Structured data and schema markup are essential for SEO. However, now, it’s not just something that helps you appear in search and its rich results, it enables you to appear in AI-generated answers.
In this new “era” of search, appearing in AI answers is becoming the new type of SEO. SEO 2.0, so to speak. This makes it incredibly important to appear in these answers, and one way to do this is to make your content more machine-readable by using structured data.
What is Structured Data in SEO
Structured data (also known as metadata or schema) is a code you can add to your webpages, normally in JSON-LD format, that explains the page’s contents in a machine-readable way.
This supports entity annotation, enabling search engine crawlers to understand specific people, places, products, and key concepts.
For example, when using the structured data “BlogPosting”, you can tell search engine crawlers the title of the post, the author, the publish date, and several other pieces of information.
Why Does Structured Data Matter for AI SEO?
Structured data for SEO helps search engine crawlers quickly understand what the page is about. Its structure, meaning, and context. This is the same for AI engines.
Instead of having AI search, parse, and interpret the content, which is what will happen without using structured data, with structured data, explicit labels and context are provided already.
Most AI algorithms use structured data to understand web pages better as well. Everything from Google’s MUM and Bard to Bing’s GPT-based model uses it to understand the page better.
For example, let’s say you write a How-to blog post. Using the schema “HowTo” will help distinguish it from a “BlogPosting”. Therefore, if someone asks an AI to perform something, like “How to XYC”, your content will more likely get cited.
The importance comes down to ease of understanding and categorising. When you make it easy for AI algorithms to understand the content and context of your page, the higher the chance it’ll get cited in an AI-generated answer.
Alongside this, it supports the shifts towards semantic SEO, where the focus on meaning, relationships, and intent is more important than keywords.
How Schema Markup Helps AI Understand Your Content
As you now know, schema markup helps AI understand your content, making it more likely to appear in AI-generated answers and zero-click results.
Here’s how it works:
- Entity Recognition & Disambiguation: Clearly defines people, places, products, etc., to help AI quickly and correctly interpret your content.
- Contextual Understanding: Adds meaning to content types, for example, articles, recipes, and how-to guides, to help AI comprehend and categorise your web pages.
- Passage-Level Citations: Makes content easier to quote from with tools like Google AI Overviews and ChatGPT, increasing chances of being cited.
- AI Trust & Preference: Structured content is simpler for AI engines to parse and trust, giving your page an edge over unstructured competitors.
Choosing the Right Schema Types
On the official Schema website, Schema.org, there are many schema types. Choosing the right one, however, is very important.
| Schema Type | Description | When to Use | Aligns with E-E-A-T |
| Article / BlogPosting | Marks content as a news article or blog post with headline, author, date, etc. | Use on blog posts, news articles, press releases. Use BlogPosting for blog content, NewsArticle for journalism. | Yes |
| FAQPage | Structures Q&A content by marking each question and its answer. | Use on FAQ sections, help centres, or product Q&A pages. Avoid using it for user-submitted questions. | Yes |
| HowTo | Marks step-by-step instructional content, including steps, tools, and images. | Use for tutorials, DIY guides, or process explanations. | Yes |
| Person (Author) | Identifies and describes the author, including name, title, and social links. | Use on author bios or within Article schema to highlight who wrote the content and their credentials. | Yes |
| Organisation (Publisher) | Describes the website or company, including name, logo, contact info, and social profiles. | Use site-wide, typically in the homepage or footer, to represent the publisher or brand. | Yes |
Mapping Schema Types
Mapping schema types is all about choosing the correct schema for your particular web page. This ensures that your web page sends off the right signals.
When performing this, think about the purpose of the page. Consider what type of information the users and AI would be looking for.
- Is it an information blog post? Use Article/BlogPosting schema, with Person and/or Organisation schema.
- Is it a product or an e-commerce page? Use Product schema with Offer or AggregatedRating (review) schema.
- Is it a question-and-answer page? Use the FAQPage schema.
- Is it a profile or about page? Use Person or Organisation schema.
- Is it a how-to guide? Use HowTo schema.
Using the correct structure data and schema markup is essential. It’ll help AI algorithms with page type classification, increasing your chances of being cited for the right queries.
