Human approval should sit at the point where a consequential action is still reversible and the reviewer has enough context to make a real decision. Approval is weak if the human only clicks “yes” after the system has already committed, or if volume makes meaningful review impossible. Design the workflow around decision rights, evidence and exception handling.
Match approval to consequence
Low-risk drafting may need sampling; payments, contracts, customer decisions or irreversible changes need explicit authorization.
Show why the AI recommends the action
The reviewer needs source information, uncertainty, relevant policy and the proposed action — not just an opaque score.
Measure reviewer behaviour
If humans approve everything automatically, the control exists on paper but not in the system.
How soapplied approaches the question
I would not start by assuming the stated problem is the whole problem. The first step is to understand the people, purpose, constraints and interactions around it, then test what intervention would improve the system rather than merely optimise one component.