There’s a growing pile of writing about agentic commerce right now — AI agents that research, compare, quote, and eventually buy on a company’s behalf. Deloitte has a maturity roadmap. BigCommerce has a preparation checklist. Mirakl has a readiness framework. Every one of them is worth reading, and every one of them starts at the same place: the agent has already found your company and is now evaluating what you sell.
Read four of them back to back, as we did, and the omission gets hard to unsee. Not one explains how the agent found you in the first place.
That’s not a minor footnote. It’s the first link in the chain, and if it breaks, none of the rest of the advice matters. You can have immaculate product data and a fully agent-ready quoting workflow, and it will do exactly nothing if the agent building a shortlist never surfaces your company at all.
What’s actually happening on the buyer’s side
The behavior driving this is real and already underway. Procurement teams increasingly do the early research themselves using AI assistants, and by the time anyone picks up a phone, the shortlist already exists.
Forrester projects that roughly one in five B2B sellers will face agent-led quote negotiations by the end of 2026. The important shift isn’t the negotiation part.
It’s earlier than that. Buyers have stopped typing company names and started describing problems — asking an assistant for suppliers who can hold a particular tolerance in a particular material with a particular certification, and getting back a short list of candidates. If your company isn’t in that answer, you were never in the running, and you’ll never know the opportunity existed.
The step every guide skips
Here’s the sequence the existing advice assumes:
- An agent finds your company
- An agent evaluates your product data
- An agent requests a quote or places an order
Almost all the published guidance addresses steps two and three. Step one gets treated as solved — or worse, as somebody else’s problem. Deloitte’s piece leaves the question open in as many words, asking how a mid-market manufacturer would ensure buyer agents can even find them, and then moving on.
BigCommerce’s guide says to test whether you show up by asking an AI assistant what it recommends in your category, which is genuinely good advice, but it assumes you’re already discoverable enough to be tested.
Step one is a visibility problem. It always was. That’s why the answer isn’t in any of these guides — the people writing them sell commerce platforms and product data systems, not visibility.
Why this lands hardest on RFQ manufacturers
The bigger issue with most of the published advice is the reader it quietly assumes: a manufacturer already running a B2B ecommerce platform, with a catalog, structured product data, and probably a PIM system behind it.
That is not most manufacturers. Plenty of shops — precision machining, custom fabrication, contract assembly — don’t sell anything online at all. There is no catalog, because everything is made to a customer’s print. There’s no product feed to optimize, because the “product” is a capability, not a SKU. What exists instead is a capabilities page, a few certifications, maybe a PDF spec sheet, and a “Request a Quote” form.
Every framework aimed at platform operators skips straight past that reader. And it’s a large, valuable slice of the market — exactly the companies that win work on capability and credibility rather than catalog depth.
What an AI agent can actually read on your site
Strip away the jargon and this is a plain content and structure problem. An agent assembling a shortlist needs to extract facts, and it can only extract facts it can actually parse.
Your specs need to be text on a page, not trapped in a PDF. This is the single biggest one for manufacturers, and it’s nearly universal. Spec sheets, tolerance tables, material lists, and capacity figures sit in downloadable PDFs because that’s how they’ve always been distributed to engineers.
A machine assembling a supplier comparison will reliably read an HTML table and may well skip the PDF entirely. The information exists; it’s just stored in the least accessible format available. We’ve written about this failure pattern before in why most manufacturing websites fail at SEO — it was already costing you human buyers, and now it costs you machine ones too.
Capabilities have to be stated, not implied. “Precision machining for demanding industries” tells an agent nothing. Materials worked, tolerance ranges, part size envelopes, volume ranges, lead times, and industries served are the actual matching criteria. Write them as plain text where they can be read, not as a line in a brochure image.
Certifications need to be findable, not just claimed. ISO 9001, AS9100, ITAR registration, ISO 13485 — these are frequently the hard filters in a sourcing query. A logo image in your footer is invisible to a parser. The text of what you hold, and for which facility, is not.
Your technical foundation has to let a crawler through at all. None of the above matters if pages are slow, blocked, or rendered in a way that hides the content. That’s ordinary technical SEO work, and it’s the prerequisite for every other item on this list.
How this is different from ranking in AI search
Worth drawing a clear line here, because these two things get blurred constantly — including on this blog, where we’ve covered the other one.
Getting found in AI search — GEO and AEO — is about being cited when someone asks a question and an AI generates an answer. The output is information, and the human reads it.
What we’re describing here is different: an agent doing procurement work on a buyer’s behalf. The output isn’t an answer, it’s a shortlist, an RFQ, or eventually a purchase order. The evaluation is harder-edged, because the agent is matching against specific requirements rather than summarizing a topic, and increasingly no human reads the intermediate step at all.
The good news is that the underlying work overlaps heavily. Clear, parseable, specific content serves both. The difference is mainly in what you emphasize: for AI search, explain and contextualize; for sourcing agents, state hard specifications plainly and make them easy to match against.
Where to actually start
You don’t need an ecommerce platform or a PIM system to do any of this, which is the whole point.
Start by taking your single most important spec sheet and publishing it as a real page — actual text, a real table, headings that say what the numbers are. Then do your capabilities page the same way, replacing adjectives with figures. Then check that your certifications appear as text somewhere a machine can read them.
After that, run BigCommerce’s test, because it’s a good one: ask an AI assistant what suppliers it recommends for the kind of work you do, in the region you serve, and see whether you appear. If you don’t, the companies that do are the ones you’re now competing against for that shortlist slot — and the gap between you and them is usually content structure, not company size.
The same principle applies as in our guide to getting found by engineers and buyers: the buyer has changed shape, but being specific and legible still wins.
How to tell if it’s working
This is harder to measure than ordinary rankings, and anyone claiming precise attribution here is overstating what’s currently possible. Some practical signals: re-run that assistant test monthly and note whether you appear and how you’re described.
Watch for RFQs arriving from companies with no prior contact and no obvious referral path, and ask new inbound leads how they found you — the answers are starting to include AI tools often enough to be worth tracking deliberately.
Beyond that, the ordinary indicators still apply, and the work overlaps with the fundamentals covered in our industrial SEO strategies guide. If your specs and capabilities become more legible, both human and machine discovery improve together.
Want to know whether AI sourcing agents can actually read your site? Contact us and we’ll take a look at what’s visible, what’s locked in PDFs, and what’s missing.
Frequently Asked Questions
vendors. If you take RFQs rather than online orders, the work is
content, clean structure, specific claims – are the same ones that have always mattered. What changes is who’s reading, and machines are less
work that helps human buyers and traditional search. That’s what makes it a low-risk thing to do early – you’re not betting on agentic commerce
Skyler Lyon
Skyler is a B2B growth expert with over a decade of experience building and managing high-growth SEO programs for manufacturing companies.