AI operating governance · Field note

Governing AI as operational change, not software activity

TL;DR

Measure the recurring work AI changes, not only tool activity.

What the paper develops

Usage counts and license activity say little about whether AI improved the work an organization funded. This paper moves the unit of governance from the tool to the recurring workflow. It shows how to establish a baseline, select operating measures, assign ownership, and review whether AI changed cycle time, quality, rework, exceptions, judgment, or outcomes without creating new hidden costs.

The operating move

Measure the recurring work AI changes: cycle time, quality, rework, exceptions, judgment, and outcomes. Tool adoption is an input, not evidence that the operation improved.

WORKFLOWCONTROL EVIDENCEHUMAN OWNER

Inside the white paper

  • Workflow baselines and value mechanisms before deployment
  • Operating measures for quality, rework, exceptions, judgment, and outcomes
  • A governance cadence for adapting or stopping the intervention

Sources and notes

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