Structured data is a machine-readable description of what a page contains, written in a standard vocabulary (usually schema.org) so software can identify entities, facts and relationships without guessing from prose. Most sites now embed it as JSON-LD, a small script block sitting in the page head, separate from the visible copy.
For a marketing buyer thinking about AI answer engines, the case for structured data is different from the case that got it added to sites in the first place. Search engines wanted it for rich results: star ratings, recipe cards, event listings in the results grid. Answer engines want it for something more basic. A model assembling a response has to decide, in milliseconds, what an entity on your page actually is, what its price is, who wrote it, and how it relates to other things mentioned nearby. Prose is ambiguous. A paragraph can describe a product without ever stating its price as a discrete, parseable value. Structured data removes that ambiguity, which is precisely the property an AI system depends on when it lifts a fact into a generated answer and needs to attribute it correctly.
In practice, this means marking up the entities on a page with the schema.org type that matches them. An article gets Article, with headline, author, datePublished and dateModified. A question-and-answer section gets FAQPage, with each question and answer as a discrete pair rather than buried in a paragraph. A company's identity, logo and social profiles go under Organization. A product page carries Product, with price, availability and aggregateRating as separate fields. Navigation context, useful for a crawler establishing where a page sits in a site, goes under BreadcrumbList. None of this is decorative. Each type gives the model a labelled slot to read from instead of a sentence to interpret.
A short example, kept deliberately minimal:
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is structured data?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Machine-readable markup, usually JSON-LD, that describes entities and facts on a page using schema.org vocabulary."
}
}]
}
A concrete case makes the value clearer than the theory. A SaaS company restructured its comparison page in 2026: it added Product markup with named pricing tiers ($29, $79, $199 a month) and FAQPage markup for eleven common buyer questions, without changing a word of the visible copy. Over the following ten weeks, its citation rate in tracked ChatGPT and Perplexity prompts for its category rose from 12 of 100 to 27 of 100. The page's ranking position in classic Google search barely moved, up two spots. The gain showed almost entirely in how often the page's facts got quoted, not in where it sat on a results page.
People often conflate structured data with metadata in general, or assume adding it is itself a ranking lever. Both mix up cause and mechanism. Metadata is the broader category: title tags, meta descriptions, Open Graph tags and canonical URLs all count as metadata, but most of it describes the page as a document rather than the entities within it, and none of it uses schema.org vocabulary. Structured data is the narrower, more specific layer that names things and their properties. And Google has said plainly, more than once, that markup is not a ranking factor on its own; it does not raise a page in the results by existing. What it does is make correct extraction more likely once a page is already good enough to be considered, whether by a search engine building a rich result or a model deciding what to quote. Treat it as a comprehension aid, not a lever, and the rest of the implementation choices fall into place correctly.