Designing AI Automation with Human Review

Automation quality does not depend only on model accuracy. It also depends on where the system places authority.

Separate execution from approval

Use AI for repeatable analysis, classification, drafting, and prioritization. Keep explicit approval for irreversible, high-risk, or externally visible actions.

Store the evidence

Every automated recommendation should retain its source data, model output, confidence indicators, and reviewer decision. This creates an auditable workflow instead of an opaque shortcut.

Measure intervention

Track how often reviewers accept, revise, or reject AI output. These rates reveal whether the automation actually reduces work or merely transfers it.