Definition
Structured data is machine-readable markup, usually written in schema.org vocabulary and embedded as JSON-LD, that labels the meaning of content on a page so search engines and AI answer engines can identify entities, authorship, products, and facts without guessing. Instead of inferring that a block of text is a price, a rating, an author, or an FAQ, the system reads it directly from the markup.
How Structured Data Works
Structured data describes entities and their relationships in a shared vocabulary maintained at schema.org. A page carries a JSON-LD block, typically in a <script type="application/ld+json"> tag, declaring what the page is and what it contains. Common types include Organization, Article, Product, SoftwareApplication, FAQPage, BreadcrumbList, and DefinedTerm (this glossary entry emits DefinedTerm markup itself). Crawlers parse the block alongside the rendered content, and the declared entities feed knowledge graphs, rich results, and the entity resolution AI systems use when deciding who is claiming what.
The cardinal rule is parity with visible content. Markup must describe what a human sees on the page; adding ratings, awards, or FAQs that do not appear in the visible content is a quality violation that can suppress eligibility for rich results rather than help it.
A Worked Example
Consider a software vendor whose pricing page shows three plans. Without markup, an engine must infer from prose which numbers are prices and which plan they belong to. With Product and Offer markup, each plan’s name, price, and currency are declared explicitly, so a system assembling a “how much does X cost” answer can extract the facts with far less risk of pairing the wrong number with the wrong plan. The same logic applies to Organization markup resolving a brand name that collides with a common word, or FAQPage markup labeling which question each visible answer belongs to. The markup did not create the information; it removed the guesswork around it.
Why This Is Important For AEO
Answer engines synthesize text from retrieved pages, and their biggest failure mode is misattribution: the wrong entity, the wrong author, a fact assigned to the wrong product. Accurate markup helps a system confirm what a page is, who published it, and what type of information it holds, which supports correct retrieval and attribution. The honest caveat: structured data is a clarification layer, not a ranking lever. Google has stated there is no special schema requirement to appear in its generative AI features, and no llms.txt requirement either. Schema helps machines understand a page; it cannot make thin content citable. Where markup fits among the other levers is covered in what AEO is and AEO versus traditional SEO.
Structured Data And AI Citations
Markup alone does not earn an AI citation; a direct answer, original evidence, and crawlable HTML do more of that work. But accurate structured data reduces ambiguity about entities and authorship, which protects the AI visibility a brand earns once its content is source-worthy: the right brand gets credited for the right claim. Treat schema as one input among several, not a shortcut.
How To Validate And Measure It
Structured data is verifiable in a way most AEO work is not. Validate syntax and type-specific rules with Google’s Rich Results Test or the schema.org validator before shipping, and monitor Search Console’s enhancement reports for errors at scale. The measurement question is binary per page: is valid markup present, does its type match the page, and does every declared fact appear in visible content? AEO Goal’s free scan on AI SEO software checks JSON-LD presence and validity across a domain as part of its AEO-readiness score, alongside crawler access and entity salience, and flags pages where markup is missing or contradicts the content.
Structured Data vs Adjacent Terms
vs schema markup. In practice synonyms: “schema markup” names the vocabulary (schema.org), “structured data” names the general technique.
vs answer engine optimization. AEO is the whole discipline of earning presence in AI answers; structured data is one technical lever inside it.
vs semantic HTML. Semantic HTML (<article>, <nav>, heading hierarchy) conveys document structure; structured data conveys meaning about entities and facts. Both help machines parse a page, and they complement rather than replace each other.
Related Terms
Related terms include schema.org, JSON-LD, answer engine optimization, AI citation, and answer engine.