Edge Artificial Intelligence (AI) is poised to usher in a new era of innovations in automotive and mobility. In concert with the transition towards software-defined vehicle (SDV) architectures, the application of in-vehicle edge AI has the potential to extend well beyond ADAS and AV. Applications such as adaptive energy management, real-time powertrain calibration, predictive diagnostics, and tailored user experiences. By moving AI model execution right into edge, i.e. the vehicle, automakers can significantly reduce data transmission and processing costs, ensure privacy of user data, and ensure timely decision-making, even when connectivity is limited. However, achieving such use of edge AI will require essential cloud and in-vehicle infrastructure, such as automotive-specific MLOps toolchains, along with the proper SDV infrastructure. Elements such as flexible compute environments, deterministic and high-speed networks, seamless access to vehicle-wide data and control functions. This paper outlines examples of edge AI use cases beyond ADAS and AD, the challenges current vehicle electrical/electronic (E/E) architectures pose, and the limitations of general-purpose MLOps tools. It goes on to discuss how the shift to Software-Defined Vehicle (SDV) architectures and MLOps toolchains that are needed to overcome the challenges.