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

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.

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.