Building lasting, scalable AI systems, not just those that work once, is crucial for companies. Velocity without discipline in AI development leads to liabilities, emphasizing continuous functionality beyond initial success. Modern AI conversational, reasoning, and autonomous capabilities demand high reliability and governance. The author's company, having extensive experience in applying AI across various traveler journeys, developed ML and AI principles to guide their system development. These principles aim to ensure business value, scalability, and safety, defining how systems are measured, designed, governed, and operated. The real challenge lies in translating these principles into practical operating mechanisms, such as mandatory checks before launching agentic AI features. Automating these requirements into the software development lifecycle is essential for embedding them in AI system design and approval. Focusing on business outcomes, optimizing for return on cost, and justifying complexity against baselines are key to measuring what truly matters. Building scalable systems involves shared foundations, treating data as a first-class product, and prioritizing generality over local optimization. Clear ownership, adherence to governance standards, and proportional risk management are vital for building trust. Finally, designing for fairness, privacy, transparency, safe rollout, and continuous monitoring ensures responsible and adaptable AI.