How Generative AI Is Boosting Innovation for Carmakers and Drivers

The automotive industry, like so many others, is undergoing a technological awakening with the advent of generative artificial intelligence (AI). From streamlining research and development (R&D) to offering in-car experiences that were once the domain of science fiction, generative AI is unlocking potential for the industry at every turn. The technology has the capacity to transform the sector across vehicle design, manufacturing and customer experience. By enabling rapid design iterations, virtual testing and optimization of manufacturing processes, generative AI could significantly reduce time to market. It can also enhance personalization, improve safety features and support the development of autonomous vehicles.

However, widespread adoption is not without its challenges. These include accurately predicting performance metrics and ensuring the manufacturability of AI-generated designs. Perhaps most pressing, automakers must steer through a nascent ethical and regulatory environment around the technology’s data privacy and security concerns. Equally challenging is building expertise and an AI-ready organizational culture. Nevertheless, what seems increasingly certain is that a new metric may soon determine the success of an automotive product line: not necessarily how well a car performs on the road, but how effectively it learns from it.

Revving Up Innovation With Generative AI

Co-Piloting the Driving Experience With Generative AI

Navigating Challenges to Generative AI in the Automotive Industry

Revving Up Innovation With Generative AI

Generative AI has the potential to become the engine of innovation in the automotive world. Investment is pouring in, and tremendous growth is on the horizon.

Generative AI marks a milestone in the history of automotive innovation.

Generative AI is revolutionizing the auto industry. The technology offers almost limitless potential to fine-tune everything from automotive design to driver experience, whether for more closely meeting consumer preferences, elevating vehicle safety or engineering more environmentally sustainable automobiles. The impact of this technology is expected to grow significantly, driving innovation and competitiveness in the sector.

93%

of automotive industry stakeholders believe generative AI is an industry game-changer, and 75% plan to adopt it this year.

Buy-in for generative AI is set to gain considerable momentum in the next 10 years.

The generative AI market in the automotive segment is expected to skyrocket from $335 million in 2023 to $2.6 billion by 2033. This increase represents a compound annual growth rate (CAGR) of 23%. Fueling this growth is widespread industry buy-in among R&D departments. A remarkable 69% of decision-makers in these departments are prioritizing early adoption of the technology.

Although North America commands more than 42% of the current global market in generative AI for the auto segment, 93% of stakeholders in European, North American and Asian markets say that this technology is a game-changer for the industry. For example, generative AI-driven personalization in automotive innovation is expected to manage 75% of customer interactions. This innovation will boost sales by 15% and customer satisfaction by 20%.

Generative AI offers a powerful R&D engine for automotive innovation.

Efficiency gains and accelerated product development timelines are two key factors leading early integration of this technology in automotive R&D and manufacturing. Testing processes — the nuts and bolts of making sure a vehicle passes regulatory muster and is prime for market approval — are seeing 20% to 30% efficiency gains through AI-driven automation of reporting and scenario simulations. One German supplier for the industry has reported a 70% uptick in productivity in test vector generation owing to the use of the technology. The benefits extend to engineering teams also, with reports of 30% productivity gains when using this technology to draft initial stakeholder requirements.

Given these gains, this technology is set to change the pace and the precision of product development in automotive R&D. Already, 75% of European automotive companies are actively test-driving at least one application. In the design segment, early use cases of generative AI show high promise, with executives estimating a 10% to 20% improvement in R&D processes.

Generative AI’s Applications in the Auto Industry

Design Innovation: Generative AI can rapidly generate multiple design options for complex automotive systems, accelerating the design process and optimizing vehicle performance, safety and efficiency.

Research and Development: The technology can assist engineers in making data-driven decisions, pinpointing optimal materials, designs and technologies to enhance vehicle performance and safety as well as streamlining the innovation process.

Virtual Testing and Simulation: Generative AI creates detailed, realistic models of cars and their components for virtual trials, including crash simulations and performance in different weather conditions, accelerating development by reducing the need for costly physical prototypes.

