The Role of Structured Data & Entities in AI Optimization

In the age of AI search, simply having great content isn’t enough. AI models also rely on structured cues and recognized entities to understand and surface information.

Structured data (like Schema.org markup) and entity metadata serve as the “language” through which websites communicate facts directly to AI. When used effectively, search marketers can ensure their content is truly understood by models.

Understanding Schema Markup

Schema markup is a standardized format for classifying specific content on a webpage. It uses terminology from Schema.org (often via JSON-LD scripts) to label elements such as articles, products, events, reviews, organizations, and more within the code.

In traditional SEO, schema markup is known for enabling rich results (like star ratings or recipe cards in SERPs). In Generative Engine Optimization (GEO), it plays a bigger role.

Schema directs AI to key details within your content and on your website. By adding the right markup, you’re essentially speaking in a language that AI systems natively understand, rather than expecting them to infer meaning from standard, unstructured on-page text.

Google’s Search Generative Experience (SGE) relies heavily on structured data to categorize information and decide what to present in its AI summaries. Implementing appropriate schema can be the deciding factor between your content being included in a summary or omitted altogether. Let’s explore some common Schema markup types that should be part of your GEO strategy:

• Organization (name, logo, url, contactPoint, sameAs): Clearly associates content with your brand by specifying important company information and external profiles.

• Article, BlogPosting (headline, author, datePublished, dateModified, image): Provides clear context about articles and blog posts, including key publication details.

• Person (name, jobTitle, affiliation, sameAs): Clarifies the identity and expertise of content creators or individuals referenced (e.g., your internal thought leaders).

• FAQPage (mainEntity): Explicitly identifies sections structured as question-and-answer pairs.

• HowTo (step, supply, tool, totalTime): Defines step-by-step instructions, enabling AI to accurately present detailed processes.

• Recipe (recipeIngredient, recipeInstructions, cookTime, nutrition): Highlights cooking-specific details so AI can surface recipes in conversational answers.

• Product (name, offers, price, availability, aggregateRating): Describes key product attributes to assist with shopping queries.

• Review, AggregateRating (ratingValue, reviewCount, bestRating, worstRating): Conveys review and rating data that helps AI summarize sentiment and compare products or services.

• Event (name, startDate, endDate, location): Provides detailed event information for accurate AI responses.

• LocalBusiness (name, address, openingHours, telephone, geo): Shares precise business details to support location-based AI responses.

• VideoObject (name, description, uploadDate, thumbnailUrl, duration):
  Clearly describes video content to improve visibility in AI-driven multimedia responses.

• BreadcrumbList (itemListElement): Clarifies your site's navigational structure, helping AI contextualize content across webpages.

• Course (courseCode, coursePrerequisites, educationalCredentialAwarded, provider): Explicitly defines educational content for queries related to training and online learning.

• JobPosting (title, hiringOrganization, jobLocation, employmentType, baseSalary): Highlights employment opportunities and makes them discoverable in AI-generated job search results.

• SoftwareApplication (name, operatingSystem, applicationCategory, downloadUrl):
  Clearly describes software products or apps for inclusion in vendor recommendations.

• PodcastEpisode, PodcastSeries (name, description, episodeNumber, series):
  Structures podcast metadata, helping AI surface relevant audio content.

• MedicalCondition, Drug, MedicalProcedure (name, symptoms, treatment, dosageForm, procedureType): Provides precise health-related information to accurately support medical queries.

• QAPage (mainEntity): Explicitly signals to AI that a page is structured around a single question with community answers (e.g., forums).

Complementary Metadata For AI Optimization

Metadata that benefits GEO extends beyond Schema markup and encompasses all signals embedded in your content. This includes HTML meta tags (titles and descriptions) and semantic HTML structure (headings, lists, tables) that organize information.

