68% of Automotive Organisations Use or Pilot Agentic AI, Capgemini Finds

Nearly seven in ten automotive organisations are using or piloting agentic artificial intelligence (AI) for product development, but only 5% report fully mature AI governance, according to a new report by the Capgemini Research Institute. The study also identifies gaps in data quality, accountability and collaboration as barriers to scaling the technology across engineering operations.

The report, Rewiring Automotive Product Development: How Organisations Can Pull Ahead in the AI Race, finds that 33% of respondents are already using agentic AI, while another 35% are piloting it. Adoption in non-core engineering activities is expected to rise from 26% currently to 68% over the next two to three years. Meanwhile, 83% of executives surveyed believe agentic AI could allow engineers to spend more time on core engineering tasks by reducing administrative and coordination work.

Agentic AI adoption creates a performance gap

The study, based on a survey of 800 senior automotive executives across 12 countries, found differences in the returns organisations are achieving from AI investments.

Organisations identified as high-value AI adopters reported 17% faster development cycles, a 19% improvement in engineering productivity and a 19% reduction in time-to-market. Capgemini attributes these gains to stronger data foundations, clearer business cases and more structured governance and operating models, rather than AI adoption alone.

AI strategy maturity also varies by market. The report found that 53% of original equipment manufacturers (OEMs) in the US and 46% in Europe were implementing AI initiatives. Comprehensive cross-functional AI strategies, including a vision and roadmap, were reported by 18% of US respondents and 16% of European respondents. In China, 40% of respondents were in the implementation phase, while 7% reported comparable strategic maturity. Implementation maturity was lower elsewhere in Asia-Pacific.

Trust remains a constraint in engineering applications

Confidence in agentic AI varies according to its intended use. Executives reported 59% trust in the technology for upstream research and concept activities, compared with 40% for development activities, where AI-generated results can have more direct implications for engineering outcomes, product quality and safety.

The findings point to the need for stronger evidence, traceability and accountability as organisations expand AI use across engineering workflows. Limited governance maturity could make it harder to move from pilot projects to wider deployment, particularly in activities with safety implications.

Vehicle data is not consistently feeding into product updates

The report also identifies gaps in the use of data generated by vehicles already in operation. Although connected vehicles produce operational and usage data, many organisations continue to use those insights primarily for future vehicle programmes rather than improving vehicles already on the road.

Nearly two-thirds (64%) of respondents said learnings from vehicles in use primarily inform future vehicle programmes. Over-the-air (OTA) updates are largely concentrated on cybersecurity and navigation, with fewer organisations using them to modify driving functions or assisted-driving capabilities.

Process inefficiencies and collaboration gaps hinder AI deployment

Organisational challenges remain a major obstacle to scaling AI in automotive product development. Six in ten executives said delays were primarily caused by process inefficiencies rather than technology limitations, while 45% of organisations had yet to define an AI strategy and roadmap for product development.

Collaboration across OEMs, suppliers and technology partners is another area of concern. Only 30% of organisations reported co-development models based on shared outcome metrics, while 10% had established shared accountability across the product lifecycle within their ecosystem.

The findings indicate that wider deployment of agentic AI will depend on organisational processes, data readiness and clearly defined responsibilities alongside technology investment.

The Capgemini Research Institute surveyed 800 senior executives, comprising 400 automotive OEMs, 160 Tier-1 suppliers and 240 technology providers across North America, Europe and Asia-Pacific. The survey was conducted in July 2026 and was supplemented by in-depth interviews with 24 automotive industry executives and technology providers.