The company is a 200-person B2B SaaS platform with approximately $30M in ARR and an 85-person engineering organization. They had deployed GitHub Copilot Business across the entire engineering team six months prior — a decision championed by the VP of Engineering, who had seen impressive results during a small pilot and wanted to scale it before competitors gained an edge.
The deployment went smoothly. Engineers liked it. Many of them said it felt like a meaningful productivity boost. Stand-ups started including phrases like "Copilot nailed that boilerplate" and "I let Copilot handle the test scaffolding." The VP of Engineering brought it up in every leadership meeting: Copilot was working, the team was faster, and this was exactly the kind of investment in developer experience that kept top engineers from leaving for companies that offered better tooling.
The CFO listened patiently for four months. Then, during a quarterly budget review, he asked the question that changed the conversation: "I see the $19,380 line item. I hear that engineers feel more productive. Can anyone show me a number — any number — that connects the spend to an output I can put in a board deck?"
The room went quiet. The VP of Engineering said he could pull some GitHub metrics. The CFO said he had been hearing "I can pull some metrics" for four months. He wanted math. Actual unit economics. Cost per output. ROI that a board member could evaluate the same way they evaluate every other line item in the engineering budget.
The VP of Engineering was not wrong — Copilot was working. The CFO was not wrong either — "it's working" is not a financial argument. Both of them needed what neither of them had: tagged, traceable, verifiable numbers that turned a gut feeling into a board-ready metric. That is what Coriven was engaged to produce.
This is the conversation that happens at every software company between Series A and IPO. Engineering adopts an AI tool. Developers love it. The VP of Engineering advocates for it passionately and personally. Six months later, the CFO looks at the line item and asks a question nobody on the engineering side can answer with precision: how much more output are we actually getting for this money?
The challenge is not that engineering is being dishonest. They genuinely feel more productive, and in most cases they are right. The challenge is that "feel" is not a unit of measure that finance can model, forecast, or defend to a board of directors. When the CFO asks "what's the ROI on Copilot?" and the answer is "the engineers say it's great," the CFO hears: "We do not have a measurement system."
And when engineering hears the CFO questioning the investment, they hear something equally frustrating: "You don't understand what we do well enough to trust our judgment." Both sides are partially right. Both sides are talking past each other. The missing piece is not better communication — it is better data. Specifically, data that connects license spend to development output in units both sides can agree on.
That is exactly what Coriven was asked to provide. Not an opinion on whether Copilot is worth it. Not a recommendation to buy more or buy less. A unit economic analysis with confidence tags on every number, so both the VP of Engineering and the CFO could look at the same data and reach their own conclusions.
Coriven connected three data sources that had never been joined: Copilot licensing data from the company's identity provider, Copilot usage telemetry from GitHub's admin API, and development output data from the company's GitHub repositories. No surveys. No self-reported "hours saved." Actual tool usage measured against actual development output measured against actual spend.
The headline numbers looked strong — and they were genuine. But the seat-level data told a more nuanced story that neither the VP of Engineering nor the CFO had seen before, because neither of them had the cross-system visibility to see it.
The aggregate story was clear: engineers were shipping more code, faster, and the cost per unit of output was modest relative to the fully loaded engineering cost structure. At a fully loaded cost of approximately $2,400 per PR (engineering salary, benefits, and overhead divided by PR output), adding $41.76 in AI tooling cost to generate 23% more PRs was, on its face, a strong investment.
But aggregate numbers hide the details that matter. The real story was in the seat-level data.
Combined, that is 20 seats paying $19/month for a tool that is generating zero measurable value. $4,560/year in pure waste — not because the tool is bad, but because it was provisioned to people who do not use it and nobody had the cross-system visibility to notice.
The engagement was not about whether Copilot was a good tool. It was about building a measurement system that connected spend to output so that the investment could be evaluated, optimized, and defended using the same rigor applied to every other line item in the engineering budget. The VP of Engineering did not need to be told Copilot was working — he needed data that proved it in a format the CFO could put on a slide.
