Decision Feedback Loop
A recurring review that compares AI-supported decisions and actions with outcomes, corrections and exceptions, then improves the data, rules, models and workflow.
Decision Feedback Loop is a recurring review that compares AI-supported decisions and actions with outcomes, corrections and exceptions, then improves the data, rules, models and workflow.
It appears repeatedly across the KUOS case library because leaders need it at a real decision point: defining a boundary, assigning an owner, choosing evidence or deciding whether to scale.
Use it in practice by naming one current workflow, the accountable human, the evidence you expect and the condition that would make you change course.
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Review evidence
OUTCOME
Improve or stop
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Real-world examples where this concept appears in our case studies.
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