A meeting preparation agent delivers a net monthly value of $4,458 by handling 1,000 interactions with a 65% success rate. This performance translates to 108 hours of net staff time saved, assuming a loaded staff cost of $55 per hour and a monthly platform cost of $1,500.
To calculate the true return on investment, organizations must look beyond license costs. The model accounts for the 2 minutes of human review required for every successful outcome and the 35% of cases that are escalated to a person. This results in a cost of $2.31 per successful outcome.
Agent ROI worked example
Worked example for Meeting Preparation Agent using stated NetLift assumptions:
| Input (stated assumption) |
Value |
| Interactions handled per month |
1,000 |
| Accepted / successful outcomes |
65% |
| Escalated to a person |
35% |
| Staff minutes saved per accepted outcome |
12 min |
| Human review per accepted outcome |
2 min |
| Loaded staff cost |
$55/hour |
| Agent platform cost per month |
$1,500 (stated assumption) |
| Computed result |
Value |
| Successful outcomes per month |
650 |
| Cost per successful outcome |
$2.31 |
| Net staff time saved |
108 h / month |
| Labour value of time saved |
$5,958 / month |
| Current net value |
$4,458 / month |
Escalation, review and rework are part of the true cost of an AI agent. Track them — an agent that resolves fewer tickets with less rework can beat one that closes more tickets badly.
Why measure cost per successful outcome?
Measuring cost per successful outcome ensures that the platform cost and human oversight time are balanced against actual results. In this model, 350 interactions out of 1,000 are escalated to staff. By focusing on the 650 successful outcomes, leaders can see that the $1,500 platform spend plus human review time effectively costs $2.31 per completed task.
How does human review impact net value?
Review and rework are essential components of the true cost of an AI agent. Even when an agent succeeds, this model tracks 2 minutes of human review per outcome. If an agent closes tasks poorly, the time required for rework can quickly erase the 12 minutes of saved staff time per task. Tracking these metrics helps determine if a deployment should be expanded or if it requires improvement to maintain a positive net value.
NetLift calculates value by comparing tracked work against a historical baseline, grading the Evidence Quality from simple estimates to verified data. By subtracting platform costs and human oversight from the labor value of realized time saved, NetLift provides a deterministic decision state—such as Expand, Continue, or Review—for every agent deployment. This ensures that AI spend is measured by work and value rather than individual productivity or invasive surveillance.