How do I measure AI agent ROI? (And why most numbers fail)
AI agent ROI is value created minus total cost of ownership, divided by that cost. The arithmetic is trivial. What breaks is the input side: no baseline captured before the agent ran, a cost figure that counts only the model bill, and a value claim tied to nothing finance already reports.
What the number actually contains
Value has to be one outcome the business already reports: cost per invoice processed, hours reclaimed at a loaded rate, speed to lead, tickets closed without a person. Cost covers inference, integration, data cleanup, oversight time, and the retries an agent burns before it produces an answer someone accepts. Agent cost scales per task rather than per seat, so growing volume raises the bill instead of spreading it thinner.
Why most agent ROI numbers fail review
McKinsey's 2026 survey of 1,719 leaders found 37% attribute any EBIT impact to AI, flat against 2025, and only 6% attribute 5% or more of EBIT. Gartner expects over 40% of agentic projects to be killed by the end of 2027, blaming escalating cost and unclear business value. Two patterns cause most of that. Nobody captured a baseline before the agent ran, so the after number has nothing to sit against. And the agent was scoped as an assistant to a person, which parks it several steps away from anything finance counts. Engineers on Hacker News describe the same trap: copilot-shaped features are hardest to justify because they sit furthest from the base metrics.
What makes a number defensible
Give one agent a whole work unit, measure that unit's cost and cycle time for four weeks before launch, then measure the same unit after. Set the kill number in the same meeting. Deployments without one run indefinitely, because nobody can prove they failed. If the outcome does not already appear in a monthly report, do not fund it past the pilot.
Last updated: Aug 14, 2026