Data and AI

Rewiring automotive product development – How organizations can pull ahead in the AI race

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How organizations are scaling agentic AI in automotive engineering and across the product development lifecycle

The automotive industry is embracing AI at pace, but technology is advancing faster than the processes, governance, and operating models needed to apply it, while scaling AI across the automotive lifecycle. Today’s competitive advantage increasingly depends on turning AI-generated outputs into trusted engineering decisions. However, building trust in AI-driven engineering decisions is proving to be a challenge.

This report draws on a survey of 800 executives from automotive manufacturers, suppliers, and technology providers across 12 countries, complemented by 24 interviews with engineering leaders from across the industry, and explores the future of automotive engineering.

Paris Motor Show 2026

October 12-18, 2026 | Paris Porte de Versailles | Hall 7.1

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Where the industry struggles and succeeds with AI

AI is paying off, and leaders are pulling ahead

A distinct group of organizations is achieving two to three times greater gains in development speed, engineering productivity, and time-to-market, underscoring the advantage AI can delivery across engineering. Their advantage stems from stronger automotive AI governance, higher-quality engineering data, and clearer accountability.

Agentic AI is moving into mainstream engineering workflows

Among organizations who work towards AI adoption in automotive OEMs and suppliers, 35% report using agentic AI in engineering processes, while 33% begin pilot programs. Adoption is expanding fastest across activities linking the engineering lifecycle.

Lack of a connected engineering backbone remains one of the industry’s biggest obstacles

One of the biggest challenges facing the industry a connected engineering backbone that links engineering data, lifecycle platforms, and workflows, giving agents the context and traceability to act across functions.  This is particularly true when it comes to applying AI to engineering-critical decisions and means engineering data governance is a strategic priority.

Automotive AI governance and decision rights

Only 5% of organizations describe their AI governance model as fully mature, while fewer than half have redefined the necessary decision-making authority in terms of engineering decision intelligence.

Vehicle data rarely becomes vehicle action

Many organizations are struggling to translate vehicle telemetry and over-the-air update data insights into engineering actions for vehicles in service, particularly in the era of AI-powered software-defined vehicles.

AI is exposing new dependencies across the ecosystem

Growing challenges around ownership in sovereignty, accountability, cybersecurity, and an AI-powered engineering lifecycle are creating mounting difficulties, particularly as AI scales across partner networks. This has increased the need for a mature automotive AI ecosystem collaboration.

Five actions for automotive leaders

To address these issues and harness the full potential of AI for automotive product development, organizations should:

  • Scale AI across product development in parallel, not sequentially
  • Build an agentic control plane grounded in evidence and traceability
  • Make engineering data machine-interpretable
  • Manage engineering across three key lifecycle metrics
  • Close the loop between vehicles in service and engineering teams to enhance AI-enabled vehicle development

This report is essential for CTOs, chief engineering officers, heads of R&D, chief digital and AI officers, product strategy leaders, and engineering transformation teams seeking to move from AI experimentation to trusted execution across the product lifecycle and build a connected engineering backbone.

Further reading

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Meet our experts

Laurence Noël

Laurence Noël

Executive Vice President, Global Head of Automotive, Capgemini 
With more than 25 years of experience in the automotive industry, Laurence Noël combines deep expertise in business, operations, strategy and engineering. A former senior executive at Stellantis and PSA, she has managed global businesses and operations, with deep knowledge of European and Asian automotive markets. Her focus today is on the major forces reshaping the industry, from AI and software-defined vehicles to engineering transformation and intelligent operations. She also holds board positions across the automotive, mobility and higher education ecosystems.
Heiko Huettel

Heiko Huettel

Executive Vice President, Automotive, Software and Chief Technology & Innovation Officer (CTIO), Capgemini
Heiko brings over 25 years of experience across automotive and technology. He combines deep expertise in cloud, mobility, and software product development with a clear vision for a software-driven automotive ecosystem. Passionate about software-defined vehicles, he supports the industry’s digital transition through open-source collaboration, strong engineering foundations, and practical business execution.
Praveen Cherian

Praveen Cherian

Executive Vice President, Group Automotive, Capgemini 
As an Executive Vice-President within Capgemini Group Automotive, Praveen Cherian connects the technical dots to find the best and simplest solutions to complex business challenges facing automotive industry clients. Along with a strong track record in automotive engineering, he brings global leadership experience in operations, supply chain, and logistics. Praveen’s specialist knowledge spans electric vehicles, fleet operators, battery integration, connected car solutions, and more.
Etienne Grass

