Every AI investment now has a CFO in the room.
Know your numbers before the project starts, build the business case from a real baseline, and calculate the exact return once it is live.
AI spend used to be an experiment. Now it is a capital decision.
A year or two ago, AI pilots were approved on promise. Today the CFO sits in on nearly every AI investment decision and expects a baseline, a business case and proof of return. Most companies cannot produce all three.
- Step 1
Baseline and benchmark
Measure throughput, cycle time and cost before anything is built, and benchmark them against peers so the starting point is credible.
- Step 2
Build the business case
Tie the project to one business KPI, size the return from the baseline, and agree the kill criteria before the pilot starts.
- Step 3
Measure in production
Track adoption, usage and fluency by team, and every dollar of license, token and agent spend, against the work AI actually completes.
- Step 4
Prove the ROI
Calculate the exact return, cost per unit of AI work and where freed capacity was redeployed, validated in a form finance signs off on.
What you walk away with.
Numbers your CFO, your board and your sponsor can all work from.
A board-ready business case
Built from your own baseline and peer benchmarks, not vendor claims.
An AI value scorecard
Adoption, spend and output in one view, by team, tool and agent.
A calculated ROI
The return on each investment, and which ones to scale, fix or stop.
Benchmarks behind every number.
AI to ROI is supported by Ray Rike, Expert Accelerator, founder of Benchmarkit and host of the AI to ROI podcast, where enterprise and AI leaders share how they turn AI spend into measurable results. Benchmarking is where every engagement starts.
AI to ROI questions
What is AI to ROI?
An AGS offering that ties every AI investment to a measured baseline, a business case and a calculated return. We benchmark where you are before you build, track adoption and spend once AI is live, and report the ROI in terms your CFO and board accept.
Why measure before an AI project starts?
Without a baseline there is nothing to compare against. Documenting throughput, cycle time and cost up front, and benchmarking them against peers, is what makes the business case credible and the ROI provable later.
Why is the CFO now involved in AI decisions?
A year or two ago most AI spend was approved as experimentation. Today AI budgets are large enough, and pilots common enough, that finance expects the same discipline as any other capital decision: a baseline, a business case and proof of return.
Walk into the next AI decision with the numbers.
Start with a baseline. Finish with a return you can prove.