Personalized Driving Experiences and Customer Interaction: The technology can create personalized driving experiences that adapt to individual preferences and needs, including changing vehicle aesthetics, displays and controls to align with user preferences.

Predictive Maintenance: Generative AI enhances operations within auto manufacturing by predicting maintenance needs, reducing equipment failure and improving productivity.

Co-Piloting the Driving Experience With Generative AI

The auto industry’s roadmap for generative AI reveals a near future in which driver experiences transcend horsepower and handling to feature personalized interactions and vehicles that anticipate needs.

Dashboards are set to become generative AI command centers.

12

Number of languages cars speak in the new Stellantis ChatGPT-powered voice assistance system to be used in 17 countries

In-vehicle interfaces will soon be interactive powerhouses with the integration of generative AI. General Motors (GM) recently announced an initiative to use Microsoft Azure and OpenAI technologies to develop a chatbot capable of helping with real-time vehicle issues. GM looks to offer drivers access to step-by-step instructions for solving problems. These include tire changes and explanations of vehicle alerts, even potentially scheduling maintenance visits, all communicated through natural conversation. Similarly, auto industry AI stalwart Cerence is working with Nvidia to create an automotive-specific large language model (LLM). This LLM aims to achieve more intuitive, real-time human-vehicle interactions highly contextualized to the automobile experience.

Generative AI could write a new chapter on driver-vehicle relationships.

This technology is turning the long sought-after goal of personalizing driver experiences into reality. Audi’s integration of Cerence’s Chat Pro, an AI assistant powered by ChatGPT, across its product lineup aims to enhance the in-car experience through advanced conversational interfaces, showing the technology’s immediate viability. Stellantis, too, is rapidly scaling its generative AI use across its European brands by adding ChatGPT to its SoundHound Chat AI voice assistance system, with rollout aimed to span 17 countries and 12 languages by the end of July 2024.

Generative AI-powered personalization stands to profoundly influence the driver-vehicle “relationship.” As generative AI automotive systems evolve, they could lead to new products that seamlessly connect with other areas of drivers’ digital lives. In the longer term, these adaptive tools could even learn from individual driver behaviors, potentially enhancing both safety and efficiency.

Navigating Challenges to Generative AI in the Automotive Industry

Major challenges to generative AI adoption in the auto industry include building an AI-ready skill set and organizational culture as well as addressing ethical, data privacy and security concerns around the technology’s use.

A critical skills gap challenges implementation in the auto industry.

The road to widespread adoption of this technology in the auto industry is still under construction. Industry stakeholders cite a lack of skilled staff (63%), data privacy concerns (53%) and complex or ambiguous regulatory regimes (41%) as the top three critical obstacles to implementing generative AI solutions within their organizations.

63%

of automotive industry stakeholders cite a lack of skilled staff as a critical obstacle to using generative AI solutions within their organizations.

A shortage of professionals with expertise in both automotive engineering and advanced AI technologies makes it difficult for companies to build and maintain generative AI systems. Implementing generative AI solutions often requires integrating them with legacy systems and processes, which can be complex and time-consuming. Moreover, with many auto companies still in the experimental stages with the technology, building an AI-ready organizational culture and overcoming resistance to change can be significant hurdles.

Ethical, data privacy and security concerns remain the number-one challenge in applying the technology.

Ethical, data privacy and security concerns represent important — and as yet unknown — risks that will need careful management for effective implementation of the technology for the industry. These concerns are particularly crucial due to the safety-critical nature of vehicles and the large amounts of personal data involved. Ensuring that generative AI systems are trustworthy, protect user privacy and are secure against potential attacks or misuse is a major challenge that automotive companies must overcome to successfully implement this technology at scale. Without handling these challenges, the industry could fail to fully realize the potential benefits of generative AI across design, manufacturing and other key areas.