The starting point was familiar: too many documents, too many versions, too many half-clear handoffs, and too much time spent re-reading the same material just to figure out what changed. The process technically existed, but only in the sense that everyone had their own version of it.
Instead of trying to automate everything in one leap, the work focused on making the process legible first. We mapped where documents were created, where they stalled, where approval language changed, and where people were doing invisible cleanup work that no one had ever documented.
Once the workflow was visible, AI became useful in a grounded way. It helped summarize differences between versions, surface likely action items, and reduce the amount of repetitive review work that was eating the team alive. The real win was not novelty. It was consistency.
By the end, the team had a repeatable review flow they could explain to a new person in plain English. That matters more than a flashy demo. When systems become teachable, they become durable.