Most AI pilots fail for operational reasons, not model reasons. What separates the ones that stay in production.
The failure mode is rarely model quality. It is that the automation sits outside the workflow, so someone has to remember to use it, and nobody owns the output when it is wrong.
Automation that lasts is triggered by a business event, writes back into the system of record, and has a defined escalation path to a named human. If any of those three are missing, the pilot quietly ends.
Grounding matters more than model choice. An assistant answering customer enquiries needs your pricing rules, availability, and policies as its source, with citations back to them, so staff can verify an answer in seconds.
Measure it operationally: first-response time, share of enquiries resolved without escalation, and the error rate that reaches a customer. Those three numbers decide whether automation stays.