The visible cost is the easy one
Per-call model pricing is published, predictable, and usually modest at business volumes. Budgeting from it alone is why so many of these projects come in over — the real costs are staff time and they land in someone else’s budget line.
1. Review time, which never reaches zero
Someone checks the output. That cost falls as trust grows but never disappears, and if reviewing takes nearly as long as doing the work then you automated the wrong step.
2. Knowledge maintenance
Business rules change, and each change has to reach the system. Budget recurring hours for whoever owns the knowledge; without them the thing decays into confidently repeating last quarter’s policy.
The model is rented. The maintenance is permanent.
3. The escalation path
Every escalation consumes a human, and escalations concentrate the hard cases. The average handling time of what reaches your team goes up even as volume goes down — a real cost that looks like a productivity regression if nobody expected it.
4. Drift you don’t notice
Context grows, prompts accumulate, retries stack. The same quiet inflation that makes AI features slower and dearer after launch applies here, and only shows up if someone tracks cost per successful outcome rather than per call.
Compare against the honest alternative
Set the total against what you would otherwise do — hire, outsource, queue it, or leave it undone. AI employees usually win on bounded, high-volume, verifiable work and lose on judgment-heavy work with low volume, and the arithmetic says which is which before you commit.