Structured data and AI Search – which schemas support visibility in AI responses?

Ever asked ChatGPT or Gemini to recommend an app for editing, a refrigerator model, or running shoes? That’s AI search. Users are increasingly directing various queries to LLM models, which is why AI search engine optimization (AISO) is gaining importance. After all, as a business owner, you want people to find your brand wherever they ask about services or products from your industry.

What distinguishes AI search from the search we have known so far? LLM models do not look at a page like the Google algorithms we know. They do not index entire pages, but search for information from available sources in real time. It is this accessibility for AI models and agents that will be key to your website’s visibility. And that is why you need structured data.

Tip: AI models try to “understand” fragments of a page and match the intent and context of the content to the user’s query. Therefore, in AI positioning, it is not so much keywords or links that will be important, but rather a clear structure and a clear purpose of the content.

Learn more about AI positioning: AISO – Your Brand’s Visibility in AI-Powered Answers.

Yes. For SEO, it is an additional element that distinguishes your website, while with AI, schema becomes even more important.

Structured data is also helpful in standard SEO. For Google bots, these are elements that aid interpretation and are additionally displayed in the results as rich snippets (e.g., stars with product ratings). This makes the results stand out visually, and users are more likely to click on them.

We have already mentioned that Google and AI use different methods of data storage and indexing. Google indexes the entire code of a website, so it can interpret it even without structured data (although this is still recommended). For LLM models, on the other hand, a correctly implemented schema is a real game-changer.

AI models scan page elements and quickly assess the content’s context to use a specific fragment in their response. So, by using structured data such as Article, we immediately signal that the models are dealing with an article, similarly to FAQPage, which clearly indicates the question and answer elements on the page.

John Muller also emphasized the importance of structured data in the context of AI positioning during his presentation at the Google Search Central Live conference in Madrid:

Source: X

If your website does not contain structured data, AI may skip it at the source selection stage, or in another scenario, it may not understand it correctly.

Thanks to structured data, LLMs can better identify the message of a given content, which results in more accurate responses. With schema, you can clearly indicate, for example, the author, product, its price, reviews, or your company’s location. In this case, the model does not have to guess the context – it has the answer given directly.

Schema also saves time and computing power for LLM models. Without a schema, the model must first analyze many elements of the content to understand that a given structure is, for example, a question and a short answer. Such analysis consumes more resources, so AI may decide not to visit such a page.

Structured data can be compared to signposts for LLM models. Imagine this situation: you are moving and packing all your belongings into boxes. If you don’t label them, it will take you much longer to unpack. You will have to open each box, check what’s inside, and then arrange it in the right room. You will manage, but if the contents were labeled, it would be much faster. The same is true for schema and AI. By marking fragments of content with dedicated structured data, you make it easier for models to work, thereby gaining visibility in AI.

In the context of AI Search, it is worth focusing on the types of structured data that directly provide context to language models:

• FAQPage – particularly effective in direct responses, where AI quotes a specific question and answer.
• Article / NewsArticle – key for expert and industry content. AI often uses these tags to generate summaries.
• Product / Offer – very important in e-commerce. AI can utilize data on the product, including its price, availability, and reviews.
• LocalBusiness – for local queries (e.g., “best café in Krakow”), a well-marked local business has a better chance of appearing in the response.
• HowTo / Recipe – very useful when a user is looking for step-by-step instructions or a recipe. AI often quotes these structures exactly.
• Author / Organization – supports building authority and brand/person recognition, which is very important for AI when selecting a source.

Want to add structured data for FAQs to your website? Check out our free FAQ Schema generator!

The best choice for structured data is definitely the JSON-LD format, which is also recommended by Google and AI models such as ChatGPT and Claude.

In addition:

• JSON-LD is easy to embed in the website code – just add <script type=”application/ld+json”> in <head> or <body>.
• It does not break the HTML structure, which means there is less risk of errors and greater control over the website code.
• AI models prefer data in a clear, separate format – and JSON-LD allows for just that.
• Choosing this format for your website also means better scalability, the ability to generate data through the backend dynamically, and compatibility with tools.

To enhance your brand’s recognition in AI Search, we recommend implementing structured data on your website. Thanks to this, LLM models will use your website more often for their responses, the number of mentions or recommendations of your products/services will increase, and this can turn into conversions.

Sources:

• https://www.searchenginejournal.com/google-confirms-structured-data-still-essential-in-ai-search-era/544141/
• https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data