In short: Structured data is a set of machine readable labels on your content. They tell crawlers and language models unambiguously who you are, what a page is about and which questions it answers. It takes a few hours, requires no new technology, and it is one of the most concrete things you can do for your AI visibility this week.
What is structured data, and why does AI care?
Structured data, often called schema markup, is a standardised way of describing content in the code of a web page. The format most people use is called JSON-LD: A small text block in the page head that says things like "this is an organisation with this name and this registration number" or "this is a question, and here is the answer".
Humans never see this information. Machines read it first. And that is exactly the point: When an AI platform decides what your page is about and whether it can be used as a source, unambiguous machine readable information is the difference between being understood correctly and being guessed at.
We see it in practice across our customers: Businesses with clean structured data get quoted more accurately, with the right name, the right location and the right products. Businesses without it get confused with others, left out, or described with outdated information.
The four schema types with the highest payoff
1. Organization: Who you are
The cornerstone. Placed across the whole site, it states your name, logo, address, registration number, contact details and links to your official profiles. This is the anchor that ties all other coverage of you together into one identity the models can trust.
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Your Company Ltd",
"url": "https://yourcompany.com/",
"logo": "https://yourcompany.com/logo.png",
"address": { "@type": "PostalAddress", "addressLocality": "Oslo", "addressCountry": "NO" },
"sameAs": ["https://www.linkedin.com/company/yourcompany/"]
}2. Article and BlogPosting: What you publish
Marks articles and blog posts with title, description, publication date and, crucially, author. The author field matters more than people think: Named experts with a role and profile are a credibility signal the models weigh when choosing sources.
3. FAQPage: The questions you answer
The most direct GEO gain. FAQPage schema packages questions and answers in exactly the format answer engines are built to consume. If you have an FAQ section without markup, you are leaving the gain on the table. The rule is simple: The answer should appear verbatim in the markup, short and complete, so it can be quoted on its own.
4. Product: What you sell
If you sell products or services with prices, Product schema with an Offer gives the models unambiguous facts to repeat. When someone asks AI "what does X cost at Y", this markup decides whether the answer is right.
How to do it in practice
- Map what you have. Run your most important pages through Google's Rich Results Test or validator.schema.org. Many discover half finished markup from an old SEO project.
- Start with Organization. One block, on every page, with complete and correct information. Check that it matches your official company registry entry and your LinkedIn profile. Consistency is half the point.
- Mark all content with Article. If you use a publishing system, this is usually a setting or a small template change, not manual work per article.
- Add FAQPage to genuine question pages. Do not invent artificial FAQs for the markup's sake. Take the actual questions customers ask, answer them well in the content, and package the same answers in markup.
- Validate and monitor. Test after every change, and watch whether AI platforms start quoting you more accurately and more often. The latter is measurable: The visibility score and source analysis in Synlig.ai show whether your site is actually being used as a source in answers.
The mistakes we see most often
- Markup that promises more than the content delivers. Schema for content that does not exist on the page violates the guidelines and erodes trust. The markup should describe what is actually there.
- Outdated information. An old address or old prices in markup spread into AI answers and stay there for a long time. Treat structured data as part of your content, not something you set up once.
- Everything on one page, nothing on the rest. The full package on the front page and zero on the content pages is a common pattern. It is the content pages that get cited as sources, so they need markup the most.
Where to go next
Structured data is the machine readable half of the job. The human readable half, how the content itself is written and structured, is covered in our guide on AI friendly content. And if you want the full picture of what drives your visibility, start with the pillar guide on AI visibility.