Measure the workflow, not the model. ROI comes from time saved, error reduction, throughput, and faster decisions, tied to business outcomes.
Define one primary workflow metric
Examples: resolution time, cycle time, approval time, tickets per agent, conversion rate, time-to-quote.
Avoid "model accuracy" as the only metric. It rarely maps to business value directly. A model that's 92% accurate but saves 40 hours per week is more valuable than one that's 97% accurate but sits unused.
Track adoption and override rate
If humans override outputs frequently, fix prompts, data, or routing before scaling. Override rate is the most honest signal you have. High overrides don't mean the AI is broken. They mean the trust bridge isn't built yet.
Prove value in 30–60 days
A good MVP should show leading indicators within 2 sprints. If not, the use case selection is wrong, not the technology.
Related reading:
- What mistakes do companies make in their first AI build?
- How do you move from AI experimentation to adoption?
Frequently asked questions
Is cost saving the only ROI?
No. Revenue lift, risk reduction, and speed-to-decision are often more valuable than headcount reduction.
How do we compare tools?
Use the same workflow benchmark and test with real examples. Don't compare demos.