How to Run a 12-Month AI Governance Value Realization Review
An AI governance value realization review compares what the program was meant to achieve with evidence of what it achieved across five dimensions: risk reduction, control effectiveness, compliance and audit readiness, operational efficiency, and AI adoption enablement. Run it by defining outcome indicators for each dimension, tracing each to a verifiable evidence source, testing documented controls against runtime records, and converting gaps into a ranked remediation and investment plan.
Review sequence
The review moves through five steps. Each step builds on the one before it, and the sections below explain how they fit together.
| Step | Activity | Focus |
|---|---|---|
| 01 | Define value dimensions | Risk reduction, control effectiveness, audit readiness, efficiency, adoption enablement. |
| 02 | Select outcome indicators | Replace activity counts with measures of changed risk and control state. |
| 03 | Collect runtime evidence | Enforcement logs, agent identity and permission records, tool-call trails. |
| 04 | Assess policy-to-runtime gaps | Test each documented control against production behavior. |
| 05 | Prioritize and report | Rank remediation and investment decisions with supporting evidence. |
Scope the review around outcomes, not activity
The review compares what the program was meant to achieve with evidence of what it achieved. Begin by defining the value dimensions the review will cover:
- Risk reduction
- Control effectiveness
- Compliance and audit readiness
- Operational efficiency
- AI adoption enablement
Keeping the scope on these outcomes, rather than on volumes of governance activity, keeps the review focused on whether the program changed anything.
Match each value dimension to an indicator and an evidence source
For each dimension, select an outcome indicator. Replace activity counts with measures of changed risk and control state.
Then trace each indicator to a verifiable evidence source. An indicator without an evidence source is a claim that cannot yet be tested, and it should be recorded as such in the review.
Identify the runtime evidence each claim requires
Each value claim should be grounded in records of what actually happened in production. Collect runtime evidence such as:
- Enforcement logs
- Agent identity and permission records
- Tool-call trails
These records are what allow documented controls to be tested against runtime behavior in the next step.
Assess policy-to-runtime gaps and rank them
Test each documented control against production behavior. Where the runtime record does not match the documented policy, the difference is a gap.
Outputs to present to executives and auditors
- An indicator scorecard per value dimension with baseline, current value, evidence source, and owner
- A control-by-control table showing documented policy, runtime evidence, and status
- A ranked gap register with severity, owner, and remediation date
- Retire, tighten, and fund recommendations, each tied to supporting evidence
- A statement of evidence limitations and indicators that could not yet be measured
- A measurement cadence so the next review starts from this baseline
Plan the evidence behind your next review
If your review shows gaps between documented policy and runtime enforcement, talk with our team about how runtime governance can supply the enforcement and audit evidence your program needs.
Talk to an Expert