For example, consistently including your company name in page titles reinforces brand association for AI models (e.g., “2025 Marketing Trends | ExampleCorp Study”). Additionally, crafting detailed meta descriptions provides concise summaries that AI models can use when generating snippets or short answers. Meta descriptions could even help with brand recognition if your company name is included.

Header tags and structured content hierarchy also contribute to AI optimization. Logical headings (H2s and H3s), bullet points, and clearly formatted tables help AI parse content and identify relevant information.

Beyond Schema.org and on-page optimization, other metadata can also positively impact GEO. Open Graph tags and Twitter Card metadata, while primarily designed for social platforms, label content with key information like image, title, and description. Some AI platforms use these tags when generating previews or interpreting on-page content.

Public-facing knowledge sources like Google Business Profile and Wikipedia also act as supporting metadata by reinforcing brand identity and providing AI models with reliable external context about your organization.

Additionally, technical metadata like XML sitemaps, which include last modified dates, and content feeds, such as RSS or Atom, signal content updates and ensure AI models have access to the most current information.

While these complementary elements aren’t schema markup per se, they contribute to an ecosystem of structured signals that make your content easily understandable to AI algorithms. Together, all this metadata serves as a clarifying layer for AI. Adding these tags is a low-effort, high-impact strategy that goes a long way in getting your content included in AI answers.

How AI Connects Entities (People, Brands, and Topics)

Back in 2012, Google coined the phrase “things, not strings,” as they began to prioritize real-world entities and their relationships over basic keyword strings. More than a decade later, this principle continues to ring true as search marketing evolves.

AI search is built on the premise of entities—people, places, organizations, concepts, or any other item that has a distinct meaning and can be described with attributes. Models seek to understand the entities mentioned in content—and the relationships between them—so they can provide users with detailed, accurate responses.

That means the people, brands, and topics mentioned on your site aren’t just words to AI, they are nodes in an interconnected system of information, commonly referred to as a “knowledge graph.”

For example, Google’s Knowledge Graph contains hundreds of billions of facts about different entities, including well-known people, companies, products, landmarks, events, and even abstract concepts.

When a user enters a query, AI will parse it for known entities. For instance, if someone searches for “What impact did the Inflation Reduction Act have on solar panel adoption?”, AI understands that “Inflation Reduction Act” refers to specific 2022 U.S. legislation and “solar panel adoption” is part of the broader renewable energy domain. By mapping the query to these known entities, it can connect the question to a wealth of related information, like laws that include clean energy incentives, implementation timelines, consumer behavior, and adoption rates for solar panels. The result is an information-rich response that answers the user’s question in detail.

This entity-focused approach applies to all sorts of searches. Consider a query like, “What’s the legal precedent for parody in U.S. copyright law?” Even if a particular case (say, Campbell v. Acuff-Rose Music, Inc.) isn’t explicitly part of the query, an AI system likely knows that it’s the key precedent associated with parody and fair use. It can then discover content that involves that case and related legal principles by drawing on its vast, interconnected knowledge graph.

In essence, AI is so sophisticated that it can connect the dots between the query and the entities that logically answer or relate to it. This has profound implications for GEO. Your content has a real chance of being surfaced, even if it doesn’t contain any of the terms used in the query. It only needs to reference the core entities and convey information that is contextually relevant to what the user wants to know.

Let’s go deeper and explore people, brands/organizations, and topics—the primary entities that search marketers should focus on.

People

AI recognizes individuals as entities with certain attributes. If your content cites a person, especially a notable figure or author, that connection can add context and authority in AI responses.

For instance, including a bio for an article author (and marking it up with “Person” schema) helps AI associate credibility or expertise with the content, especially if that person is already part of its broader knowledge graph.

Similarly, citing an expert within the content and explaining their qualifications helps AI connect that individual’s identity to the subject matter, potentially enhancing E-E-A-T by bringing authority to the topic.

Brands & organizations

Your brand itself is an entity. Ensuring that your website highlights key information about your organization (via “Organization” schema and supporting metadata) can solidify your presence in the knowledge graph.

