In 2019, we deployed AI-powered product recommendation and re-order recommendation software at Hisco. The technology worked. The results with our smaller customers were real and measurable. The AI identified products customers should be buying and surfaced re-order opportunities our reps weren’t seeing.

But here’s what stopped us: the rest of the organization wasn’t ready to believe the AI could see something they couldn’t.

These were experienced people. Decades in distribution. They’d built relationships, learned customer buying habits, developed instincts over years. And now a machine was telling them there were products and re-orders they’d missed?

That didn’t sit well.

It wasn’t a technology problem. It was a belief problem. The unspoken assumption was: I’ve been doing this for 20 years—what can an algorithm tell me that I don’t already know about my customers?

Turns out, a lot. But only if you’re willing to hear it.

The Rocket Engine on a Bicycle

I’ve been thinking about that 2019 experience a lot lately as I watch how distributors are approaching AI in 2025 and beyond. And I keep coming back to the same observation: the biggest mistake isn’t ignoring AI. It’s using AI to do the same things we’ve always done—just faster.

Faster emails. Faster summaries. Faster reports. Faster proposals.

That’s like using a rocket engine to power a bicycle. You can do it, but you’re missing the entire point of the engine.

AI isn’t a productivity tool. It’s a platform shift. And like every platform shift before it—the internet, the smartphone, even electricity—the winners won’t be the ones who use the new technology to accelerate their existing playbook. The winners will be the ones who ask a fundamentally different question: What does this make possible that wasn’t possible before?

Why Experience Becomes the Obstacle

Here’s the uncomfortable truth I learned at Hisco: the very experience that makes your people valuable can also make them resistant to AI.

Your best sales reps have spent years—sometimes decades—developing intuition about customers. They know the buying patterns. They recognize the signals. They’ve built mental models about who buys what and when. That knowledge is real. It’s valuable. It took a long time to develop.

And when an AI system tells them something that contradicts that hard-won knowledge, the natural human response isn’t curiosity. It’s skepticism. The machine must be wrong. I know these customers.

But here’s what the AI can do that even your best rep can’t: it can analyze every transaction, every customer, every product combination simultaneously. It doesn’t get tired. It doesn’t forget. It doesn’t have favorites. It sees patterns across your entire customer base that no human—no matter how experienced—could hold in their head at once.

The AI at Hisco wasn’t replacing our reps’ knowledge. It was seeing things their knowledge couldn’t reach. Different scope. Different scale. Different kind of insight entirely.

We just weren’t ready to believe it.

The Safe Zone That Keeps You Small

When I look at how most distributors are using AI today, I see a pattern. They’re deploying it in safe zones—places where it can’t challenge anyone’s expertise or authority.

Summarize this document. Write this email. Generate this report.

That’s comfortable. Nobody feels threatened by an AI that drafts an email. Nobody’s 20 years of experience gets questioned by a meeting summary.

But that’s also where AI creates the least value. You’re using a revolutionary technology to shave minutes off tasks that weren’t your bottleneck in the first place.

AI becomes transformational when it surfaces insights that contradict what you thought you knew. When it shows you the products a customer should be buying. The re-order timing you missed. The segment you’ve been underserving. The pattern you’ve been blind to.

That’s where the real value is. It’s also where the real resistance lives.

From Tool Users to System Builders

There’s a useful distinction I’ve been thinking about lately: the difference between using AI as a tool and using AI to build a system.

A tool user says: “Help me write this quote.”

A system builder says: “Build me a process that identifies which customers need quotes, generates draft proposals based on their buying history, and flags the highest-probability opportunities for my reps to prioritize.”

Same technology. Completely different outcomes.

The tool user gets incremental efficiency. The system builder creates compounding advantage. And over time, that gap becomes insurmountable.

The distributors who will lead in the next decade aren’t the ones who adopt AI first. They’re the ones who are willing to let AI challenge their assumptions, surface uncomfortable truths, and reveal opportunities they didn’t know they were missing.

The Question That Matters

Back in 2019, our AI-powered recommendation engine wasn’t just suggesting products. It was showing us a different way to serve a customer segment we’d undervalued. The smaller customers. The ones that didn’t get as much attention from experienced reps because the individual transactions seemed less important.

The AI saw what we couldn’t: those customers, in aggregate, represented significant untapped revenue. The buying patterns were there. The opportunities were there. We just needed to look at them differently.

We weren’t ready to see it that way. Too many people had too much invested in believing they already knew the answers.

Most organizations still aren’t ready.

So here’s my challenge to you: Where in your business might AI be surfacing something you’re not ready to accept? What insights are you dismissing because they conflict with what your experienced people believe they already know?

The question isn’t whether you’re using AI.

It’s whether you’re willing to believe it when it tells you something you didn’t want to hear.

That’s the difference between using a rocket engine to power a bicycle—and actually learning how to fly.