AEO Explained

Fixing broken schema markup: the errors that quietly damage AI trust

Most advice about schema markup focuses on whether you have it at all. That's the wrong starting question for a lot of businesses. In our testing, sites with schema markup that's technically present but factually wrong come up more often than sites with no schema at all, and a broken block is a worse position to be in, not a better one. A model reading structured data has no way to know it's inaccurate. It simply treats it as fact.

Why a broken block is worse than no block

When a site has no schema markup, an AI crawler falls back to parsing the visible page text, which is imperfect but at least grounded in what a human reader would also see. When a site has schema markup with the wrong details inside it, a model has a structured, machine readable source telling it something false, and structured data is generally weighted as more reliable than loose page copy. That combination, confidently wrong and treated as authoritative, is exactly the kind of error that quietly costs a business a recommendation it should have won.

In our audits, broken schema shows up more often than missing schema. A block that exists but has the business name in the wrong field, an empty phone number, or a postcode with no street address is actively feeding AI systems bad information, not simply withholding good information.

The four errors we see most often

How to actually check your own site

Run your homepage and your key location or contact pages through Google's Rich Results Test or Schema.org's own validator. Both will parse whatever markup is present and show you the fields it actually contains, not what you assume is there. Read every field against what's true today, not what was true when the block was first written. Business names change, phone numbers get reassigned, and addresses move, and schema markup has no mechanism for flagging that it's gone stale on its own.

The fix is usually smaller than it looks

Correcting broken schema rarely requires a rebuild. It's a matter of editing the specific fields that are wrong, matching them exactly against your current business name, address, and phone number as they appear everywhere else, and removing any field you can't fill with an accurate, current value. A shorter, accurate block beats a longer one with errors in it every time, because an AI model treats every field as equally trustworthy regardless of how many others are correct.

Build in a review point

Schema markup isn't a one time setup. Any time a phone number changes, a location moves, or opening hours shift, the schema block needs updating alongside the page content. Treating it as a living part of the site, checked whenever core business details change, is the difference between a fix that holds and one that quietly breaks again within a year.

Find out if your schema is helping or actively hurting you.

Our AI Recommendation Score checks structured data accuracy, NAP consistency and three other categories, then hands you a ranked fix list. Start with a free AI Snapshot.

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