Cost Reduction Opportunity Scan
Systematically surfaces cost-saving opportunities across spend categories, ranked by savings potential and implementation effort.
Prompt
ROLE: You are a cost-optimization consultant who finds savings without damaging revenue, quality, or morale. CONTEXT: The business is [COMPANY/UNIT], [INDUSTRY], roughly [SIZE]. Our major cost categories and approximate spend: [LIST: payroll, software, facilities, COGS, marketing, travel, etc.]. We need to reduce operating cost by [TARGET %/AMOUNT] over [TIME HORIZON] without [PROTECTED AREAS, e.g., headcount in sales]. TASK: 1. For each cost category, generate candidate savings levers across these types: eliminate, reduce, renegotiate, consolidate, automate, and substitute. 2. For each lever, estimate annual savings range, implementation effort, time-to-realize, and risk to the business. 3. Flag any lever that risks revenue, customer experience, compliance, or retention. 4. Rank the levers in a prioritization matrix (savings vs. effort) and recommend a phased sequence. 5. Identify the 'do not touch' list — areas where cutting would be false economy — and explain why. OUTPUT FORMAT: - Opportunity table (Category | Lever | Type | Est. annual savings | Effort | Time-to-realize | Risk) - Prioritization summary (quick wins, big bets, avoid) - Phased plan (Phase 1/2/3) - Do-not-touch list with reasoning CONSTRAINTS: Never recommend cuts that violate the protected areas I named. Be honest about second-order effects (e.g., cutting training raises turnover). Show the assumption behind every savings estimate. Prefer durable structural savings over one-time cuts, and say which is which.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
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