Summary
- Accuracy scores do not define who is accountable when a model’s output changes a customer, payment or system.
- Safe adoption starts with bounded permissions, traceable evidence, human review and rollback.
An AI tool combines a model with data, an interface and often access to other systems. Its practical risk depends less on the label than on what it is allowed to read, decide and change. Teams should separate assistance from execution, test representative failures and record the evidence behind consequential outputs. The next useful proof is a controlled trial showing false-positive rates, escalation routes and a reliable undo path. Automation deserves broader authority only after those boundaries work under pressure.
