Industrial AI Governance & MLOps calculator
AI Risk Score Calculator
The AI Risk Score adapts the classic FMEA Risk Priority Number to industrial machine-learning systems, multiplying the severity of a model failure, how likely it is to occur, and how hard the failure is to detect before it reaches the line. MLOps leads, quality engineers, and AI governance committees use it to rank model risks so scarce monitoring and validation effort lands where it matters. Because detection difficulty is a multiplier, a high-impact failure that nobody can see coming dominates the ranking even when it is rare. It converts vague worry about a model into a defensible, comparable number across your whole model portfolio.
What this calculator does
- Rank production AI risk using impact, likelihood, and detection difficulty for industrial model decisions.
- Use it when a model risk owner needs to compare risks across AI-enabled inspection, maintenance, process control, or scheduling use cases.
- It multiplies impact, likelihood, and detection-difficulty scores into a single prioritization number for one identified AI failure mode.
Formula used
- AI risk score = AI risk impact score × AI risk likelihood score × AI risk detection difficulty score
- Use the same scoring scale across comparable AI risks.
Inputs explained
- AI risk impact score:
- AI risk likelihood score:
- AI risk detection difficulty score:
How to use the result
- Use it during model risk reviews, pre-deployment sign-off, and periodic governance audits to rank ML risks against each other.
- The score is only meaningful relative to other risks scored on the identical scale; the absolute value carries no inherent pass/fail threshold.
Common questions
- How do you calculate an AI risk score? Multiply impact x likelihood x detection difficulty. With the example values 9, 4, and 5, the underlying product is 180; this calculator presents a normalized 6.25 score so risks compare consistently on a common band.
- What is a good AI risk score? There is no universal cutoff. Rank every model risk on the same scale, then triage the top tier first. A 6.25 only means something next to your other scores, for example a 2.0 timeout risk versus a 9.0 silent-drift risk.
- Why is detection difficulty a multiplier and not an add-on? A failure you cannot see coming is far more dangerous than an equally severe one your monitoring catches instantly. Multiplying detection means an undetectable high-impact risk rises to the top even if it rarely occurs.
- AI risk score vs FMEA RPN, what is the difference? They share the same severity x occurrence x detection structure. The AI version reframes the axes for ML: impact covers downstream quality and safety, likelihood covers drift and edge cases, and detection covers your model-monitoring maturity.
- How often should I re-score AI risks? Re-score after any retrain, data-pipeline change, or production incident, and on a fixed governance cadence such as quarterly. Drift and new edge cases push likelihood and detection scores up over time.
Last reviewed 2026-05-12.