I am not a technical person.

I don't say that as a disclaimer. I say it because it's true and because I think it's important to say out loud. I have an engineering degree I never used, three graduate degrees that were all about business and people, and a career built in operations and strategy. I have never written a line of production code. I don't know what a neural network actually does under the hood. When people start talking about parameters and fine tuning, I nod politely and wait for the conversation to come back to something I can work with.

And yet AI has become one of the most useful tools in my professional life.

Not because I learned to be technical. Because I learned that AI doesn't require you to be technical. It requires you to think clearly, ask good questions, and know what a good answer looks like in your domain. Those are skills I've been building my entire career. If you work in operations, strategy, marketing, HR, finance, or really any function that involves solving problems with imperfect information, you've been building them too.

This post is for the people who feel like the AI conversation is moving too fast and they're getting left behind. I want to tell you what I've learned. Not from a textbook. From actually using these tools as someone who is decidedly not a tech person.

The Moment It Clicked for Me

I'll be honest about how I started. I was skeptical. Not skeptical in the "AI is a fad" way. Skeptical in the "this probably isn't for someone like me" way. I assumed you needed a certain kind of brain to use these tools well. A coding brain. A data science brain. A brain that thinks in systems architecture and API endpoints.

Then I tried using an AI tool for something simple. I had a piece of writing I was working on and I wanted to restructure it. I described what I was trying to accomplish, explained the audience, told it what wasn't working, and asked for suggestions.

What came back was genuinely useful. Not perfect. But useful in a way that saved me an hour of staring at a blank screen.

And the thing that surprised me was that the quality of the output depended entirely on how clearly I described the problem. Not on any technical skill. On the same skill I use every day at work. Define the objective. Explain the constraints. Describe what good looks like. Iterate based on what comes back.

That's when it clicked. I had been doing this my entire career. I just called it something different.

You Already Have the Skills. You Just Call Them Something Else.

Here is something I wish someone had told me earlier. The skills that make someone good at prompting AI are the exact same skills that make someone good at operations.

Think about what you do when you're building a process or improving a workflow. You break a complex goal into clear steps. You define success criteria. You think about what could go wrong. You test, you look at the results, and you adjust. You iterate until the output meets the standard.

That is prompt engineering. That is exactly what it is. The only difference is that instead of writing an SOP or briefing a team, you're directing an AI tool. The medium changed. The thinking didn't.

People with operations backgrounds, strategy backgrounds, project management backgrounds have been doing this kind of structured thinking for years. The AI era doesn't ask you to learn a new way of thinking. It gives you a new place to apply the thinking you already do.

I find that genuinely exciting. Because it means the barrier to entry is not technical knowledge. It's willingness to start.

What AI Actually Does Well (and Where It Falls Apart)

One of the most important things I've learned is where AI helps and where it doesn't. Understanding this boundary is what separates people who use AI effectively from people who either overhype it or dismiss it.

AI is excellent at speed. It can summarize a long document in seconds. It can draft a first version of something that would take you an hour to write from scratch. It can analyze patterns in data and surface things you might have missed. It can generate options when you're stuck. It can take a messy set of notes and organize them into something coherent.

What AI cannot do is judgment. It doesn't know your organization. It doesn't understand the politics of your team. It doesn't know which stakeholder will push back on a recommendation and why. It doesn't know whether the data it's looking at is trustworthy or stale. It doesn't know the difference between a technically correct answer and the right answer for your situation.

That judgment is yours. And it's the most valuable thing you bring.

AI does the first 70% of the work in about 5% of the time. Then I do the last 30% — the part that requires knowing my context, my audience, and my standards.

The combination is dramatically faster than doing everything myself. But the human part is not optional. It's the part that makes the output actually good.

The Spreadsheet Analogy

When I talk to colleagues, friends, or family who feel intimidated by AI, I often use this comparison.

Think about spreadsheets. In the 1990s, knowing how to use Excel was a differentiator. The people who could build a pivot table or write a VLOOKUP had an edge. It wasn't a "tech skill" in the way we think about tech today. It was a practical skill that made you more effective at your job. Over time it became a baseline expectation. Nobody lists "proficient in Excel" on a resume anymore and expects it to impress anyone. It's just assumed.

AI is going through the same transition right now. Today, knowing how to use AI tools effectively is a differentiator. In a few years, it will be a baseline. The professionals who start now will be the ones who set the standard. The ones who wait will spend their time catching up.

I don't say this to create anxiety. I say it because I waited longer than I needed to. And when I finally started, I realized the gap between "not using AI" and "using AI effectively" was much smaller than I thought. It wasn't a cliff. It was a step.

What 70% Actually Means

There's a statistic that shows up in a lot of the research I've read. About 70% of AI success in organizations depends on people and processes, not technology. Microsoft found this in their manufacturing research.

I think this is the most underreported story in the entire AI conversation.

Everyone is talking about the technology. The models are getting better. The tools are getting cheaper. The capabilities are expanding. All true. But the reason most organizations struggle with AI isn't that the technology doesn't work. It's that the people and the processes aren't ready.

That is a change management problem. And a communication problem. And a training problem. It's an organizational design problem. These are not technical problems. They are the exact problems that operations and strategy professionals have been solving for decades.

If you work in any of these areas, the AI era doesn't make you less relevant. It makes you more relevant than ever. The world has plenty of people who can build AI systems. What it needs is people who can make AI work inside real organizations, with real humans, and real constraints. That is your lane.

How I Actually Use AI in My Life

I want to be practical about this because I think too many AI articles stay at the 30,000 foot level. So let me just tell you what I've actually built and used. As a reminder, I have never written a line of code.

I built this website. The one you're reading right now. AI helped me build it from scratch. I manage my desktop, my emails, and my writing workflow through AI tools that I set up myself.

I built an agent that scoops up competitive intelligence, earnings calls, and relevant external sources from across the internet. The important part is that it knows what's relevant without me having to tell it every time. It maps what it finds to internal strategy so I can quickly spot gaps and wins. That's not a side project. That's something I built to do my actual job better.

I also built a personalized fitness application that tracks my diet, my workouts, and my mood. It's tailored to me. Not a generic app I downloaded. Something I described and shaped and now use every day.

And I'm just getting started.

None of this required technical skill. All of it required knowing what I wanted, describing it clearly, and iterating until it worked. The same skills I use in every part of my professional life.

The Real Barrier Is Not Skill. It's Starting.

If you've read this far and you're still thinking "but I'm not a tech person," I want to tell you something directly.

Neither am I. And that has not been a barrier. It has been irrelevant.

The AI era does not belong to the people who understand the technology best. It belongs to the people who understand their domain best and are willing to use new tools to do their work better. That's a meaningful distinction. The technology is built to be accessible. The domain expertise is not. You spent years building yours. That's your advantage.

The only barrier is starting. And starting looks like this. Pick one thing you did this week that was repetitive and took longer than it should have. Open an AI tool. Describe the task in plain language. See what comes back. Adjust your description. Try again.

That's it. That's the whole first step.

You're not learning to be a technologist. You're learning to use a tool. The same way you learned to use a spreadsheet, or PowerPoint, or a project management platform. You figured those out. You'll figure this out too.

And if you're someone who works in operations or strategy or any function that requires you to break down complex problems, communicate clearly, and iterate until something works, you're going to be surprised by how natural it feels.

You've been training for this your whole career. You just didn't know it yet.