Introduction to the New Era of Search
The way people find and consume information has shifted. As marketers, we 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.
Understanding the Role of Structured Data
Earlier this year, Google, Microsoft, and ChatGPT each commented about how structured data can help LLMs to better understand 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 and Its Importance
Schema markup can help build a data layer for AI. This schema markup data layer, or what can be called a “content knowledge graph,” tells machines what a brand is, what it offers, and how it should be understood. This data layer is how content becomes accessible and understood across a growing range of AI capabilities, including AI Overviews, chatbots and voice assistants, and internal AI systems.
The Model Context Protocol
In November 2024, Anthropic introduced the Model Context Protocol (MCP), “an open protocol that standardizes how applications provide context to LLMs” and was subsequently adopted by OpenAI and Google DeepMind. MCP provides a standardized way to connect AI models to different data sources and tools. The combination of structured data and 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.
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, enterprises can contribute to both external search performance and internal AI enablement.
Implementing a Scalable Schema Markup Strategy
A scalable schema markup strategy requires defined relationships between content and entities, entity governance, content readiness, and technical capability. For enterprise teams, structured data is a cross-functional capability that prepares web data to be consumed by internal AI applications.
Preparing Your Content for AI
Enterprise teams can align their content strategies with AI requirements. To get started, teams should audit their current structured data, map their brand’s key entities, build or expand their content knowledge graph, integrate structured data into AI budget and planning, and operationalize schema markup management.
The Benefits of Structured Data
Structured data provides 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.
Conclusion
In conclusion, structured data is essential for enterprises to succeed in the new era of search. 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. As AI continues to evolve, it’s crucial for marketers to prioritize structured data and develop a robust content knowledge graph to stay ahead of the curve.