An HR Support Agent delivers value by resolving routine inquiries, resulting in a cost per successful outcome of $2.31. This figure is derived from the total platform cost and the human labor required to review the AI's work, ensuring quality isn't sacrificed for speed.
When handling 1,000 interactions per month with a 65% success rate, the agent saves a net of 108 hours of staff time. At a loaded staff cost of $55 per hour, the labor value of this saved time exceeds $5,900 monthly, providing a clear path to positive ROI.
Agent ROI worked example
Worked example for HR Support 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 HR agent interaction?
The total cost of an AI agent includes more than the platform subscription. To find the cost per successful outcome, you must account for the platform fee and the cost of human review for each accepted task. In this HR model, the platform costs $1,500 per month, and each successful outcome requires two minutes of human oversight, leading to the $2.31 unit cost.
How does escalation impact ROI?
Efficiency is measured by the percentage of outcomes accepted without needing full human intervention. With a 65% success rate, the agent resolves 650 tickets monthly while escalating 35% to staff. ROI calculations must only credit the agent for these successful outcomes, minus the time humans spend reviewing them, to maintain an accurate picture of labor value.
NetLift determines whether AI spend pays back by comparing realized time savings against a tracked baseline. We categorize evidence quality from 'Estimate Only' to 'Verified' to ensure leaders make decisions based on objective data rather than hype. By measuring the work rather than monitoring the employee, NetLift provides the financial clarity needed to decide whether to expand, improve, or stop an agent deployment.