Decision Performance Loops

Executive summary

Use outcome data, confidence scores, override rates, and evaluation sets to recalibrate AI over time. This advanced practitioner guide places that work inside AI Amplification. It helps leaders turn a broad concern into a specific operating decision without treating the topic as a stand-alone transformation. Use the detailed model below to clarify the current state, make trade-offs visible, and assign ownership for the next move. Apply it when AI quality needs to improve from real outcomes, overrides, confidence, exceptions, and evaluation results. The practical result is a decision-performance loop with feedback signals, owner, recalibration trigger, and review cadence. Keep that output connected to adjacent layers so upstream constraints remain visible and downstream execution can show whether the design is working.