AI Pilot Purgatory Is an Orchestration Failure
Most AI initiatives don’t die in the model. They die in the seams between teams.
I recently helped a client push two AI initiatives into live testing. The technology worked. What almost didn’t was the coordination — and the pattern is instructive because it applies broadly.
Getting an AI agent from prototype to production requires four parties in tight lockstep: domain experts making decisions on critical functionality, IT enabling data access and services, AI developers tuning agent accuracy and usability, and program management keeping everything synchronized. Remove any one from the cadence and progress fragments — not from a technical failure, but a coordination one.
What kept these two initiatives moving:
- Weekly status focused on achievement, not problem-solving. Every party in the room, every week. Keep it tight; surface blockers and route them, don’t analyze them.
- Active interim follow-up. Four interdependent teams can’t afford to wait until next week. Gaps compound fast.
- Disciplined scope. Generative AI invites endless “what if” conversations. Aspirations go in the backlog. Phase deliverables stay fixed.
- Time-bound accountability. Every deliverable has an owner and a date. No ambiguity about who owes what by when.
None of this is new. It is the same discipline that gets any cross-functional initiative across the line. But AI amplifies the coordination cost because the work spans domains that rarely share a natural operating rhythm — domain experts, IT, and AI developers operate on fundamentally different cadences and modes. That gap doesn’t close on its own.
The organizations shipping AI into production aren’t necessarily the ones with the best models. They’re the ones that treat orchestration as a core competency.