Why AI Pilots Fail in Quarter Two and the Governance Fix
Why promising AI pilots often fail after the demo and how to install enough governance to operationalize them.
Quarter one is for excitement. Quarter two is where AI pilots reveal whether they have an operating system.
The failure pattern
The demo worked. The sponsor was interested. Then production exposed the missing pieces: unclear owner, incomplete data, no evaluation, weak escalation, and no budget decision.
The governance fix
Governance should answer operational questions, not slow everything down:
- What workflow does this enter?
- Who owns the outcome?
- What data is required?
- What risk boundary applies?
- What evidence moves this from pilot to production?
Pilot-to-production gate
Before a pilot expands, require owner, metric, eval set, escalation path, user feedback, and stop criteria.
One action this week
Review every active pilot and classify it as prove, operationalize, pause, or stop. Anything without an owner and metric should not expand.
If you want an outside operator view of your own workflows, agents, owners, risks, and 90-day plan, view diagnostic details.