced it would never be a serious technical tool. I said so pretty loudly. But I now feel like I was wrong. I am quite sure it truly is going to be a game changer at minimum, like it or not.

My first real "aha" moment came designing a flow measurement training system for one of my I&C training courses. AI made numerous mistakes — mistakes I caught because I had the experience to recognize them. But with each correction it adapted and improved, and within a few hours we had accurate combinations of flow rates, pump curves, head calculations, piping sizes, and differential pressure values for equipment I already owned (so I didn't have to buy expensive new equipment). That would have taken me far longer to do manually and I couldn't afford the cost or learning curve for high end fluid flow software. No high-powered systems. Minimal time investment. Accurate results. Just iterative problem solving with a tool I knew how to supervise. I've since built several systems and even some cool online simulations I couldn't possibly have completed without AI help.

The pattern holds across every substantial project I have undertaken since: I provide guidance and judgment, AI handles the gritty iterative work. That division of labor works extraordinarily well — when the human half is strong enough to hold up their end.

Are there still major weaknesses? Does it sometimes generate frustrating results? Absolutely. But it is also an amazingly powerful tool in the right hands and overall it has not only saved me tons of money, but it has also helped me do way more than I could without the help.

The Same Pattern, Different Scale

I have spent nearly four decades in the I&C realm — from Navy Nuclear and old tubes and magamps technology, to digital circuits and microprocessors, to PLCs, DCS, the PC revolution, industrial networking and so on. My observation is that in every case the professionals who engaged early, had strong fundamental skills and kept them sharp, and built genuine competency on emerging technologies came out ahead. Those coasting on procedural knowledge or avoiding learning the new stuff, and/or who lacked deep conceptual understanding of the core craft found themselves in a gap that was hard to close.

AI is a different animal in speed and scale. The dynamic is identical.

What It Does Well — And Where It Fails

AI genuinely excels at pattern recognition, documentation drafting, information summarization, complex analysis, and iterative calculations. For professionals who know how to direct it, these capabilities are transformative.

But it fails regularly — and sometimes dangerously. It will confidently cite standards that don't exist, or suggest solutions that won't work, or overlook serious factors or issues. In a low-stakes environment that's an inconvenience. In more critical environments it could result in serious risk, harm, or damage.

The Most Overlooked Opportunity — AI as a Learning Tool

In forty years in this field I have not encountered a better learning tool. When something fades from memory I no longer immediately start sorting through my old reference books — I start an AI chat and get a clear, cited answer in minutes, then follow up, go deeper, and apply it to the problem at hand. Textbooks can't do that.

Example — a quick prompt a technician might use:

"Remind me how a capacitor filter works — explain it like I'm in 8th grade."

The response from my AI tool was clean, accurate, and immediately useful for someone asking that level of question:

"Think of a capacitor like a tiny rechargeable battery built into the circuit. When the voltage coming in gets too high, the capacitor absorbs the extra energy and stores it. When the voltage dips too low, the capacitor releases that stored energy to fill the gap. The result is that instead of a bumpy, uneven voltage — like a road full of potholes — you get a smooth, steady flow. That's filtering. The capacitor is basically the shock absorber of the electrical circuit."

The person could easily expand or dig deeper from there. The adaptability of AI in self-learning is beyond what any textbook can deliver. For the true self-learner, AI is unbelievably powerful. I am confident that those who become proficient at using AI as a learning and retention/refresher tool will become tops of their respective teams and fields.

Current limitation with LLM based AI (chat) type systems for learning: most AI tools can't generate technical diagrams yet (at least not good ones in most cases) and graphics are really helpful in the learning process for most people. The workaround is to simply include a request for links to online references with good visuals in the prompt.

Here is a practical starting prompt for any learning task:

"Please explain [topic / concept] at a [beginner / intermediate / advanced] level and include trustworthy authoritative online references. Where available, include links to resources with video or graphical explanations."

Not sure where to start with AI at all? Try this prompt. Learn AI by using AI to learn how to use AI — sorry for that, but it works!

"I work in [your field or role] and want to learn how to use AI as a daily work and learning tool. Ask me questions about my background and the problems I deal with — then give me a practical plan for getting started in ways that will have the most immediate impact on my work."

Be prepared for early disappointment when you first start using AI (that is partly AI and partly bad or incomplete prompts). Push through it. The capability compounds quickly once it clicks and you learn to steer and guide the AI.

The Warning Most Organizations Aren't Ready to Hear

Some organizations probably should not be deploying AI right now — not because the technology isn't capable, but because they simply don't have the experienced talent needed to guide it. Without strong technical people steering it, AI in an industrial environment is like putting an F-16 in the hands of an untrained teenager with a need for speed. They might get airborne. But it probably won't end well.

If your organization still has deeply experienced technical people — hold on to them and start a transition plan immediately that includes transferring and encapsulating their knowledge. An AI expert alone cannot guide sound industrial implementations. Site-specific experience, intuition built over decades, and the ability to correlate real-world conditions are irreplaceable — and essential to any successful AI deployment.

That talent is leaving industry right now at a pace most organizations weren't prepared for. When those skilled personnel are gone, rebuilding from scratch is extraordinarily difficult. The years of deferred training and missed knowledge transfer are beginning to show up on the bottom line for many organizations already. For some it will mean downtime. For others, costly failures. For a few, something worse.

The solution isn't to avoid AI. It's to recognize that deep technical expertise just became more valuable — not less — and invest accordingly.

The Bottom Line

AI is not the villain and not the savior. It rewards those who combine strong fundamentals with the capability to steer and verify what it produces. The professionals who thrive won't necessarily be the most AI-experienced — they'll be the ones with deep domain knowledge who are able to use AI well as a force multiplier.

For individuals: engage now, start simple, stay skeptical, keep building your fundamentals. They matter more than ever.

For organizations: protect your experienced talent and begin transitioning their knowledge and experience asap, invest in the next generation, and stop treating AI as a shortcut around the hard work of building capable people. It isn't one — it is a force multiplier. 1000 times zero is still zero.

The shift is already underway. The question isn't whether AI will change your field or role — it's whether you'll be ahead of that change or behind it.

Ready to Strengthen Your Team's Capabilities?

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Mike Glass | ISA CAP, CCST III
Owner, Orion Technical Solutions LLC | orion-technical.com
(208) 715-1590 | [email protected]