The AI Expertise Gap: Why Answers Alone Aren’t Enough

This voice experience is generated by AI. Learn more.This voice experience is generated by AI. Learn more.Avy Punwasee is a Partner at Revenue Management Labs. We help companies develop and execute pricing solutions to maximize profits.

gettyA few weeks ago, I found some strange white crystals at the bottom of my freezer. Then I found the same thing at the bottom of my fridge on the floor. Instead of calling an appliance repair company, I took two photos, uploaded them to Claude and asked what was going on. The AI nailed it: drain valve at the back of the freezer. It even told me exactly where to find it. I snapped a photo of the model number, and the AI walked me through which panel to remove and what to do next. Finishing the job meant taking off that back panel, the one with a small light attached to it, then defrosting the line and flushing it with warm water—and a few more steps after that. That is the point where I stopped and called a professional.

That small experience has stuck with me because it captures something important about where AI actually helps and where it runs out of road. Today, it seems everyone has the answer, but far fewer have the experience. ​And that matters in business.

The question I hear constantly is some version of: Why do I need an expert when I can just ask ChatGPT, Claude or Gemini? You can. And you’ll get an answer. But an answer is not the same thing as expertise.

An expert, in my view, is simply someone who has enough “scar tissue” that they already know what not to do. They’ve seen a strategy go sideways. They’ve watched a “clean” plan blow up the moment it met a difficult customer, an outlier account or a team that had its own agenda. AI has never made those mistakes. It has read about them, but it has never lived through the fallout.

To use another home analogy, I think about this the same way I think about tiling a floor. You can watch three YouTube videos, buy the materials at Home Depot and probably lay a floor yourself. At the end of the day, you’ll have a floor. The question is how well it’s laid and how long it lasts. Mix the grout wrong or skip a step you didn’t know mattered, and in a year, you’re redoing the whole thing. A good professional is trained to do it faster and better to ensure it lasts. That gap only shows up over time.

Strategy works the same way. You can dump your data into an AI tool and get back an analysis that looks exactly right. The math isn’t the hard part anymore. The hard part is everything AI can’t see: the relationships, the internal politics, the years of “we tried that already and here’s why it failed.”

The riskiest part of an AI-built strategy isn’t a bad chart or a wrong formula. It’s the cost nobody notices.

If a strategy leaves value on the table, that rarely shows up as a line item. You just never learn what you could have had instead: the better margin, the higher win rate, the deal that could have gone the other way. There’s no red flag for money you didn’t lose—only money you never made. And because a team built the strategy themselves, often with real pride, that pride can make it harder to go back and ask whether it was actually optimal.

In my experience, most people never ask. They built the solution with the AI, they are proud of it and they trust it without question.

Here’s the part I think gets lost in every AI conversation: Most strategy work is roughly 20% analysis and 80% execution. AI has compressed the 20% dramatically. It has done almost nothing for the 80%.

You still have to convince your own team that a new approach is worth the discomfort. You still have to sit down with your most demanding customer and figure out how to shift the relationship without losing them. You still have to walk into the room and make the case, in person, with all the friction that involves. No prompt does that for you.

And without a clear story for why a decision was made, teams can fall back into old habits fast. The loudest voice in the room wins the argument, and things drift back toward the path of least resistance because nobody built the muscle to hold the line.

We shouldn’t be asking, “Can AI do this?” The answer is almost always yes. The better question is, “What are we actually optimizing for?” Is it a quick answer, or confidence that the answer will hold up once it meets real customers, real politics and real resistance?

I use AI constantly, for research, for first drafts, for getting unstuck. It has genuinely made me faster. What it hasn’t done is replace the years of watching a strategy succeed, fail and succeed again for reasons that never showed up in the data.

AI can tell you exactly where the drain valve is on your fridge. It still can’t tell you how to take the panel off without breaking the light attached to it. That only comes from having done it before, more than once, and having gotten it wrong at least once along the way.

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