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A senior technical writer posted on LinkedIn recently that she had been told her product does not need documentation for mass adoption. The reasoning came in four parts: a good enough product explains itself, people will find it and figure it out, mass adoption comes from products that need only a sentence or two, and nobody reads the docs anyway.

Within a day, the technical writing community had rebutted every point in the comments. I added a comment about the last claim, because it is the one that is most out of date.

Suppose nobody reads the docs

Grant the claim for a moment, and assume no human ever opens your documentation.

Your users still have questions about your product, and more of them are asking AI tools those questions instead of searching your site. Those tools read everything they can find, and they answer whether or not what they found is accurate. When a product has no official documentation, the AI fills the gap with whatever else exists, such as forum threads, outdated blog posts, a competitor's docs, or plain guesses.

The writer checked this herself, and the AI tools she asked gave answers she described as wildly wrong. Those are the answers her future users will get, and when the instructions fail, they will blame the product.

Wrong answers are worse than no answers

An AI agent that finds nothing tells the user it does not know. An agent that finds wrong information acts on it with full confidence, and you pay for it in support tickets and failed setups, and eventually in users who stop trusting the product.

Clear pages are only half the job now. The other half is making sure what the machine finds is still true after the next release. That is a process question, and technical writers usually see the gaps before anyone else does.

Five processes that keep AI answers accurate

Assign an owner to every page. When accuracy is everyone's job, nobody checks. A named owner, even an engineer who reviews one page per release, means someone is responsible when a page goes stale.

Tie documentation to releases. If a feature can ship without its docs changing, eventually one will, and AI tools will keep describing the old behavior. Add a docs check to the release checklist or the pull request template so someone asks the question every time.

State limits explicitly. Say what the product does not support, which versions a page applies to, and what is deprecated. When a page says nothing about a limit, AI tools tend to assume the capability exists.

Retire stale and duplicate content. An outdated page that is still live gets retrieved and repeated with the same confidence as your current docs. Archive or redirect old pages, and keep one canonical page per topic.

Test what the AI says. Once a month, or after each major release, ask two or three AI tools the questions your users ask most. Log what comes back wrong and trace each error to its source, whether that is a missing page, a stale page, or a gap in a page that exists. That log shows you where your process has holes, and it gives you evidence the next time someone tells you the docs do not matter.

Try it this week

Pick five questions a new user would ask about your product. Put them to the AI tools your users are most likely to use, and read the answers as if you were that new user.

Then share what you found with your team. Most teams have not checked yet.

Diana Payton