What Is Structured Data?
Structured data is a standardized way of describing the content of a web page. It uses a specific format, such as JSON-LD, RDFa, or Microdata, to provide additional context about the content of the page.
Structured data helps search engines and other tools understand the content of a page, including the type of content (article, product, event), author information, and key data points. This makes it easier for search engines to index and display your content in relevant search results, and it can improve eligibility for enhanced search-result features where supported.
Where Structured Data Fits in the Discovery Process
It helps to keep three stages separate. Crawling and indexing determine whether a page is discovered and stored. Source selection determines which indexed pages a system chooses for a given answer. Structured data sits alongside these stages: it does not replace crawlable content, and it does not control selection — it improves machine understanding of pages that are already discoverable.
In other words, schema markup is a supporting signal. A page still needs to be crawlable, well-structured, and relevant before structured data can help systems interpret it accurately.
How AI Search Systems Use Structured Data
AI search systems use structured data to understand the content of a page more effectively. By providing additional context about your content, you can help AI systems extract and use your content more efficiently.
For example, if you have an article about a product, adding Product schema markup to your page can help AI systems understand that the content is about a product, and it can extract information like the product name, price, and description. This information can then be used to generate a more relevant answer for users searching for that product.
Common Types of Structured Data for AI Search
There are many types of structured data that can improve your content's visibility in AI search systems. Some of the most common types include:
- Article schema: Describes articles, blog posts, and news content.
- Product schema: Describes products, including names, prices, and descriptions.
- Event schema: Describes events, including dates, locations, and organizers.
- FAQ schema: Describes frequently asked questions and their answers.
- HowTo schema: Describes step-by-step instructions.
- LocalBusiness schema: Describes local businesses, including addresses and opening hours.
These types of structured data can help AI systems understand the content of your page and extract relevant information. By adding these schemas to your website, you can increase the likelihood that your content will be included in relevant AI search results.
How to Implement Structured Data
Implementing structured data on your website requires adding schema markup to your pages. The most common format for structured data is JSON-LD, which is a lightweight data-interchange format.
There are several ways to implement structured data on your website, including: using a plugin or tool, editing your website's code directly, or using a structured data generator. The method you choose will depend on your website platform and technical expertise.
Testing and Validating Structured Data
It's important to test and validate your structured data to ensure it's being correctly implemented and understood by search engines and other tools. Google provides validation tooling that you can use to test your structured data.
To validate, enter the URL of the page you want to test, and the tool will analyze the page's structured data and provide feedback on any errors or warnings.
Best Practices for Structured Data
To get the most out of structured data, it's important to follow some best practices:
- Use the most relevant type of structured data for your content.
- Keep it accurate and up-to-date so it matches the visible page content.
- Prefer JSON-LD format, which is widely supported and easy to maintain.
- Add structured data to all relevant pages, not just the homepage.
- Test and validate regularly, especially after site changes.
By following these best practices, you can increase the likelihood that your structured data will be correctly understood by search engines and other tools, which can improve your content's visibility and discoverability in AI search systems.
Important Note
Structured data improves machine understanding and can support search-result eligibility, but it does not guarantee AI citations, rankings, or inclusion in AI-generated answers. BrandLens does not directly monitor every AI assistant. The signals described here may support AI-assisted discoverability; they are heuristic indicators to investigate rather than guaranteed outcomes.
How BrandLens Can Help
BrandLens helps you review the machine-readable information on your website. Our platform analyzes your pages' metadata, structure, and technical setup, providing recommendations you can verify and implement yourself.
Metadata and Structure Review
Review titles, descriptions, headings, and page structure to identify gaps in the machine-readable information your pages provide.
Related Reading
To understand how sources are discovered and used, see How AI Search Finds and Uses Web Sources. For practical content structure guidance, see How to Structure Content for AI Search. To put these signals into practice, use the AI Search Visibility Checklist.