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Can We Automate Quotes and Orders for a Wholesale Business?
What AI can read off an emailed purchase order, where a rep still has to decide, what your product and pricing data has to look like before any of it works, and whether to buy a product or build around the ERP you already run.
By Victor Xu, Founder · LinkedIn
Last updated · 2026-10-20
The short version
Yes for the reading and the typing, and that is most of the work. A customer emails a purchase order as a PDF, a photographed delivery note, a spreadsheet with their own part numbers in it, or three lines in the body of an email. Software can now read all of that, match it to your catalogue and your customer-specific pricing, and put a draft order or quote in front of a rep to approve. What it should not do is decide the price, release the substitution or send the quote without a human on the approval. Two things decide whether this works in your business, and neither is the AI: whether your product data is good enough to match against, and whether your customer-specific pricing lives somewhere a system can read. Distribution is early here, which is an advantage rather than a worry. The category productised itself in 2026, so the common failure modes are known before you spend anything.
Can we automate quotes and orders for a wholesale business?
Yes, and the right scope is narrower than the pitch: automate the transcription, keep the judgement. An inbound order that arrives as an email attachment has to be read, matched to the right SKUs, priced against the right agreement, checked for stock and entered. The first four of those are reading and lookup tasks that software does well. The fifth is entry. The judgement calls sitting around them, whether to substitute, whether to hold for a truckload, whether this customer gets the contract price or the spot price, are decisions your reps are paid for.
The design that holds up in practice puts the system in the drafting seat and the rep in the approval seat. Software reads the document, produces a draft order with every line matched and flagged where it is unsure, and a rep approves, corrects or rejects it. Corrections teach the matching. Nothing goes to a customer or into the ledger without a person saying yes.
That design is also what the products in this category now ship. Proton.ai announced availability of order and quote entry automation for distributors in July 2026, reported by Distribution Strategy Group on 21 July 2026: the platform takes forwarded emails, spreadsheets and PDF documents, converts them into draft quotes using customer-specific pricing and catalogue data, recommends substitutes when a product is unavailable, and has sales representatives review and approve every quote before it goes to a customer. Those are the vendor's claims carried in trade coverage rather than measured results, and the approval step in them is the part worth copying.
Our board on what AI is actually doing in manufacturing businesses carries the same work from the maker's side, including turning whatever the customer sends into an order, with the maturity of each job marked. The maker's version of the quoting problem is in AI automation for small manufacturers.
How does order and quote entry automation actually work?
Order and quote entry automation runs in five steps, and the fragile one is matching rather than reading. Understanding the sequence tells you where your own build will struggle.
Capture. The system watches a shared inbox, an EDI feed, a portal or a scanned pile, and picks up anything that looks like an order or a request to quote. Attachments get read, including PDFs, spreadsheets and photographs of a written order.
Extraction. Each document becomes structured lines: part reference as the customer wrote it, quantity, unit of measure, requested date, ship-to, any notes. Customers do not use your part numbers and they do not use your units, which is why extraction alone is not enough.
Matching. Each extracted line is matched to a SKU in your catalogue, using the customer's own cross-reference history where you have it. Confidence varies line by line, and the usable systems say so per line rather than per document.
Pricing. The matched lines get priced from the contract, the price list or the customer's last paid price, in the order of precedence your business actually uses. If that order of precedence is not written down anywhere, this is the step that stops the project.
Draft and approve. A draft order or quote appears in the ERP, with the uncertain lines flagged. A rep approves it, and only then does it become real.
What makes this different from the EDI you may already run is that EDI requires your customer to send a structured file. Order automation works on what the customer actually sends, which for most distributors is still email. Does AI automation work with my software? covers what the common ERPs allow a system to write back.
What does our product and pricing data have to look like first?
Your product data and your pricing rules have to be good enough to match against, and that is where most of the first phase goes. A system that reads an order has to land each line on one part in your catalogue, and it cannot do that if the catalogue does not distinguish them.
Four specific things decide it. Your SKU data needs enough description and attributes to tell two similar items apart, because a line that says "1/2 inch brass ball valve, 600 WOG" has to land on one part and not a shortlist of nine. Your units of measure need to be consistent, since a customer ordering 10 boxes of something you stock in eaches is a reliable way to produce a wrong order. Your customer cross-references, the mapping between their part numbers and yours, are worth whatever they cost to assemble, because they turn ambiguous lines into certain ones. And your pricing precedence has to be explicit: contract first, then customer price list, then last paid, then list, or whatever your actual rule is.
Most distributors have three of those four in workable shape and one in poor shape, and the poor one sets the timeline. That is a data project wearing an AI hat, and it is better to know it at the start.
Cleaning that data pays off whether or not you automate anything, because the same gaps are already costing you wrong shipments and credit notes. Is our data good enough for AI? sets out how to tell what state yours is in before anyone quotes you for a build.
What does each order channel need from a person?
Orders arrive in more ways than any one tool covers, so the table below sorts the channels by what automation can take and what has to stay with a person.
| How the order arrives | What automation can do | What still needs a person |
|---|---|---|
| EDI from a large customer | Everything, already. This is solved and is not an AI problem | Exception handling when a line rejects |
| Emailed PDF purchase order | Read it, match the lines, price it, draft the order | Approve, and decide any substitution |
| Spreadsheet attachment | Read it including the customer's own columns and part numbers | Approve, and confirm units where the sheet is ambiguous |
| Order typed in the body of an email | Read it, match, draft, and ask the customer about anything missing | Approve, and judge whether the request is complete |
| Photograph of a written or faxed order | Read it where the image is legible, flag what it cannot read | Approve, and re-key the illegible lines |
| Phone order to a rep or counter | Transcribe the call, draft the order from the transcript | The whole conversation, and the approval |
| Request to quote on a project | Assemble the quote from the bill of materials, price it, format it | The price strategy, the margin call and the sign-off |
| Reorder of a known basket | Draft it from history and flag what has changed | A glance, and approval |
The pattern down that table is that automation is strongest where the customer has already written the order down and weakest where the order is still a conversation. That is also why a shared inbox is usually the best place to start: it is the highest volume of already-written orders in most distributors.
