The Last Two Weeks Changed Something for Me
Over the past two weeks, I’ve been building apps.
Not with a development team. Not with a six-month roadmap and a steering committee. Just me, a laptop, and AI—using what I know about this industry to build tools that are already changing how we operate.
I want to be precise about what made that possible. Because the easy answer is “AI.” The more honest answer is more complicated than that.
I had to let go of my pride first.
That’s not a throwaway line. For someone who has spent more than 30 years earning expertise in distribution—running operations, managing P&Ls, living through the decisions that kept businesses solvent and the ones that didn’t—saying “I had to let go of my pride” is the most operationally significant thing I can tell you about this moment.
The Ceiling Nobody Talks About
Here’s what happens to experienced operators when they first engage with AI.
They use it to confirm what they already believe. They ask questions they already know the answers to. They run the outputs through a mental filter that says: “does this match what I know?” If yes, they accept it. If not, they dismiss it. Then they walk away thinking AI is overhyped.
That’s not a technology problem. That’s experience becoming a closed loop.
The same confidence that makes a veteran operator effective—the pattern recognition, hard-won judgment, the instinct that’s been calibrated by years of real consequences—can also make them the hardest person in the room to teach. Not because they’re arrogant. Because they’ve been right so many times that the cost of being wrong feels higher than it is.
I’ve been that person. I know what it feels like to sit across from a new tool and unconsciously run it through every frame I’ve already built. It’s not skepticism. It’s fluency in your own worldview—and it quietly blocks everything that doesn’t fit inside it.
What Letting Go Actually Looks Like
I want to be specific, because “let go of your pride” sounds like soft leadership advice. It isn’t.
What it means in practice: I stopped entering every AI interaction trying to steer it toward a conclusion I’d already reached. I started entering those interactions with a genuine question—what do you know that I don’t? What are you seeing in this problem that I’m filtering out?
That shift is small and behavioral. It’s enormous in terms of what comes out the other side.
The apps I built over the past two weeks came from exactly that dynamic. I knew where the pain was. I’ve lived inside distribution businesses long enough to feel the inefficiencies that get papered over, the processes that work until they suddenly don’t, the decisions that get made slowly because nobody has the right information fast enough. That’s my contribution. I can walk into a problem and diagnose it in a way that takes most people months to develop.
What I couldn’t do was build a solution at the speed and scale the problem needed. Not alone. Not without tools, I didn’t have.
AI had the capability I was missing. I had the context AI was missing. When I stopped treating that as a competition and started treating it as a collaboration, something got created that neither of us could have produced on our own.
Why This Matters More Than Most People Realize
The combination of deep industry experience and real AI capability is genuinely rare. Most organizations have one or the other.
You have experienced operators who know every wrinkle in the business but can’t build at speed. Or you have younger teams who are technically fluent but don’t know what problem is worth solving. The rare person, the rare organization—is the one that figures out how to combine both.
Here’s what the data from DSG’s research keeps showing: early AI adopters in distribution aren’t winning because they have better technology. They’re winning because they’re applying that technology with better context. They know their customers, margins, operational friction points, and supplier dynamics. AI amplifies that knowledge. It doesn’t replace it.
But that amplification only happens if the experienced operator is willing to be a genuine participant—not just a filter.
The operators who aren’t willing to do that will still use AI. They’ll use it to move faster in the direction they’ve already decided to go. They’ll get some efficiency gains. They’ll stay mostly inside their existing frame.
The operators who are willing to hold their expertise a little more loosely—who bring their experience to the collaboration without making it the final word—will build things the rest of the industry can’t replicate. Not because they’re smarter. Because they’re more open.
What I’d Tell My Younger Self
I spent a long time in distribution thinking the job was to be the most knowledgeable person in the room. That instinct served me. It also cost me—in moments where being the most knowledgeable person made me the last one to consider I might be wrong.
What I know now: the most valuable thing I bring to any AI interaction isn’t my certainty. It’s my context. My ability to frame the problem correctly. My knowledge of where real friction is. My willingness to test
a conclusion against operational reality before acting on it.
That’s expertise that took decades to build. It doesn’t go away when AI enters the room. It becomes more powerful—but only if I stop treating it like something I need to defend.
The question every experienced distribution leader needs to sit with is this: are you bringing your expertise to AI as a resource, or as a gate?
One of those postures builds something new. The other confirms what you already know.
Decide which one you’re doing.
Join us at the Applied AI for Distributors Conference in Chicago, June 23–25, 2026. Details at appliedaifordistributors.com.
Share this article:
