Alvaro Peña de Luna 
Structured data for AI is machine-readable markup, usually schema.org vocabulary written as JSON-LD, that states what a page is about and which entity it belongs to, so search engines and the AI features built on them can read facts such as names, prices, authors and relationships without inferring them from the text.
It is useful, but it is often oversold. Markup describes your content; it does not replace it, and it does not put you in AI answers on its own.
How structured data works
Schema.org is a shared vocabulary of types (Organization, Product,
Article, Person) and properties (name, price,
author). You describe a page with it inside a
<script type="application/ld+json"> block. This is a minimal organization node
for a home page:
{
"@context": "https://schema.org",
"@type": "Organization",
"@id": "https://www.example.com/#organization",
"name": "Acme",
"url": "https://www.example.com/",
"logo": "https://www.example.com/logo.png",
"sameAs": [
"https://www.linkedin.com/company/acme",
"https://www.youtube.com/@acme"
]
}
Two properties do most of the entity work. @id gives the brand one stable identifier
that other nodes on your site can point to (an article's publisher, a product's brand).
sameAs links that identifier to your official profiles elsewhere, which helps a system
confirm that the Acme on your site, on LinkedIn and in a news article is the same company.
What structured data does and does not do in AI search
Google's guidance on AI features and your website says you do not need special markup to appear in AI Overviews or AI Mode. Those features build on Google Search, where structured data already helps Google understand pages, qualify them for rich results and connect them to entities. Assistants such as ChatGPT and Perplexity mostly read the visible text of the pages they retrieve.
In practice, structured data helps AI visibility in three indirect ways:
- Entity clarity. A consistent Organization node with
sameAslinks makes it easier to tie mentions of your brand across the web to one entity. - Reliable facts. Prices, availability, ratings and dates in Product, Offer and Article markup are unambiguous, where the same facts in prose can be misread.
- Authorship. Article markup with a Person author connects content to the people behind it, one of the signals of E-E-A-T.
Which schema types matter most
- Organization on the home page: name, logo, url, sameAs, contact details.
- Product and Offer for what you sell: price, currency, availability.
- Article with a Person author for editorial content.
- LocalBusiness for physical locations: address, opening hours, phone.
- BreadcrumbList to describe where a page sits in your site.
How to check your structured data
The free schema validator takes a URL or pasted code, reads every JSON-LD block and Microdata item, and checks that each type and property exists in schema.org, that values such as dates, URLs and prices are well formed, which properties Google requires for a rich result, and whether your pages are tied to your brand as one entity. Run it on your home page, one product or service page and one article: most errors repeat across templates.
Markup is only read if crawlers can reach the page, so check access with the AI crawler checker as well.
Common mistakes
- Markup that does not match the page. Ratings, prices or FAQs that visitors cannot see break Google's guidelines, even when they validate.
- Facts only in JSON-LD. If a price or a feature appears only in markup, assistants that read the visible text will not see it. Write it on the page too.
- Inconsistent names. "Acme", "Acme Inc." and "ACME Software" across templates weaken the single entity you are trying to describe.
- Broken syntax. One trailing comma invalidates the whole JSON-LD block.
- Expecting citations from markup alone. Structured data supports good content; it does not make thin content quotable.
How to see whether it changes anything
The result that matters is whether AI engines name your brand and cite your pages. Mencoro's AI brand monitoring checks your prompts on ChatGPT, Perplexity, Google AI Overview and Google AI Mode, records every mention of your brand and every link to your domains, and shows how that changes over time next to your competitors. Compare the weeks before and after an entity cleanup rather than a single answer, because AI answers vary from run to run.
Related glossary terms
- Entity SEO: optimizing for entities and their relationships rather than keywords.
- llms.txt: a curated map of your key pages for AI models.
- E-E-A-T: the quality signals behind trustworthy content.
- Grounding: how AI engines anchor answers in retrieved sources.
