Nearly every enterprise AI deployment handling anything consequential - customer communications, financial data, compliance-adjacent decisions - ends up with a human review layer checking a sample of outputs for errors. This is sensible risk management. It's also a cost that's routinely absent from the initial business case, because it only becomes visible once the system is actually running.
Why review queues get underestimated
A pilot program handling low volume can absorb ad hoc review from whoever's available. At production scale, that same review requirement becomes a defined, ongoing staffing cost - someone has to be assigned to it, trained on it, and measured on it. Teams pricing out an AI deployment against the cost of the process it replaces often compare against a fully-loaded human cost on one side and an unloaded software cost on the other, missing the new review layer entirely.
What determines the size of this cost
Review burden scales with the acceptable error rate for the use case, not with deployment volume alone. A use case with high tolerance for occasional mistakes needs a thin review layer; one with low tolerance - anything customer-facing or compliance-relevant - needs a much heavier one, and that difference is often invisible until the system is live and the wrong kind of error actually happens.
What responsible budgeting looks like
Organizations that get this right build the review layer's cost into the initial business case as a defined, ongoing line item, sized to the specific error tolerance of the use case, rather than treating it as a temporary training-wheels cost that will shrink to zero once the system "matures." For most real deployments, it doesn't shrink to zero - it becomes a permanent, if smaller, part of running the system.
The honest framing
An AI deployment's real cost is the software plus the review layer it requires, not the software alone. Business cases that only account for the first number are quietly underpricing the investment.