In the context of AI systems, transitioning from impressive demonstrations to reliable production environments requires a tailored production readiness framework that addresses AI's unique failure modes, such as model drift, hallucinations, and token cost surges. Robust architecture, including industrial-grade data pipelines, modular software, and comprehensive security measures, is essential to prevent crises and maintain system integrity. Load and stress testing, failure scenario planning, and efficient rollback procedures are crucial for understanding system limits and ensuring rapid recovery. Monitoring and observability provide proactive insights to prevent issues before customers experience them, while operational capacity planning translates technical needs into strategic business discussions. Risk mitigation involves addressing technical, regulatory, ethical, reputational, and operational threats, shifting focus from component reliability to overall enterprise resilience. Continuous post-mortems and reliability improvements transform incidents into learning opportunities, fostering a culture of prevention and prediction. Galileo's platform exemplifies how AI governance can be implemented, offering automated quality control, real-time protections, and human-in-the-loop optimizations to ensure trustworthy AI performance at scale.