AI governance guide
How Boards Can Measure AI Performance
How boards can measure AI performance through productivity, quality, risk, adoption, customer outcomes and strategic value.
Guide
Practical board-level starting point.
Boards should measure AI performance using a balanced view of value, risk and adoption. Cost savings alone rarely tell the full story.
Useful measures include time saved, quality improvement, revenue support, error reduction, customer outcomes, employee adoption and risk events avoided.
AI performance reporting should compare expected benefits with actual results and should flag where experiments should be scaled, paused or redesigned.
Next steps
Turn the guide into action.
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