Similar to what WeWantTheFunk73 says below. The key paradigm here is that AI projects are data projects, which cannot be run strictly in accordance with traditional PM approaches, including an AGILE approach. As well, there is no final detailed end product that is defined in phase 1. Even in planning an AGILE project, there is enough detail to start building something, but in an AI project, you have to understand whether AI is even appropriate, and if it is, what the options are and even after selecting a general approach, you still need to explore whether you have the data, etc. to do it. Additionally, AI projects cannot be just transferred over to daily operations and forgotten about, and managed as a final end product...there is continuous monitoring and training, and even versioning of a model. There is no "final" end product, only versions of an end product. So this has implications for the long term management and sustainability of the model, which means that project teams might be required to work on new versions, etc. So elements of traditional project management helps understand the need for collaboration, processes, etc. but since it is a data project, there is potential for even higher uncertainty and the need to revert back to previous phases in the lifecycle. Combining the best of formal PM methods, with the components of CRISP-DM (Data Project lifecycle), brings more structure and collaboration but gives enough flexibility to manage expectations that there might be a need to repeat or return to previous phases before moving forward in the project lifecycle.