By Mehdi HassanWhat is schema markup?
Schema markup is structured data: a block of code that describes what a page contains in terms a machine can parse without guessing. It uses the vocabulary maintained at schema.org, which Google, Microsoft and other search companies created together, and it's most often written as JSON-LD inside a script tag.
A human sees a page with a logo, a byline and a price. A crawler sees text and images. Schema closes that gap by stating, for example, that a block of text is an Article, that "Mehdi Hassan" is its author and that the publisher is an Organization with a specific website and social profiles. It's one of the core entries in our SEO and AEO glossary.
What does schema markup look like on a real B2B site?
On a B2B site, schema markup usually describes the company, the pages and the content, not products with star ratings. A typical studio or SaaS site carries an Organization block on every page and an Article block on every blog post.
Example: the markup on a blog post like this one
A post on our own blog would carry an Article block naming the headline, the author (Mehdi Hassan, linked to an author page), the publisher (Designify Studio) and the published and updated dates. Alongside it sits a BreadcrumbList that tells Google the path: Home, Blog, this post.
The Organization block adds sameAs links pointing to the company's official profiles elsewhere. That's what helps a search engine connect "Designify" on this site with the same Designify on Contra or LinkedIn. A glossary entry can add a DefinedTerm block for the term it defines.
The types that matter for most marketing sites are covered one by one in structured data for marketing sites. If your site is built in Next.js, the Next.js SEO checklist shows where the JSON-LD goes.
Does schema markup help with AI search?
Schema markup doesn't measurably help pages get cited in AI answers, based on the best 2026 evidence. In Ahrefs' controlled test, pages with added schema saw AI Overview appearances fall 4.6%, while AI Mode and ChatGPT citations stayed flat. Google's own AI features guidance also says no special schema is needed.
What schema does | What schema doesn't do |
|---|---|
Makes pages eligible for rich results | Guarantee a rich result appears |
States entity facts clearly (name, logo, profiles) | Lift rankings on its own |
Helps search engines parse FAQs and breadcrumbs | Get you cited by ChatGPT or AI Overviews |
Ties an author to their content | Replace a real author page with real credentials |
So keep it, but treat it as hygiene. The full evidence, test by test, is in does schema markup help AI citations, and the practical take for ChatGPT and AI Overviews is in schema markup and AI citations.
What are common schema markup mistakes?
The most common schema markup mistakes are marking up things the visitor can't see, copying a plugin's defaults without checking them, and expecting schema to fix weak content.
- Invisible claims. FAQ or review markup for content that isn't on the page goes against Google's structured data guidelines.
- Conflicting blocks. A theme and an SEO plugin each output an Organization block with different names or logos.
- Wrong types. A services company marked up as a LocalBusiness with a fake address, or every page marked as a Product.
- Never tested. Broken JSON fails silently. Run pages through Google's Rich Results Test after every template change.
How does schema markup relate to other SEO terms?
Schema markup is a technical layer that supports several other ideas rather than standing alone. Structured data is the broader term; schema.org is the vocabulary most structured data uses; rich results are the visible payoff; and entity SEO is the strategy that schema helps express. An AI Overview, by contrast, is built from your visible text, which is why the words on the page still matter most.
