You can’t scroll through LinkedIn these days without encountering elaborate diagrams of AI architecture. They are everywhere. With spooky colors and black backgrounds, they illustrate how large language models (LLMs), retrieval systems, AI agents, orchestration tools, and governance mechanisms work together to deliver business results.
I’ve been looking at a number of these models recently. Some are remarkably detailed, with layers devoted to everything from data management to security and human oversight. But something stands out: Many acknowledge the importance of enterprise data and knowledge, yet few explicitly address structured content or the professionals who manage it.
That’s a consequential omission. For decades, technical communicators, content strategists, and information architects have developed methods to organize, classify, maintain, and reuse information. These methods have renewed relevance as organizations build trusted AI systems that depend on reliable organizational knowledge.
Overlook that relevance at your own risk.
Structured Content: Established Principles, New Relevance for AI
Structured content has been extensively defined, described, and standardized by international bodies and leading institutions. It is information that is organized into modular, discrete, self-contained components that are:
- Consistently architected for human and machine readability
- Identifiable
- Semantically tagged (without regard to formatting)
- Version-controlled
- Reusable
- Made relative and relevant with component-level metadata
