68% of automotive organisations use or pilot agentic AI for product development, but only 5% report mature AI governance

According to the Capgemini Research Institute’s latest report ‘Rewiring automotive product development: how organisations can pull ahead in the AI race’, automotive organisations are rapidly moving agentic AI from pilots into product development. Nearly seven in ten organisations already use (33%) or pilot (35%) the technology, while adoption across non-core engineering activities is expected to increase from 26% today to 68% within the next two to three years. The perceived value is significant, with 83% of executives believing agentic AI will enable engineers to spend more time on core engineering work rather than administrative and coordination tasks.However, very few (5%) organisations report fully mature AI governance, highlighting ongoing challenges around data quality, traceability, accountability and ecosystem collaboration.

Agentic AI is creating a new performance gap across the industry

Based on a global survey of 800 automotive senior executives across 12 countries in North America, Europe and Asia-Pacific, the research identifies agentic AI as the next frontier in automotive product development. While most automotive organisations are investing in AI, a select group is already achieving significantly greater returns than its peers.

These high-value realisers report 17% faster development cycles, 19% gains in engineering productivity, and a 19% reduction in time-to-market. According to the report, their advantage stems less from adoption of AI itself and more from stronger data foundations, clearer business cases, and more disciplined governance and operating models. As agentic AI adoption accelerates, these capabilities are emerging as the key differentiators between organisations generating measurable value and those still struggling to scale.

AI maturity varies significantly across markets. Respondents in the US and Europe report the highest levels of AI strategy execution and cross-functional AI planning. Among OEMs in these regions, 53% and 46%, respectively, are already implementing AI initiatives, while 18% (US) and 16% (Europe) have a comprehensive cross-functional AI strategy, vision, and roadmap in place. In China, four in ten respondents report being in the implementation phase, while just 7% have reached a comparable level of strategic maturity. Elsewhere in Asia-Pacific, implementation maturity remains lower.

“Over the past decade, the automotive industry has undergone a profound transformation with the shift to software-defined vehicles, establishing the digital foundations that are now enabling the next wave of innovation. Agentic AI is creating a new performance divide in the automotive industry,” said Laurence Noël, Global Head of Automotive at Capgemini. “The organisations generating the most value are not simply adopting AI faster, they are redesigning workflows, strengthening ecosystem collaboration, building trusted data foundations and establishing clear accountability for AI-enabled decisions. The leaders of tomorrow will be those that successfully embed AI into the engineering processes at scale.”

Trust emerges as the key barrier to scaling agentic AI in product development

As organisations expand the use of agentic AI across engineering workflows, confidence in technology varies substantially depending on where it is applied. Executives say they are most comfortable using agentic AI in upstream research and concept activities, where trust reaches 59%, but confidence falls to 40% in development activities, where AI-generated results can have more direct implications for engineering outcomes, product quality, and safety.

The findings suggest that many automotive organisations have yet to establish the evidence, traceability and accountability layers required to deploy it confidently at scale. Trust is becoming a critical factor in determining how quickly organisations can move from experimentation to enterprise-wide transformation.

The vehicle feedback loop remains incomplete

The research also highlights a challenge beyond AI adoption itself: the industry’s ability to turn real-world vehicle data into continuous product improvement. While connected vehicles generate vast volumes of operational and usage data, many organisations still struggle to translate these insights into action.

Nearly two-thirds (64%) of organisations report that learnings from vehicles in use primarily inform future vehicle programmes rather than improvements to vehicles already on the road. Meanwhile, over-the-air updates remain concentrated on areas such as cyber security and navigation, with comparatively fewer organisations deploying updates that modify driving functions or assisted-driving capabilities. The findings suggest that industry’s next competitive advantage will depend not on collecting more data, but on accelerating the journey from vehicle insight to engineering action.

Scaling AI will require more than technology

According to the report, the industry’s biggest barriers to scaling AI are increasingly organisational rather than technical. Six in ten executives report that delays are primarily driven by process inefficiencies rather than technology limitations, while 45% have yet to define a strategy and roadmap for AI in product development.

The research also points to the growing importance of ecosystem collaboration. As software-defined vehicles become more complex and development becomes increasingly distributed across OEMs, suppliers and technology partners, success will depend on greater alignment across the value chain. However, only 30% of organisations report co-development models built around shared outcome metrics, while just 10% have established shared lifecycle accountability across their ecosystem. This highlights the challenge many organisations face in aligning partners around common goals and shared responsibility for product outcomes.