By Raj Kanaya, General Manager, Automotive Business Unit, Aeris The automotive industry is rapidly evolving into a more connected, software-defined future. This shift is creating a new challenge for OEMs as vehicles become increasingly software-led, Cloud-connected and reliant on real-time diagnostics, OTA updates and always-on connectivity. This opens new opportunities to improve driver experiences and accelerate software innovation, but it also creates a more complex environment to manage. A vehicle is no longer a static product that changes only during scheduled service visits; it is part of a wider IoT ecosystem, where data flows continuously between the vehicle, network, Cloud platforms, applications and service providers. For OEMs, the challenge is no longer simply how quickly they can build software into vehicles. It is how effectively they can operate, monitor, update and secure connected vehicles once they are on the road. Connected vehicles are creating a visibility problem Connected vehicle data has huge potential, but only if OEMs can interpret and act on it quickly. When an issue occurs, teams need to understand whether the root cause sits in the vehicle, an IoT device or sensor, SIM profile, mobile network, cloud environment, application layer or a third-party service. At scale, even a small percentage of vehicles failing to connect can create a significant burden, with teams spending days identifying the issue before they can fix it. The problem is not necessarily a lack of data, but that it is fragmented across different systems, partners and operational teams. This is where AI becomes operationally valuable. It can help OEMs move from reactive troubleshooting to more proactive, data-led decision-making. Rather than relying solely on manual alerts, AI can analyse patterns across multiple sources and help teams understand what is happening more quickly. Why OEMs need an AI maturity model The automotive industry already