Artificial intelligence supervision burden includes prompt refinement, monitoring, review, repairs, retries and approval. A model that requires frequent correction can create more total work even if its per-token price or raw speed appears attractive.
Teams can measure supervision through human touches, review time, correction rate and unresolved failures. The appropriate level also depends on risk because high-impact work may require deliberate oversight even when the model is consistent.
