What Are Rich Results and Do They Help AI?

Understanding Rich Results and Their Role in AI

Rich results are enhanced search listings that appear in search engine results pages (SERPs) with additional visual elements beyond standard blue links. These enhancements include review stars, product pricing, FAQ expandable sections, recipe ingredients, event dates, and other structured information. Rich results are created through structured data markup, a standardized format that tells search engines exactly what your content represents. The most common vocabulary for this markup is schema.org, which defines hundreds of content types and their associated properties. When properly implemented, rich results not only improve click-through rates in traditional search but also play an increasingly important role in how artificial intelligence systems understand and cite your content.

The relationship between rich results and AI is more nuanced than many realize. While rich results were originally designed to enhance visual presentation in search results, they now serve a critical function in helping large language models (LLMs) and AI search engines comprehend your content with greater accuracy and context. This shift represents a fundamental change in how structured data functions in the digital ecosystem.

How Structured Data Powers Rich Results

Structured data is implemented using three primary formats: JSON-LD (JavaScript Object Notation for Linked Data), Microdata, and RDFa. Of these, JSON-LD is Google’s recommended format because it’s cleaner, more scalable, and easier to maintain. JSON-LD places structured data inside a <script type="application/ld+json"> block, keeping it separate from your page’s HTML layout. This separation makes it flexible and less prone to errors compared to older methods that embed attributes directly into HTML elements.

The implementation process involves marking up specific elements on your page with relevant schema types. For example, on a product page, you would mark up the product name, price, availability, and customer reviews. On a blog post, you would identify the author, publication date, and article headline. On an FAQ page, you would structure each question-and-answer pair so machines can parse them individually. Each piece of information you mark up becomes a signal that helps search engines understand not just what your page contains, but what each component means and how it relates to user queries.

The Impact of Rich Results on Traditional Search Visibility

Rich results have long been a cornerstone of SEO strategy because they directly improve click-through rates (CTR) and visibility in search results. When your page displays with review stars, pricing information, or an expandable FAQ section, it stands out visually among competitors’ plain text listings. This visual differentiation makes users more likely to click on your result, which translates to increased organic traffic. Studies consistently show that pages with rich results experience significantly higher CTR compared to standard blue links, sometimes by 20-30% or more depending on the industry and result type.

However, the landscape of rich results has been volatile. In 2023, Google made significant changes to its rich result policies. The company stopped showing Video rich results on the SERP for most pages, instead favoring YouTube results or pages where video is the primary content element. Additionally, Google deprecated How-To rich results entirely and dramatically reduced the frequency of FAQ rich results, restricting them to “well-known, authoritative government and health websites.” These changes initially caused concern among SEO professionals who relied on rich results as their primary metric for measuring structured data success. However, these deprecations revealed an important truth: the value of structured data extends far beyond achieving visual enhancements in search results.

How AI Systems Use Structured Data to Understand Content

The emergence of generative AI search engines like Google’s AI Overviews, Microsoft Bing Chat, ChatGPT with web browsing, Perplexity AI, and Claude has fundamentally changed how structured data matters. Unlike traditional search algorithms that primarily use structured data to generate rich snippets, AI systems use structured data as a signal to interpret page meaning and establish context. When an LLM encounters well-structured data, it can more confidently identify what each part of your page represents—whether it’s a question, a product specification, an author credential, or a review rating.

Microsoft has publicly confirmed that Bing uses schema.org markup to help its models, including Bing Chat and Copilot, understand page content more deeply. The company explicitly recommends implementing structured data as a way to prepare for AI-powered search. While Google hasn’t released detailed documentation on how it uses schema in its AI systems, the behavior of Google’s AI Overviews suggests that structured data plays a significant role in content selection and citation. OpenAI’s GPTBot crawler also parses static HTML, and schema embedded as JSON-LD can be processed by these crawlers to better understand content context.

The key insight is that structured data is not a shortcut to AI visibility, but rather a vital support mechanism. It helps AI models understand what each part of your page is: a question, a product, an author, a review, or an instructional step. This understanding, in turn, increases the chances your content will be cited in AI-generated answers. When an AI system needs to provide an answer to a user query, it looks for content that clearly addresses that query. Content marked up with appropriate schema is easier for the AI to parse, verify, and cite as a source.

Which Schema Types Matter Most for AI Visibility

Not all schema types are equally valuable for AI systems. If your goal is to improve visibility in AI-powered search experiences, focus on these high-impact schema types:

FAQPage and Question/Answer schemas align naturally with how AI delivers answers. When you mark up a visible FAQ section with proper schema, you make it easy for LLMs to extract accurate, ready-to-cite content blocks. These schemas are particularly valuable because they directly answer specific questions that users ask AI systems. Google still supports FAQ schema in search results for eligible websites, and other AI search engines prioritize well-structured Q&A content.

HowTo and HowToStep schemas are among the most valuable for AI systems because step-by-step guides are among the most common queries in AI tools. When you use HowTo schema to structure your instructional content, you allow AI models to generate structured, logical answers that maintain the integrity of your original guidance. This schema type helps AI systems understand the sequence and dependencies between steps, making your content more useful for citation.

