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Structured data is no longer an SEO trick - it is the language in which AI systems communicate with your website. When ChatGPT, Perplexity, or AI search answers answer a question, they draw on machine-readable information. Schema.org is the most widely used vocabulary worldwide. This article explains which schema types are actually relevant for AI visibility, why JSON-LD is the only sensible implementation method - and what Schema.org explicitly does not deliver.
Schema.org defines a shared vocabulary for structured data. How that vocabulary is technically embedded in a web page is a separate question - and the answer is clear: JSON-LD (JavaScript Object Notation for Linked Data) is the recommended method, for several concrete reasons. Microdata embeds schema attributes directly in HTML tags: itemscope, itemtype, itemprop nested through the DOM. The problem: Microdata is tightly coupled to the HTML structure. Anyone who changes the layout risks inadvertently destroying schema attributes. Moreover, Microdata is only defined for HTML5 - not for JSON responses or API endpoints. RDFa (Resource Description Framework in Attributes) is the oldest of the three formats, originates from the Semantic Web environment, and is considerably more complex to implement. RDFa is accepted by Google, but offers no advantages over JSON-LD - only more complexity. JSON-LD, on the other hand, lives in the script tag, completely decoupled from HTML markup. This has three practical advantages: first, maintainability - the JSON-LD object can be managed centrally without touching the HTML template. On a product page, the layout often changes monthly - the schema stays stable. Second, completeness - JSON-LD allows nested objects without DOM constraints. A Product schema with Offer, AggregateRating, Review, and Brand can be mapped as a clean object. Third, AI crawler compatibility - Google's own documentation (Google Search Central) explicitly recommends JSON-LD as the preferred format. Since AI search answers and many other AI systems use Google's crawling infrastructure or follow similar approaches, the industry follows this recommendation. The practical recommendation is therefore clear: use JSON-LD. Microdata and RDFa work technically, but offer no advantages and create significantly more maintenance overhead.
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Not all schema types are equally valuable for AI visibility. Based on analysis of Google Search Central documentation and observed citation patterns in AI search answers, seven types can be identified that have particularly high relevance. FAQPage achieves the highest citation rate in AI search answers - and that is no coincidence. FAQ structure corresponds exactly to the format in which AI systems generate answers: a question, a clear answer. AI search answers retrieve FAQPage schema directly to populate answer blocks. The implementation is simple: a FAQPage object with mainEntity as an array of Question-Answer pairs. Product is indispensable for e-commerce. A complete Product schema contains name, description, brand (with @type Organization), offers (with price, priceCurrency, availability), aggregateRating, and review. Missing fields - especially GTIN, MPN, or availability - significantly reduce the chances of appearing in shopping features. Service is the equivalent of Product for service providers. Important fields: serviceType, provider (Organization), areaServed, hasOfferCatalog. Without Service schema, it is harder for AI systems to understand exactly what is being offered and for whom. LocalBusiness with its over 80 subtypes is decisive for local visibility. Instead of the generic LocalBusiness, use the most precisely matching subtype: Plumber for plumbers, Dentist for dentists, Restaurant for hospitality, Attorney for lawyers, Bakery for bakeries. AI systems that give local recommendations interpret subtypes more precisely than the generic type. Organization builds trust through machine-readable company identity: legalName, address, telephone, email, sameAs (with links to LinkedIn, Google Business, Wikidata). Particularly important: sameAs links your data to external sources and increases the confidence rate in AI systems. Person is relevant for advice-intensive industries: lawyers, doctors, consultants, coaches. A complete Person schema with jobTitle, worksFor, knowsAbout, and sameAs increases the probability of being named as an expert in AI answers. Review and AggregateRating act as trust signals. Important: reviews must reflect genuine evaluations - fake reviews violate Google's guidelines. Validate your implementations with the Google Rich Results Test and the Schema.org Validator. Both tools are free and display errors and warnings.
A widespread misconception is that Schema.org markup alone is sufficient to appear in AI answers. This is wrong - and it is worth naming the limits clearly. Schema.org is a vocabulary, not a ranking signal. It helps AI systems and search engines understand the context of your content. It does not guarantee appearance in Rich Snippets, citation in AI search answers, or better rankings. Google itself emphasises in the Search Central documentation that structured data is treated as a hint - not as a binding instruction. Another misconception concerns llms.txt: this file, which some describe as a robots.txt for AI systems, is not an official standard at this point in time. It is read by some AI crawlers, but most major systems - ChatGPT, Perplexity, Gemini - do not follow a standardised llms.txt specification. For a detailed analysis, the llms.txt reality check article on this blog is worth reading. Also not an official schema: there is no special AI schema or GEO markup. Anyone selling you proprietary schema extensions for AI visibility is selling hot air. What actually counts alongside Schema.org: content depth and source citations (AI systems cite verifiable facts more often than unsupported claims), consistent company data across all platforms (NAP consistency: Name, Address, Phone), regular updates (outdated data causes hallucinations - AI systems interpolate missing information from the training corpus), and technical crawlability (robots.txt must not block relevant pages, the server must be accessible to crawlers). Schema.org is the foundation - but it is only one layer in a multi-layered strategy. Those who only implement Schema.org and change nothing else will see no dramatic effects. Those who use it as part of a coherent data strategy have a measurable advantage.
The hierarchy is clear: JSON-LD as the implementation format, FAQPage and Product/Service as priority schema types, LocalBusiness subtypes instead of the generic type, Organization for trust, Review for authority. Validate regularly with the Google Rich Results Test. And do not forget: Schema.org is the technical foundation - it does not replace content quality or consistent data maintenance. The combination of both decides whether AI systems recognise your business as a trustworthy source.
Check GEO Score for freeMarvin Malessa
Founder, Beconova
Founded Beconova in Germany in 2025 to help shops and service businesses become visible in AI search engines. Writes about GEO, AI visibility, and the future of search.
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