What is human-in-the-loop?
Human-in-the-loop is a design approach where a person reviews, corrects or approves an AI system's work at defined points, so the AI handles volume and drafting while accountable people keep control of decisions that matter.
The idea is simple: the AI does the repetitive part and a person makes the call. In practice, the design work is deciding exactly where the checkpoints sit. Too few and errors reach clients. Too many and staff spend their day clicking approve on things that never need changing, which defeats the purpose and trains people to stop looking carefully.
A useful rule is to match review to consequence. Anything that sets a price, gives legal, medical or financial advice, or goes out under your name to a client gets a human approval. Internal sorting, tagging and first drafts can often run with spot checks instead. Write these rules down so everyone applies them the same way.
Track what reviewers actually change. If a step is approved untouched almost every time for months, you may be able to move it to sampling. If reviewers keep correcting the same kind of mistake, fix the prompt, the data or the process upstream. The review record is also your best evidence when a client or regulator asks how AI is used.
Example
For example, a bookkeeping firm could use AI to categorize bank transactions for small business clients in QuickBooks Online. Low-dollar, recurring vendors are auto-categorized with a weekly spot check, while anything new, large or unusual lands in a review queue for a bookkeeper. The firm tracks how often reviewers change the suggested category. Thresholds are illustrative.