The Problem

You have added structured data to your website. Maybe you even ran it through a validator and saw zero errors. Yet when you ask ChatGPT, Perplexity, or Google AI Overviews about your business, the information is wrong, incomplete, or your site is absent entirely. The uncomfortable truth is that most schema markup on the web is either irrelevant to how AI systems actually extract information, or it is implemented in ways that get silently ignored.

Developers and SEO professionals often treat schema.org as a checklist — add BreadcrumbList, sprinkle in some LocalBusiness, maybe throw a WebSite type on the homepage. But AI-powered search engines do not weight all schema types equally. They prioritize the types that help them answer user questions with confidence, and they discard or deprioritize everything else.

Why It Matters

When an AI system generates a response, it needs to trust the source. Structured data is one of the strongest trust signals available because it provides machine-readable facts that can be cross-referenced. If your schema is missing, generic, or incorrect, the AI has to rely on unstructured text alone — and it will often prefer a competitor whose structured data clearly answers the query.

Worse, incorrect schema actively poisons your credibility. If your Organization schema lists the wrong founding date, or your Product schema claims a price that does not match the page content, AI systems learn to distrust your markup entirely. This penalty is silent — you will never receive a notification that your structured data has been deprioritized.

The Solution

Focus on the Five Schema Types AI Actually Parses

Organization schema establishes entity identity. AI systems use it to connect your website to a known entity in their knowledge graph. Include name, URL, logo, founding date, contact information, and sameAs links to your social profiles and Wikidata entry if one exists.

Article schema tells AI systems that a page contains editorial content. Include headline, datePublished, dateModified, author (linked to a Person or Organization), and publisher. The dateModified field is especially critical — it is how AI determines content freshness.

FAQ schema is arguably the most powerful type for GEO. AI systems directly extract question-answer pairs from FAQPage markup and use them to generate responses. Each question should be a real query your audience asks, and each answer should be a complete, self-contained response.

Product and HowTo schema serve similar purposes for their respective content types. Product schema provides structured pricing, availability, and review data. HowTo schema breaks procedural content into discrete, extractable steps that AI can present directly.

Implement Exclusively with JSON-LD

While schema.org supports Microdata and RDFa syntaxes, JSON-LD is the only format consistently parsed by AI crawlers. Place your JSON-LD blocks in the head of your document. Keep one block per schema type — do not nest unrelated types in a single block. Ensure every required property for the type is present; optional properties should only be included when you have accurate data to populate them.

Avoid These Common Mistakes

Do not fabricate data to fill schema fields. An empty field is better than an incorrect one. Do not duplicate schema across pages unless the data genuinely applies to each page. Do not use schema types that do not match the page content — adding FAQ schema to a page without visible questions and answers will be flagged as spam. Finally, do not forget to update your schema when the underlying content changes. Stale schema is treated as inaccurate schema.

Test with the Right Tools

Google's Rich Results Test validates whether your schema qualifies for rich results, but it does not catch all errors. The Schema.org Validator checks structural correctness against the full specification. Use both tools, and then manually verify that every value in your schema matches the visible content on the page. Automated testing catches syntax errors; only human review catches semantic errors.

What Success Looks Like

When your schema is correctly implemented, AI systems can confidently extract and attribute facts to your website. Your organization appears as a verified entity in AI responses. Your articles are cited with accurate dates and authorship. Your FAQ answers are quoted directly. Your products show up with correct pricing. This is not a theoretical advantage — it is the difference between being cited as a source and being invisible. Schema that matters is schema that AI can trust, verify, and use.