Benchmarks
AI Maturity Benchmark: context, workflow, tools, measurement, and governance
Benchmark AI maturity by operating capability rather than model access or prompt volume.
Executive summary
Measure the operating outcome, not the AI activity.
Benchmark AI maturity by operating capability rather than model access or prompt volume.
Methodology
Rule 1
Define the business outcome and the start/end state before measuring activity.
Rule 2
Use first-party runtime, workflow, connector, and product evidence where available.
Rule 3
Separate observed measurements from estimates, modeled scenarios, and qualitative interpretation.
Rule 4
Do not publish a benchmark value until its source, population, period, and calculation are reproducible.
Rule 5
Retain human review for consequential financial, legal, clinical, employment, coverage, or other material decisions.
Measurement framework
Five dimensions worth measuring repeatedly.
Outcome completion
Qualified intents that reach the expected business outcome
Activity counts do not prove that the workflow delivered value.
Cycle time
Elapsed time from trigger to completed outcome
Faster completion is one of the clearest benefits of connected execution.
Human intervention
Manual touches, approvals, retries, and escalations per completed outcome
Automation should reduce avoidable work without removing appropriate oversight.
Exception rate
Runs that leave the expected path or require recovery
Exception frequency exposes brittle workflows and poor context.
Data provenance
Share of material decisions supported by current authoritative sources
AI output quality depends on trusted operating context.
What to do next
Action 1
Measure one bounded workflow first.
Action 2
Record the baseline and evidence period.
Action 3
Compare like-for-like workflows and populations.
Action 4
Treat modeled ROI separately from observed outcomes.