How Distributors Should Evaluate AI: Cross-Referencing and Match Accuracy Are the Hard Part
By Jason Sullivan, Founder and CEO, Distro
Short answer: When a distributor evaluates AI, the deciding factor is not how fast the tool reads an order. Reading a purchase order or RFQ is the easy part, and nearly every tool will soon do it well. What separates AI that grows revenue from AI you abandon by spring is how accurately it cross-references messy inputs to the right product in your own catalog, and whether it tells your people when it is not sure. Start with the front office, your quoting and sales team, because that is where AI turns into more quotes won and more revenue.
Where should a distributor start with AI?
Start with the front office. AI can go almost anywhere in a distribution business, from the warehouse to accounts payable, and plenty of it is worth doing. But the front office, your quoting and sales team, is where the money is made and where the retirement wave will hit hardest, whether it has reached you yet or not. AI built for that work gets more quotes out the door and wins more of them. That is what grows revenue, and it is not what most demos are selling you.
Why is order entry the easy part of AI for distributors?
Reading the document is the easy part. Most AI demos are built around one moment designed to get a reaction: someone drops a messy order or an RFQ into the software, and it pulls every line off the page in seconds, even the notes scribbled in the margin. The room is impressed, but that is the whole point of it, and not much comes after. Pulling text off a page is close to solved and will soon be table stakes for every tool. On its own, it has not won you a single job.
What actually matters: cross-referencing to your catalog
The value is in what happens after the read. Cross-referencing is the work of matching an incoming line, such as a competitor's part number or a loose description, to the right product in your own catalog, at the right price, with the right substitution.
A line on a page is not a quote yet. The part number might be a competitor's, or something you quit stocking two years ago. Sometimes there is no number at all, just "1-1/2 inch bronze circulator, flanged." The right answer is the Taco or the Bell & Gossett you actually carry, at the price this customer gets, with the substitution your best counter person would have reached for without thinking.
This is the product knowledge your veterans built up over decades and carry in their heads. It decides whether a quote goes out fast and right or slow and wrong. Get it wrong and you lose the quote, or worse, win it with the wrong part.
A distributor told me recently that the quotes that kill his team are not the ones for products he already stocks. Those are easy. It is the ones built around a brand he does not carry, where his people have to cross them over to what he does. Two of his salespeople will cross the same competitor product to two different part numbers, because there is no standard way to do it. That is where the time goes, and where they lose deals.
Why does AI confidence scoring matter as much as accuracy?
An AI tool has to know how sure it is. A shaky match cannot look as confident as a solid one. The tool has to flag the doubtful ones and say, in effect, "double-check me here." When it does not, the failure is predictable: one of your best people gets a confident answer that is flat wrong, stops trusting the tool, and within a week the whole team is back to the old way. Speed means nothing if no one trusts the results enough to use them.
What questions should you ask an AI vendor?
When the demo wraps up, keep asking:
How do you match a line to the right product when the part number is wrong or missing?
How accurate is that matching on my catalog, not a generic one?
How do you handle competitor cross-references and substitutions?
How does the tool tell my people when it is not sure?
A tool that reads an order beautifully but fumbles those questions always looks better in the conference room than on a Tuesday afternoon with a customer on hold.
How is this different from order-entry tools like Canals?
Most AI sold to distributors is built to read the document. Order-entry and extraction platforms, including tools like Canals and Conexiom, are designed to turn a purchase order or RFQ into structured data quickly. That is useful, but it is the easy part, and it stops short of the work that wins the deal. Reading a line is not the same as knowing which product it maps to in your catalog, at your price, with the right substitution, and how confident that match is. Cross-referencing and confidence scoring are a different and much harder problem, and they are not what an extraction tool is built to do. That gap is exactly where Distro focuses.
How Distro approaches cross-referencing and confidence
This is the exact problem we built Distro to solve: the cross-referencing and the confidence behind it, not just reading the page. Distro cross-references incoming lines to the right item in a distributor's own catalog more than 90% of the time, and it flags the lines it is not sure about instead of bluffing.
That standard holds whether you buy from us or not. Put your money on the hard part, the part that helps your front office sell, and hold any vendor to it. AI really is part of the answer for distributors, as long as you point it at the right place. Reading the order was never the hard part. Turning it into the right product, quoted fast and at a margin you can live with, is the job.
Frequently asked questions
Is order entry automation the most important AI feature for distributors? No. Reading and extracting lines from a purchase order or RFQ is the easy part and is quickly becoming a commodity. The higher-value work is cross-referencing those lines to the right product in your catalog and scoring how confident the match is.
What is cross-referencing in wholesale distribution? Cross-referencing is matching an incoming line, such as a competitor's part number or a vague description, to the correct product you carry, at the correct price, including the right substitution. It is the core of accurate quoting.
How is Distro different from order-entry tools like Canals? Order-entry and extraction tools, such as Canals or Conexiom, are built to read a purchase order or RFQ and turn it into data. Distro goes further: it cross-references each line to the right product in your own catalog, applies your pricing and substitutions, and flags matches it is not confident about. Reading the document is the easy part; cross-referencing and confidence scoring are the hard part, and that is what Distro is built for.
How accurate should AI cross-referencing be for distributors? Measure accuracy against your own catalog, not a generic one. Distro cross-references to the right item more than 90% of the time and flags low-confidence matches for human review.
Why does AI confidence scoring matter? If a tool presents an uncertain match with the same confidence as a certain one, your team will eventually get burned by a wrong answer and stop trusting it. Flagging uncertain matches is what keeps a tool in daily use.
Where should a distributor start with AI? Start in the front office, with quoting and sales, because that is where AI most directly grows revenue by producing more accurate quotes and winning more deals.
