Generative Artificial Intelligence (GenAI) is reshaping multiple industries by enabling autonomous generation of diverse content types including text, images, and videos. However, each industry needs to determine whether these new AI models are truly value-adding beyond the hype. Through a systematic analysis of academic literature, we identify and categorize potential use cases of GenAI in automotive process and product innovation, such as R&D processes and in-vehicle software. Further, we discuss the degree of innovation of these applications from incremental to radical. Our results show that in product development, GenAI improves customer review analysis, vehicle design, sound design, and crash test simulations. Software development sees enhanced code generation and data augmentation for autonomous driving, battery management and in-vehicle security. Manufacturing benefits from better quality control by improved defect detection, and operational planning. GenAI also advances real-time in-vehicle applications through sensor data enhancement, improved anomaly detection, and additional security measures. Incremental innovations largely relate to improved training data augmentation for autonomous driving and manufacturing software, while radical innovations feature vehicle co-pilot systems based on large language models (LLM), enhanced safety and increased technology acceptance due to explainable autonomous driving. Lastly, the novel GenAI models offer opportunities for new players to enter the field, for example in the field of training or sensor data improvement and applications built upon LLM for improved human-vehicle and human-robot interaction. This paper provides structured insights into the innovation potential of GenAI, highlighting current applications and identifying opportunities for future research. This study aims to bridge the existing gap in academic knowledge by using a replicable approach based on high-quality publications, thereby offering valuable perspectives for both researchers and practitioners in the automotive field.