I've seen technical SEO discussions focus heavily on whether schema markup is valid.
I think the more interesting question is whether it tells the right story about the business.
Imagine a search engine trying to connect these dots:
Who published this article?
Which organization does the author work for?
What product is being discussed?
How does this page fit into the website?
Is the information consistent across the site?
Your schema markup can pass a validation test without answering those questions well.
A business might have multiple versions of its name across pages. An author might have no clear connection to their articles. Product markup might describe a price that's no longer available.
These aren't always syntax problems.
They're information architecture problems.
That's why I approach schema as part of technical SEO, not a checklist of markup types to install.
The right implementation depends on the page, the entity being described, and the information users can actually see.
I put together a guide to 10 schema types worth evaluating for SEO and AI search, with practical notes on what they describe and where they make sense.
Not every website needs all 10.
And no schema implementation guarantees rankings or AI citations.
The goal is to make your website's information more explicit, accurate, and consistent.
When you audit schema, do you start with validation errors or the underlying entity relationships?
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