According to @DeepLearningAI, most AI teams stall not because of poor models but due to misaligned success criteria, where teams simultaneously chase accuracy, recall, latency, and edge cases, leading to paralysis; high-performing teams instead select a single north-star metric and align data, evaluation, and rollout around it (as reported in the tweet by DeepLearning.AI on Feb 14, 2026). According to DeepLearning.AI, this focus enables faster iteration cycles, clearer trade-offs, and reduced scope creep in MLOps, improving time-to-value for production AI systems. As reported by DeepLearning.AI, teams can operationalize this by setting business-tied metrics (for example, task success rate for customer support copilots), enforcing metric gates in CI for model releases, and separating exploratory evaluation from production KPIs to unlock measurable gains in deployment velocity and reliability.