Data Governance for Distributors: Why AI Implementation Starts with Step One, Not Step Two
On the first morning of the AI for Distributors event in Chicago, I stood in front of a room full of distribution executives and digital commerce leaders and told them something most vendors won’t: everybody in this room is trying to sell you step two.
The conference itself is a time-lapse of how fast this market is moving. It’s the fourth year Distribution Strategy Group has run it, and the trajectory is steep; from legacy vendors hastily bolting AI features onto outdated systems, to fully AI-native platforms for order management, product enrichment, and AI-first ERPs. My colleague Ryan made a telling observation: a founder who showed up last year with nothing but questions and ambition returned this year with an actual product. That’s how quickly someone who understands AI can assemble something that looks like a SaaS product. That’s also how saturated the market is getting with solutions nobody is quite ready for yet.
The Real Problem Distribution Leaders Face
The most revealing conversations weren’t with vendors. They were with the distribution executives who’d already bought a tool last year. For most of them, something had gone wrong. Usually it wasn’t the tool.
It was the ground the tool got planted in.
Data governance. Or rather, the complete absence of it.
Duplicates everywhere. Two ERPs and two product information management systems mashed together from a merger. No master data rules. No consistent naming conventions. No real understanding of what data actually lives where. These are distribution leaders running sophisticated, billion-dollar operations on infrastructure that’s fundamentally chaotic at the data layer.
When they came to the booth asking about the next tool, the honest advice was: stop. Look at the digital supply chain. How data comes into your business and how it leaves. Before you spend another dollar on technology, fix that first.
That’s what I meant when I talked about step one and step two. Step two is the shiny tool; the AI that generates product descriptions, optimizes pricing, predicts demand. Step one is the infrastructure that makes that possible: data governance, data quality, integration architecture, change management. The unsexy but non-negotiable foundation.
Ryan added what he calls step zero, and it’s where half the room hadn’t gotten yet: knowing where you are, where you want to be, and the actual path between them over the next three to five years. Some of the attendees admitted, a little sheepishly, that they were miles behind. Still running the business out of spreadsheets. Still managing orders in systems never designed for modern commerce. The fantasy is that you can drop AI on top of a messy data practice and it’ll politely work around the problem.
It won’t.
Integration Is Where Most AI Implementation Fails
Here’s what distribution executives don’t always anticipate: the integration wall.
Take a brand-new AI tool. Bolt it onto a system that’s been running since 1998. Now ask: can they talk? Will this tool that dazzles in a demo actually survive on your infrastructure? On systems older than half your staff? Integration isn’t a technical nicety. It’s most of the battle.
And it’s getting more complicated. Distributors are now doing something I’ve never actually seen before in my career: hiring people specifically to build AI; vibe-coding custom apps for company-specific problems in tools like Claude. It’s exciting. It’s also a fresh source of risk. If 200 people are all spinning up their own agents and integrations and nobody knows who’s building what, you get duplicated work, duplicated spend, and security holes with nobody minding them. That’s AI governance.
We’ve seen this at Pivotree. Customers show up with a prototype they built over a weekend and say, “Make this real.” Standing up a pretty prototype is easy now. Turning it into something multi-tenant, secure, and wired into a live enterprise system is a completely different job, and that’s where the real work of enterprise AI implementation happens. Let the experts handle this.
This Is Business Transformation, Not Digital Transformation.
Ryan made his sharpest point about the language itself. Everyone at distribution conferences talks about “digital transformation,” and he’s right to push back on it. The shift needed to serve buyers who grew up clicking to buy a car and tracking dinner to their door isn’t digital.
It’s business transformation.
A 180-degree turn in where your company aims itself. From sell-to-order to buy-from-anywhere. From ERP-centric to customer-centric. From batch processes to real-time. Whoever executes this cleanest and fastest takes market share at a rate we haven’t seen since online shopping first appeared. Distribution executives who get this right become the default player in their segment.
But it doesn’t start with the AI tool. It starts with data governance. With integration strategy. With a clear three-year vision. Then you layer in AI. In that order, or not at all.
If you’re ready to move beyond step two, listen to Episode 7 of Data vs. Commerce here where we break down the exact sequence distribution leaders need to follow, and what each step actually requires to get right.
Ready to assess where your distribution company really stands? Book a complimentary diagnostic call and walk away with real human intelligence about the state of your data and integration architecture so you can take the first step toward actual AI implementation that works.
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