A Contract Review Agent produces a monthly labor value of $5,958 by saving 108 hours of staff time. After subtracting the $1,500 monthly platform cost, the net value of adoption is $4,458 per month. This assumes a loaded staff cost of $55 per hour and 650 successful outcomes from 1,000 monthly interactions.
Measuring the true cost of an agent requires looking past the platform fee to account for human review and rework. In this model, every accepted outcome requires two minutes of human oversight, while 35% of all interactions are escalated to a person entirely. Successful outcomes only occur when the agent saves 12 minutes of staff time, resulting in a net cost of $2.31 per outcome.
Agent ROI worked example
Worked example for Contract Review 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.
What determines the cost per successful outcome?
The cost per outcome is calculated by dividing the total platform spend by the number of successful results, rather than total interactions. While the agent handles 1,000 interactions, the 35% escalation rate means only 650 outcomes are considered successful. Efficient agents maintain a low cost per outcome by maximizing the success rate and minimizing the need for human intervention.
How does human review affect the final ROI?
ROI is based on net time saved, which subtracts human review time from the gross staff minutes saved. Even when an agent outcome is accepted, the two minutes spent on human review reduces the effective time saved per contract. Tracking this review time is essential for a realistic value model, as an agent that requires excessive rework can quickly erode the labor value of the time it saved.
NetLift measures AI adoption by comparing actual time spent on work against objective historical baselines. We focus on evidence quality, grading data from simple estimates up to verified outcomes. By applying a deterministic value model, NetLift helps leaders decide whether to expand or improve a deployment based on realized labor value rather than speculative productivity claims.