sensitivity analysis
This glossary entry explains sensitivity analysis for AI governance and model risk programs. The sections below summarize what the term means in plain language, why chief AI officers and cross-functional committees track it, where teams often get confused, and—when you are signed in—how it shows up across major industries and in expectations tied to the EU AI Act and NIST AI RMF. Use related links at the end of the page to explore neighboring concepts without losing context.
What It Means
Sensitivity analysis is a stress test for your business decisions that shows which factors could make or break your outcomes. It reveals which variables in your plans are most critical by testing what happens when you change them slightly.
Why Chief AI Officers Care
It helps executives identify the biggest risks and opportunities in their strategies, prioritize what to monitor closely, and build more resilient plans by understanding which assumptions their success depends on most.
Real-World Example
A retailer planning store expansion might test how sensitive their ROI projections are to factors like rent costs, foot traffic, and average purchase size - discovering that a 10% change in rent barely affects profits, but a 5% change in foot traffic kills the entire project.
Common Confusion
People often think it predicts the future or tells you what will happen, when it actually shows you what could happen and which variables matter most for your planning and risk management.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, sensitivity analysis helps evaluate how changes in key variables like patient volume, reimbursement rates...
Finance: In finance, sensitivity analysis is used to assess how changes in key assumptions (interest rates, default rates, market...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"A “what-if” type of analysis to determine the sensitivity of the outcomes to changes in parameters. If a small change in a parameter results in relatively large changes in the outcomes, the outcomes are said to be sensitive to that parameter."Source: OECD
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