The transition to software-defined vehicles (SDVs) has surpassed advanced driver-assistance systems (ADAS) and electric vehicles (EVs) to become the primary strategic focus for the automotive sector. According to the Software-Defined Vehicles Adoption Report 2026 by IoT Analytics, 45% of automotive OEMs and suppliers currently rank the transition to SDVs as their number one strategic priority. The report is based on a survey of 80+ automotive OEMs and suppliers and numerous expert interviews.
“Our research indicates the shift to software-defined vehicles is now a top technology priority for automotive OEMs. Tesla, Rivian, and several Chinese OEMs are moving fastest, while many incumbents still face major architectural and organisational gaps,” says Knud Lasse Lueth, CEO at IoT Analytics.
Harsha Anand, senior at IoT Analytics, says: “The SDV transition isn’t just about the in-vehicle applications; it’s about Software-Driven Engineering. By integrating cloud-based compute and AI into the design phase including designing vehicle architectures, OEMs can scale the AI training necessary to move from software platforms currently used in SDVs to fully autonomous AI-Defined Vehicles (AIDV).”
Key findings in the report include:
Structural shift to zonal architecture: The industry is moving away from distributed or domain-based systems. Data indicates that over 90% of automotive OEMs are committed to zonal architecture. Further, 80% have already started the migration. This consolidation into centralised or advanced zonal architectures is driven by benefits such as reduced wiring complexity, weight reduction, and increased manufacturing efficiency.
Hybrid cloud strategy prevails: Vehicle-to-cloud integration is a critical enabler for SDVs. 73% of OEMs and 71% of suppliers identify over-the-air (OTA) updates as the top role for cloud integration. However, security concerns persist. 91% of OEMs expect critical software workloads to remain on on-premises servers or in a private cloud rather than public cloud environments.
Divergent adoption speeds: Tech-native companies like Tesla and Rivian, alongside Chinese OEMs such as BYD and NIO, are identified as top innovators. These players leverage software-first architectures to develop new models significantly faster than legacy peers.
Organisational challenges: The transition presents cultural and skill-based hurdles. 18% of OEMs cite insufficient internal skills as a key challenge. To address this, companies are increasingly utilising generative AI to support software development and validation phases.
A publicly available research article detailing insights derived from the report is available at Software-defined vehicles: The 4 dimensions of adoption and the OEMs moving fastest.