Start with the unit cost
Forrester puts the cost of a resolved support ticket at roughly $0.46 for an AI agent and $4.18 for a human. That is close to a ninefold difference on the single most repeated event in a support operation.
The point is not to remove people. It is to stop spending human attention on the tier-one questions that a well-built agent resolves end to end, so your people handle the cases that actually need judgment.
Why "well-built" is the whole sentence
A cheap ticket is only cheap if it is actually resolved. An agent that deflects without resolving just moves the cost downstream, into a frustrated follow-up and a second contact. That is worse than doing nothing.
Well-built means the agent is grounded in your real knowledge base, wired into your help desk, and measured against a resolution baseline, not a deflection rate. It means evaluation frameworks and human checkpoints so quality does not drift. This is engineering, and it is where most automation efforts quietly fail.
The math that matters to you
Take a team resolving 10,000 tier-one tickets a month. Shifting even 60 percent of those to a reliable agent is the difference between a large human cost and a rounding error, every month, forever. The build pays for itself fast, then keeps paying.
That is why we scope every agent against your own numbers. The average is a headline. Your baseline is the business case.
The takeaway
The unit economics of AI support are not subtle. The risk is never the model, it is shipping an agent that deflects instead of resolves. Build for resolution, measure it, and the savings are not a projection, they are a monthly line item.
Dartnox
We make businesses AI-native. Not AI-curious.