Does schema markup help AI citations?
The short answer
On its own, no. The best causal evidence available — a matched difference-in-differences study by Ahrefs across 1,885 pages that added JSON-LD schema — measured citation changes of −4.6% in Google AI Overviews, +2.4% in AI Mode and +2.2% in ChatGPT. All three are statistically indistinguishable from zero. Schema markup helps machines parse a page reliably, and it remains worth implementing for that reason, but it is not what causes an answer engine to recommend a brand.
- Ahrefs, 1,885 pages, difference-in-differences: −4.6% / +2.4% / +2.2%. Effectively zero.
- Semrush's +22% figure across 304,805 URLs is an association, not a causal test.
- Pages with schema do get cited more — because sites that implement schema well also invest in content and press coverage.
- The exception that does work is entity linking: deep sameAs arrays measured +34% citations, +52% on Gemini and Bing.
The study
Ahrefs took 1,885 pages that added JSON-LD schema markup and compared them against matched pages that did not, using a difference-in-differences design. That design matters: it isolates the effect of adding schema rather than observing which pages happen to have it.
The measured citation changes were:
| Engine | Change after adding schema |
|---|---|
| Google AI Overviews | −4.6% |
| Google AI Mode | +2.4% |
| ChatGPT | +2.2% |
None of these is distinguishable from zero. Two of them are positive and one is negative, which is roughly what you would expect from noise.
The counter-evidence, and why it does not hold
Semrush studied 304,805 URLs and reported a +22% citation lift associated with structured data, ranking it the fifth-strongest predictor they measured. This is frequently quoted as proof that schema works.
It is not, and the word doing the work is associated. Semrush measured which pages get cited more. Ahrefs measured what happens when you add schema to a page. Those are different questions:
Pages with schema do get cited more frequently. But high-authority sites implementing schema also invest heavily in content quality, media coverage and brand building — these are the actual drivers, not the markup itself.
If a better-powered causal study reverses this, the honest position changes with it. As of September 2026, the causal design says zero and the correlational design says positive, and causal designs win that argument.
The one structured-data change that did work
There is an important exception, and it is a specific one.
The 90-day GEO Measurement Study ran twelve controlled changes across roughly 50,000 citations. The single highest-impact change was adding deep sameAs arrays to Person and Organization JSON-LD blocks — “deep” meaning at least eight entries: Wikidata, Crunchbase, LinkedIn, GitHub, the company About page, a personal site, an X profile, a Mastodon or Bluesky handle.
That produced +34% citation lift across all engines, and +52% on Gemini and Bing specifically.
Note what this is actually doing. It is not helping the engine parse your page. It is helping the engine be certain who you are by tying your identity to records it already trusts. The markup is the delivery mechanism; the entity resolution is the effect. That distinction explains the whole result set — generic page-level schema does nothing, identity-level schema does a lot.
What to do instead
In descending order of measured effect:
- Earned third-party coverage. Earned media outperforms brand-owned content by roughly 325% for AI citation rates.
- Deep entity linking. The
sameAswork above, plus consistent naming everywhere you appear. - An active review profile. Brands with none appeared in 1% of answers; brands actively collecting and responding appeared in 75.3%.
- Extractable content. Quotations, statistics and inline citations measured lifts of roughly 41%, 32% and 30% respectively.
- Ungate everything. Two gated whitepapers earned 14 citations in 90 days; open equivalents on the same topics earned 1,847.
And one thing that measurably does nothing at all: keyword density. Varying keyword frequency by 3× across a control set showed zero correlation with citation share on any engine.
Questions people actually ask
Should I remove my schema markup then?
No. Schema still drives rich results in traditional search, helps machines disambiguate your content, and costs almost nothing to maintain once implemented. The finding is narrower than it sounds: adding schema will not by itself cause AI engines to cite you. Keep it, stop paying a premium for it as an AI visibility service.
Why do so many agencies still sell schema as AEO?
Because it is legible, billable and easy to show on a report. You can screenshot a validator passing. Earned media and entity work are slower, harder to demonstrate in month one, and much harder to package. That is a commercial incentive, not evidence.
Which schema types matter most if I am implementing it anyway?
Organization and Person, because they carry the sameAs property that the entity-linking research found to be the single highest-impact change measured. Article, FAQPage and Product are useful for traditional rich results but showed no independent citation effect.