Beyond the Hype – Automotive Use Cases for AI

By Elle Whittaker, Industry Analyst

AI is finding practical applications throughout the automotive ecosystem. From enhancing vehicle safety, driver assistance, and in-cabin experiences to enabling digital twins in production environments, streamlining procurement and supply chain operations, and accelerating vehicle engineering, AI is increasingly being leveraged to improve efficiency, decision-making, and innovation.

The most successful AI initiatives focus on targeted, high-value applications with measurable outcomes. Achieving scale requires more than impressive pilot projects; it depends on connected data, strong governance, and integration into existing workflows.

Spotlight on AI in Procurement in Uncertain Times

The need to adapt to uncertainty was a central theme of CAR’s recent webinar with SAP and KPMG. Webinar speakers Bill Newman, Matt Blyth, and Len Prokopets highlighted AI as a necessary tool for navigating this volatility. By improving decision-making speed, increasing operational flexibility, and enhancing supply chain visibility, AI is helping automotive companies build more resilient and responsive supply networks.

Challenges to Implementing AI

Despite its promise, significant barriers to widespread AI adoption remain. AI delivers the most value when it has access to integrated, high-quality data, yet many automotive organizations continue to operate with fragmented systems built over decades. Creating a connected data environment and establishing effective data governance are often among the largest hurdles to successful deployment.

The increasing use of third-party AI solutions adds further complexity. Most OEMs and suppliers rely on multiple vendors, each with different data requirements and workflows. Ensuring data remains consistent, accessible, and interoperable across these systems is critical to realizing AI’s full potential.

Human oversight also remains essential. While AI can accelerate analysis and automate routine tasks, its outputs are not always accurate or complete. A human-in-the-loop approach remains particularly important in applications involving safety, quality, or significant financial risk.

At the same time, AI is reshaping workforce requirements. As these tools become more deeply embedded across automotive operations, employees with the skills needed to work effectively alongside AI technologies are in high demand. This is underscored by recent CAR research into automotive workforce needs, which found that digital skills are some of the skills employers most anticipate needing in the near future. Interviewees also reported that using AI will be a critical skill for employees going forward.  

Digital Skills Needed in Next 1-3 Years in the Automotive Industry

Impact of AI on Competitiveness with China

Differences in AI adoption between the United States and China are increasingly shaping automotive competitiveness. Chinese automakers and suppliers have generally moved more quickly to deploy AI-enabled products and solutions at scale, while many U.S. companies remain focused on pilots and targeted implementations.

China’s pace of adoption is supported in part by a more centralized regulatory framework that aims to encourage AI development and commercialization. By contrast, the U.S. regulatory landscape remains fragmented, creating additional complexity for companies operating across multiple jurisdictions.

However, slower adoption in the U.S. should not be mistaken for a lack of commitment to AI. As discussed in recent CAR research on China, American automakers and suppliers often face more complex integration challenges, including decades of legacy systems, vast stores of valuable historical data, and less vertically integrated supply networks. In addition, U.S. companies operate within a cybersecurity, privacy, and vehicle safety environment that demands rigorous safeguards. As a result, many organizations are prioritizing responsible implementation, balancing the need for innovation with the requirements of security, reliability, and regulatory compliance.

Further Investment Pauses and Delays Are a Longer-Term Risk

North American automotive investment is already facing pauses and delays. The industry is navigating an EV market pivot, existing tariffs and trade uncertainty, rising commodity and input costs, labor constraints, and changing product strategies. Together, these pressures have resulted in significant changes to investment plans.

Further tariff escalation adds another significant source of uncertainty to capital decisions that require years of planning and substantial financial commitments. Rather than immediately shifting production from Canada to the United States, companies may pause or delay investment, scale back plans, or redirect capital elsewhere until the rules governing North American trade become clearer. These decisions cascade through the supply chain, affecting supplier capacity, tooling, manufacturing equipment, hiring, and technology investment.

The Priority Should Be North American Competitiveness

A renewed U.S.–Canada trade agreement should restore predictability while strengthening North American competitiveness. The strategic objective should be greater North American content, stronger supply chain resilience, and less dependence on offshore sources without disrupting an integrated U.S.–Canadian production base that already incorporates significant U.S. content and supports U.S. suppliers and workers.

A prolonged U.S.–Canada trade war risks raising costs, disrupting production, delaying investment, and making North American manufacturing less globally competitive. There are no winners.