GenAI pilots often falter not due to technological shortcomings but because organizations struggle to adapt AI into their processes, with 95% failing and only 5% succeeding, typically through external partnerships. These failures are attributed to poor integration with existing systems, lack of trust in AI outputs, unclear ownership, and tools that don't evolve with use. MIT's report reveals that early adopters gain a competitive edge through accumulated training data, while the rest face challenges such as agent sprawl and vendor lock-in. Successful AI implementation requires careful documentation, goal-setting, controlled automation, transparent autonomous workflows, and intelligent scaling using modular approaches. Continuous governance, monitoring, and iteration are vital for transitioning from experiments to enterprise-ready AI, with Dataiku's unified Ops strategy providing a framework for integrating diverse operational practices into a cohesive model. This approach emphasizes adaptability, resilience, and the ability to learn from failures, ensuring AI systems deliver consistent and meaningful business impact.