You are accountable for the output. That changes everything.

What usually brings people here

Rarely a productivity problem. More often one of these.

• An unnerving near-miss. Something plausible and wrong nearly reached a client, and you realised the only thing that caught it was luck and a good day.
• The plateau. AI made the early, generative part of the work much faster and the difficult final part no faster at all, and you cannot see why.
• The evidence question. Someone asked how you use AI in your work and you did not have a defensible answer, only an honest one.
• The reset problem. Every project starts from scratch. Nothing you learned last time made this time structurally easier.

What you get out of it

A documented workflow you own and can operate repeatedly. A verification layer designed for the specific ways AI fails in your kind of work. Explicit allocation of what you must own versus what may be drafted. Escalation triggers, so the workflow stops rather than guesses. And a record of operation that turns "I use AI carefully" into something you can actually show.

The workflow survives model changes because it was designed around your professional standards, not around one tool's behaviour.