AI ROI measurement, from spend to outcome.
A baseline
How long the work takes and what it costs before AI, so later numbers mean something.
Adoption and usage
Who uses which tools and skills, by team, and whether use sticks after the first month.
Spend
Model and seat costs by team, tool and skill, so you know what each one costs to run.
Outcomes
The business numbers the work moves, such as cycle time, tickets resolved or deals worked, measured against the baseline.
Quality
Whether the output holds up: evals for AI features and, for coding agents, how often changes are reverted.
An AI ROI measurement framework you can run monthly.
Mostly data you already have.
AI tools already record usage and sessions, and your business systems already hold the outcomes. The work is joining them, choosing a few measures that matter and making the report easy to read.
Before you ask.
How do you measure AI ROI?
Set a baseline for the work, track usage and spend by team and skill, measure the outcome the work affects, and compare the value against the full cost, including upkeep.
Which AI adoption metrics matter?
Active use by team, use that sticks past the first month, and the outcome each use case is meant to move. Seat counts on their own say little.
Do we need to buy a new analytics tool?
Usually not at first. Most of the data is in your AI tools and business systems already. A tool can come later, once you know which measures you need.
How soon can we see results?
The baseline comes first. After that, a few weeks of usage data is usually enough to see which uses are worth keeping.
Get your free AI audit.
Twenty minutes, and we are honest about where AI helps your business and where it doesn't. If there's nothing worth building yet, we'll tell you.