How Generative AI Can Help Drive the Auto Industry Forward

by Jeff Schlageter , General Manager, Global Manufacturing and Energy Industry, IBM

From the dawn of the assembly line, the history of the automotive industry has been defined by bold technological and process innovations that elevate efficiency and drive society forward—and recent history is no exception. For the past decade, manufacturers have begun to harness the evolving power of artificial intelligence and deep learning in the creation of next-generation driver assistance systems and the push toward fully autonomous vehicles. New competitors in e-Mobility are challenging established OEMs at light-speed.

Now, as generative AI takes the capabilities of artificial intelligence to new heights, opportunities are emerging for auto OEMs to streamline nearly every facet of their operations, from design and manufacturing to open up new streams of revenue in the process. It enables the car industry with new routes for faster scaling and focus on user experience and completely new customer interactions as a design imperative.

Here are some Auto Industry key business areas that stand to benefit most from gen AI, and how IBM can help auto manufacturers gain a competitive advantage in them.?

Software-Defined Vehicles

As autonomous driving technology matures, the reliance of vehicles on software is growing by leaps and bounds. By 2025, it’s projected that vehicles will contain up to 650 million lines of software code [1], 16 times more than a smartphone and even more than a passenger jet. This software controls almost every vehicle function and allows for firmware updates that can bring performance upgrades and new features down the road—and with them, new opportunities for monetization.

However, to take part in the software-defined vehicle (SDV) revolution, OEMs must evolve from simple providers of hardware and begin addressing skills gaps in software development. Some companies, such as Mercedes-Benz AG and Hyundai, have opted to take on the design of their vehicle operating system stacks in house; others, like Renault Group and Volvo are using third-party software from Amazon and Google[2]. Still others, like Tesla and RIVIAN, have built their companies around the concept of SDVs, with a core emphasis on software from the outset. According to some estimates, up to 50% of vehicle costs [3] will be associated with software-driven components by 2030—and automakers that tackle more of the technology development on their own will be better positioned to compete for their share of tomorrow’s roads.

I attended the IAA auto show in Munich from September 4th – 8th and as I walked around to see all the vehicles, it struck me how similar the cars looked on the interior – streamlined dashboards with a large screen in the middle and another in front of the driver – and with the shift from Internal Combustion Engines to Battery Electric Vehicles, how will the Auto OEM’s differentiate their lineups? The answer is software. The companies which provide the most immersive, seamless, and autonomous experience will ultimately be the winners in the next generation car market.

The challenge for every auto OEM is the daunting prospect of building all the code required to enable next-generation vehicles. However, new enterprise-grade generative AI tools are beginning to make this time-consuming work much more manageable. Using IBM’s watsonx Code Assistant , software developers at OEMs can speed the creation of code for future cars in a trustworthy, secure way. Purpose-built for targeted use cases and highly customizable, IBM watsonx Code Assistant uses generative AI to craft code recommendations based on natural language inputs, and features data-source matching so developers know exactly where the code originated.

IBM Red Hat has also been helping major OEMs such as Volkswagen and BMW Group more easily develop and support next-gen SDVs with AI-powered tools. With Red Hat technology, these automakers are creating open-source, Linux-based in-vehicle Operating Systems and platforms for safety- and non-safety-related applications[4]. Beyond simply accelerating innovation, Red Hat can also enhance an SDV’s entire lifecycle with its enterprise-grade, long-term support model.

Smarter Customer Service

As vehicles evolve, so too is the way OEMs are engaging with their customers. Multiple paths to the customer are emerging, including digital channels such as direct digital sales or agency models. ‘Ownership’ models are also varying to a much greater extent, including leasing, subscription or multi modal traveling options. ?The shift in paths can put automakers in direct touch with the end customer, allowing them to better manage the customer buying experience, open new lines of communication, and vehicle pricing[5]. In conjunction with the rise of SDVs as avenues for in-car service offerings, auto manufacturers will soon be able to deliver a fully customized, omnichannel sales experience. Regulatory requirements pose increasing responsibility on the OEMs to maintain car and equipment inventory over lifetime.?

