I recently had a long conversation with one of my close friends about AI, and it genuinely stuck with me longer than I expected.
At one point, he opened his IDE and walked me through a few personal projects he’s been building. What impressed me most wasn’t just the projects themselves, but how he has integrated AI into his workflow. In IT, AI is already part of the toolkit. Meanwhile, in OT environments, we’re still observing from a distance.
That contrast hit me.
At times, it honestly feels like OT is operating a few decades behind—not in capability, but in adoption mindset.
“Oh, That’s AI-Generated”
Something my friend said during that conversation really made me pause.
He pointed out how often, when someone produces something impressive today, the response is:
“Oh, that’s AI-generated.”
There’s usually an unspoken tone behind it. Almost like it diminishes the achievement. As if using AI somehow makes the work less legitimate, less skillful, or less “earned.”
But when you step back, that logic doesn’t really hold.
For years—decades even—we’ve been building on layers of abstraction created by other people. None of us are writing everything from scratch. We use programming languages, frameworks, content management systems, and open-source libraries every single day.
And no one questions that.
No one says:
“Well, it was built with JavaScript, so it doesn’t count.”
“That’s just a WordPress site, so it’s not real development.”
Because we understand what goes into it.
We know:
- You still have to learn the syntax
- You still have to understand the architecture
- You still have to debug, design, and solve real-world problems
The tools don’t remove the thinking—they support it.
AI feels like the next step in that same progression.
AI Isn’t Magic—It’s a Skill Multiplier
There’s a common misconception that AI is just about typing a sentence and getting a perfect answer back.
Anyone who’s actually used it seriously knows that’s not how it works.
Getting real value from AI requires:
- Knowing what to ask
- Framing problems clearly
- Iterating and refining outputs
- Validating accuracy
- Applying your own context and experience
In other words, it still demands critical thinking.
Prompting well is a skill. Interpreting results is a skill. Knowing when not to trust the output is a skill.
The difference is that AI compresses time.
What might have taken hours to research, prototype, or troubleshoot can now take minutes. That doesn’t eliminate effort—it changes where the effort is spent.
Instead of grinding through repetitive tasks, you spend more time:
- Exploring ideas
- Testing possibilities
- Making higher-level decisions
That’s where the real leverage comes from.
The Opportunity for OT
This is where things get interesting.
Everything happening in IT right now—the acceleration, the experimentation, the increased velocity—can absolutely translate into OT environments.
But only if we’re willing to shift perspective.
OT has always been more cautious, and for good reason. The stakes are higher. Systems are critical. Mistakes aren’t just inconvenient—they can be dangerous.
But that doesn’t mean standing still is the safer option.
There’s a growing gap between:
- Those who are learning how to work with AI
- And those who are choosing to dismiss it
Over time, that gap will turn into a capability divide.
The people who adopt AI thoughtfully—combining it with real-world experience—will be able to:
- Diagnose issues faster
- Simulate and test ideas more efficiently
- Build smarter, more resilient systems
And that applies just as much on the plant floor as it does in a software team.
Responsibility Still Comes First
That said, none of this works without responsibility.
AI isn’t risk-free, especially in industrial and operational contexts.
We have to be deliberate about:
- Data privacy
- Security boundaries
- Model reliability
- Ethical use
Feeding sensitive data into the wrong tool, trusting unverified outputs, or automating decisions without oversight—those aren’t small mistakes. They can have real consequences.
So the goal isn’t blind adoption.
It’s informed adoption.
The Bottom Line
AI doesn’t replace intelligence.
It amplifies it.
It gives an edge to people who already understand their domain and are willing to evolve how they work within it.
And just like every major shift before it, the advantage won’t go to the people who resist it the longest—it’ll go to the ones who learn how to use it well.
The real question isn’t whether AI belongs in your workflow.
It’s whether you’re willing to figure out how it should.
