A leading payment bank deployed automation bots to process hundreds of depository transactions monthly. Standard cases ran cleanly. Business logic applied correctly. The system identified incorrect entries, made routing decisions, and generated confirmation letters at volume with exactly the accuracy the bank needed. Then a transaction arrived that fell just outside the approved threshold. The customer profile was inconsistent with available history. Two policies appeared to conflict. The evidence was present but incomplete. The model was technically capable of generating a recommendation. Whether it was equipped to handle what it was seeing was a different question entirely. The system knew how to process the normal path. Nobody had designed the exception path with the same precision. This is the gap that appears repeatedly across enterprise AI programs as systems move from advisory to operational. Enterprise AI is genuinely capable of handling defined patterns at scale and spee...