How does structured data influence Generative Engine Optimization results?

Tony Finn

22 Sep 2025 • 11 min read

BLUF: Structured data is no longer optional; it's a foundational layer for Generative Engine Optimization (GEO), directly shaping how AI models understand, prioritize, and present your content.

Introduction: The Rise of Semantic SEO for AI

For years, Search Engine Optimization (SEO) has revolved around algorithms that parse HTML, analyze backlinks, and interpret keyword densities. But with the advent of large language models (LLMs) powering generative AI, a seismic shift is underway. We're moving from a world of syntactic analysis to one of semantic understanding.

In this new landscape, Generative Engine Optimization (GEO) focuses on making content not just readable but understandable to AI. This means crafting information in a way that enables LLMs to accurately extract facts, identify entities, and synthesize coherent answers. One of the most potent tools in this arsenal is structured data.

Structured data, in its simplest form, is metadata that provides context and meaning to your content. It transforms raw text into machine-readable information, allowing search engines—and increasingly, generative AI—to interpret content with greater precision. This blog will dive deep into how structured data influences GEO results, covering:

The fundamentals of structured data and its role in semantic understanding.
Specific schema types that are most impactful for GEO.
Practical strategies for implementing structured data to improve AI visibility.
Case studies showcasing the tangible benefits of structured data for GEO.
Insights into the future of structured data and its evolving relationship with generative AI.

Understanding Structured Data and Semantic SEO

What is Structured Data?

Structured data is a standardized format for providing information about a page and classifying the page content. Search engines use it to understand the content on the page, as well as to gather information about the web and the world in general. Structured data is coded in-page using markup, and that markup tells search engines things like:

"This is a recipe."
"This is a product review."
"This is a list of upcoming events."

The most common vocabulary for structured data is Schema.org, a collaborative initiative supported by Google, Microsoft, Yahoo, and Yandex. Schema.org provides a comprehensive collection of schemas (i.e., data vocabularies) that can be used to describe a wide range of entities, from articles and books to events and organizations.

How Structured Data Powers Semantic Understanding

The power of structured data lies in its ability to make content explicitly understandable to machines. While traditional SEO focuses on keywords and backlinks, structured data adds a layer of semantic context. It helps AI models go beyond surface-level parsing and truly grasp the meaning and relationships within your content.

Consider the following example:

Unstructured Text: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France."

Structured Data (using Schema.org):

{
  "@context": "https://schema.org",
  "@type": "Landmark",
  "name": "Eiffel Tower",
  "description": "A wrought-iron lattice tower on the Champ de Mars in Paris, France.",
  "address": {
    "@type": "PostalAddress",
    "addressLocality": "Paris",
    "addressCountry": "France"
  },
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": 48.8584,
    "longitude": 2.2945
  }
}

In the structured data example, we've provided precise information about the Eiffel Tower, including its type (Landmark), location (Paris, France), and geographical coordinates. This allows AI models to not only understand what the Eiffel Tower is but also its relationship to other entities, such as Paris and France.

This semantic understanding is crucial for GEO. When a user asks a question like "What are some landmarks in Paris?", an AI model can use structured data to quickly identify and present relevant information.

The Shift from Rich Snippets to Generative Answers

Historically, structured data was primarily used to enhance search engine results pages (SERPs) with rich snippets. Rich snippets are visual enhancements, such as star ratings, event dates, and product prices, that appear alongside standard search results. They make results more eye-catching and informative, increasing click-through rates.

While rich snippets remain valuable, the role of structured data is expanding in the age of generative AI. Now, structured data directly influences the content and accuracy of AI-generated answers. When a user poses a question to ChatGPT, Gemini, or Claude, the AI model may draw upon structured data from various sources to synthesize a comprehensive and accurate response.

This shift from rich snippets to generative answers represents a fundamental change in the way information is accessed and consumed. Instead of simply providing links to relevant web pages, AI models are now acting as knowledge synthesizers, curating and summarizing information from across the web.

Key Schema Types for Generative Engine Optimization

While Schema.org offers a vast array of schema types, some are particularly impactful for GEO. These schema types are often associated with specific types of content that are commonly used to answer user queries.

1. Article Schema

The Article schema is used to mark up news articles, blog posts, and other types of informational content. It provides valuable information about the article, such as its headline, author, publication date, and image.