AI ROI
Analysis

We define which AI initiatives the analysis covers and what decision it must support — fund, defer, or stop. Together with your stakeholders we agree the benefit hypotheses, the baselines they will be measured against, and the time horizon of the case.

We build a total-cost-of-ownership model covering data preparation, development, licensing, infrastructure, inference at production volumes, retraining cycles, and the operating team. Build-versus-buy alternatives are costed side by side so the comparison is honest.

Each claimed benefit — hours saved, conversion uplift, error reduction, churn avoided — is tied to a measurable baseline from your own data. Where a baseline does not exist yet, we define how to establish one before money is committed.

We stress-test the case against the variables that actually move it: adoption rates, model accuracy, usage volumes, and token or compute prices. The output is a range of outcomes with explicit best, expected, and worst cases rather than a single seductive number.

Costs, benefits, and risks are consolidated into payback period, NPV, and break-even calculations per use case. Initiatives are ranked so leadership sees not just whether each one pays off, but in which order to fund them.

We present the findings in a decision-focused session with the financial model handed over, not just a PDF. Your team keeps a working workbook it can update as assumptions change — turning a one-off analysis into a reusable investment tool.