AI has become the decision layer of the enterprise, shaping pricing, operations, risk, and customer outcomes. But unlike traditional systems, AI is probabilistic, dynamic, and often opaque. As organizations scale AI, executives face a critical challenge: how to balance innovation with control, cost, and trust. This keynote reframes AI governance and observability as core executive imperatives - not technical afterthoughts. Drawing on real-world enterprise examples, it highlights common failure patterns such as shadow AI, model drift, and fragmented ownership, and introduces a practical governance framework spanning policy, control, observability, and continuous learning. Key takeaways: → Govern AI as a decision-making capability not just technology → Establish observability to measure performance, cost, and risk in real time → Avoid scaling pitfalls through clear ownership, controls, and accountability ap→Organizations that lead will not just build AI they will govern, observe, and continuously improve it.