Should we buy a product or build this around our ERP?
Buy the product when your volume sits in one channel and your ERP is one the vendor already supports, and build when the work crosses systems that no vendor covers together. The honest sorting question is how much of your order flow a single product would actually touch.
A product gets you a supported path and somebody else's roadmap, and it will be running sooner. The costs are the ones every productised category carries: your process has to fit its assumptions, your ERP has to be on its integration list, and the pricing precedence it supports has to match yours. For a distributor whose orders are mostly emailed PDFs into a mainstream ERP, that is often a good trade.
A build makes sense when the order desk is not the only problem. Orders arriving in five channels, customer cross-references living in a spreadsheet one person maintains, quotes assembled from a bill of materials in a different system, supplier acknowledgements that nobody reconciles until something fails to arrive. The value there is in the handoffs, and products are sold by channel rather than by handoff.
What should decide it is a count rather than a preference: how many of your order lines a single product would cover, and what the rest would still cost you. That is the first thing we work out on a call, and sometimes the answer is to buy something and build nothing.
Where we end up building, the system lives on our infrastructure and we run it for you from there, which for an order desk matters more than it sounds: a reading system drifts when a customer changes their document format, and somebody has to notice. Who maintains an AI automation? covers what that ongoing work actually involves.
Is wholesale behind other industries on this?
Wholesale trade adopts AI below the national average, and that is useful information rather than a warning. The US Census Bureau's Business Trends and Outlook Survey, published 26 May 2026, put overall business AI use between 17 and 20 percent from December 2025 to May 2026, with another 20 to 23 percent expecting to adopt within six months. Distribution Strategy Group's May 2026 reading of the same data has wholesale trade, manufacturing and retail all below that average, and notes that on an employment-weighted basis adoption reached 32 percent, meaning the larger distributors are moving faster than the count of companies suggests. One caution on older numbers: the Bureau widened its question in November 2025 from use in producing goods or services to use in any business function, so figures either side of that date are measuring different things.
Being later to a category means the expensive lessons are already public. The products exist, the approval-step design has settled, and the data prerequisites are known, which was not true two years ago.
What has changed on the customer side is less about AI and more about expectation. McKinsey's 2026 Global B2B Pulse, the tenth edition of that survey, published in May 2026 and covering nearly 4,000 decision makers across 13 countries, found buyers using an average of ten channels across a purchase journey. It also reports that 71 percent of respondents say their organisation offers e-commerce, and that among those that do, roughly a third of total revenue now flows through digital channels.
Read together, those two say something specific about where to spend. Your buyers will keep sending orders in whatever form suits them, and the competitive question is how fast and how accurately your desk turns that into a confirmed order. Explore strategy is where we work out which part of that is worth building first.
Frequently asked questions
What does it cost to automate order and quote entry for a distributor?
Scope and data quality drive it rather than seat count. The things that move the number are how many channels the orders arrive in, how clean your SKU and pricing data is, whether customer cross-references exist, how many ERPs or portals the system has to write into, and how much of the pricing logic has to be encoded rather than looked up. A single shared inbox feeding one ERP with clean product data is a contained piece of work. Five channels, a spreadsheet of cross-references and three pricing rules that live in your reps' heads is a larger one. Count the order lines your team keys by hand each week and multiply by a loaded hourly cost before asking anyone for a figure.
How accurate is AI at reading a purchase order?
Accurate enough to draft and not accurate enough to post unreviewed, which is why every workable design has an approval step. Accuracy depends far more on your catalogue than on the model: a clean, well-described SKU set with cross-references matches most lines with high confidence, and a sparse one produces shortlists a rep has to resolve. The right measure is not a single accuracy percentage but the share of lines a rep approves without changing, tracked weekly. If that share is rising, the matching is learning. If it is flat after a month, the problem is the data.
Is this different from EDI?
Yes, and the two sit side by side rather than competing. EDI needs your customer to send a structured file to an agreed specification, which works well with large customers and badly with everyone else. Order automation reads what the customer actually sends, which for most of your accounts is an email with an attachment. If you already run EDI with your largest accounts, the automation is for the long tail underneath them, which is usually where the manual keying is concentrated.
Can quotes be automated too, or only orders?
Both, with a harder limit on quotes. Assembling a quote, pulling the lines, checking availability, applying the pricing rules and formatting the document is the same reading and lookup work as an order. What cannot be handed over is the margin decision on a competitive project, because that depends on what you know about the customer, the competition and your own capacity. The useful split is to let the system produce the quote in minutes and keep the number and the sign-off with whoever owns the account.
Will our reps resist this?
Less than you expect when it drafts and more than you expect when it decides. Reps generally dislike keying orders and like keeping control of pricing and customer relationships, so a system that removes the typing and leaves them the decisions tends to land well. What causes resistance is an approval queue that feels like extra work, so the queue has to be faster than keying was. Measure how long an approval takes in week one and fix it before you widen the rollout.
The rest of the adoption problem, the part about getting people to actually use what you build, is in how to get your team to use AI.
Work out which part of your order desk is worth building
We follow an order from the inbox to the ledger, put a number on every place it gets retyped or waits, and say what is a product purchase, what is a data job and what is worth building. Then our engineering team builds what the roadmap calls for and we run it for you from there. We own the outcome, not the deliverable.