Experimentation is cheap to start and expensive to live in. Each team has a tool, a prompt library, a pilot that never quite got a budget. Operating practice is the opposite: named workflows, permission boundaries, evaluation, and a decision about when a person is still required.
The whitepapers on this site are the longer argument. The short case is that you do not get from one to the other by buying more seats. You get there by teaching the people who will run the system, advising on what is worth running, and putting the work on a runtime — Platform, a specialist product, or a bespoke build — that can survive contact with production.
If you are still choosing whether AI belongs in a given workflow, start with consulting. If the organisation already knows and cannot yet operate, start with training and a system, not another trial.