By 2035 cars will be software-defined and AI-powered
The automotive industry is undergoing a once-in-a-century transformation, transitioning from traditional vehicles to software-defined and AI-powered cars. This revolution is expected to progress further into 2035, but the journey involves overcoming significant challenges.
The shift to software-defined vehicles (SDV) and electrification
In a recently published study “Automotive 2035”, The IBM Institute for Business Value (IBV) predicts that by 2035, 82% of new cars will be electric to some degree, including pure electric vehicles (EVs), hybrids, and fuel cell vehicles. Additionally, 75% of industry executives believe the software-defined experience will be the core brand value. This shift will require significant changes, for example, in electrical and electronics (E/E) architectures, product development processes and methodologies, and service operations, as it requires continuous updates throughout a vehicle’s lifecycle.
Shifting business models
The move towards software-defined vehicles (SDVs) is motivated by the desire to enhance the driver experience. The focus is shifting beyond infotainment and device integration, to vehicle control domains, making safety, reliability, security, and privacy foundational capabilities. As cars become more software-centric, the industry aims to shift from one-time car sales to recurring digital revenue models. By 2035, digital revenue is expected to triple from 15% to 51%, with R&D investment in software rising from 21% to 58%. However, the success will depend on the clear values offered to customers and strong feedback loops from users and ecosystem partners.
Technical and cultural challenges to SDV
Despite the promising outlook, the transition to SDVs faces significant challenges. Although 68% of executives believe their SDV transitions are on track, 74% acknowledge their organizations are still rooted in a mechanical-driven culture, making the shift to a software-centric approach difficult. The industry also faces a critical skills gap, with the necessary talent not expected to be fully in place until 2034.
The biggest challenge cited by 79% of respondents is the technical complexity of separating software and hardware layers. Other major challenges include cost management and product lifecycle management. Collaboration on standard technology interfaces and the use of open-source technologies for non-competing domains are seen as potential solutions. Working with new ecosystem partners is another strategy to address these issues.
The role of AI to speed up SDV transformation
Some executives say the SDV future may come sooner than 2035. Taking advantage of recent advancement of AI technologies may be a key to speed up. Generative AI has the potential to significantly accelerate the SDV development and operations. Prominent use cases for generative AI in the next three years include testing and simulation, code generation, and over-the-air (OTA) update operations.
Testing and Simulation: Generative AI can automate the creation of test scenarios, enhance test coverage, generate test data, and improve simulation accuracy. This can speed up the testing process, reduce workload, and lower costs. Extensive testing and simulation are necessary to ensure the safety and reliability of advanced autonomous systems, and AI can help with productivity improvement.
Code Generation: Generative AI can automate code generation, improving development productivity by 39%. This can reduce the time required to launch new products and services by 21%, allowing companies to respond more swiftly to market demands and regulatory changes.
OTA Update Operations: Generative AI can optimize the delivery of OTA updates, ensuring efficient and secure software updates. AI can predict maintenance needs, generate targeted updates, and enhance security by identifying and mitigating potential threats. This ensures that vehicles remain up-to-date, secure, and optimized.
By leveraging generative AI, companies expect to increase the perceived value of their products by 22% and the perceived value of digital products by 37%. To capitalize on these benefits, enterprises are planning to increase their generative AI investment by 46% over the next three years.
Conclusion
The automotive industry is on the cusp of a monumental transformation towards software-defined and AI-powered vehicles. While significant challenges remain, generative AI offers promising approaches to address some challenges. By enhancing testing, code generation, and OTA update operations, AI can help auto companies improve development efficiencies, helping to deliver safer, more reliable, and innovative products to the market.
This IBV Automotive 2035 report was conducted in collaboration with Alliance for Automotive Innovation, Amazon Web Services, and Red Hat and gleans insight from over 1,200 global automotive industry executives.
Explore the full insights of the Automotive 2035 report at: https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/automotive-2035
To discover how IBM solutions and services can help automakers lead the software-defined vehicle and generative AI revolution, visit our webpage: https://www.ibm.com/industries/automotive
Meet the author:
Noriko Suzuki, Global Research Leader, Automotive, Electronics, Energy and Utilities Industries, IBM Institute for Business Value
