Exploring the future of GenAI in automotive

In this Q&A, Espina shares insights on how GenAI is shaping safety features, transforming supply chains, and reimagining the role of automotive engineers. From the increase of in-car companions to the sustainability implications of AI integration, Espina shares his insights on what the future holds for both manufacturers and drivers.

Is there anything that you’re noticing from any car manufacturers to suggest GenAI is the next automotive trend? If so, what?

While generative artificial intelligence (AI) permeates every aspect of our lives, it isn’t completely new to the automotive industry. For years, car manufacturers have leveraged AI to power safety and driver assistance features such as voice recognition systems, anti-lock braking, and lane-keeping assistance. Now, the advent of new AI capabilities and generative AI is introducing a new era in the industry. Car manufacturers like Mercedes-Benz are just one of many companies introducing autonomous vehicle systems, while others like BMW and Subaru have introduced driver-focused features like monitoring systems to ensure drivers are focused on the road ahead. Following a busy year of automotive and AI-focused announcements, 2025 is poised to be full of new tech-focused capabilities that will completely transform the industry.

GenAI in the automotive industry is also going beyond autonomous capabilities or safety features to be focused on personalised driving and in-car entertainment experiences, a trend I expect to see grow further this year. Features like entertainment, music, in-car phone calls, navigation, and security are being driven by GenAI abilities to make every car personal to the driver. This is done through recommendation engines, seamless mobile connections, customised dashboards, and more integrations. For example, the imminent launch of the Sony-Honda Afeela.

When you talk about GenAI in automotive, what does that mean? For example, GenAI is seen as a creative tool, so how will it enhance the driver/driving experience?

GenAI capabilities are becoming more prevalent in a myriad of areas in the automotive industry. This includes manufacturing, design, supply chain, production, and integrating driving assessment systems into vehicles. GenAI can drastically improve the manufacturing process from accelerating the time-to-market through vehicle data and predictive modelling to optimising the logistics and shipping process by analysing shipping methods and weather patterns. In some cases, GenAI systems can create variations of designs for different parts, using guidelines from designers and engineers. Beyond manufacturing and design, GenAI is used for the driver experience and the software implemented in today’s vehicles – including entertainment features, security, and navigation. I expect to see more customised and personalised vehicle experiences powered by GenAI.

What specifically would need to happen to allow AI assistants to become true in-car companions?

Before AI assistants can truly become in-car companions, they need to mature in their digital journeys to understand the nuances of vehicles, the driving experience, and safety. While car experiences are rich and complex, safety must be at the forefront of new capabilities as research from the World Health Organization shows over a million people die in crashes annually. AI assistants need to evolve their environmental awareness and real-time data processing, improve their data privacy and security measures, and enhance their autonomous capabilities. While current AI advancements are steps in the right direction, AI models need more time to mature before they can be true passenger companions.

How do you imagine GenAI will transform driving experiences? And what benefits will it offer? For example, voice control without taking eyes off the road?

Driving experiences are multifaceted which opens the door to a multitude of AI capabilities that can level up the in-car experience. From improving safety, and offering hands-free assistance to new in-car entertainment, autonomous driving and monitoring solutions. AI can also help make simple auto-related activities more personalised like offering easier access to driver manuals, searching for routing applications, and providing real-time traffic updates.

How will integrating GenAI into automotive affect the cost of buying a car?

GenAI can lower production costs by streamlining the supply chain, accelerating manufacturing, and reducing waste, ultimately leading to more affordable vehicles over time. However, integrating advanced AI-driven features such as autonomous driving capabilities, personalised in-car experiences, and predictive maintenance would increase development costs. The additional cost of integrating advanced technology may be passed on to consumers, leading to higher prices. Nonetheless, these prices could stabilise as AI becomes more standardised, making AI-enhanced vehicles more affordable. Achieving this affordability will depend on balancing innovation costs, operational efficiencies, and consumer demand.

With increased use of power consumption/data, how will this impact sustainability goals?

The growing use of power and data in automotive technologies will be fuelled by GenAI advancements, which present both challenges and opportunities for sustainability goals. GenAI systems, like those used in autonomous driving and smart manufacturing, require significant energy, which can increase emissions if powered by non-renewable sources. This creates an opportunity to develop more efficient GenAI and balance technological advancements and sustainable practices, allowing the automotive industry to continue innovating while minimising its environmental impact.

What role will GenAI play in enhancing the software defined vehicle? And what role will it have in transforming not only consumer vehicles, but fleet mobility and supply chains? Smart maintenance/scheduling, or real time data feedback, for example.

GenAI will enhance software-defined vehicles by enabling more intelligent systems to process real-time data to improve performance and safety. This will help vehicles optimise operations through predictive maintenance, more innovative scheduling, and real-time feedback, reducing downtime and costs. This technology will transform fleet and mobility, and supply chain by enabling route planning, better resource management, and more efficient operations. These advancements will create a more connected and responsive transportation system, benefitting businesses and consumers.

What does the widespread adoption of GenAI mean for the future of autonomous and connected vehicles?

The widespread adoption of GenAI will play a key role in advancing autonomous and connected vehicles, making them smarter, safer, and more efficient. GenAI can enhance connected systems by providing real-time data analysis, predictive maintenance, and personalised user experiences. Integrating GenAI, autonomous, and connected vehicles will become safer and better equipped to meet evolving consumer demands, transforming how people live, work, and commute. Advancements will contribute to a future where transportation systems are more integrated, responsible, and sustainable, shaping how people move in the years to come. With the global automotive AI market projected to grow at a compound annual growth rate of 29.2%, these developments will have far-reaching impacts across industries and daily life.

In five years, how different do you think the role of an automotive engineer will be compared to today?

An automotive engineer's role will likely undergo significant transformations due to technological advancements and industry trends. While core engineering principles will remain foundational, a shift in automation and technology will redefine the flow of automotive engineering, most likely becoming reliant on emerging technologies. GenAI and data integration will be a significant focus as engineers will use AI-driven tools for predictive maintenance, design optimisation, and real-time data analysis, shifting their role toward interpreting and applying data for better outcomes. While traditional mechanical engineering expertise will remain essential, they will also need to develop key programming and machine learning skills to support the advancement of connected and autonomous vehicles.