The role of structured data in AI Search visibility

A new way of searching

Internet search is increasingly shifting toward generative AI experiences. Instead of traditional lists of blue links, users receive direct answers from tools like Perplexity AI, ChatGPT (with browsing mode), Claude, or Google AI Overview. 

Language models analyze content from multiple sources and generate concise summaries with cited references. For website owners, this opens a new field for optimization – what matters is not just what you write, but how algorithms interpret it.

So, what role does structured data play in this process? And can it truly impact whether your content gets selected and cited in AI-generated answers? That’s what we’ll explore in this article.

How LLMs choose and cite sources

Language models don’t rely solely on HTML tags or keywords. Instead, they read content similarly to humans: they look for meaning, context, clear answers, and coherent claims. 

That’s why content clarity and structure are crucial. Well-organized text, including:

• logical headings, 
• bullet points, 
• and highlighted key statements, 

increases the likelihood that the content will be cited. 

At the same time, content that is unstructured, or overly promotional, may be ignored – even if structured data is technically present.

Some tools, such as Perplexity, use curated indexes and prioritize trusted sources like Wikipedia or Reddit. However, smaller websites can still compete – as long as they provide specific, expert-level information in an accessible format. 

What matters is not just what you publish, but also how you present it.

What is structured data?

Structured data is a standardized way of describing content of a webpage to make it easier for external systems – such as search engines – to understand its purpose and fragments. It helps machines not only “see” the content, but also to “know” that a given text refers to, for example, a product name, review rating, publication date, or a question-and-answer pair.

The most common vocabulary for structured data is schema.org, which defines hundreds of content types and associated properties.

There are three main formats used to implement structured data:

• JSON-LD (JavaScript Object Notation for Linked Data) – the most recommended format nowadays. It places structured data inside a <script type=”application/ld+json”> block, separate from the page’s HTML layout. This makes it clean, scalable, and easy to maintain.
• Microdata – an older method involving adding attributes directly to HTML elements (e.g., itemprop, itemscope). Still supported, but can clutter code and be harder to manage on complex sites.
• RDFa (Resource Description Framework in Attributes) – a more advanced technique used primarily in Linked Open Data projects. Less common in standard SEO but still valid.

Regardless of the format, the goal is the same: to clarify the meaning of content for machines. 

Nowadays, JSON-LD is a standard thanks to its flexibility and widespread support.

Structured data and how AI understands content

Structured data, especially in JSON-LD format, plays a role far beyond just supporting traditional SEO. While it still enables rich snippets (e.g., ratings, FAQs, product availability) in Google search, it now also helps large language models (LLMs) understand content context more deeply.

Rather than relying solely on HTML structure, LLMs use structured data as a signal to interpret the page meaning and establish, if it’s an expert article, a product with reviews, or a direct answer to a question.

• Microsoft has confirmed that Bing uses schema.org markup to help its models (including Bing Chat and Copilot) understand page content. Microsoft also recommends IndexNow for faster discovery of fresh content, which is valued by LLMs.
• Google hasn’t publicly detailed how it uses schema in LLMs, but AI Overviews behavior suggests it plays a role.
• OpenAI also parses static HTML, and it’s likely that schema embedded as JSON-LD can be processed by crawlers like GPTBot.

In short: structured data is not a shortcut to AI visibility, but it’s a vital support mechanism. It helps models understand what each part of the page is: a question, a product, an author, a review. This, in turn, increases the chances your content will be cited in AI-generated answers.

Which types of structured data are most useful for LLMs?

Not all schema types are equally helpful for large language models. If you want to make your site more AI-friendly, focus on the following:

1. FAQPage, Question, Answer
   • Q&A formats align naturally with how AI delivers answers.
   • Marking up a visible FAQ section helps LLMs extract accurate, ready-to-cite content blocks.
   • Google still supports this format in search results.
2. HowTo, HowToStep
   • Step-by-step guides are among the most common queries in AI tools.
   • Using HowTo schema allows models to generate structured, logical answers.
3. Article, BlogPosting
   • Even readable text benefits from context: author, publish date, update history.
   • This data helps models assess credibility and freshness.
4. Product, Offer, Review, AggregateRating
   • In e-commerce, structured data helps models parse product details: price, stock status, reviews.
   • This increases chances of inclusion in AI-powered product recommendations.
5. Other useful types:
   • QAPage – for forums and community-driven content
   • Organization, LocalBusiness – for brand and location data
   • Recipe – popular in culinary queries
   • Dataset, TechArticle – for scientific and technical content

Implementation and validation

The best practice is to use JSON-LD, placed inside a <script type=”application/ld+json”> block. This format is recommended by Google and is easily parsed by crawlers, including those that do not execute JavaScript.

Once you implement it, you can validate your structured data using tools like:

• Google’s Rich Results Test
• schema.org validator

Is structured data worth implementing?

Absolutely. Structured data is one of the most effective ways to make content machine-readable and semantically clear.

For traditional SEO, it unlocks rich snippets (stars, prices, dates, FAQs) that boost click-through rates (CTR) and visibility in search results.

For AI-powered tools like Perplexity, ChatGPT, and Bing Copilot, structured data acts as a bridge between your content and the model’s understanding. It improves recognition of meaning and relevance – increasing your chances of being cited in AI-generated answers.

Even without clicks, such citations build brand awareness and credibility. 

Additionally, clean structured data enhances:

• crawlability,
• integration with third-party services (e.g., aggregators, voice assistants),
• code quality and long-term site maintenance.

Conclusion

Positioning today means optimizing for both traditional search results and AI-generated answers – simultaneously. Structured data plays a crucial role in both areas, so it shouldn’t be overlooked. Instead, treat it as a strategic opportunity to enhance your visibility and strengthen your brand presence wherever your audience is searching for information or solutions.

Not sure if your site uses structured data – or whether it’s implemented in the recommended JSON-LD format? We can help.

We can also support you in discovering your current visibility in AI-generated responses and develop a holistic, actionable plan for both SEO & AI Search Optimization.