Start with bounded tasks
Impact analysis, release-note preparation, source comparison, terminology checks, first-draft outlines, and extracting documentation needs from structured work items are strong starting points.
These tasks are easier to review than open-ended requests to produce finished documentation from incomplete context.
Keep product truth outside the model
The model should help organize and transform trusted information. It should not become the source of truth for product behavior.
Writers still need direct access to product requirements, code, subject-matter experts, testing environments, and review.
Measure the workflow, not the novelty
A useful AI workflow should reduce cycle time, improve consistency, surface missed impacts, or create capacity for higher-value work. If it merely produces more text to review, the team has automated the wrong bottleneck.
The goal is not to use AI everywhere. It is to use it where the documentation system becomes healthier.