The findings were not about whether Copilot was good or bad. They were about optimizing a deployment that was already working — removing waste, establishing measurement, and building the data infrastructure to evaluate AI tooling investments with the same rigor as any other engineering spend.
| Finding | Score | State at Audit | State After |
|---|---|---|---|
|
12 Seats — Zero Copilot Suggestions Accepted in 60 Days
Utilization · Cost Waste
|
4.80 Do First | 14% of Copilot licenses showing zero usage — disabled, never activated, or misconfigured plugins generating no value | All 12 seats reclaimed — licenses removed from inactive users, 2 JetBrains plugin misconfigurations identified and fixed for developers who wanted to use it |
|
8 Seats — Assigned to Non-Coding Roles
Provisioning · Role Alignment
|
4.50 Do First | Engineering managers, team leads, and a director provisioned with Copilot seats during blanket rollout — do not write code daily | All 8 seats removed — provisioning policy updated to require active coding role verification before Copilot license assignment |
|
No Unit Economics or ROI Measurement
Governance · Financial Accountability
|
4.30 Do First | $19,380/year in Copilot spend with zero connection to development output — CFO unable to evaluate, forecast, or defend the investment | Unit economic model built — cost per PR, cost per incremental PR, and net ROI calculated with confidence tags, delivered as board-ready metric |
|
No Seat-Level Usage Monitoring
Observability · Optimization
|
3.70 Do Next | Copilot usage only visible at aggregate level — no seat-level data on acceptance rates, suggestion frequency, or active usage patterns | Usage dashboard deployed — monthly seat-level reporting with automatic flagging of seats below minimum usage threshold |
|
No Quarterly Review Cadence
Governance · Continuous Optimization
|
3.20 Do Next | Copilot deployed once with no scheduled review — seat allocation and ROI never reassessed after initial rollout | Quarterly review process established — seat utilization audit, unit economics recalculation, and board reporting on 90-day cycle |
The outcome of this engagement was not a recommendation to keep or cancel Copilot. The outcome was something more valuable: a measurement system that made the question answerable. The VP of Engineering can now point to verified data showing a 23% increase in PRs merged and an 18% reduction in cycle time. The CFO can now point to a unit economic model showing a 3.2x return on investment after right-sizing. The board can now see AI tooling evaluated with the same rigor as infrastructure spend, headcount, or any other engineering investment.
The direct savings from right-sizing — $4,560/year from reclaiming 20 unused seats — are modest. That was never the point. The point was building the connective tissue between spend and output that makes AI tooling investments defensible, optimizable, and scalable. When the company evaluates its next AI tool purchase, it will not be starting from "the engineers say it's great." It will be starting from "here's our measurement framework — let's apply it."
We do not claim AI tools are worth it or not worth it. We show you the math and let you decide. In this case, the math said Copilot was a strong investment — once you removed the 20 seats that were generating zero value. The VP of Engineering was right about the tool. The CFO was right about needing proof. Both of them got what they needed: tagged, traceable, verifiable numbers that ended the debate and started the optimization.
Copilot was the first AI tool evaluated with this methodology, but it will not be the last. The company uses AI-assisted code review, AI-powered testing tools, and AI documentation generators across the engineering organization. Each one is a line item with no unit economics attached. The measurement framework built for Copilot is now being extended to create a comprehensive AI tooling ROI dashboard — one number per tool, one cost per output, one confidence tag per claim.
We connect your AI tool licensing data to your actual development output. Not surveys. Not sentiment. Unit economics with confidence tags on every number. The math your board meeting has been missing.
Start the Conversation →Disclaimer: This use case is based on a composite engagement profile using the Coriven Method. The company described is a representative profile, not a specific client. All findings reflect the methodology Coriven applies to real engagements. Green numbers are verified from source data. Indigo numbers are calculated using defined methodology. Gold numbers are estimated from baseline data and implementation modeling. 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.