The biggest shift from prototype to production is changing the success criteria from “can the model do it?” to “can the system fail safely when the model gets it wrong?” Evaluation, permissions, integration boundaries, and exception handling all become part of the product rather than supporting infrastructure.

In production AI work at IT Path Solutions, the human escalation path is especially important because autonomy should be earned per workflow, not granted to the whole system. A useful maturity signal is not just accuracy, but knowing exactly which actions remain supervised, what evidence triggers escalation, and whether those boundaries still hold after a model, API, or business-rule change.