Why Most AI Projects Fail Before They Ship

Most AI initiatives fail in the handoff between prototype and production. Teams optimize for model novelty instead of deployment constraints, then discover late that reliability, observability, and data quality were the real blockers.

Successful teams start with operational constraints first: latency budgets, cost ceilings, fallback behavior, and ownership boundaries. They treat AI as one component in a larger system instead of the whole product.

The practical playbook is straightforward: define business metrics before training, ship a thin vertical slice early, add strong monitoring, and iterate in tight loops with domain stakeholders. Execution discipline beats model complexity.