Industrial AI Governance & MLOps worked example
AI Risk Score with ai risk impact score of 23 score: a worked example
This scenario runs the ai risk score calculation on the strong side: ai risk impact score of 23 score, with every other input held at its documented default. Use it when a model risk owner needs to compare risks across AI-enabled inspection, maintenance, process control, or scheduling use cases.
The inputs for this scenario
- AI risk impact score: 23 score (raised for this scenario; the documented default is 9)
- AI risk likelihood score: 4 score (unchanged)
- AI risk detection difficulty score: 5 score (unchanged)
Working through the calculation
- Applying the documented formula (AI risk score = AI risk impact score × AI risk likelihood score × AI risk detection difficulty score) to the inputs above produces each figure below.
- At this operating point the engine returns 11.85 score for ai risk score, the number this scenario is built around.
- At this operating point the engine returns 23 score for ai risk impact score.
- At this operating point the engine returns 4 score for ai risk likelihood score.
- At this operating point the engine returns 5 score for ai risk detection difficulty score.
How this compares with the baseline
- Against the tool's baseline example, where ai risk impact score sits at 9 score and the headline result is 6.25 score, this scenario comes in 89.6% above the baseline at 11.85 score.
- Use it during model risk reviews, pre-deployment sign-off, and periodic governance audits to rank ML risks against each other. Treat this as a target state: the delta against the baseline quantifies what the improvement is worth before you commit to chasing it.
Results at a glance
- AI risk score: 11.85 score (headline result)
- AI risk impact score: 23 score
- AI risk likelihood score: 4 score
- AI risk detection difficulty score: 5 score
Run it with your numbers
- Every input above is editable in the live AI Risk Score calculator, which recalculates instantly and can be shared with the inputs intact.
Last reviewed 2026-05-12.