Tech Writers May Be Solving the Wrong AI Problem

Many organizations are preparing to invest heavily in AI systems that consume documentation. At the same time, much of that documentation was written for human readers who are good at filling in gaps, making assumptions, and interpreting context that was never explicitly stated.

Humans do this so naturally that we rarely notice it. Machines don’t.

I was thinking about that while reading a recent LinkedIn post from knowledge graph researcher Benny Cheung. He described the progression from data to taxonomy to ontology to knowledge graphs and argued that organizations that stop too early in that progression are likely to struggle with AI.

The post resonated with me because technical writers have been dealing with parts of this problem for years, although we don’t usually use the same vocabulary.

Organizing Information Isn't The Same Thing As Defining Meaning

Most organizations have plenty of places to store information. They have databases, content management systems, knowledge bases, SharePoint sites, and file repositories.

Storage isn’t the problem.

Cheung points out that a database schema and an ontology solve different problems. A schema describes how information is organized. An ontology describes what something is, how it relates to other things, and what rules govern those relationships.

Many organizations blur those distinctions. They assume that once information is structured, categorized, and stored, meaning somehow comes along for the ride.

It doesn’t. Not even close.

Consider the difference between a content label such as “firmware update” and a description that explains who performs the update, what event triggered it, what conditions must be true before it can begin, and what happens when it succeeds or fails.

👉🏾 One helps identify a topic.
👉🏾 The other helps explain how something works.

For AI systems, those details matter.

Taxonomy Helps You Find Things

Technical documentation teams have spent years building taxonomies. We classify products, features, audiences, document types, and metadata values. That work is useful because it helps people find information.

The challenge is that classification only answers part of the question.

AI systems increasingly depend on structured context, not just text. Knowledge graphs and RDF (Resource Description Framework) represent entities and the relationships between them, while action and process models make it explicit who performs an action, what object is affected, what events or conditions shape a workflow, and how activities connect across a process. That structure helps AI systems retrieve information with context and perform multi-step tasks more reliably.

Those relationships are often scattered across multiple documents or left unstated because human readers can usually infer them. AI systems don’t always infer them correctly or consistently.

That’s where many documentation repositories start to show their age. The information exists, but the relationships are incomplete, inconsistent, or implied.

Why Documentation Can Confuse AI

When people read our documentation, they bring a lifetime of experience to the process. They know that customers and technicians have different responsibilities. They recognize that a setup procedure may not apply during routine operations. They can usually tell when a warning belongs to a specific circumstance rather than every circumstance.

Documentation often depends on that kind of judgment.

Large language models don’t read the way people do. Not even remotely. They generate responses based on patterns and probabilities. When key relationships are missing, the model may connect things that shouldn’t be connected, skip conditions that matter, or assign actions to the wrong person or system.

The result can sound reasonable while being