Traditional software product management was built around deterministic systems where precise specifications reliably produced predictable outcomes, but AI systems are probabilistic, requiring PMs to define success through evaluations, confidence thresholds, and data quality rather than binary acceptance criteria. As AI coding agents rapidly operationalize ambiguous requirements at scale, organizations that fail to improve upstream specification quality, evaluation design, and data governance will continue to see weak returns from AI investments despite advances in coding tools and models.