Implementing JSON-LD Schema Correctly
After reading the above, you may want to start implementing JSON-LD. If so, here’s a quick guide:
Where to Place JSON-LD Schema
JSON-LD schema markup can be implemented anywhere in your HTML, in a webpage’s <head> or <body> areas. Most people, however, put it in the <head> section.
For sitewide schema, like Organisation or WebPage, adding it to your CMS’s global or footer script is recommended.
Best Practices Using JSON-LD Schema
- Use the Correct Schema Type: Always choose the right schema type so algorithms can understand your content correctly.
- Follow JSON-LD Format: Google recommends that you use JSON-LD format as it’s easier to implement and to maintain.
- Ensure Consistency: The schema markup should match the data that’s visible on the page to avoid penalties.
- Validate Your Markup: Use tools like Google’s Rich Results Test to validate markups to check for errors.
- Avoid Over-Markup: Though exciting, don’t go overboard with markup. Only add schemas that are visible and relevant to users.
How to Scale JSON-LD Schema Implementation
If you have thousands of web pages, performing schema implementation manually will take years. Luckily, however, there are plenty of tools you can use to implement structured data at scale.
You could automate it with tools, for example. Tools like Yoast SEO, Rank Math, etc., can automate schema deployment across similar page types. You could also use Google Tag Manager (GTM) and inject JSON-LD schema across many pages.
Plus, alongside this, you could create templates where you can copy and paste—for example, the Product schema. You could create a template for this and then just change out certain details to match the product page.
All the schemas you’ll ever need to use will be on Schema.org. You can also generate schema using AI tools, like ChatGPT, Bard, and Perplexity, increasing the scalability of the implementation.
Schema Examples
Article
This is a broad type of schema. Generally, it’s used to mark up general articles, think news stories, magazine features, and other types of written content.
BlogPosting
BlogPosting is a little different to Article schema. The main difference is how it’s written and the style of content. Blogs are more informal, whereas articles are more formal.
FAQPage
FAQPage schema markup is best used for Frequently Asked Questions (FAQs) pages. This will also help you appear in search rich snippets, like the “People Also Asked” section.
Person
The Person schema is used to describe an individual. These are used for author pages, staff directories, and any page about a specific person. It can include their name, job title, social profiles, biography, etc, which can help extract data for a knowledge graph on search engines.
Organisation
Similar to the Person schema, however, it’s used for companies, businesses, or other organisations. It’s best used on about pages, contact pages, or anywhere you want to clarify the details about an organisation.
With this type of schema, you can provide details like the company name, address, logo, social links, etc. Like with the Person schema, this type of schema can help develop knowledge panels for branded search results.
Tools to Test and Validate Structured Data
| Tool | Purpose | Best For |
| Google Rich Results Test | Tests schema for rich result eligibility and previews | Blog posts, FAQs, recipes, articles |
| Schema Markup Validator | Validates general schema syntax (not limited to Google types) | Niche or non-rich result schema types |
| SEO Crawlers (e.g. Screaming Frog) | Audits schema site-wide and flags errors | Large-scale schema audits |
| CMS Plugins (e.g. Yoast) | Adds schema and shows issues within WordPress | WordPress users managing blog or content schema |
| Browser Extensions (e.g. JSON-LD Viewer) | View and inspect schema on any live page | Quick checks and competitor research |
| Google Search Console | Monitors structured data health across your site | Ongoing tracking and issue alerts |
| Bing Validator | Bing-specific schema validation | Sites with significant Bing traffic |
| Lighthouse (DevTools) | Flags schema issues in SEO audits | Quick diagnostics during site checks |
| Schema Generators (e.g. Merkle Schema Generator) | Creates schema code templates | Easy schema creation for beginners |
Final Word
Structured data and schema markup are your “translator” between human content and AI models. Without it, they’ll struggle to understand the content. With it, they can understand it in a way that’s easy for them.
Because of this, I encourage all website owners to perform a structured data audit. First, start with the key pages, articles, about, product pages, etc., and then work out from there. Little by little, you’ll make your content more machine-readable and LLM-friendly.
