The Biggest Names in Distribution Score Worst on AI Readability

The Industrial AI Visibility Benchmark measured 984 manufacturer and distributor catalogues on a single question: can an AI assistant actually retrieve a product specification from this site? The median score was 50 out of 100.

That number was published anonymised. This article is not anonymised, because the more useful finding was hiding inside the distribution.

The largest, best-known companies in industrial distribution score below the median of the market they lead. Their median is 40. Fifteen of the twenty-five household names we re-measured fall under the cohort's own midpoint, and the highest score anywhere in the full 984 — an 80 — belongs to a small specialist distributor with no brand recognition outside its niche.

The table

Measured live on 13 August 2026, not lifted from the earlier fieldwork. Every figure below can be reproduced by anyone using the free grader, including by the companies named.

Median of these 25: 40. Median of the full 984: 50. Range here: 31 to 78.

Why we re-measured before publishing

The benchmark fieldwork ran on 10 August. Publishing a named score three days old would have been careless, so every company in the table was re-graded on the morning of publication.

Twenty-one of the twenty-five reproduced exactly. That is a reassuring result for the instrument. Three did not:

• Rexel UK: 57 → 66. A nine-point improvement in three days.
• TE Connectivity: 53 → 40. A thirteen-point fall.
• Schneider Electric: 51 → 39. A twelve-point fall.

Nobody at those companies is likely to have been working on AI readability that week. A change to an edge rule, a WAF policy update, a CDN configuration pushed for an unrelated reason — any of these can move the score double digits overnight, in either direction, without anyone noticing.

Then it happened again while this article was being written. A second pass a few hours after the first found Toolstation had moved from 63 to 60 — a smaller change, but inside the same day, and the table above carries the corrected figure rather than the one drafted that morning.

That is the most operationally useful thing in this article. Your machine readability is not a property you fix once. It is a live configuration that drifts, and it drifts silently, because nothing in your monitoring watches for it. Four of these twenty-five moved within seventy-two hours, one of them within a single afternoon, and it is safe to assume nobody at those companies knew.

What actually separates 78 from 31

The four things measured are not equally hard to fix, and the gap between top and bottom is rarely about the top scorer having done something clever.

Crawler policy — the cheapest points on the board

Most sites in the cohort have a robots.txt that says nothing whatsoever about AI crawlers. It is not hostile; it is silent, written before this category of client existed. Silence increasingly resolves to refusal, because the safe default for a crawler facing an unstated policy is to stay out.

Naming the answer-engine crawlers you welcome is an afternoon's work and it is the single highest-leverage change available to almost every company in the table.

Edge reachability — where the big estates lose

This is where scale actively hurts. Enterprise bot management exists to stop scraping, credential stuffing and price harvesting, and it is good at it. It is also, by default, suspicious of any client that does not look like a browser — which describes every answer-engine crawler.

Several of the lowest scores in that table are not content problems at all. The content is fine. A non-browser client simply never reaches it.

On-page readability — the JavaScript trap

If your specifications are assembled in the browser by a JavaScript application, most crawlers see an empty container. The product data exists, is accurate, and is invisible. Server-rendered structured data — Schema.org Product with real attributes, not a name and a price — is what closes this.

Agent surfaces — where essentially nobody has started

Across all 984 catalogues, not one exposed an endpoint an agent could query directly. No MCP server, no machine-readable feed designed for retrieval rather than for a shopping comparison engine. The bar here is not high. It is simply that almost nobody has cleared it, which makes it the cheapest available differentiation for anyone who moves first.

What this is not

It is worth stating the limits of the claim, because a benchmark that oversells itself deserves the scepticism it gets.

This is not a measure of catalogue quality. Several companies with poor scores have excellent product data. The problem is access, not accuracy.

It is not a claim that a low-scoring brand is absent from AI answers. Large brands are described extensively across the web, and an assistant will happily talk about them using third-party sources. The risk is more specific: the assistant answers a technical question about your product using a distributor's data, an old cached page, or a competitor's comparison table, and cites them instead of you.

It is not a fixed ranking. As three companies demonstrated within three days, these scores move.

And it is not an accusation. Nothing in the table is the result of a decision anyone made badly. It is the accumulated residue of sensible individual choices — block the scrapers, render in the client, ship the SPA — taken before a new kind of reader arrived.

If you are in the table

Run the grader on your own domain. The breakdown shows which of the four probes failed and precisely why, and it is free with no signup because an argument you can check yourself is worth more than one you have to take on trust.

If you think the result is wrong — a regional storefront misidentified, a geo-redirect followed incorrectly, a rate limit tripped — tell us. We will re-run it and correct the record publicly. We have done that before. A measurement nobody is willing to be wrong about in public is not a measurement, it is marketing.

Method

Each site is probed as an AI crawler would approach it. We fetch the homepage and robots.txt, resolve the catalogue through the sitemap, and sample product pages with a standard non-browser client. Four dimensions are scored: crawler access policy, machine reachability at the edge, on-page machine readability, and agent surfaces. Nothing behind a login is touched, no rate limits are deliberately exceeded, and a site that blocks us is recorded as blocked rather than retried in disguise — the block is the finding.

The full cohort of 984 remains published anonymised as the Industrial AI Visibility Benchmark 2026. The 25 companies named here are named because they are large, publicly traded or widely known, the measurement is reproducible by anyone, and the finding is materially more useful with the names attached than without them.