In recent years, artificial intelligence (AI) has evolved from a visionary concept into a foundational component of modern vehicle architectures. While initial applications were limited to rule-based systems and basic automation, today’s AI technologies enable data-driven, adaptive functionalities that fundamentally enhance safety, efficiency, and user experience. AI in the automotive sector spans far beyond the vehicle itself. It plays a growing role across the entire value chain – from internal processes and organizational decision-making to intelligent functions embedded in the vehicle.

Accordingly, the term “automotive AI” does not exclusively refer to large-scale language models (LLMs), but also includes compact, domain-specific models and edge AI modules (edge in this context onboard deployed AI modules) – for example, machine learning or deep learning-based optimization of comfort features such as heating or seating systems. Within the vehicle, AI is found in both visible domains – such as human-machine interaction (HMI) and advanced driver assistance systems (ADAS) – and invisible layers that work in the background: processing sensor data, enabling predictive maintenance, or generating real-time insights for OEMs and platform providers. A further distinction must be made between AI systems operating directly on the edge (within the vehicle), and those supported or orchestrated by backend infrastructures.