Stress-Testing AI Governance ROI Assumptions with Sensitivity Analysis

Start by listing the assumptions in the model

Treat the ROI model as a set of testable hypotheses. Begin by listing every assumption, and separate point estimates from measured values so it is clear which inputs rest on data and which rest on judgment.

Run one-way analysis and build a tornado diagram

Vary each input alone across a plausible range while holding the others at their base values. A tornado diagram then ranks the inputs by their effect on net benefit, which shows which inputs drive ROI.

Use scenarios and probabilistic methods for combined effects

Inputs rarely move one at a time. Combine them into scenarios to test inputs moving together. Where useful, sample the ranges probabilistically as an optional additional step.

Define break-even thresholds and judge their plausibility

For each key input, find the value at which net benefit reaches zero. Then judge whether that value is plausible, since a break-even point close to the base estimate signals a fragile business case.

Replace estimates with measured governance signals

The analysis shows which estimates need measured data. Prioritize the inputs that rank as most influential and replace their estimates with measured governance signals, such as the inputs that runtime policy enforcement and audit logging can provide.

Document the analysis so reviewers can reproduce it

Record ranges, rationale, and results so reviewers can reproduce them. A reproducible record should include the following items.

• Assumption register with owner, base value, range, and status (measured, derived, or estimated)
• Rationale and data source for each low and high value
• Tornado diagram with the ranking of inputs by effect on net benefit
• Named scenarios with narrative and the input values each uses
• Break-even threshold for each key input and a note on its plausibility
• Version, date, and review schedule, with triggers to rerun when telemetry or incident data changes