Metrics
AI Adoption Metrics That Actually Show Workflow Change
Usage metrics show activity. These metrics show whether AI is genuinely changing work, and whether that change is safe to scale.
Adoption depth
How consistently AI is used across the relevant people, teams and workflows.
Workflow ownership
How much of the end to end workflow AI reliably owns, measured against the four autonomy levels.
Output quality
Whether AI outputs remain acceptable as autonomy increases, measured against workflow specific quality criteria.
Human handoff
Where humans review, correct or escalate AI outputs, and how often.
Risk exposure
Governance gaps, quality drops, compliance risk and AI debt accumulating in the workflow.
Business value
Measurable impact on cycle time, cost, throughput, revenue or service quality tied to workflow change.
Board readiness
Whether AI adoption evidence can withstand board and audit scrutiny.
Why these metrics matter
Boards and executive teams do not need another activity dashboard. They need a small set of workflow level metrics that connect AI adoption to real business outcomes and to the risk of scaling AI without evidence.
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