A 300-employee SaaS company generating $120M in annual revenue. Six departments. Fourteen AI tools. A monthly AI bill that had tripled in 18 months — from $15,800/month to $47,000/month — and nobody could explain why. The CFO brought it to the board as a line item. The board asked three questions: What are we spending? What are we getting? Is it worth it? The CFO could answer the first. She couldn't answer the other two. That's when she called us.
The first thing the board needed was attribution. Not "AI spend is $47K." But whose $47K? We mapped every dollar to the department that consumed it. The results changed the conversation entirely.
Not all spend is waste. But $18,400/month of this company's AI bill was producing zero measurable value. We categorized every waste dollar by type, tagged it with confidence level, and gave the CFO a number she could defend to the board.
| Finding | Score | State at Audit | State After |
|---|---|---|---|
|
Unused Seat Waste — $12,200/mo
Cost Waste · License Management
|
4.80 Do First | 142 seats across 6 tools with zero logins in 90 days — $12,200/month in dead licenses | Seats reclaimed — 142 licenses cancelled or reassigned, saving $12,200/month immediately |
|
Duplicate Tool Spend — $3,400/mo
Cost Waste · Tool Rationalization
|
4.50 Do First | 2 content generation platforms, 2 meeting summarizers, overlapping code assistant coverage — $3,400/month in redundancy | Consolidated to single tool per category — 4 duplicate subscriptions cancelled |
|
Premium Model Misuse — $2,800/mo
Cost Efficiency · Model Tiering
|
3.90 Do Next | GPT-4o used for email drafts, data formatting, basic summarization — tasks where GPT-4o-mini produces identical output at 85% lower cost | Model routing rules implemented — premium models reserved for complex reasoning, commodity tasks on efficient models |
|
No Department Chargeback Model
Governance · Financial Attribution
|
4.60 Do First | All AI spend rolled into a single IT line item — no department visibility, no accountability, no incentive to optimize | Department-level chargeback live — each department sees their AI spend, usage, and unit economics monthly |
|
Zero ROI Measurement Framework
Governance · Business Case
|
3.70 Do Next | Not one AI tool had a defined success metric, baseline measurement, or ROI owner — $564K/year with no proof of value | ROI framework established — 8 high-value tools now have defined metrics, baselines, and quarterly measurement cadence |
|
No Renewal Negotiation Data
Cost Efficiency · Vendor Management
|
2.80 Plan For | 3 major AI vendor contracts renewing in next 6 months — no usage data, no leverage, no negotiation strategy | Usage dashboards built for all 3 vendors — negotiation briefs prepared with utilization data and competitive alternatives |
Raw spend is meaningless without context. The board didn't want to know "we spend $47K/month on AI." They wanted to know what that buys. We built unit economics for every major AI investment — cost per output, cost per employee, cost per business action. Every number confidence-tagged.
These unit economics transformed the board conversation. Instead of "we spend $47K/month," the CFO could say: "Our AI-assisted engineering cost per PR is $41.76, our support resolution cost dropped to $4.20/ticket, and marketing asset production cost fell 63%. The waste we've identified and eliminated is $18,400/month. The remaining $28,600/month is producing measurable returns."
The board report included confidence tags on every number. Green for verified from billing data. Indigo for calculated with documented methodology. Gold for estimated from baselines. The CFO's exact words: "This is the first time I've presented AI spend where I could defend every number."
The waste is eliminated. The chargeback is live. The unit economics are measured. Phase 2 turns cost control into strategic advantage — using the data to make smarter AI investments and negotiate better vendor terms.
We map every AI dollar to the department that spends it, the output it produces, and the ROI it delivers — confidence-tagged, hash-verified, and board-ready.
Get Your AI Spend Audit →Disclaimer: This use case is based on a simulated engagement using the Coriven Method. Company details are representative. All findings reflect the methodology Coriven applies to real engagements. Green numbers are verified from source billing data. Indigo numbers are calculated with documented methodology. Gold numbers are estimated from baselines. Actual results vary.
Every number in this use case is confidence-tagged by color — because we believe if we can't prove it, we should say so.