AI in wholesale and distribution: where it helps fastest and where to start

In a typical wholesale business, orders arrive through several channels: by email with a PDF or spreadsheet, through a B2B platform (an online shop for business customers), by phone and from large customers’ portals. Each channel means different work for the order processing team: read the order, find the product, check the price and stock, enter it into the system, confirm.

The table below lists the areas where AI makes sense in distribution, from the fastest results to the most demanding. The order is not accidental. The first two areas work on data you already have, while the others need a well-organised history.

This is most often a wholesaler’s first AI project, because the problem is repetitive and highly visible. The customer sends a list of products in their own format, often with their own names or old codes. An employee guesses which product code is meant and retypes everything into the ERP, the core system for orders, warehouse and invoices.

AI does the same work in the steps described below. The last step stays with a person, at least at the beginning. This is not a weakness of the solution but a safeguard: the employee approves the order with one click and corrects only what AI has flagged as uncertain.

Connecting to the ERP requires an API – an interface through which one program can write data into another. Most popular systems have one. If yours does not, the options are file import or a program that enters data the way a person would – this works, but breaks more often.

Take a hypothetical wholesaler with 60 employees that receives 150 orders a day by email. Entering one manually takes 4 minutes on average. That is 150 × 4 = 600 minutes, or 10 hours of work a day.

Assume that AI prepares the order and an employee checks and approves it in 1 minute. The saving is 3 minutes per order, i.e. 150 × 3 = 450 minutes, or 7.5 hours a day. Over 250 working days, that comes to 1,875 hours a year.

This is an estimate, not a promise. Plug in your own figures: how many orders arrive by email, how long they take to enter and how many of them have exceptions. Orders with many unusual lines will be shortened less, because an employee still has to go through them.

What happens to the time saved also matters. In many companies the gain is not fewer jobs, but the same team handling a growing number of orders without overtime and confirming delivery to customers faster.

EDI is the electronic exchange of documents between companies’ systems according to a common standard. An order from a retail chain then arrives as structured data that the ERP accepts without retyping. Where EDI or your B2B platform is in place, you do not need AI to read orders.

AI is useful at the edges: with smaller customers who will never adopt EDI, with orders containing wrong codes and with handling exceptions. A sensible split looks like this: large customers via EDI, mid-sized ones via the B2B platform and the rest by email, with help from AI.

The same goes for purchase invoices. Since 1 April 2026, most invoices from Polish suppliers have arrived through KSeF, Poland’s National e-Invoicing System, as ready-made data. Reading them from PDFs therefore no longer makes sense. Tools for recognising text in documents are still useful for foreign invoices, specifications and delivery documents.

In many wholesale businesses, price lists and discounts live in spreadsheets, and discount decisions are made in conversations between salespeople and customers. After a few years, nobody knows why one customer has terms a few percentage points better than a similar one. AI works well here as a tool for spotting such discrepancies.

For example, AI compares customers with similar turnover and order patterns and flags those whose discount clearly stands out. It also adds in the cost to serve: the number of small deliveries, returns and late payments. The sales director gets a list to review, not an automatic price change.

Automatic real-time pricing rarely suits a wholesaler with regular customers and negotiated terms. A customer who sees a different price every week will soon start asking your competitors. We therefore recommend starting with a report and recommendations, and leaving the decision to a person.

A distributor usually ties up one of the largest shares of its working capital in inventory. A better forecast means less stock sitting on the shelves and fewer stock-outs that send customers to competitors. That is why demand forecasting is tempting – but it is a harder project than it looks.

AI forecasts based on sales history, seasonality, promotions and fixed contracts. To see seasonality, you need at least two full years of data. If product codes changed during that time, products were merged or stock-outs depressed sales, the data has to be cleaned first.

Good practice is to start with a small group of products that tie up the most money. The questions below will help you judge whether your company is ready.

Complaints take up time out of proportion to their number, because each one requires gathering data from several places. AI identifies the topic of the complaint, attaches the order number and delivery documents, and offers the employee a draft reply in line with company rules.

Complaints collected in one place also reveal patterns: the same supplier, the same product group, the same carrier. For the management board, such a report is often more valuable than the time saved.

The safest approach is a narrow pilot with a success measure agreed before you start. An example measure is the share of orders approved without corrections and the average time from email to confirmation to the customer.

The schedule below assumes that the ERP allows orders to be written in from outside. If a workaround has to be built, the pilot usually takes longer.

Not every wholesaler needs AI to begin with. If most orders arrive through a B2B platform or EDI, a better investment is persuading more customers to use those channels.

The mistakes below come up most often, and any one of them can stop a project at the testing stage.