Most wholesale distributors have solved order entry for their largest customers. Those accounts send orders through EDI or a customer portal, and the orders land in the ERP without anyone touching them. The problem is the long tail: hundreds of smaller customers who email a PDF, paste a list into the body of a message, or attach a spreadsheet exported from their own system.
For those orders, someone on the order desk opens the email, finds the customer in the ERP, matches each line to a SKU, checks the price, and keys it in. It is careful, repetitive work, and it is where errors creep in: a transposed quantity, the wrong unit of measure, a customer part number that maps to the wrong item.
Why templates and OCR were not enough
Earlier attempts at automating this relied on templates: define where the PO number sits on each customer's form and extract it. That works until a customer changes their form, and it never works for orders written in free text. Plain OCR reads the characters but does not understand that "2 cs" means two cases of twelve, or that a customer's internal part number corresponds to your SKU.
Modern language models change the first half of the problem. They can read a purchase order in almost any layout and pull out the customer, ship-to, requested date, and line items with their quantities and units. What they cannot do on their own is know your catalog, your contract prices, or your customer's history. That knowledge lives in your ERP.
The workflow that works
A reliable order entry workflow has four stages. First, extraction: the AI reads the email and attachments and produces structured lines. Second, matching: each line is matched against your item master and the customer's cross-reference table, and each match carries a confidence level. Third, validation: prices are checked against the contract, quantities against pack sizes, and dates against lead times. Fourth, approval: an employee reviews the prepared order, resolves anything flagged, and approves it into the ERP.
The approval step is not a formality. It is where the order desk's judgment stays in the process. The difference is that the employee now reviews a prepared order with the exceptions highlighted, instead of building it from scratch.
What to measure
Before a pilot, measure three things on your current process: the time from email received to order entered, the number of manual touches per order, and the rate of orders that need correction after entry. Those are the numbers the pilot should move. If they do not move, the workflow is not working, regardless of how impressive the extraction looks in a demo.
See how this applies in practice: AI for wholesale distributors.
