A Quality Assurance agent delivers ROI by automating the initial review of interactions, saving 108 net hours of staff time per month at an assumed volume of 1,000 interactions. With a 65% success rate and a $1,500 monthly platform cost, the net labor value created is $4,458 per month.
Operational impact depends on the true cost of the agent, which includes the 35% of cases escalated to humans and the 2 minutes of human oversight required for every accepted outcome. Measuring value based on successful resolutions rather than total attempts provides a realistic view of the agent's contribution to the bottom line.
Agent ROI worked example
Worked example for Quality Assurance 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 is the true cost of an AI-driven outcome?
Calculating ROI for a QA agent requires looking beyond the subscription fee. The true cost per successful outcome includes the platform license and the cost of human review for accepted work. When accounting for these factors, the cost per success in this model is $2.31.
Why track escalations and human review?
Escalation, review, and rework are part of the total cost of ownership. If an agent closes tickets quickly but poorly, requiring significant human intervention, the net value drops. By measuring the 12 minutes of potential time saved against the 2 minutes of required human review, leadership can determine if the agent is actually reducing the department's workload or simply shifting it.
NetLift calculates the delta between your historical baseline and the time spent with AI to establish a deterministic value. By grading Evidence Quality from Estimate Only to Verified, we help leaders decide whether to Expand, Continue, or Improve an agent deployment based on realized labor value rather than optimistic projections.