A bank's fraud-detection AI pilot ran informally, with the data science team manually reviewing flagged transactions each morning. As the bank moves this initiative to production-grade status enterprise-wide, what governance and operational requirement is most critical to add?

Production-grade operation at enterprise volume requires a defined escalation process and a clearly accountable owner, since the informal morning reviews a small data science team could handle during the pilot won't hold up once flagged transactions scale across the whole bank. Requiring every flagged transaction to be personally reviewed by the original data science team, regardless of volume, keeps the pilot's informal review process in place but ties it to a fixed team's limited capacity, which doesn't scale and isn't a real operational requirement for production use. Removing human review entirely because the pilot already demonstrated the model's accuracy strips out oversight at the exact moment volume and stakes increase, which is risky in fraud detection, where an error has direct financial and regulatory consequences. Treating a one-time compliance sign-off obtained before the pilot began as sufficient, with no further review needed, mistakes governance for a single event completed at the start, when production-grade operation actually requires ongoing review as transaction volume and patterns change. The operating principle: the experimental-to-production transition adds durable, scalable governance — defined ownership and escalation — rather than preserving an informal team-dependent process, eliminating oversight, or treating compliance as a one-time checkbox. Operational check: is there a named owner accountable for acting on flags today, regardless of how many come in?