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Define the review process and baseline
Start by drawing a boundary around the process you want to measure. Specify the event that marks a case entering governance review and the terminal state that ends measurement, such as an approval, rejection, or documented escalation. Define how reopened cases, exclusions, and paused reviews count.
Choose a baseline before enabling automation, then preserve the same case scope and metric definitions in the comparison period. Record the observation windows, business-hours convention, workflow version, and relevant changes to staffing, policy, workload, or review criteria. NIST calls for documented methods, metrics, benchmarks, and results in its AI Risk Management Framework (AI RMF) 1.0; APQC recommends internal benchmarking to support meaningful comparisons.
Build a scorecard for speed, flow, and quality
Select a focused set of measures that answers whether reviews are moving more quickly, whether queues are healthier, and whether governance remains effective. Definitions below are candidates to adapt to your workflow, not universal standards or target values.
| Question | Measure | Definition to set before comparison |
|---|---|---|
| Is a review taking less time? | End-to-end cycle time, including median and a slow-tail percentile | Time from intake to the defined terminal decision; identify the period and case segments. |
| Is work waiting less? | Open backlog, queue age, and wait time at each stage | Define queue states, calendar-time or business-time convention, and how reopened cases count. |
| Is the team completing more work? | Completed reviews per period and arrival/completion balance | Count comparable completed cases; show incoming volume and staffing or capacity context. |
| Is service more reliable? | SLA attainment and SLA-at-risk or violation rates | Define the target, eligible cases, pause rules, and exclusions. |
| Is quality preserved? | First-pass quality, rework or reopen rate, exception rate, and control-evidence completeness | Define errors, corrections, valid exceptions, and what makes a record complete. |
| Did automation change the process as intended? | Automation coverage, handoff rate, and exception rate | Specify automated steps, required human steps, and how failures or overrides count. |
Separate elapsed time from human work
End-to-end cycle time includes both active work and waiting across queues and handoffs. Active review time measures human effort. If elapsed time falls while touch time stays similar, reduced waiting may explain the change. If touch time falls but elapsed time does not, the customer-facing delay or queue may not have improved.
Look beyond averages
Use a median and a slow-tail percentile where useful, and inspect the age of open cases. An average can conceal a small group of reviews that remain severely delayed. Report queue volume, arrivals, completions, and waits at individual stages to see where work accumulates.
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Keep quality and controls in view
Pair speed and throughput with first-pass quality, correction loops, exceptions, and evidence that required controls were followed. NIST’s Measure function calls for documenting risk measurements and tracking risk over time. Its framework describes methods that may be quantitative, qualitative, or mixed; the AI RMF 1.0 is being revised, so identify the version informing your approach.
Compare equivalent cases and periods
- Freeze the definitions. Document intake and terminal events, eligible cases, exclusions, time convention, and the observation windows before calculating results.
- Compare like with like. Segment by review type, risk tier, complexity, business unit, or other factors that affect effort or risk, when the data supports it.
- Show flow and quality together. Report absolute values and changes for cycle time, queue measures, throughput, service levels, rework, exceptions, and control evidence.
- Expose capacity and demand. Include arrivals, completions, staffing or capacity, and case mix so that volume or reviewer changes are visible.
- Document the analysis. Keep a traceable record of event definitions, data extraction, exclusions, transformations, and decisions made from the results.
If rollout is staged, compare eligible groups and periods where feasible, and document how the groups and comparison were chosen. There is no universal sample-size threshold or single causal evaluation design established for this specific intervention. A before-and-after improvement shows that results changed over time; it does not isolate automation as the cause when staffing, workload, policy, or review criteria also changed.
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Interpret workflow telemetry as operational evidence
Workflow software can help show that a case moved, waited, failed, or breached an SLA. For example, Microsoft Power Automate monitoring documentation describes flow duration, queued and processed items, SLA risk or violations, and exceptions. Some listed queue measures are marked public preview. These are useful operational indicators, not proof that a meaningful governance review occurred.
Governance evidence should also show whether the required reviewer examined the relevant material, supplied a rationale, had appropriate approval authority, and applied the required risk controls. A status record that says “reviewed” may show a transition without establishing what the reviewer considered. NIST’s framework emphasizes documented measures and repeatable assessment processes; its AI RMF Playbook identifies itself as a companion resource based on AI RMF 1.0, released January 26, 2023.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set owners and reporting rules
Assign an owner to each measure and record its data source, collection cadence, definition, and locally chosen threshold. APQC advises evaluating measures for reliability, impact, visibility of trends, accessibility, and familiarity, and cautions against overloading dashboards with metrics. Choose enough measures to detect bottlenecks and quality regressions without obscuring the decisions they should inform.
There is no established industry-wide percentage for how much AI governance review bottlenecks should fall because of automation, nor a universal acceptable review time. Set targets for your process, explain how uncertainty and limitations affect interpretation, and avoid presenting operational movement alone as evidence of improved governance.
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