Category: Technical optimization

I’ve often wondered how much schema markup actually aids AI search optimization. There are claims it can increase citations or significantly enhance AI visibility, yet the truth is more complex and nuanced.

Let’s dive into separating facts from assumptions and explore how schema truly integrates into an AI search strategy.

How Schema Fits into AI Search Now

Search is evolving from simple SERP links to dynamic AI Overviews, with generative answers and chat-style summaries compiling content beyond just links. My goal is to ensure my content is recognized within this model, and that’s achieved by focusing on ‘entities’—distinct concepts such as a person, place, or event—not just strings of text.

Schema markup is a powerful tool I use to clarify these entities and their relationships, making them comprehensible to AI. For instance, identifying a person, their organization, the price of a product, or the author of an article.

AI systems focus on three key elements:

• Entity definition: Identifying brands, authors, services, or SKUs on the page.
• Attribute clarity: Distinguishing which properties relate to which entity (like prices or ratings).
• Entity relationships: Understanding connections between entities (using tags like offeredBy or authoredBy).

By employing schema with stable values and structured methods, it begins to function like a mini knowledge graph. AI systems no longer guess who I am or how my content ties together; they follow explicit links between my brand, authors, and subjects.

Dig deeper: Why entity authority is crucial for AI search visibility

How AI Search Platforms Use Schema

Two primary platforms acknowledge that schema markup enhances their AI’s ability to comprehend content. It’s a confirmed infrastructure for them.

• Google AI Overviews: In April 2025, Google Search confirmed that structured data offers an edge in search results.
• Microsoft Bing Copilot: Microsoft’s product manager confirmed in March 2025 that schema aids Microsoft’s LLMs in understanding content for Copilot.

Exploring ChatGPT, Perplexity, and Other AI Search Platforms 

The usage of schema by these platforms remains uncertain. They haven’t publicly clarified if they maintain schema during crawling or use it for data extraction. Though LLMs can technically process structured data, it doesn’t guarantee their search systems do.

Dig deeper: Using knowledge graphs and entities for SEO

Research on Schema and AI

Here are some studies that shed light on schema’s impact on AI search.

Understanding Citation Rates

A December 2024 study revealed no direct correlation between schema and citation rates. Sites with extensive schema markup didn’t consistently outperform those lacking it.

It doesn’t negate schema’s value, but highlights that schema alone doesn’t drive citations. LLM systems prioritize relevance, authority, and clarity over structured markup presence.

The Role of Extraction Accuracy

A study in February 2024 found that LLMs extract data better with structured prompts compared to unstructured ones.

LLMs excel when given a structured format to fill out instead of a blank canvas, minimizing errors when extracting defined data fields.

Schema markup resembles this structured format, providing clear entity, brand, and topic fields.

Interpreting the Research

The findings suggest that LLMs can better process structured data than unstructured text. However, we still lack confirmation on whether AI search systems preserve schema data during crawling or use it during extraction.

For Microsoft Bing and Google AI Overviews, schema likely improves data extraction accuracy, given their confirmed usage. Other platforms remain unverified regarding implementation.

Dig deeper: Entity-first SEO and Google’s Knowledge Graph

Exploring the Unknowns of Schema and AI Search

Given the novelty of AI search—exemplified by ChatGPT’s launch in October 2024—companies haven’t revealed their indexing methods. Measuring impact remains challenging due to non-deterministic AI responses.

No peer-reviewed studies yet explore schema’s AI search visibility impact, nor are there controlled studies on LLM citation behavior with schema.

This gap persists as AI search is relatively new, with companies withholding indexing details and difficulties in assessing AI interactions.

Building an Entity Graph with Schema

In traditional SEO, schema is often limited to adding individual markup like Article or Organization. For AI search, connecting nodes into a cohesive graph through @id is more beneficial.

• Create an Organization node with a permanent @id for your brand.
• Develop a Person node for each author linked to your organization.
• Form an Article node linking the author to the publication with detailed topics.

{  "@context": "https://schema.org",  "@graph": [  {  "@id": "https://example.com/#organization",  "@type": "Organization",  "name": "Example Digital"  },  {  "@id": "https://example.com/#person-jane-doe",  "@type": "Person",  "name": "Jane Doe",  "worksFor": { "@id": "https://example.com/#organization" }  },  {  "@type": "Article",  "@id": "https://example.com/blog/schema-markup-ai-search",  "headline": "Schema Markup for AI Search",  "author": { "@id": "https://example.com/#person-jane-doe" },  "publisher": { "@id": "https://example.com/#organization" }  }  ]  }

This interconnected pattern transforms schema into a useful entity graph. For AI systems preserving the JSON-LD, it clearly identifies brand ownership, human responsibility, and topic focus, unaffected by page changes over time.

Dig deeper: Supporting local visibility through structured data

Recommendations for Implementing Schema for AI Search

I recommend the following for leveraging schema in AI search:

• Ensure entities and relationships are machine-readable for platforms utilizing structured data (as confirmed by Bing Copilot and Google AI Overviews).
• Clarify brand, author, and product identity to ensure clean and consistent data extraction.
• Strengthen topical depth and authority to complement clear brand signals.

Implement schema markup to:

• Boost visibility in Bing Copilot.
• Facilitate inclusion in Google AI Overviews.
• Enhance traditional SEO efforts.
• Simplify content parsing for better comprehension.
• Maintain a cost-effective approach with potential for future platform evolution.

Avoid assumptions that schema alone will:

• Guarantee citations from ChatGPT or Perplexity.
• Substantially enhance visibility on its own.
• Compensate for weak content or lack of authority.

Key schema types, based on platform insights, include:

• Organization for brand identity.
• Article or BlogPosting for content and authorship.
• Person for author authority and entity links.
• Product or Service for commercial clarity.
• FAQPage for Q&A formats.

Dig deeper: Enhancing brand perception with entity-focused home pages

Implement Schema for AI Search Today

Schema markup acts as infrastructure rather than a miracle solution. Although it may not automatically raise citation rates, it’s an aspect I control that’s explicitly used by platforms such as Bing and Google AI Overviews.

The key isn’t just implementing schema in isolation, but integrating structured data with proper entity connections, high-quality authoritative content, and clear entity identity and brand signals. Strategic use of @graph and @id to build these connections is crucial.

Inspired by this post on Search Engine Land.