Make decisions from what you measured

Once controlled use starts, swap your assumptions for measured values. Keep the original budget alongside so the differences stay visible. I'd look at cost per completed unit, exception rate, active review time, source maintenance and correction work together.

Break the results down far enough to support an actual decision. One input type or one business unit might be causing most of the exceptions. A model change might cut inference cost while pushing review up, because the answers got less consistent. An aggregate cost per case can hide both of those.

Any optimisation should go after a cost or failure you've measured. The options include tightening input validation, dropping a model call you don't need, caching stable context, using a smaller model for a bounded classification, changing retrieval, making the review interface clearer, or turning away unsupported cases earlier. Test one material change at a time against a fixed evaluation set and a sample of real operation.

Watch for costs just moving somewhere else. Shorter prompts might mean more retries. Less review coverage might mean more support and correction. A cheaper provider might need new integration and governance work. Look at the effect on the whole workflow before you call something a saving.

I'd set decision points ahead of time. For example: pause intake when exceptions go past what the team can handle, go back to manual handling when source quality drops below the agreed level, or require a fresh approval when monthly spend goes over the funded range. The exact thresholds need local evidence and someone accountable signing off on them.

Some workflows will stay expensive and still be worth running, because they cut delays, make results more consistent or make it possible to handle work that couldn't be managed before. Say what the benefit is meant to be and how you'll see it. You don't have to turn every benefit into a speculative dollar figure.