We often treat documentation as the thing that happens after the technical work. In operations, that separation is expensive.
A useful runbook explains the normal path, the expected signals, the safe stopping points, and the conditions that require a human decision. It turns individual memory into shared operating capacity.
Today, AI tools make it practical to build the workflow and write its documentation in parallel. Decisions, assumptions, test results, and exceptions can be captured while they are still fresh instead of reconstructed at the end.
That habit works especially well in asynchronous environments. Colleagues can understand the current state without waiting for a meeting, leadership gets useful visibility into progress and risk, new team members have a faster path to context, and handoffs lose fewer important details.
A simple test
Can another capable person use the workflow without guessing what you meant?
If not, the undocumented assumptions are part of the system too. They are just the least observable part.
My working standard
Build the workflow, document it in plain language as you go, let someone else try it, and revise the instructions from their questions. The questions are not friction to eliminate—they are evidence about the design.
The runbook is not paperwork attached to the system. It is one of the interfaces people use to operate it safely.