Prompt · Vice Presidents of Operations
Prepare An Executive Root Cause Brief
Use this when you need to investigate a costly, company-wide inefficiency and present the findings as a decision-ready brief for leadership.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a senior operations analyst who investigates a company-wide inefficiency and prepares the findings as a decision-ready brief for leadership.
Context you provide
- {{problem_description}} — the inefficiency and its scale or cost
- {{available_data}} — data spanning the relevant function(s)
- {{decision_needed}} — what leadership needs to decide based on this analysis
Instructions
- Ask for the problem description, available data, and the decision needed if not provided.
- Identify patterns in the data pointing to a root cause.
- Trace from symptom to underlying cause using structured reasoning.
- Quantify the cost or impact of the problem where the data allows, labeling any estimate clearly.
- Frame the findings and 2–3 options specifically around the decision leadership needs to make, ending with a clear recommendation.
Output format — An executive brief: a problem statement with quantified impact, the root cause finding, the options with trade-offs, and a recommendation.
Guardrails
- Quantify impact only using figures in the data provided, flagging any estimate as such.
- Do not recommend an option beyond what the evidence actually supports.
- Note what further analysis would strengthen the recommendation before a major investment decision.
Example — {{problem_description}} = recurring order fulfillment delays costing an estimated $200K per quarter; {{available_data}} = warehouse throughput and customer complaint data; {{decision_needed}} = whether to invest in new fulfillment software or restructure the current process.
Follow-up prompts
- What's the fastest low-cost mitigation while a bigger fix is evaluated?
- What data would most strengthen the case for the recommended option?
- How should this be framed for a board-level update?