Article and BlogPosting schemas provide essential context that helps AI systems assess credibility and freshness. By marking up author information, publication date, and update history, you give AI systems the signals they need to determine whether your content is authoritative and current. This is particularly important for topics where recency matters, such as news, technology, or evolving best practices.

Product, Offer, Review, and AggregateRating schemas are critical for e-commerce and consumer-focused content. These schemas help AI models parse product details including price, stock status, and customer reviews. This increases your chances of inclusion in AI-powered product recommendations and shopping-related queries. When an AI system needs to provide product information, it looks for pages with complete, accurate product schema.

The Semantic Value Beyond Rich Results

The most important shift in how we should think about structured data is understanding its semantic value—the underlying meaning and context it adds to content. While rich results come and go based on Google’s strategic decisions, semantic understanding is instrumental in laying the groundwork for contextual content that will shape the future of search. By implementing proper connected schema markup and establishing connections between entities on your site and external authoritative knowledge bases, you create your organization’s content knowledge graph.

This content knowledge graph is a structured information data layer that helps search engines and AI systems disambiguate the entities mentioned on your site. For example, if you mention “Apple,” your schema markup can clarify whether you’re referring to the technology company, the fruit, or something else entirely. By providing this disambiguation, you shape how search engines and AI systems understand your content, gaining greater control over how users perceive your brand. This ultimately provides users with more accurate and relevant search results while improving your visibility in AI-generated answers.

The semantic approach to schema markup involves more than just adding the minimum markup required for rich results. It requires thinking about how entities on your site relate to each other and to external authoritative sources. When you establish these connections through schema markup, you’re essentially building a machine-readable representation of your expertise and authority. AI systems rely on this kind of semantic clarity to confidently cite your content as a source.

Implementing Schema Markup for AI Optimization

To maximize the impact of structured data on both traditional search and AI visibility, follow these implementation best practices:

Use JSON-LD as your primary format. Google explicitly recommends JSON-LD because it’s flexible, scalable, and easy to maintain. Place your JSON-LD markup in a <script type="application/ld+json"> block in your page’s head or body. This format is also the most widely supported by AI crawlers and LLM systems.

Mark up only visible content. Never include information in your schema that isn’t visible to users on the page. Hidden or misleading markup can lead to penalties from search engines and reduces trust signals for AI systems. Your schema should accurately represent what users see and read.

Keep schema accurate and updated. Dates, prices, availability, and reviews must match what’s displayed on your page. Inconsistencies between your schema and visible content reduce trust signals and can cause search engines to discard your markup entirely. Implement a process to update schema whenever your content changes.

Validate your markup regularly. Use Google’s Rich Results Test and schema.org validator to ensure your structured data is correct and error-free. Test both your live pages and development environments to catch issues before they go public. After any site updates, re-validate your schema to ensure it remains intact.

Focus on evergreen schema types. Key types like Article, Product/Offer/Review, FAQPage, HowTo, and Organization are widely used and recommended for content visibility. These types have strong support across search engines and AI systems, making them reliable investments.

Avoid schema bloat. Use schema liberally where it adds clarity, but don’t overdo it. Google’s John Mueller has cautioned against excessive schema markup on pages like product listings. Only mark up what truly helps explain your content and provides value to search engines and users.

Measuring the Impact of Structured Data on AI Visibility

One of the challenges in the AI era is that search engines don’t yet provide detailed analytics on how your content performs in AI-generated answers. Unlike rich results, which show impressions and clicks in Google Search Console, AI visibility metrics are harder to track. However, you can monitor several indicators:

Monitor your brand mentions in AI search results. Periodically search for your brand, products, and key topics in AI search engines like Perplexity, ChatGPT, and Google’s AI Overviews. Note whether your content is cited and how it’s presented. This gives you qualitative feedback on your AI visibility.

Track structured data errors in Google Search Console. Under the “Enhancements” section, monitor whether your structured data is being indexed and recognized. Errors or warnings indicate that your schema needs attention.

Measure traditional SEO metrics as a proxy. While not a direct measure of AI visibility, improvements in rich results, featured snippets, and organic traffic suggest that your structured data is working effectively. These improvements often correlate with better AI visibility as well.

Analyze content performance by schema type. If you’ve implemented multiple schema types, compare the performance of pages with different markup. This helps you understand which schema types are most valuable for your specific content and audience.

The Future of Structured Data in AI Search

The trend is clear: structured data adoption is growing as AI search matures. We expect the vocabulary of schema.org to expand further to accommodate AI-specific needs. Crucially, structured data is becoming part of the semantic layer that underpins AI. As generative models demand verifiable facts and clear context, schema provides the grounding they need. SEO leaders have noted that investing in structured data today is “not just about SEO anymore—it’s about building the semantic layer that enables AI.”

In other words, schema turns your site into a machine-readable knowledge graph, and future AI tools will rely on that graph to answer questions accurately and cite your content as authoritative sources. For digital marketers and content creators, this means structured data will remain a priority. Watch for new schema types and ensure your content is marked up accordingly. At the same time, keep core SEO strong: rich content, good user experience, and technical hygiene like open crawl paths for AI bots.

The relationship between rich results and AI is evolving, but the fundamental principle remains: clear, accurate, machine-readable content wins. Whether your goal is to achieve visual enhancements in search results or to ensure your content is cited in AI-generated answers, structured data is an essential investment in your digital visibility.