GEO Intelligence
Structured data is doing quiet work in AI answers
Schema is how a page states, in a form a machine does not have to guess at, what it is and what it claims. The markup has to match the page.
Structured data has a reputation as a box to tick for rich results: add the markup, maybe get stars in search. In AI answers its role is quieter and, in some ways, larger. Schema is how a page states, in a form a machine does not have to guess at, what it is and what it claims.
It removes ambiguity the model would otherwise resolve badly
A language model reading raw HTML has to infer who wrote a piece, when it was updated, what it is about, and whether a number on the page is a price, a rating, or a phone extension. Markup answers those questions directly. An Article type with an author and a modified date, an Organization with its real identifiers, a FAQ block that pairs a question with its answer: each one closes off a guess. Fewer guesses means fewer confident mistakes about you.
The markup has to match the page
This is where most implementations go wrong. Schema that claims a review score the page never shows, or an author who does not exist, is worse than no schema at all, because it reads as manipulation the moment anyone checks. The rule is simple and unglamorous: mark up only what is genuinely on the page and genuinely true. If the page has real sources, mark them. If it does not, do not invent a citation field to look authoritative.
Where to spend the effort
For a publication, the high-value types are the obvious ones done properly: Article with a real author and dates, Organization that resolves to your actual entity, and Breadcrumb so the piece sits in a clear structure. Resist the urge to stack every type you can find. A small amount of accurate, matching structured data earns more trust than an elaborate graph that contradicts the visible page.