AI in distribution & B2B trading

If you run a trading or distribution business, your day probably includes some version of these: someone re-typing orders from emails into the ERP, quotes going out slower than customers want them, and payments that get chased whenever someone remembers. None of these feel like technology problems. They feel like staff problems, or just how the business is. They’re neither — and they’re exactly where AI now earns its keep in businesses like yours.

“Every order gets typed in twice.”

A customer emails a purchase order as a PDF. Someone opens it, reads it, types the lines into your system, files the email. It works — until the person is on leave, until a digit slips, until month-end when fifty of them pile up. What changes with AI: the system reads the email and the attachment, pulls out the order lines, checks them against your own records, and posts them — and anything it isn’t sure about goes to a person, with the original alongside. Your team stops typing and starts checking. The measured gap between the manual and automated way of handling documents is stark — roughly four to five times the cost per document, and days versus weeks in turnaround (source: Ardent Partners’ payables research). In plain terms: the same team handles far more volume, faster, with fewer errors reaching customers.

“Quotes take too long, and slow quotes lose orders.”

In trading, the first usable quote often wins. If yours depends on someone finding the price list, checking stock, and typing a reply, you lose orders to nothing but speed. What changes: AI drafts the reply the moment the inquiry lands — prices pulled from your real price data, never guessed — and a person approves it with one click. The customer who used to wait a day hears back in minutes, and your staff handle the exceptions instead of the routine.

“We find out about problems when the customer calls.”

Wrong stock posted, a return stuck in limbo, a payment overdue that nobody flagged — problems that surface late cost the most. What changes: the boring, reliable half of AI systems — watchers. A returns claim that stalls gets escalated. A receivable that ages past terms triggers a polite, automatic follow-up in your voice. A mismatch between documents stops the process instead of sliding through. Nothing clever; just nothing forgotten.

What this looks like in practice

These aren’t hypotheticals — they’re the systems in our own current work: an order-inbox-to-ERP pipeline live in production at a national ICT distribution business; the same pattern being applied to returns and warranty claims in a current engagement; and a unified reply-and-quotation inbox in delivery for an 88-year-old electrical components trader handling 80+ inquiries a day. In every one, the design rule is the same: AI reads and drafts, your rules and your people decide, and money is always looked up, never guessed.

Where to start

Not with a platform. With the one flow that costs you most — orders in, quotes out, or payments chased — automated properly, with people on the exceptions. That single flow usually pays for itself within months, and it quietly builds the clean data every later system needs.

The examples are from Suviq engagements, anonymised. The one benchmark cited links to its source; your numbers will differ — finding them is what an audit is for.