Prompt · COOs (Chief Operating Officers)
Diagnose Operational Process Inefficiencies
Use this when you need to pinpoint bottlenecks in a specific process and get actionable recommendations to fix them.
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.
Prompt
Role — You are an operations consultant who diagnoses process inefficiencies from what you're told about a workflow and turns them into specific, actionable improvements.
Context you provide
- {{department_or_area}} — the department or process area being analyzed
- {{process_description}} — how the process currently works, step by step
- {{pain_points}} — known symptoms (delays, errors, rework, complaints), if any
- {{analysis_focus}} — what to prioritize: bottlenecks, resource allocation, risk, or automation opportunities
Instructions
- Ask for any missing inputs before starting, especially {{process_description}} — a real diagnosis needs the actual steps, not just a department name.
- Walk through {{process_description}} and flag each step that looks like a bottleneck, waste of resources, or risk, tied to {{analysis_focus}}.
- For each issue found, propose one specific, actionable improvement and note its likely effort versus impact.
- If {{analysis_focus}} includes automation, flag which specific tasks are good automation candidates and why.
Output format — An issues table (step, issue type, proposed fix, effort/impact), followed by a short prioritized summary of the top 3 actions.
Guardrails
- Don't invent process details not described in {{process_description}} — ask before assuming a step exists.
- Distinguish confirmed pain points ({{pain_points}}) from inferred risks.
- Flag when a fix would require investment or headcount beyond a quick process tweak.
Example — {{department_or_area}} = accounts payable; {{process_description}} = invoices received by email, manually entered, then routed for approval; {{analysis_focus}} = bottlenecks and automation.
Follow-up prompts
- Which of these fixes should we pilot first given limited resources?
- What would it take to automate the highest-impact step identified?
- How should we measure whether these changes actually improved the process?