A sales qualification agent achieves a cost per successful outcome of $2.31 when handling 1,000 interactions per month. This figure accounts for a 65% success rate and the $1,500 monthly platform cost, resulting in a monthly net value of $4,458 for the business.
To find the true return, you must subtract the cost of human review and rework from the gross labor value. In this model, while each success saves 12 minutes of staff time, it requires 2 minutes of human review, leading to a net saving of 108 hours per month at a loaded cost of $55 per hour.
Agent ROI worked example
Worked example for Sales Qualification 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 a successful outcome?
The cost of an AI agent extends beyond the monthly subscription. A successful outcome only occurs when a lead is qualified without requiring immediate human intervention for the primary task. With a 65% acceptance rate, 650 outcomes are achieved from 1,000 interactions. Factoring in the $1,500 platform fee, the direct cost per success is $2.31.
How does escalation affect ROI?
ROI is highly sensitive to the escalation rate. When 35% of interactions are escalated to a person, the agent's value is capped by the remaining human workload. NetLift methodology treats review and rework as part of the total cost of ownership. An agent that resolves fewer total interactions but requires less human rework often delivers a higher net value than one with higher volume but higher error rates.
NetLift measures the shift in work from human to agent by comparing realized time saved against a deterministic baseline. By grading Evidence Quality from 'Estimate Only' to 'Verified,' we move beyond self-reported productivity gains to hard financial data. This allows department leaders to decide whether to Expand, Continue, or Improve an agent deployment based on actual payback periods rather than vendor hype.