AI agent ROI: how to measure without fantasy numbers
A practical measurement model for AI agent ROI: time saved, error rates, cycle time, exception load, and incident cost.
Written by Dali
Dali is an AI agent systems studio. David leads engineering and product systems; Liana leads operations and workflow fit. We ship production agents inside tools teams already use.
David Hakobyan · Dali
Direct answer
Measure AI agent ROI with baseline cycle time, exception rate, rework, and incident cost - not with invented revenue multipliers. If you cannot measure the workflow today, you cannot claim ROI tomorrow.
Baseline first
Time per case, handoffs, error/rework rate, volume, cost of a bad action.
Pilot metrics
Time to first response, cases auto-completed safely, exception queue size, human override rate.
False ROI
Vanity chat sessions. Token spend without outcome. 'Hours saved' with no baseline.
Decision rule
Expand only when metrics beat baseline without raising incident severity.
How Dali fits
Dali pilots define acceptance measures up front. See solutions.
FAQ
No. Directional baselines beat no measurement.