Etienne Grass

Managing Director at Capgemini Invent France
Etienne serves as the Managing Director of Capgemini Invent for France. Having joined the Capgemini Group in 2017, he dedicated his initial four years to the Public Services sector in France and later expanded his scope globally within Capgemini Invent. Prior to his current role, he played a pivotal role in leading the BLEU sovereign cloud project for the entire Group.
Eric Dalla-Vecchia

Eric Dalla-Vecchia

Vice President, Intelligent Industry Accelerator, Capgemini
Eric brings over two decades of invaluable experience in the automotive sector, having held key R&D management roles within both OEMs and suppliers. At Capgemini Automotive, Eric spearheads the management of portfolios, strategies, and partnerships within the Intelligent Industry Accelerator. Eric’s expertise lies in orchestrating software-based transformative journeys for industry OEMs and Tier1 software entities.
Oussama Ben Moussa

Oussama Ben Moussa

Automotive Industry Business Leader | CTO Autonomous Mobility
Oussama Ben Moussa is the Automotive Industry Business Leader and Chief Technology Officer for Autonomous Mobility at Capgemini, leading the evolution of “Intelligent Industry” in the automotive sector. With over 20 years of experience, he has been at the forefront of helping automotive players develop and implement advanced solutions that leverage cutting-edge technologies to improve performance and optimize operations.
Christian Hummel

Christian Hummel

Executive Vice President and Head of Automotive at Capgemini Invent, Germany
Christian supports large national and international automotive companies in their digital transformation and growth initiatives. His consulting focus include strategies for Marketing, Sales & CRM, Market Development and Control, organizational development and the implementation of strategic transformation programs. He joined Capgemini in 2005 and has more than 20 years of industry and consulting experience in the automotive and financial services sectors.
Luz Mauch

Luz Mauch

EVP, Head of Embedded Systems Engineering
Luz G. Mauch is a senior executive at Capgemini Engineering with over 25 years of international leadership experience across the automotive, manufacturing, and technology sectors. He specializes in strategy, growth, and large‑scale transformation, with a strong focus on digital transformation, software‑driven business models, and industrial innovation. A trusted advisor to executive and supervisory boards, he brings deep expertise in governance, stakeholder management, and value creation in highly regulated, technology‑intensive environments.

    FAQs

    The report explores how AI, particularly agentic AI, is reshaping automotive product development. It examines where organizations are generating value, what prevents adoption at scale, and how stronger data, governance, engineering processes, and ecosystem collaboration can accelerate progress.

    Agentic AI refers to AI systems that can coordinate activities, orchestrate workflows across tools and functions, and execute defined tasks with varying levels of autonomy. In automotive engineering, potential applications range from documentation and data preparation to coding, testing, simulation, defect analysis, and workflow coordination.

    Thirty-five percent of OEM and supplier executives say their organizations already use agentic AI in engineering processes and tasks, while a further 33% are actively piloting it. The report finds that 65% use generative AI in engineering processes and tasks.

    The strongest reported benefits include higher engineering productivity, faster development cycles, improved testing efficiency, better engineering quality, and reduced time-to-market. High-value realizers report 19% productivity gains, a 17% improvement in development-cycle time, and a 19% reduction in time-to-market.

    The leaders do not necessarily use more AI. They are more likely to have clear business cases, trusted data foundations, stronger governance, aligned incentives, effective cross-team collaboration, and operating models that free engineers from labor-intensive, non-core work.

    AI adoption and value realization are currently strongest in software-focused environments, where workflows and data are already highly digitalized. In mechanical and electrical/electronic engineering, promising areas include performance monitoring, test-result analysis, calibration, simulation-led refinement, and issue diagnosis.

    The main obstacles are not simply technical. They include inconsistent engineering data, fragmented toolchains, unclear decision rights, limited traceability, immature governance, process inefficiencies, and difficulty converting AI-generated outputs into trusted engineering decisions.

    AI outputs in automotive engineering can affect product quality, compliance, safety, and performance. Although 68% of surveyed organizations require human approval for high-impact decisions, only 41% say they can trace the data, assumptions, and rationale behind an AI-influenced outcome.

    Organizations need to connect vehicle telemetry, customer feedback, service data, engineering decisions, validation, and deployment through an automated feedback pipeline. While 81% collect connected-vehicle telemetry, only 40% report having an automated pipeline that connects data collection, insight generation, validation, and deployment.

    The research is based on a July 2026 survey of 800 senior automotive executives, including 400 OEM executives, 160 tier-1 supplier executives, and 240 executives from technology providers. It covers 12 countries across North America, Europe, and Asia-Pacific and is supplemented by 24 in-depth interviews with automotive executives and industry experts.