Measuring the ROI of AI automation

AI projects are easy to start and hard to justify later. The fix is to agree on how you will measure success before anything is built, then track the same numbers after launch.

Set a baseline first

Measure how the work happens today: volume, time per task, error rate and the people involved. A two-week sample is usually enough to give you an honest starting point.

Count the full cost

Include build time, model and hosting costs, monitoring and the hours your team spends reviewing output. ROI that ignores maintenance looks great for one quarter and then quietly disappears.

• Build and integration effort

• Model, API and hosting spend

• Human review and exception handling

• Monitoring, retraining and support

Track value in three buckets

Most of the return lands in hours saved, errors avoided and revenue the team can now pursue. Report all three every month, next to the cost, so the decision to scale is obvious.

If a project cannot show its number after ninety days, it should be fixed, narrowed or switched off.