Whenever your brand is mentioned, AI can connect that mention to the established entity (your company) and collect relevant information to share in responses.

For instance, if your company has an informative study you hope will surface in AI responses, being a recognized entity helps make that happen. AI will view your company as an established source within your industry and be more inclined to present data from the study for relevant queries.

You can also build out your brand entity by linking to official profiles. Using the “sameAs” property in “Organization” schema to point to your Wikipedia page, LinkedIn profile, or other well-known sources will tell AI that all those profiles represent the same entity, helping it form a deeper understanding of your brand.

Topics & concepts

Not every topic is a proper noun. However, many abstract concepts exist within knowledge graphs, and AI often connects these topics to related entities and categories.

When you write about a niche concept, consider whether it ties into other entities or a broader category. You can make that connection explicit by referencing well-known concepts and using schema properties like “about.”

For example, an article about a new marketing tactic (say, AI optimization) could include an “about” property linking to a more established concept on Wikipedia (like SEO). This way, even an emerging topic gets anchored to something the AI model recognizes, helping it build out its knowledge graph using your information.

A powerful capability of AI is how it infers relationships between entities. Search engines have spent years building relational databases (who is part of which organization, which products a brand offers, what events are associated with which places, and so on). When your content includes multiple entities, AI pays attention to how they’re connected.

If you publish blog posts that frequently mention your brand alongside a specific type of product or industry, you’re educating AI on your offering and the type of customers you serve. Over time, that helps establish your authority on relevant topics within your niche.

In fact, marketing teams that deliberately build their own “content knowledge graph” by linking topics, subtopics, and entities make it easy for AI systems to understand facts about their brand and expertise.

Actionable Tips for Highlighting Entities in Content

From a practical GEO standpoint, connecting entities requires being explicit and consistent. Here are some tips for helping AI models clearly understand the people, organizations, and concepts mentioned in your content:

• Use the full names of people and organizations once or twice before switching to pronouns or abbreviations so AI doesn't have to make assumptions.

• Provide context for lesser-known entities on first mention (e.g., “ObscureTech, a fintech startup,…”).

• Link to known profiles or references for important entities (e.g., official websites, LinkedIn profiles, Wikipedia pages) to contribute to AI’s knowledge graph.

• Aim to get your brand and key authors listed on Wikipedia or industry databases, as AI actively draws on these sources when building its knowledge graph.

• Use straightforward language and clearly name people, brands, and topics so AI can accurately understand and cite your content.

Expanding on the last bullet point, be aware when a particular entity may be ambiguous. Many words can refer to multiple things (e.g., “Apple” the company vs. apple the fruit, “Mercury” the planet vs. the element).

AI uses context—such as surrounding text, schema markup, and knowledge graph data—to determine which entity is intended. You can support this process through clear writing. If your article is about the planet Mercury, mentioning related entities like “solar system” or “NASA’s Messenger probe” helps signal to AI that you're referring to astronomy, not chemistry.

You can also use the “about” or “mentions” properties in Schema markup to link to the Mercury Wikipedia entry. Doing so helps ensure that AI accurately interprets your content and associates it with the correct entity.

Using Structured Data to Improve Content Recognition

When an AI model “reads” web content—whether it’s Google using its Multitask Unified Model (MUM) to better understand queries for SGE or Bing’s infrastructure retrieving content to be interpreted by ChatGPT and Copilot—having structured data is like having signposts that highlight your content’s most important information.

First and foremost, structured data feeds factual information directly into AI. Unlike a traditional search snippet that just shows a few highlighted words, an AI answer often needs to provide an exact fact (e.g., a price or date). If your page includes that fact as plain text, the model has to comprehend what’s written and make a judgment call if it’s the answer.

But if you’ve also marked it up (e.g., using “priceCurrency” and “price” in a Product schema or “startDate” in an Event schema), AI can understand that fact with high confidence from the structured data.

