MarkdownPlaybook
Executive Briefing Template — TokenOps
Purpose: Present TokenOps program results to C-suite executives
Duration: 15–20 minute presentation
Update Frequency: Monthly or Quarterly
Slide 1: One-Page Executive Summary
AI Cost Operations — [Month/Quarter] [Year]
| KPI |
Value |
Trend |
Status |
| Monthly AI Spend |
$____K |
↓ __% MoM |
🟢 Under Budget |
| Optimization Savings (cumulative) |
$____K |
↑ __% QoQ |
🟢 On Track |
| Token Yield Rate |
__% |
↑ __pp |
🟡 Improving |
| Budget Adherence |
__% of teams within budget |
— |
🟢 / 🟡 / 🔴 |
Headline: _"AI spend reduced __% since program inception while supporting _% more AI-powered features."
Key Wins This Period:
- _____________ → saved $____/month
- _____________ → saved $____/month
- _____________ → improved quality by __%
Attention Items:
- _____________ (action: _____________)
Slide 2: Cost Dashboard
Monthly AI Spend Trend
| Month |
Budget |
Actual |
Variance |
Optimized Savings |
| Jan |
$__K |
$__K |
+/−__% |
$__K |
| Feb |
$__K |
$__K |
+/−__% |
$__K |
| Mar |
$__K |
$__K |
+/−__% |
$__K |
| Apr |
$__K |
$__K |
+/−__% |
$__K |
| May |
$__K |
$__K |
+/−__% |
$__K |
| Jun |
$__K |
$__K |
+/−__% |
$__K |
| H1 Total |
$__K |
$__K |
|
$__K saved |
Spend by Team
| Team |
Monthly Spend |
% of Total |
Budget Status |
Trend |
| Engineering |
$__K |
__% |
🟢 Within |
↓ |
| Product |
$__K |
__% |
🟡 Near Limit |
→ |
| Marketing |
$__K |
__% |
🟢 Within |
↓ |
| Data |
$__K |
__% |
🟢 Within |
↓ |
| Support |
$__K |
__% |
🔴 Over |
↑ |
Cost per Revenue Dollar
AI Cost Ratio = Monthly AI Spend / Monthly Revenue
Target: < 2% Current: ___%
Slide 3: Optimization Wins
Initiatives Completed This Period
| Initiative |
Owner |
Savings/Month |
Quality Impact |
Status |
| Model tiering — support chatbot |
Eng |
$__K |
−1.2% accuracy (acceptable) |
✅ Complete |
| Semantic caching — FAQ |
Eng |
$__K |
No impact |
✅ Complete |
| Batch API — data pipeline |
Data |
$__K |
No impact |
✅ Complete |
| Prompt compression — all services |
Eng |
$__K |
No impact |
✅ Complete |
Pipeline (Next Period)
| Initiative |
Owner |
Expected Savings |
Timeline |
Risk |
| _________ |
___ |
$__K/mo |
Q_ |
Low |
| _________ |
___ |
$__K/mo |
Q_ |
Medium |
| _________ |
___ |
$__K/mo |
Q_ |
Low |
Cumulative Savings
Program inception: [Month Year]
Total cumulative savings: $___K
Annualized run-rate savings: $___K
ROI on TokenOps investment: ___x
Slide 4: Risk & Compliance
Vendor Concentration
| Provider |
% of Spend |
% of Calls |
Risk Level |
| OpenAI |
__% |
__% |
🟡 Moderate |
| Anthropic |
__% |
__% |
🟢 Low |
| Google |
__% |
__% |
🟢 Low |
Mitigation: Multi-provider routing active. Failover tested monthly.
Budget Adherence
| Metric |
Target |
Actual |
Status |
| Teams within monthly budget |
100% |
__% |
🟢 / 🟡 / 🔴 |
| P1 cost incidents this period |
0 |
__ |
🟢 / 🔴 |
| Mean time to detect anomaly |
< 1 hour |
__ min |
🟢 / 🟡 |
| Cost attribution coverage |
100% |
__% |
🟢 / 🟡 |
Compliance Status
Slide 5: Strategic Recommendations
Recommendations for Executive Decision
[Recommendation 1]
- Context: _____________
- Investment: $___ (one-time) / $___ (monthly)
- Expected return: $___/month savings
- Timeline: __ weeks
- Risk: Low / Medium / High
[Recommendation 2]
- Context: _____________
- Investment: $___
- Expected return: $___/month
- Timeline: __ weeks
[Recommendation 3]
- Context: _____________
- Decision needed: _____________
Budget Request (if applicable)
| Item |
Amount |
Justification |
Expected ROI |
| _____ |
$__K |
_____________ |
__x in __ months |
| _____ |
$__K |
_____________ |
__x in __ months |
| Total |
$__K |
|
|
Appendix: Detailed Metrics
Model Usage Distribution
| Model |
Monthly Calls |
Input Tokens (M) |
Output Tokens (M) |
Monthly Cost |
Cost/Call |
| GPT-4.1 |
___K |
___ |
___ |
$__K |
$____ |
| GPT-4.1-mini |
___K |
___ |
___ |
$__K |
$____ |
| Claude Sonnet 4 |
___K |
___ |
___ |
$__K |
$____ |
| Gemini 2.5 Flash |
___K |
___ |
___ |
$__K |
$____ |
| Total |
___K |
___ |
___ |
$__K |
|
Unit Economics
| Metric |
Current |
Last Period |
Target |
| Cost per API request |
$____ |
$____ |
$____ |
| Cost per active user |
$____ |
$____ |
$____ |
| Cost per customer (SaaS) |
$____ |
$____ |
$____ |
| Blended cost per 1M tokens |
$____ |
$____ |
$____ |
| Token yield rate |
__% |
__% |
≥ 80% |
| Cache hit rate |
__% |
__% |
≥ 40% |
Presentation Tips
For Non-Technical Executives
- Lead with business impact — "We saved $X" not "We reduced tokens by Y%"
- Use the cost-per-customer metric — Executives understand unit economics
- Show trend lines — Direction matters more than absolute numbers
- Highlight quality preservation — "We cut costs 40% with no quality degradation"
- Compare to industry benchmarks — "Our AI cost ratio of 1.8% is below the 3% industry average"
Common Executive Questions & Talking Points
| Question |
Talking Point |
| "Can we cut costs further?" |
"We've captured __% of identified savings. The next phase targets [area] for an additional $__K/month." |
| "Are we spending too much on AI?" |
"Our AI cost ratio is __% of revenue, which is [below/at/above] industry benchmarks. The key metric is ROI per dollar, not absolute spend." |
| "What happens if AI costs double?" |
"Our multi-provider architecture and model tiering protect us. A 2× price increase from any single vendor would increase total costs by only __% due to routing flexibility." |
| "How does this compare to competitors?" |
"Based on industry data, our blended cost per token of $__ is in the [top/middle/bottom] quartile." |
| "What's the biggest risk?" |
"Vendor concentration — __% of our spend is with [Provider]. We're mitigating with multi-provider routing and quarterly vendor reviews." |
Template from the TokenOps Atlas — tokenops-atlas