Nearly seven in 10 automotive companies are already using or piloting agentic artificial intelligence (AI) in product development, but only 5% have fully mature AI governance systems, highlighting a widening gap between technology adoption and organisational readiness, according to a new report by Capgemini Research Institute.
The report, Rewiring automotive product development: how organizations can pull ahead in the AI race, found that 33% of automotive organisations are already using agentic AI, while another 35% are piloting the technology.
Adoption in non-core engineering activities is expected to rise from 26% currently to 68% over the next two to three years, indicating the potential for AI agents to take on a larger role in automotive development workflows.
The findings also point to significant productivity gains. As many as 83% of executives believe agentic AI will allow engineers to devote more time to core engineering work instead of administrative and coordination tasks.
However, shortcomings in data quality, traceability, accountability and collaboration across the automotive ecosystem remain obstacles to scaling the technology.
AI leaders report faster development cycles
The study, based on a global survey of 800 senior automotive executives across 12 countries, found that companies extracting the greatest value from AI are outperforming their peers on key engineering measures.
These high-value realisers reported 17% faster development cycles, 19% higher engineering productivity and a 19% reduction in time-to-market.
According to the report, these gains depend not simply on adopting AI tools but on establishing reliable data foundations, defining clear business cases and building effective governance and operating models.
The findings suggest that automotive companies that integrate AI into engineering processes and redesign workflows around the technology could gain a competitive advantage as vehicle development becomes increasingly software-driven.
Capgemini Global Head of Automotive Laurence Noel said agentic AI was creating a new performance divide in the automotive industry.
Companies generating the greatest value were not merely adopting AI faster but were redesigning workflows, strengthening collaboration across their ecosystems, building trusted data foundations and establishing clear accountability for AI-assisted decisions, she said.
The companies that successfully embed AI into engineering processes at scale will be better positioned to lead the next phase of automotive innovation, Noël added.
US and Europe ahead in AI strategy
The report found significant differences in AI maturity across major automotive markets.
In the US, 53% of original equipment manufacturers (OEMs) surveyed are implementing AI initiatives, compared with 46% in Europe. Comprehensive cross-functional AI strategies, visions and roadmaps are in place at 18% of US respondents and 16% of European respondents.
In China, 40% of respondents reported being in the implementation phase, but only 7% had reached a comparable level of strategic maturity.
Implementation maturity remains lower elsewhere in Asia-Pacific, according to the report.
The differences indicate that AI adoption is not progressing uniformly across the global automotive industry. Having a clear strategy that connects engineering, technology and business objectives is emerging as an important factor in translating experimentation into measurable gains.
Trust remains a barrier to wider adoption
Despite growing interest in agentic AI, executives remain cautious about deploying the technology in activities where errors could affect engineering outcomes, vehicle quality and safety.
The survey found that trust in agentic AI was highest in upstream research and concept development, at 59%. Confidence fell to 40% in development activities, where AI-generated outputs can have more direct implications for product performance and safety.
Only 5% of organisations reported having fully mature AI governance.
Vehicle data not fully utilised
The report also identified weaknesses in the automotive industry’s ability to use data from vehicles already on the road to improve products continuously.
Although connected vehicles generate substantial volumes of operational and usage data, nearly two-thirds, or 64%, of organisations said insights from vehicles in use primarily inform future vehicle programmes rather than improvements to vehicles already on the road.
Over-the-air software updates are also concentrated in areas such as cybersecurity and navigation. Updates that modify driving functions or assisted-driving capabilities remain less common.
The findings point to an opportunity for automakers to shorten the feedback loop between real-world vehicle performance and engineering teams.
Faster analysis of vehicle data, combined with more effective software development and validation, could help manufacturers identify issues and improve products more quickly. However, safety-critical changes require appropriate testing and regulatory compliance.