Schema markup and AI answers: 0 lab pages, 1 Google slide, +7.5% and -5.3%

What is structured data documented to do today?

Earn rich results in classic search, on a feature list that has been shrinking for three years.

Google’s gallery of supported markup now lists 25 features: Article, Breadcrumb, Carousel, Course list, Dataset, Discussion forum, Education Q&A, Employer aggregate rating, Event, Image metadata, Job posting, Local business, Math solver, Movie, Organization, Product, Profile page, Q&A, Recipe, Review snippet, Software app, Speakable, Subscription and paywalled content, Vacation rental and Video. FAQ, HowTo, Fact check, Book actions and Practice problems are not on it.

They came off deliberately. HowTo rich results ended on desktop on 13 September 2023, and FAQ rich results stopped appearing on 7 May 2026. Seven more types went in June 2025 because, in Google’s words, “our analysis shows that they’re not commonly used in Search, and we found that these specific displays are no longer providing significant additional value for users.” The same post adds the sentence that matters for anyone pricing markup as a ranking play: “This update won’t affect how pages are ranked.”

Google’s own guidelines are blunter still. “Using structured data enables a feature to be present, it does not guarantee that it will be present.” A manual action for bad markup “means that a page loses eligibility for appearance as a rich result; it doesn’t affect how the page ranks in Google web search.”

So the documented return is eligibility for a display feature, on a list that no longer includes the two types most agencies built their schema offer around.

Who says markup helps AI answers?

Microsoft, in three places, without data. Google, once, on a slide. Nobody else.

Bing’s webmaster guidelines are the strongest published provider statement that exists, and they are explicitly about AI surfaces: they describe how Bing “discovers, crawls, indexes, evaluates, and surfaces content across Bing search experiences, Copilot, and grounding API results.” Section 14 reads: “Structured data may support clearer grounding but does not guarantee visibility or grounding traffic. Markup must accurately reflect visible content.” The same document applies the same discipline to the whole field: “SEO does not guarantee rankings or traffic, and GEO does not guarantee grounding or citations in AI experiences.”

Two Microsoft blog posts go further than the guidelines. A Microsoft Advertising post from October 2025 says schema “helps search engines and AI systems understand your content”. A Bing webmaster post from November 2025 says structured content “helps AI systems interpret and summarize your content more effectively” and that this “increases the chances of your being cited”. Neither publishes a sample, a test or an effect size.

Google’s documentation says close to the opposite, and says it twice. The AI features page: “There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary.” And: “You don’t need to create new machine readable files, AI text files, or markup to appear in these features. There’s also no special schema.org structured data that you need to add.” The generative-AI optimization guide files structured data under a heading about overfocusing: “Structured data isn’t required for generative AI search, and there’s no special schema.org markup you need to add. However, it’s a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.”

Then there is the slide. At Search Central Live Toronto on 21 April 2026, a Google Search engineer presented a diagram titled “Structured Data Oversimplified”. It shows a box labelled schema.org Data feeding, through specific processing and cleaning, a box of indexed data containing Events, Shopping and Reviews, from which one arrow labelled “Retrieve” goes to the results page and another labelled “Use as Context” goes to AIO and AIM. Google said in advance that the event “won’t be recorded or live streamed”, and we found no Google-hosted copy of the deck, so the only reading available is from photographs taken by an attendee.

Read it for exactly what it shows. Google’s own account is that schema.org data is processed, for three commercial verticals, into index data that feeds AI Overviews and AI Mode as context. It does not show generic markup on an arbitrary page being read by those features, and Google titled the diagram oversimplified. The same talk opened with two bubbles, “Structured Data is Useless.” and “Structured Data is the Future.”, followed by a slide reading “Both have some truth…”. Quoting either bubble as Google’s position misrepresents the deck.

For shopping, where the AI surfaces genuinely do consume structured input, Google’s documented pathway is a feed and not page markup. Checkout inside Gemini and AI Mode is switched on by a Merchant Center product attribute: “To enable native checkout for your products, you must add the native commerce [native_commerce] group attribute and its checkout eligibility [checkout_eligibility] sub attribute to your product data source in Google Merchant Center.” OpenAI’s shopping documentation has the same shape. It cites “Structured metadata from first-party and third-party providers” and asks merchants to share “a structured product feed with OpenAI”, delivered as files. Neither mentions schema.org Product markup as the gate.

John Mueller of Google has given the most useful short answer anyone has given, on Reddit in January, with the caveat that it is his view and “not official guidance”: “the short answer is yes, no, and it depends.” His worked example is the feed case. “Pricing, shipping, availability for shopping is basically impossible to read in high fidelity & accurately from a text page, for example.” His worked counterexample is the thing being sold: “Your ‘best geo insurance comparison site’ isn’t going to rank better by adding insurance markup.” On Bluesky in 2025 he answered the ranking question in one line: “Structured data won’t make your site rank better.”

Does the retrieval path read it?

It depends which path, and the capability tests disagree with each other.

