The way people find and consume information has shifted. We, as marketers, must think about visibility across AI platforms and Google.
The challenge is that we don’t have the same ability to control and measure success as we do with Google and Microsoft, so it feels like we’re flying blind.
Earlier this year, Google, Microsoft, and ChatGPT each commented about how structured data can help LLMs to better understand your digital content.
Structured data can give AI tools the context they need to determine their understanding of content through entities and relationships. In this new era of search, you could say that context, not content, is king.
Schema Markup Helps To Build A Data Layer
By translating your content into Schema.org and defining the relationships between pages and entities, you are building a data layer for AI. This schema markup data layer, or what I like to call your “content knowledge graph,” tells machines what your brand is, what it offers, and how it should be understood.
This data layer is how your content becomes accessible and understood across a growing range of AI capabilities, including:
AI Overviews
Chatbots and voice assistants
Internal AI systems
Through grounding, structured data can contribute to visibility and discovery across Google, ChatGPT, Bing, and other AI platforms. It also prepares your web data to be of value to accelerate your internal AI initiatives as well.
The same week that Google and Microsoft announced they were using structured data for their generative AI experiences, Google and OpenAI announced their support of the Model Context Protocol.
What Is Model Context Protocol?
In November 2024, Anthropic introduced Model Context Protocol (MCP), “an open protocol that standardizes how applications provide context to LLMs” and was subsequently adopted by OpenAI and Google DeepMind.
You can think of MCP as the USB-C connector for AI applications and agents or an API for AI. “MCP provides a standardized way to connect AI models to different data sources and tools.”
Since we are now thinking of structured data as a strategic data layer, the problem Google and OpenAI need to solve is how they scale their AI capabilities efficiently and cost-effectively. The combination of structured data you put on your website, with MCP, would allow accuracy in inferencing and the ability to scale.
Structured Data Defines Entities And Relationships
LLMs generate answers based on the content they are trained on or connected to. While they primarily learn from unstructured text, their outputs can be strengthened when grounded in clearly defined entities and relationships, for example, via structured data or knowledge graphs.
Structured data can be used as an enhancer that allows enterprises to define key entities and their relationships.
When implemented using Schema.org vocabulary, structured data:
Defines the entities on a page: people, products, services, locations, and more.
Establishes relationships between those entities.
Can reduce hallucinations when LLMs are grounded in structured data through retrieval systems or knowledge graphs.
When schema markup is deployed at scale, it builds a content knowledge graph, a structured data layer that connects your brand’s entities across your site and beyond.
A recent study by BrightEdge demonstrated that schema markup improved brand presence and perception in Google’s AI Overviews, noting higher citation rates on pages with robust schema markup.
Structured Data As An Enterprise AI Strategy
Enterprises can shift their view of structured data beyond the basic requirements for rich result eligibility to managing a content knowledge graph.
According to Gartner’s 2024 AI Mandates for the Enterprise Survey, participants cite data availability and quality as the top barrier to successful AI implementation.
By implementing structured data and developing a robust content knowledge graph you can contribute to both external search performance and internal AI enablement.
A scalable schema markup strategy requires:
Defined relationships between content and entities: Schema markup properties connect all content and entities across the brand. All page content is connected in context.
Entity Governance: Shared definitions and taxonomies across marketing, SEO, content, and product teams.
Content Readiness: Ensuring your content is comprehensive, relevant, representative of the topics you want to be known for, and connected to your content knowledge graph.
Technical Capability: Cross-functional tools and processes to manage schema markup at scale and ensure accuracy across thousands of pages.
For enterprise teams, structured data is a cross-functional capability that prepares web data to be consumed by internal AI applications.
What To Do Next To Prepare Your Content For AI
Enterprise teams can align their content strategies with AI requirements. Here’s how to get started:
1. Audit your current structured data to identify gaps in coverage and whether schema markup is defining relationships within your website. This context is critical for AI inferencing.
2. Map your brand’s key entities, such as products, services, people, and core topics, and ensure they are clearly defined and consistently marked up with schema markup across your content. This includes identifying the main page that defines an entity, known as the entity home.
3. Build or expand your content knowledge graph by connecting related entities and establishing relationships that AI systems can understand.
4. Integrate structured data into AI budget and planning, alongside other AI investments and that content is intended for AI Overviews, chatbots, or internal AI initiatives.
5. Operationalize schema markup management by developing repeatable workflows for creating, reviewing, and updating schema markup at scale.
By taking these steps, enterprises can ensure that their data is AI-ready, inside and outside the enterprise.
Structured Data Provides A Machine-Readable Layer
Structured data doesn’t assure placement in AI Overviews or directly control what large language models say about your brand. LLMs are still primarily trained on unstructured text, and AI systems weigh many signals when generating answers.
What structured data does provide is a strategic, machine-readable layer. When used to build a knowledge graph, schema markup defines entities and the relationships between them, creating a reliable framework that AI systems can draw from. This reduces ambiguity, strengthens attribution, and makes it easier to ground outputs in fact-based content when structured data is part of a connected retrieval or grounding system.
By investing in semantic, large-scale schema markup and aligning it across teams, organizations position themselves to be as discoverable in AI experiences as possible.
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