Three seismic trends  are buffeting the R&D function in the automotive industry, creating the need for profound change.

First, the transition from internal combustion engine (ICE) to electric vehicle (EV) technology is a fundamental shift, the likes of which the industry has not experienced since surging oil prices and competition sparked demand for more fuel-efficient vehicles more than a half century ago.

Second is the trend of software-defined vehicles with a central architecture that is more geared toward consumers. Software provides many opportunities for automotive players to differentiate themselves, with such applications as infotainment and advanced driver-assistance systems. However, software also presents companies with the substantial challenge of transforming hardware-centric operations to support their added role as software providers.

The third trend is the emergence of generative AI (gen AI). Gen AI is becoming a powerful technology with the potential to completely reconfigure how R&D teams operate. Although the technology is still in its early days, its ability to generate and process language and imagery, integrate insights from various sources, process information across diverse formats, and produce detailed documentation for regulatory purposes points to a radically different R&D future.

New entrants to the sector—EV manufacturers in China, the United States, and elsewhere—have already successfully implemented R&D process innovations that accelerate new-vehicle time to market, gaining considerable strategic advantages over established players, whose margins are already squeezed.

To better understand the impact and opportunity of these trends, we spoke with executives from leading European automotive and manufacturing companies. The detailed discussions focused primarily on gen AI and the lessons that are emerging from the many gen AI pilots and a few at-scale deployments.

One clear lesson emerged from these discussions: by following a value-focused approach that supports the integration of gen AI throughout the R&D process, companies can capture substantial value in the form of reduced costs, accelerated time to market, improved quality, and more innovation.

• Testing and homologation. The executives we consulted estimated that using gen AI to automate reporting and to generate documentation and scenario-based simulation could improve testing and homologation processes by 20 to 30 percent. Automation could add value by simplifying the creation of essential reports, manuals, and documentation for compliance, product documentation, and quality assurance purposes.
  
  And some use cases can deliver exceptional efficiencies: for example, a German tier-one automotive supplier achieved a 70 percent gain in productivity—including the time required for human review of the gen AI–produced output—byusing gen AI to generate test vectors such as full branch coverage and modified condition/decision coverage (MCDC). By integrating gen AI into its development process for embedded software and its generation of requirements—using gen AI to help determine requirements for stakeholder requests that could serve as first drafts—the company achieved productivity gains of up to 30 percent for engineers.
• Design applications. Within the design segment of R&D, the leaders we consulted estimated that generative-design use cases could improve R&D processes by 10 to 20 percent. They also estimated that reverse and black-box engineering use cases could yield 5 to 10 percent improvements in R&D processes by using gen AI to reveal and decode proprietary technologies such as knowledge extraction, algorithm decoding, or reengineering.

For gen AI applications to add value across the R&D process, a holistic, value-centered approach that goes beyond tech and data is needed. Only by building the range of necessary capabilities and culture can companies expect to reap the benefits of new technologies such as gen AI.

Stay tuned, more to follow!