Pick one metric per use case, before you start
The discipline that separates real programs from vibes: every AI use case gets one primary metric chosen in advance. Proposals: hours from request to sent. Support: first-response time and tickets resolved without escalation. Bookkeeping: minutes per invoice. Marketing: pieces shipped per week at stable quality. If you cannot name the metric, you are not ready to buy the tool.
Measure the before, honestly
Spend one boring week measuring the current state before rollout. Without a baseline, every later claim is an estimate, and CFOs can smell estimates. A simple time log per task for five working days is usually enough.
The ROI math
Value = hours saved per month times loaded hourly cost, plus any revenue effect you can defend (faster quotes win more deals; measure win rate if you claim it). Cost = subscriptions plus setup time plus ongoing review time. Be conservative on value and complete on cost; a modest number that survives scrutiny beats an impressive one that does not.
Watch quality, not just speed
Track one quality indicator next to every speed metric: error rate, customer satisfaction, rework percentage. Speed gains that degrade quality are not gains; catching that early keeps trust in the program.
FAQ
How soon should ROI be visible?
For assistant-and-workflow use cases: within one to two months. Custom builds take longer; that is why they come after proven basics.
What return is realistic?
Common SMB results are several saved hours per user per week on text-heavy roles. Whether that is enough depends on your loaded costs, which is exactly why you calculate.
What if the numbers are disappointing?
First check adoption: most weak ROI is usage weakness. Fix training and workflow fit before concluding the tool failed.