Before any AI automation project, we force a simple exercise: state the manual process being replaced in hours-per-week and error-rate-per-month. If a client can't quantify this, the project isn't ready to scope.
We then model three numbers — time saved, error reduction, and revenue unlocked by faster turnaround — and weigh them against build and maintenance cost over 18 months. Automation that saves 10 hours a week but requires constant babysitting is a net negative; automation that saves 3 hours but runs unattended for years is a clear win.
The projects with the best ROI are rarely the flashiest. Document classification, invoice extraction, and lead-routing automations consistently outperform more ambitious generative AI features in pure payback terms.
We report these numbers back to clients monthly post-launch, because an automation that isn't monitored tends to silently degrade as source data changes.