What executives need to approve
A strong AI FinOps business case does not only say “we can reduce tokens.” It shows where savings come from, how integrity is protected, what data is required, and how quickly the pilot can prove value.
- Current monthly AI spend baseline.
- Waste source breakdown by workflow.
- Safe savings estimate and payback scenario.
- Controls required by deployment level.
- Risk, privacy and operational review notes.
Business case sections
1. Spend baseline
LLM/API spend, RAG-related tokens, retries, self-hosted inference and training jobs.
2. Savings portfolio
Context reduction, RAG selection, retry prevention, routing, cache, GPU right-sizing and training controls.
3. Integrity guardrail
Clarifies that savings only count when critical facts, citations, policy constraints and answer quality remain protected.
4. Pilot plan
Defines a low-risk 14-day path from read-only telemetry to a first production control candidate.
| Audience | What they need to see | ML Mind asset |
|---|---|---|
| CFO | Spend baseline, forecast, payback and governance value. | Executive ROI brief and savings report. |
| CTO | Architecture fit, integration risk and quality protection. | Deployment levels and implementation playbook. |
| AI platform team | Telemetry fields, routing path, RAG controls and cache policy. | Technical audit report and pilot plan. |