A fetch-tool test run in August and published in October put a canary string in six places on one static page and asked five AI tools to read it. JSON-LD appeared in 0 of 25 tool reads. Text in a hidden div appeared in 4 of 5. That is the cleanest direct evidence that the chat-side fetch path discards script blocks, and its limits are severe: one page, one day, five tools, and part of the result rests on the tools reporting their own behaviour.

A December 2025 test of the same kind put a price only in JSON-LD and asked five systems for it. None returned it. The same test found Claude returned nothing for 0 of 8 queries even when the price was visible in the page text, which tells you how much of that result is about schema and how much is about the tool.

Pointing the other way: a February 2026 test found ChatGPT and Perplexity returning an address that existed only in a JSON-LD block, from markup that was invalid. Cloudflare’s agent-facing markdown conversion, documented and updated this month, explicitly preserves JSON-LD while stripping other script tags, which means at least one widely deployed converter sitting in front of a lot of websites keeps the markup in the text an agent sees.

The honest synthesis is narrow. Say which tool, on which date, through which access path. A sentence that begins “AI tools strip all JSON-LD” or “AI tools read JSON-LD” is wrong in both directions on this evidence.

Anthropic’s own description of its fetch tool is the one place a model provider says something mechanically useful, and it stops short of the question: “The API retrieves the full text content from the specified URL.” Whether a script block counts as full text content is not stated anywhere we could find.

What do the measurements say?

Two studies have a design good enough to argue about. They agree on the part everyone skips.

The newer one is the first from a university. Chen and Koudas at the University of Toronto analysed 143,249 answers and 396,188 citations across one 651-query consumer market between 24 June and 21 September 2026, plus a smaller business niche. Pages carrying schema.org markup were associated with 7.5% more citations when websites were compared with one another. Inside a single website the association disappeared: -5.3%, which the authors report as not significant, and odds of 0.92 at equal retrieval rank.

That gap between the two comparisons is the whole finding. Across websites, markup travels with everything else that marks a well-maintained site. Within one website, where those things are held constant, it stops predicting anything. The limits are real and the authors state them: these are associations and not effects, the models queried were small API models, and markup was recorded as a yes or no flag for any JSON-LD or microdata rather than by type. The acknowledgments also thank a commercial AI-visibility vendor for help with the data, which is worth knowing before calling this the independent study the field was waiting for.

The older one is the matched field test we covered three days ago: 1,885 pages that added JSON-LD between August 2025 and March 2026, measured 30 days either side against control pages that never added it, giving -4.6% on AI Overviews, +2.4% on AI Mode and +2.2% on ChatGPT, the last two indistinguishable from zero. Four caveats belong next to it that we did not print at the time. The analysis was run by the publisher’s own AI agent. The window is 30 days. All schema types were pooled. And the article prints its control count two ways, as “4,000 control pages” and as “3 control URLs” per treated page, which do not reconcile.

A third paper, written by an agency that sells answer-engine optimization, states in prose that the pooled positive effects of structured data “reverse or collapse to zero once domain fixed effects are applied”. It prints no coefficient, interval or table row for schema anywhere, so it can be cited as a claim and never as a number.

What nobody has published is the test the question actually needs: markup added to pages with no AI citations at all, watched against matched controls. The field test started from pages that already had more than 100 AI Overview citations, which excludes the population most likely to benefit, and the Toronto study is observational. A 45-study survey of this literature from July lists separating structured data from the other variables as an open task. It still is.

A correction to our 27 October ranking

Three days ago we wrote, about provider statements recommending schema for LLM retrieval, that we “did not find one in documentation” and “could not source it to anything a provider publishes and maintains”. That was wrong, and the fault was ours rather than the evidence’s.

Microsoft publishes and maintains the Bing webmaster guidelines, and section 14 of them is exactly such a statement, hedged but published. Two Microsoft blog posts from autumn 2025 go further. All three predate our sentence. The conference remark we were referring to is real and is still unsourceable, but it was never the only Microsoft material on the subject.

The ranking itself stands. Schema sat at 7 of 9 on the strength of the matched field test, and the Toronto study published since then is consistent with that placement rather than against it. What changes is the provider row: the correct statement is that two search providers publish hedged recommendations without data, four model providers publish nothing, and one Google slide shows a processing path for three verticals.

So why keep doing it?

Because the case for markup never needed AI to be true, and that case is now the only one with a documented mechanism behind it.

Structured data makes a page eligible for 25 named search features. It is how price, availability and shipping get read accurately, which Mueller describes as “basically impossible to read in high fidelity & accurately from a text page”. Google has documented since March 2026 that it uses schema.org markup along with the og:image tag when picking image thumbnails for Search and Discover. The Google engineer’s slides name four plain reasons the company wants the data: precision on complex fields like sale pricing, non-visible metadata such as full ISO dates and stable identifiers, lower extraction cost than running a model over every page, and less noise from adjacent content.

What that adds up to is an infrastructure argument. Markup is cheap, it is machine-readable by anything that chooses to read it, and schema.org itself says the vocabulary is there for whoever finds a use: “since the markup is publicly accessible from your web pages, other organizations may find interesting new ways to make use of it as well.” That sentence predates large language models and still covers them.

