Human-in-the-loop (HITL) is often framed as a temporary safeguard. In reality, it is a product requirement for any AI system that touches real decisions.
Executives care about accountability. When an AI system produces a recommendation, someone must own the outcome. HITL provides that ownership by defining who reviews, who approves, and what evidence is required.
The best HITL designs are explicit: they define thresholds, escalations, and response times. They are instrumented so the organization can see when humans intervene and why.
When HITL is designed intentionally, it improves AI outcomes. You get better feedback loops, higher trust, and a clear audit trail. That is not a compromise — it is the foundation for scaling responsibly.