Enterprise AI projects fail at rates of 80-95% for the same organizational reasons ML projects failed a decade ago: no clear path to production, governance as an afterthought, and velocity mistaken for strategy. Leaders who built MLOps maturity have a structural advantage because the same disciplines — defined production paths, embedded governance, reproducibility, and platform-enforced standards — apply directly to AI-assisted application development. The post outlines three core failure modes (pilots without production paths, velocity without strategy, and late-stage governance), draws a direct parallel between today's vibe coding culture and the research notebook era that gave rise to MLOps, and provides a diagnostic checklist for assessing organizational readiness. The key shift for leaders is recognizing that when AI can generate prototypes in hours, the bottleneck moves upstream to strategy, prioritization, and governance — not execution speed.
