Automotive companies are accelerating the adoption of agentic artificial intelligence (AI) in product development, with its use across non-core engineering activities expected to rise from 26% today to 68% within the next two to three years. However, only 5% of organizations report having fully mature AI governance, according to a new report by the Capgemini Research Institute.
The report, titled Rewiring Automotive Product Development: How Organizations Can Pull Ahead in the AI Race, found that nearly seven in ten automotive organizations already use or pilot agentic AI, with 33% reporting active use and 35% still in the pilot stage.
The technology is also expected to help engineers focus more on core engineering tasks. Some 83% of executives surveyed believe agentic AI can reduce the time engineers spend on administrative and coordination activities. However, confidence in the technology declines when its applications move closer to safety-critical tasks.
Agentic AI widens the performance gap in automotive
Based on a global survey of 800 senior automotive executives across 12 countries in North America, Europe and Asia-Pacific, the report identifies agentic AI as a major emerging driver of product development.
While many automotive organizations are investing in AI, a smaller group is already achieving stronger business results. These high-value organizations report development cycles that are 17% faster, engineering productivity gains of 19%, and a 19% reduction in time-to-market.
According to the report, these results are driven not simply by adopting AI, but by stronger data foundations, clearer business cases, and more disciplined governance and operating models.
AI maturity also varies across markets. Among original equipment manufacturers (OEMs), 53% of respondents in the United States and 46% in Europe are already implementing AI initiatives. Meanwhile, 18% of US organizations and 16% of European organizations have established comprehensive cross-functional AI strategies, visions and roadmaps.
In China, four in ten respondents said their organizations were in the implementation phase, while only 7% had reached a comparable level of strategic maturity. Implementation maturity remains lower in other parts of Asia-Pacific.
Laurence Noël, Global Head of Automotive at Capgemini, said the shift toward software-defined vehicles over the past decade had established the digital foundations for the next wave of innovation.
She added that organizations generating the greatest value from agentic AI are redesigning workflows, strengthening collaboration across their ecosystems, building trusted data foundations and establishing clear accountability for AI-enabled decisions.
Trust remains a barrier to scaling AI in product development
Confidence in agentic AI varies depending on the engineering activity involved. Executives report a 59% trust level for upstream research and concept development, compared with 40% for development activities, where AI-generated outputs can have a more direct impact on engineering outcomes, product quality and safety.
The report highlights the need for stronger evidence, data traceability and accountability frameworks to support wider deployment. Without these safeguards, organizations may struggle to move from pilot projects to enterprise-wide implementation.
Vehicle data is not fully translating into product improvements
The study also points to a gap between collecting operational data from connected vehicles and using it to improve products already on the road.
Around 64% of organizations said insights from vehicles in use primarily inform future vehicle programs rather than improvements to existing vehicles. Over-the-air (OTA) updates also remain concentrated in areas such as cybersecurity and navigation, while updates affecting driving functions and assisted-driving capabilities are less common.
The report suggests that the next competitive advantage will depend not on collecting more data, but on turning vehicle insights into engineering decisions and product improvements more quickly.
Governance and collaboration are key to scaling AI
Organizational challenges are emerging as major obstacles to wider AI adoption. Six in ten executives said delays are primarily driven by process inefficiencies rather than technological limitations, while 45% of organizations have yet to define an AI strategy and roadmap for product development.
Collaboration across the automotive value chain is another challenge. As software-defined vehicles become more complex, product development increasingly involves OEMs, suppliers and technology partners.
However, only 30% of organizations have co-development models built around shared outcome metrics, and just 10% have established shared lifecycle accountability across their ecosystems.
The findings underline the importance of aligning partners around common objectives, clear responsibilities and measurable product outcomes.
Report methodology
The Capgemini Research Institute surveyed 800 senior automotive executives, including representatives from 400 OEMs, 160 Tier-1 suppliers and 240 technology providers. Respondents represented organizations across North America, Europe and Asia-Pacific and included leaders in product development, engineering, technology, data and AI strategy, and related functions.
The Institute also conducted in-depth interviews with 24 automotive executives and technology providers. The survey was conducted in July 2026.
