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…
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