AI ROI Analysis and Business Case
A defensible financial model for an AI initiative, built on your numbers, with the assumptions exposed rather than hidden.
Most AI business cases fall apart in the same place: a benefit figure with no visible derivation, sitting next to a cost figure that counts the build and forgets the running. Finance teams have learned to discount both. The fix is not a bigger number, it is a model somebody can audit.
The cost side
We build total cost of ownership over three years across five categories. The ones people forget are the last two.
| Category | What goes in |
|---|---|
| Build | Engineering, data preparation, integration work, evaluation harness construction, security review. |
| Inference | Token or call volume at projected usage, modelled per model tier, with a sensitivity band for price movement. |
| Platform | Hosting, vector storage, observability tooling, secrets management, and any licence fees. |
| Maintenance | Retraining or re-evaluation cycles, prompt and pipeline updates as upstream models change, dependency upgrades. |
| Internal time | Review queues, human in the loop checks, exception handling, and the ongoing product ownership the system needs. |
Internal time is where optimistic cases usually break. A document system with a ninety percent automation rate still routes ten percent to a person, and if that person did not exist before, the saving is smaller than the slide claimed.
The benefit side
Every benefit is traced to a measurable quantity you already track, or one we define how to start tracking. We separate benefits into three tiers and label them clearly, because mixing them is how business cases lose credibility.
- Hard savings: cost that leaves the budget. Reduced vendor spend, avoided hiring, lower error remediation cost.
- Capacity release: hours freed. Real, but only becomes money if the hours are redeployed or headcount actually changes. We do not convert these to cash without an explicit decision from you.
- Risk and quality: fewer errors, faster response, better compliance position. Quantified where possible, described where not.
Sensitivity and break even
A single point estimate is not a business case. We model three scenarios and identify which assumption the outcome is most sensitive to, then state what would have to be true for the initiative to fail. Usually one or two variables dominate: adoption rate, automation rate, or inference volume. Those become the things you monitor after launch.
What you receive
- A working spreadsheet model with every assumption labelled, sourced and editable.
- Payback period, three year net position and internal rate of return under conservative, expected and optimistic scenarios.
- A sensitivity table showing which variables move the outcome most.
- A written business case suitable for finance and board review, with the methodology stated.
- A short list of the metrics to instrument at launch so the model can be checked against reality.
We hand over the model, not just the answer
You get the spreadsheet with formulas intact. If a model price drops or your volume forecast changes, you rerun it yourself in ten minutes. Business cases delivered as static PDFs stop being useful the moment an input moves.
Output
Cost model, benefit model, sensitivity analysis, written business case. Delivered in editable formats. Full IP transfers to you on final payment.
FAQ
What if we do not have baseline data for the current process?
That is common. We use a short measurement exercise, typically a one or two week sample, to establish a defensible baseline. A model built on a measured sample is far stronger than one built on somebody's estimate of how long a task takes.
Will you produce the number we need to get approval?
No. We produce the number the evidence supports. If it does not clear your hurdle rate we will say so, and usually we can identify what would have to change for it to. A business case that is engineered backwards from a target gets found out at the first post-implementation review.
How do you handle inference cost uncertainty?
With a band rather than a point. We model current published pricing, then run the scenario at higher and lower unit costs. For most enterprise use cases inference is a smaller line than people expect and internal time is larger, but that varies sharply with volume.
Can this be done for an initiative already in flight?
Yes, and it is often more useful then, because actual usage data replaces forecasts. We can also run it as a post-implementation review to check whether the original case held.
Often paired with
AI Strategy and Roadmap
A sequenced plan for what to build, in what order, at what cost, with the dependencies made explicit.
Read moreBuild vs Buy Advisory for AI
A structured comparison of building, buying or combining, costed over three years with switching risk priced in.
Read moreAI Use Case Discovery
A structured workshop that converts a long list of AI ideas into a ranked shortlist with feasibility and value scored.
Read moreIs this the right engagement?
Tell us what you are trying to decide. If a different service fits better, or if you do not need us at all, we will say so.