Gil Golan is a former Chief Technology Officer and Vice President, Global Research and Development for General Motors and President, GM Ventures. In these roles, Golan was responsible for innovation, investment, and technology solutions in the development of GM vehicles. He left GM after a 25 year career at the end of 2023 and is currently working on a new startup which is still in stealth mode.
During Golan’s career at GM he served as Vice President, of Technology Acceleration and Commercialization, leading the team responsible for defining and implementing a comprehensive battery technology strategy to maintain GM’s leadership position in this domain. He also served as Managing Director of the General Motors Technical Center in Israel. Golan led the establishment of the Tech Center in 2008 and led the teams responsible for advanced technology development and product engineering in support of GM’s key SW/AI, autonomous, and connected vehicle programs. Golan also held several executive roles in the U.S., including Director of Global Strategy for GM Research & Development and Director with GM Corporate Venture Capital.
He earned his bachelor’s and master’s degrees at the Technion – Israel Institute of Technology, received a Stanford Graduate School of Business degree (tailored & sponsored by GM) and completed executive education programs at Harvard Business School. Golan is a scheduled speaker on a panel that will be moderated by The Innovator’s Editor-in-Chief at the Sparks Innovation Summit in Tel Aviv on March 26. He recently spoke to The Innovator about AI’s impact on the automotive industry.
Q: How is AI changing the way cars are made?
GG: The latest generative AI foundation models have the potential to transform the automotive industry in many ways. GenAI can accelerate product development across the board and expedite the training and robustness needed for safer autonomous vehicles. AI-powered tools can also accelerate vehicle design by creating multiple design iterations based on specified inputs and parameters like aerodynamics, weight and aesthetics. AI-driven systems can create realistic simulations for crash tests, environmental conditions or predict battery pack performance under extreme conditions. AI can optimize assembly lines by creating digital twins, modeling factory layouts, and streamlining manufacturing operations. It can additionally help discover new materials (e.g. battery chemistry or lighter alloys) by generating molecular structures with desired properties.
Q: What about the cars themselves?
GG: Specifically, with respect to the product itself, these advanced models could reshape studio design and vehicle development processes, enabling highly customized and personalized products. For self-driving cars, foundation models play a critical role in advancing key components such as synthetic data generation and the simulation of complex driving environments and edge cases, ultimately enhancing decision-making algorithms. These advancements are crucial for addressing safety challenges, reducing reliance on costly real-world data collection, and effectively training self-driving systems to handle rare or edge-case scenarios. More importantly, these techniques enable a gradual yet significant shift from traditional full-stack modular development to an end-to-end approach that more closely mimics the functionality of the human brain.
Q: The Innovator recently interviewed a neuromorphic computing company. The CEO said the combination of cameras and sensors with built in intelligence could permit cars to hit the brakes faster than human drivers to avoid an accident. And if intelligence were to be distributed through sensors instead of centralized, vehicle systems would be able to respond faster, consume less energy, and operate more reliably in the event of failures. Do you agree?
GG: I generally agree. Over the last few decades, the automotive industry has evolved from distributed mechanical systems to distributed electrical systems, and then to electronics and software, where multiple CPUs [Central Processing Units] each control an individual feature or system. In recent years, a more modern approach has emerged: centralized large-scale computing. All ADAS [Advanced Driver Assistance Systems], autonomous capabilities, and safety-related features, often powered by numerous CPUs, have converged into one high-performance computing architecture. Similarly, all communication and infotainment CPUs and related tasks have also been consolidated. This approach can be costly, since it requires more GPUs [Graphic Processing Units] and memory, but it delivers significantly faster and more robust performance. With these modern architectures, self-driving cars can already react faster than human drivers. It’s possible that future electrical architectures will pivot back to a distributed model, using next-gen AI-driven capabilities (like neuromorphic computing) to process most data at the sensor level (the edge). Ultimately, automakers will need to weigh the benefits against the cost prior to adopting another new electrical architecture change and investment.
Q: You mentioned that AI could help the auto industry introduce a host of new personalized services. Can you elaborate?
GG: The integration of AI-powered capabilities enables cars to collect and analyze vast amounts of data on driving behavior, vehicle performance, and user interactions. This could reshape not only how vehicles operate but also how users interact with vehicles and pave the way for a host of new services. For example, AI-driven systems can learn driver preferences over time, adjusting seating, climate control, preferred music, ambient light setting, navigation routes or even calibrate the powertrain to fit the preferred driving style. The driver’s profile can then be stored in the Cloud and migrate to all other cars, rentals or robotaxi’s ride-sharing services as soon as the driver enters. Another example could be AI-powered voice-activated personalized virtual assistants offering a recommendation system (including concierge services). Carmakers could introduce new business models such as flexible subscriptions that are based on usage and data-driven insights, to drive new growth.
Q: The ten biggest car companies by trailing 12 months (TTM) revenue as of February 2025 are Volkswagen, Toyota, Stellantis, Mercedes-Benz, Ford, General Motors, Honda, Tesla, Nissan, and BYD. How will AI impact this list
GG: Most of the current top ten companies are traditional carmakers with massive production capacities and extensive distribution networks. The competitive landscape could shift if tech-focused players or newcomers leverage AI more effectively. Emerging competitive Chinese EV makers, such as Huawei and Xiaomi, could break into the top ten in the next five years, following BYD, which is already a major player.
Q: As the former CTO of General Motors what advice do you have for car companies? What do they need to do to stay competitive in the age of AI?
GG: Recognize the power of AI, create an organizational culture that embraces change and be open to pursuing deep transformation. The winners will be those who not only optimize current operations using AI but also innovate to deliver new products and services that exceed rapidly evolving consumer expectations.
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