{"id":2013,"date":"2026-10-08T13:27:11","date_gmt":"2026-10-08T17:27:11","guid":{"rendered":"https:\/\/www.precisioncontent.com\/blog\/?p=2013"},"modified":"2026-10-08T14:37:10","modified_gmt":"2026-10-08T18:37:10","slug":"information-typing-the-missing-layer-in-ai-ready-content","status":"publish","type":"post","link":"https:\/\/www.precisioncontent.com\/blog\/information-typing-the-missing-layer-in-ai-ready-content\/","title":{"rendered":"Information Typing: The Missing Layer in AI-Ready Content"},"content":{"rendered":"

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Structured Content & AI-Ready Knowledge<\/p>\n

AI Doesn’t Just Need Your Content. It Needs to Understand What the Content Is For.<\/h1>\n

Why information typing, a foundational principle of structured authoring, is becoming essential to the next generation of enterprise AI.<\/p>\n

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Made with AI. Grounded in real people.<\/strong> Illustrative studio scene generated with AI using speaker photographs. Not a photograph of the actual recording.
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Featured Conversation \u00b7 Digital Practice Insights<\/p>\n

Information Typing in DITA: Rob Hanna and Lance Cummings<\/h2>\n

Precision Content CEO Rob Hanna joins Dr. Lance Cummings, professor of professional writing at the University of North Carolina Wilmington, for a conversation with host Larry Swanson about information typing, human cognition, structured authoring, and what these disciplines mean for AI.<\/p>\n

Listen to the podcast \u2192<\/a><\/p>\n

Episode 215 \u00b7 June 28, 2026<\/p>\n<\/section>\n

Enterprises are investing heavily in AI. They are building retrieval-augmented generation (RAG) systems, deploying copilots, experimenting with autonomous agents, and connecting increasingly sophisticated models to their corporate knowledge.<\/p>\n

But there’s a fundamental problem hiding beneath all that technology.<\/p>\n

Most enterprise content was never designed to communicate what kind of knowledge it contains, what that knowledge is intended to accomplish, or how it should be used.<\/strong><\/p>\n

That distinction matters more than ever. In a recent episode of Digital Practice Insights<\/em>, Rob Hanna and Lance Cummings explored a principle that’s been central to structured authoring for decades: information typing<\/strong>.<\/p>\n

Their discussion points to an important shift. Information typing offers a method for designing knowledge that people can understand and that AI systems can interpret with greater precision.<\/p>\n

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The white paper behind the conversation<\/p>\n

Information types are not templates.<\/p>\n

Go deeper into the ideas that anchor Rob, Lance, and Larry\u2019s discussion. Explore information typing as a foundation for content designed around what people and AI need to understand, decide, and do.<\/p>\n

Read the white paper \u2192
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PDF \u00b7 Opens in a new tab<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Information typing: The missing intelligence in enterprise content<\/h2>\n

Most organizations classify content by its format or where it lives: documents, PDFs, knowledge articles, web pages, manuals, and procedures.<\/p>\n

Information typing asks a different question:<\/p>\n<\/section>\n

What is this information supposed to help someone understand, decide, or do?<\/p><\/blockquote>\n

The distinction is fundamental. Information has different functions, and those functions call for different structures.<\/p>\n

DITA established three foundational information types: concept, task, and reference. Precision Content’s methodology extends this model with principle and process, helping teams structure a wider range of enterprise knowledge.<\/p>\n

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01 \/ UNDERSTAND<\/p>\n

Concept<\/h3>\n

Explains what something is, how it works, and why it matters.<\/p>\n<\/div>\n

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02 \/ ACT<\/p>\n

Task<\/h3>\n

Describes the steps required to complete a specific action.<\/p>\n<\/div>\n

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03 \/ LOOK UP<\/p>\n

Reference<\/h3>\n

Provides facts, specifications, definitions, and other information for quick retrieval.<\/p>\n<\/div>\n

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04 \/ GOVERN<\/p>\n

Principle<\/h3>\n

Establishes rules, policies, conditions, and constraints that guide decisions.<\/p>\n<\/div>\n

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05 \/ COORDINATE<\/p>\n

Process<\/h3>\n

Explains how activities, roles, decisions, and handoffs work together to achieve an outcome.<\/p>\n<\/div>\n<\/div>\n

These are more than document templates. They are ways of organizing knowledge according to its intended function.<\/p>\n

For human readers, this reduces the effort required to interpret information. For AI, it can provide clearer signals about what a piece of information represents and how it relates to a question or action.<\/p>\n

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From AI-ready content to agent-ready knowledge<\/h2>\n

Much of today’s enterprise AI strategy focuses on helping an assistant find and summarize information.<\/p>\n

But the next generation of AI systems will increasingly be expected to do more than answer questions. Agents will need to interpret situations, evaluate conditions, follow procedures, and initiate actions within defined boundaries.<\/p>\n

Consider a financial services organization handling a disputed transaction.<\/p>\n

An AI assistant might be asked, \u201cHow do I dispute a transaction?\u201d An agent, however, could eventually be expected to determine eligibility, identify applicable policies, select the correct workflow, and escalate exceptions.<\/p>\n<\/section>\n

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Answer-first content<\/p>\n

What should I know?<\/h3>\n

Explains policies and procedures so a user or chatbot can retrieve an answer.<\/p>\n<\/div>\n

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Agent-ready knowledge<\/p>\n

What can I do, and under what conditions?<\/h3>\n

Makes rules, constraints, decisions, actions, and process relationships explicit.<\/p>\n<\/div>\n<\/div>\n

This is where the distinction between information types becomes particularly valuable.<\/p>\n

A task<\/strong> describes an action. A principle<\/strong> establishes the rules that govern it. A process<\/strong> defines how actions and decisions fit together.<\/p>\n

Information typing alone does not make an AI agent reliable or authorize it to act. Systems still require permissions, validation, orchestration, and safeguards. But well-structured knowledge can give those systems a more intelligible foundation to work from.<\/p>\n