The argument it does not support is a schema programme funded out of an AI-visibility budget, sold on a citation lift that no published measurement has isolated. If your markup is wrong or missing, fix it on the rich-results case, which is documented, and treat any AI effect as a bonus you have not paid for.

What this evidence cannot tell you

Whether markup helps a page that is currently cited by nobody. That is the one question a small site actually has, and both of the studies with a real design exclude it, one by sampling pages with 100 or more existing citations and the other by being observational.

Whether any specific engine reads it. The provider statements cover Google Search and Bing grounding. The model providers document nothing, so every claim about Claude, ChatGPT or Perplexity and schema is an inference from a small capability test, and those tests disagree.

Whether the type matters. Every study here pooled schema types or used a yes or no flag. Nothing published separates Product from Organization from Article.

Whether this survives next quarter. Google’s feature list has lost seven types in 18 months, Bing rewrote its guidelines in February without announcing it, and Google’s structured-data gallery was last updated on 16 October with no changelog line explaining what changed.

Sources

Google Search Central, AI features and your website, last updated 10 December 2025, for the no additional requirements and no special schema.org structured data statements.

Google Search Central, Optimizing your website for generative AI features on Google Search, last updated 10 July 2026, for the overfocusing line and the rich-results rationale.

Google Search Central, General structured data guidelines, last updated 10 July 2026, for the no-guarantee sentence and the manual-action sentence.

Google Search Central, Structured data markup that Google Search supports, last updated 16 October 2026, for the list of 25 features.

Google Search Central, Changes in Search: HowTo and FAQ rich results, 8 August 2023, updated 14 September 2023, for the HowTo desktop deprecation.

Google Search Central, Simplifying our search results, 12 June 2025, for the seven retired types, the stated reason and the ranking sentence.

Google Search Central, Search documentation updates, for the 7 May 2026 end of FAQ rich results, the June 2026 documentation removal, and the 2 March 2026 clarification that Google uses schema.org markup and the og:image tag as thumbnail sources.

JC Chouinard, Google Search Central Live Toronto slides, April 2026, 22 April 2026, for photographs of Ryan Levering’s “Structured Data, Quality & AI” deck, including the “Structured Data Oversimplified” diagram and the four value slides. Google’s event announcement states that the event was not recorded.

Bing, Bing Webmaster Guidelines, for the scope sentence covering Copilot and grounding, section 14 on structured data, and the GEO sentence. The page is client-rendered, so a command-line fetch returns the markup without the text.

Microsoft Advertising, Optimizing your content for inclusion in AI search answers, 8 October 2025, for the claim that schema helps AI systems understand content.

Bing Webmaster Blog, How AI search is changing the way conversions are measured, 20 November 2025, for the claim that structured content increases the chance of being cited.

Google Merchant Center Help, Native commerce, for the attribute that enables checkout inside Gemini and AI Mode.

OpenAI, Shopping with ChatGPT search and Get started with commerce, for structured metadata from providers and the product-feed integration.

OpenAI, OpenAI bots, Anthropic, Web fetch tool, Perplexity, Perplexity crawlers, and Google, URL context, as the provider documentation that contains no statement about structured data. The Anthropic page is the source of the full text content sentence.

John Mueller, r/TechSEO comment, 1 January 2026, for the yes, no, and it depends answer, the shopping-fidelity example and the insurance-markup line, with his own note that it is not official guidance. His Bluesky post, 13 April 2025, for the ranking sentence.

Chen and Koudas, University of Toronto, arXiv:2610.15033, 10 October 2026, for 143,249 answers, 396,188 citations, the 651-query market, the 24 June to 21 September 2026 window, +7.5% across websites, -5.3% within websites, the 0.92 odds ratio at equal retrieval rank, and the vendor acknowledgment.

Ahrefs, We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved., 11 May 2026, for the matched design, the three surface results, the pooled schema types, the 30-day window, the AI-agent analysis and the two control counts.

Moore and Dunne, Discovered Labs, arXiv:2609.35077, 28 September 2026, for the stated collapse of pooled structured-data effects once domain fixed effects are applied, with no schema coefficient printed.

Pilot Digital, Research finding: JSON schema doesn’t help optimize for AI search, 6 October 2026, for 0 of 25 tool reads and 4 of 5 hidden-div reads.

searchVIU, Schema markup and AI in 2025: what ChatGPT, Claude, Perplexity and Gemini really see, 2 December 2025, for the JSON-LD-only price that no system returned and for Claude’s 0 of 8 on visible prices.

Cloudflare, Markdown for agents, updated October 2026, for the documented preservation of JSON-LD during conversion.

Martinez, arXiv:2607.14035, 15 July 2026, for structured data as one of the factors that must be separated in tests on commercial systems.

Schema.org, home and FAQ, for the 45 million domains figure as of 2024 and the sentence about other organizations finding new uses for publicly accessible markup.