Structured data is machine-readable markup (JSON-LD using Schema.org vocabulary) embedded in web pages that states facts explicitly (what a product is, what it costs, how it's rated) so crawlers and AI systems don't have to infer them.
For AI visibility, structured data serves the retrieval layer: pages with explicit Product, Organization, FAQ, and Review markup are easier to parse, quote, and cite correctly. It reduces the chance a model mangles your facts.
It is not a ranking hack (models do not reward markup for its own sake), but it removes ambiguity, and ambiguity is where misattribution and hallucination breed.
Schema.org and structured data: the same thing?
Nearly. Schema.org is the shared vocabulary, a public dictionary of types and properties like Product, Organization, FAQPage and AggregateRating. Structured data is the practice of putting that vocabulary on a page. JSON-LD is the format almost everyone uses to do it: a script tag carrying the facts as data rather than as prose.
The three names get used as synonyms because in practice they travel together. If someone says "add schema", they mean embed Schema.org types as JSON-LD in the page, which is structured data.
Why it matters for AI answers, not just rich results
Structured data earned its reputation through rich results: star ratings and FAQ dropdowns in a search page. That was always the smaller prize, and Google has narrowed which sites get those. The larger one is that an answer engine reading your page has to decide what is a fact and what is marketing.
Prose forces inference. "Loved by thousands of teams, starting at just a few dollars" tells a model very little it can safely repeat. `offers.price` with a currency tells it something exact. When a model is assembling a recommendation from several sources, the source it can parse without guessing is the one it can quote without risk.
This is also how misattribution starts. If your pricing lives only in an image or a table with no markup, a model reaching for a number may take a competitor's, or a stale one from a roundup. Markup does not make a model recommend you. It makes it harder for the model to get you wrong.
What to mark up first
Start with identity. Organization on every page, with the name, the canonical URL and a logo, so the entity behind the site is unambiguous. Then Product or SoftwareApplication on the pages that describe what you sell, carrying price, currency and availability where they apply.
Then add the shapes that match how buyers ask. FAQPage where you genuinely answer questions, with the answer visible on the page and identical to the markup. Article for editorial. BreadcrumbList so the hierarchy is explicit. Dataset if you publish data others might cite.
The rule that matters more than the list: the markup must describe what a human actually sees. Structured data that claims a rating no visitor can find, or an FAQ hidden from the page, is the one form of this that carries a real penalty.
How WhereDoIRank uses it
Every public page on this site carries the markup it is describing. The rankings are published as Dataset with an ItemList of the ranked brands, the guides as Article with FAQPage, each glossary term as DefinedTerm inside a DefinedTermSet, and the site itself as Organization and WebSite. The point is that a model quoting a ranking should be able to read the standings as data rather than parse them out of a table.
It is a fair test of the thesis, too: if structured data helps a source get cited accurately, an index that marks up its own numbers should be quoted more accurately than one that does not.