Despite all these AI project differences, the goals of these efforts are the same: the application of cognitive technologies that leverage the emerging capabilities of machine learning and associated approaches to meet a range of important needs. Yet, existing methodologies that are either application development-centric or enterprise architecture focused or rooted in hardware or software development approaches face significant challenges when faced with the unique lifecycle requirements of AI projects. This is because what drives AI and ML projects is not programmatic code, but rather the data from which learning must be derived. What is needed is a project management methodology that takes into account the various data-centric needs of AI while also keeping in mind the application-focused uses of the models and other artifacts produced during an AI lifecycle. Do we need to create a new methodology out of whole cloth or can we simply revise existing approaches in a way that makes them AI-relevant?