With the explosive growth of AI and ML-driven processes, companies are under more pressure than ever to drive innovation. For many, adding a Data Science capability into their organization is the easy part. Deploying models into a large, complex enterprise that is operating at scale is new, unchartered territory and quickly becoming the technology challenge of this decade. This article takes an in-depth look at the primary organizational barriers that have not only hindered success but often prevented organizations from moving beyond just experimentation. These obstacles include challenges with fragmented and siloed enterprise data, rigid legacy systems that cannot easily be infused with AI processes, and insufficient skills needed for data science, engineering and the emerging field of AI-ops. Operationalizing AI is hard, especially at the fast pace at which the business operates today. This paper uses real-world examples to guide a discussion around each of these hurdles and will equip industry leaders with a better understanding of how to overcome the challenges they will face as they navigate their data and AI journey.