A third of retail product pages cannot be read by AI. Yours might be one.

Adobe Analytics looked at how much retail content is actually readable by AI systems. Roughly 34% of product pages came back inaccessible. About a quarter of homepage content and a quarter of category page content were not optimized for language models either.

One in three product pages. Not slow, not badly designed, not poorly worded. Unreadable by the thing that increasingly decides which products a shopper hears about.

Why this is worse than it sounds

An unreadable page is not a page that ranks lower. It is a page that does not enter the consideration set at all.

A human who lands on a badly structured page can still squint at it, scroll, and work out what the product is. An assistant building a shortlist does not squint. If it cannot extract the product, the price, the specification, and the terms, it moves to a competitor it can extract them from. There is no partial credit and no second chance in that turn.

Now put that against the traffic trend. Adobe measured AI traffic to US retail sites up 393% year over year in the first quarter of 2026, and that same traffic converting 42% better than every other source in March, with 37% higher revenue per visit.

The fastest growing and best converting source of traffic in retail cannot read a third of the pages it is being asked about.

What makes a page unreadable

The specifics vary, but the patterns repeat.

• Content that only exists after JavaScript runs. If the meaningful text is assembled in the browser, a crawler or model working from the raw document sees an empty shell where your product description should be.
• Facts locked inside images. Sizing charts, ingredient lists, comparison tables, and specification panels shipped as pictures. A human reads them fine. A parser gets nothing, unless the alternative text carries the content, which it almost never does.
• Information hidden behind interaction. Tabs, accordions, and modals that only load their content on click. The detail is on the page in a sense that matters to a designer and not in a sense that matters to a machine.
• Missing or thin structured data. Product markup that omits price, availability, review count, or return policy leaves the assistant guessing at exactly the fields a shopper asks about.
• Claims with no anchor. Dermatologist tested with no study, sustainably made with no standard named. These are weak with skeptical humans and useless to a machine that is looking for something citable.

The reason this persists at large brands

It is rarely negligence. It is usually the accumulated result of good decisions made for a different reader.

Tabs and accordions exist because a product page with everything visible looks overwhelming. Image-based sizing charts exist because they were easier to produce and looked better than a table. Client-side rendering exists because it made the site feel fast. Every one of those choices optimized for a human eye on a screen, which was the correct target right up until it was not the only one.

The result is that the brands with the most sophisticated front ends are sometimes the least legible to the new reader, while a plain competitor with boring HTML gets read cleanly and cited.

How to find out where you stand

You can get most of the way with an afternoon and no budget.

• Read your own page as a machine would. Fetch the raw HTML of a top product page and look at what is actually in the document before any script runs. If the description, price, and specifications are not there, an assistant may not see them either.
• Ask the assistants about your product directly. Not your brand, your product, the way a customer would ask. Note what comes back wrong, vague, or missing. Those gaps are usually extraction failures rather than opinions.
• Check your five best sellers first. This is not a whole-catalog project to begin with. The revenue concentration in most catalogs means a handful of pages carry the outcome.

What to fix first

Fix the facts a shopper asks about before you fix anything else: price, availability, sizing, materials or ingredients, shipping time, return terms. Get those into the document as plain text and into your structured data accurately. Everything else on the page can wait.

This is unglamorous work and it competes badly for attention against a redesign. It is also one of the few remaining places where a modest engineering effort changes whether you are considered at all, rather than changing how you look once you are.

eLLMo runs test buyers matched to your real customers against your page and returns a ranked list of what stops people from buying. A claim a buyer cannot find and a claim a machine cannot extract are usually the same claim. For the research on how AI buyers weigh page content, see AI buyer behavior and structural bias.

*Related Links: AI traffic to US retailers rose 393% in Q1, and it's boosting their revenue too (TechCrunch, on Adobe Analytics data), Shopping's New First Step: Agentic Search Grows 200% (Salesforce).*