Despite the rise in online vehicle research and purchasing, only 61% of consumers were happy with their car buying journey in 2022, down from 66% in 2021 and 72% in 2020[6]. This represents a tremendous opportunity for OEMs to improve the experience with technology, especially as online car shopping continues to grow.[7]

Today, OEMs can use the generative AI capabilities of IBM watsonx to quickly build models for marketing, enabling them to get personalized offers in front of the right customers at the right time. These tools can also be leveraged in the creation of advanced customer service chatbots to answer customer questions and guide them in their purchasing process. For existing customers, AI can aid in the scheduling of maintenance and service; as SDVs begin to arrive in greater numbers, OEMs can leverage the data generated from these vehicles to provide even more intelligent service recommendations.[8]

Further down the line, the data from SDVs will serve as an extremely valuable resource for various third-party entities. For example, it can help city planners optimize their traffic flow and better plan for new infrastructure, and it can inform insurance companies’ risk assessment models and enable them to offer personalized coverage[9]. These applications will require advanced analytics, and AI will play an integral role in preparing and parsing the data that fuels them.

Sustainability

As companies of all kinds ramp up their sustainability efforts, the auto industry has a distinct opportunity to lead the way, not only by reducing the carbon footprint of its own operations, reduce the carbon footprint across the supply chain and but also though innovation in electric and hybrid vehicles. The latter has tremendous implications for society: the transportation sector is responsible for nearly one-fourth of global CO2emissions[10], with road transport contributing to 75% of these emissions.[11] As such, the industry is also coming under intense scrutiny from investors and regulators to ensure it adheres to ESG initiatives.

AI can play a significant role in helping the auto industry succeed on both sides of its sustainability push. Some leading manufacturers have begun using generative AI to improve their vehicle design process, helping to make vehicles more efficient. With text-to-image-based generative AI[12], designers can incorporate detailed engineering parameters like drag, chassis dimensions and ride height into the earliest phases of prototyping, streamlining the task of developing more efficient hybrid and electric cars.?

ESG reporting is an onerous task for any organization; for sprawling multinational corporations like large auto OEMs, the challenge is amplified. Effective tracking, measurement and analysis of sustainability efforts requires the integration of data from various business units spanning multiple global regions and often originating from disparate systems. For full transparency, OEMs must also be able to trace the origin of raw materials, such as minerals and metals, used in the production of vehicles. Further, the automotive sector uses several different ESG reporting frameworks, including the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB), which can make it difficult to compare performance accurately.

IBM has a vast suite of AI-enabled software to help companies in this journey. In fact, the IBM Envizi ESG Suite is designed specifically to simplify the capture, consolidation, management, analysis and reporting of ESG data. Envizi’s suite of cloud-based software products work together to build a data foundation that delivers auditable, robust ESG data and GHG calculations, and features reporting tools to meet internal and external requirements for comprehensive ESG reporting and disclosure. The suite’s analytical tools can also help identify opportunities to reach low-carbon goals and track performance against commitments.

Taking things further, the various elements of IBM watsonx can greatly improve the process of gathering, analyzing and reporting on diverse data. Starting with watsonx.data , OEMs can unify their disparate data with a cutting-edge lakehouse architecture to facilitate the use of AI workloads. With watsonx.ai , OEMs can train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities with ease and build AI applications much more quickly. Finally, watsonx.governance empowers organizations to monitor their AI activities, with software automation to mitigate risk, manage regulatory requirements and address ethical concerns.

The next few years will prove pivotal for the fortunes of automotive OEMs. Those that embrace AI and build it into the heart of their processes across design, development, service and sustainability will enjoy greater opportunities for financial success than their less technically advanced peers. They will also serve as torchbearers on the road to a more intelligent, connected and sustainable future.

[1] Source: Goldman Sachs, “Software Is Taking Over the Auto Industry”

[2] Source: Futurice, “The rise of the software defined vehicle”

[3] Source: IEEE Spectrum, “How Software Is Eating the Car”

[4] Source: Red Hat, “Build the future of driving with software-defined vehicles”

[5] Source: PwC, “The Agency Distribution Model: Picking and mixing”

[6] Source: Cox Automotive 2022 Car Buyer Journey Study?

[7] Source: Cox Automotive 2022 Car Buyer Journey Study?

[8] Source: IBM SME call with Jeff Schlageter, 8/15/23

[9] Source: IBM SME call with Jeff Schlageter, 8/15/23

[10] Source: IEA, “Global energy-related CO2 emissions by sector”

[11] Source: IEA, “Transport sector CO2 emissions by mode in the Sustainable Development Scenario, 2000-2030”

[12] Source: Toyota Newsroom, “Toyota Research Institute Unveils New Generative AI Technique for Vehicle Design”