Module 6 • 22 min
Disclosing Risk and Uncertainty Honestly
Make projections more credible by stating their uncertainty.Core Concept
Every ROI projection contains assumptions about adoption, delivery, data quality, and realized benefit. Naming those assumptions and their confidence level helps reviewers understand what could change the result.
This decision affects the value, delivery effort, operating ownership, and risk profile of the initiative long after the first release.
How to Apply This in Your Organization
Separate facts, estimates, and dependencies. Then define the action that would improve confidence before a final approval.
Use one real workflow, its current systems and data, and the people accountable for the outcome. The purpose is a decision-ready recommendation, not a theoretical evaluation.
Decision Checklist
- List material assumptions.
- Assign a confidence level.
- Name mitigation or validation work.
Worked Decision Scenario
A team is evaluating disclosing risk and uncertainty honestly for a current automation initiative. It uses the framework to identify the narrowest viable next step, the dependency that could change the decision, and the owner responsible for resolving it.
An executive approves discovery rather than implementation because the business case clearly identifies an uncertain data dependency and the evidence needed to resolve it.
Apply it
Working Exercise
Document one assumption and its uncertainty.
Saved locally for this review prototype. Your response will become private account data when the approved course backend is connected.