Google’s SGE has already shown instances of pulling in product specs and details from schema markup when generating product summaries. Likewise, Microsoft Copilot (on Bing and within the Edge browser) uses structured information for quick answers about companies or people. Microsoft has even confirmed that its AI model incorporates Schema.org markup.

In previous articles, we’ve covered how FAQ and how-to content directly answers the questions users pose to AI. By using “FAQPage” schema, you help AI models identify question-answer pairs and use that information when answering a user’s query. That way, the AI model doesn’t have to analyze your prose to figure out that a certain sentence is a question and the following paragraph is its answer. It’s explicitly flagged by the Schema.

The same goes for how-to content. A properly marked-up how-to article (with steps, tools, durations) enables AI to deliver a step-by-step solution and potentially credit your site in its response. In contrast, an unstructured article might be excluded since it requires AI to work much harder to extract the steps.

Structured data also improves AI recognition in a broader sense by matching your content to more queries. Let’s say you specify that an article is about artificial intelligence in healthcare via schema (“about: AI in Healthcare” with links to relevant entities). By doing so, you’re also increasing the chance that your article surfaces in a variety of AI + healthcare queries, even when the exact phrasing differs. This is because the model isn’t relying on specific wording but rather recognizing the content’s core entity/topic.

Entity-based recognition is particularly beneficial for long-tail queries or conversational questions that can be phrased in a variety of ways. Essentially, schema markup serves as expanded keywords, helping AI associate synonyms or related terms with your content.

For example, tagging a blog post with “diabetes” for “MedicalCondition” schema tells AI that the post is about diabetes even if it focuses on blood sugar issues. A response to a user query like, “What are the effects of diabetes?” could use your content since AI associates the detailed information (blood sugar issues) with the broad topic (diabetes).

To maximize the benefits of Schema, here are some tips for including it in your content:

• Use the most specific schema types available: Don’t just settle for the generic “Article” schema if your content calls for a more specific subtype (e.g., “NewsArticle” for news content, “TechArticle” for technical documentation, “VideoObject” for videos, etc.). The more specific the type, the more context you give AI.

• Include key properties and attributes: Fill out all the important fields in your schema. For a product, that means “price,” “availability,” “brand,” “sku,” etc. For an article, ensure you have a “headline,” “description,” “author,” “datePublished,” and ideally an “about” or “keywords” field. Comprehensive schema equates to comprehensive understanding.

• Keep schema updated and accurate: AI models continuously learn from your structured data so update Schema as things change (e.g., a product going out of stock or an event date changing). Use automation feeds to keep data fresh and prevent AI from incorrectly citing your information. Bing’s team suggests using rapid indexing tools, like IndexNow, to quickly feed updates to LLMs.

• Leverage identifiers for entities: When possible, use identifiers like “@id” or “sameAs” to link to well-known sources. If you have a “Person” schema for an author, using these markups to link to their LinkedIn profile or Wikipedia page helps AI expand its knowledge graph for them as an entity.

• Validate and test your structured data: Use Google’s Rich Results Test or Schema markup validator tools to ensure your JSON-LD or microdata has no issues. While AI might be tolerant of minor errors, broken markup could prevent it from understanding what you intend it to. Clean schema, on the other hand, is definitely being ingested by algorithms.

• Combine schema with quality content: AI parses raw text along with your schema. If both are optimized, the chances of your content being cited are even greater. For example, presenting a statistic in an HTML table, calling it out within on-page text, and using markup like “FactCheck” or “Dataset” schema emphasizes its relevance to the model.

• Monitor AI search results: Keep an eye on how your content is appearing in the AI platforms you’re optimizing for. If you notice they are pulling incorrect info or missing something important that is on your page, that’s a cue to improve (or possibly expand) your structured data.

Structured data and entity optimization are among the most effective components of a GEO strategy, simply because they clearly convey the information you want AI to know. As a result, your content becomes more usable by AI models and more likely to surface in answers, summaries, and citations for the queries your target audience is asking.