There is no disputing the increasing proliferation of Artificial Intelligence in every sector, and the automotive industry is certainly no stranger to the importance of digital transformation to business success.
A recent Grant Thornton CFO survey showed digital transformation is a top priority for CFOs – to help them manage uncertainty and business risk, improve customer experience, and optimize financial and operational performance. In addition, CFOs believe that even in times of great economic upheaval, improvements in technology provide a reliable return on investment while helping to navigate a continuously shifting business environment.
The Role of AI in Automotive Transformation
As the auto industry grapples with massive uncertainty due to shifting trade policies and geopolitical tensions, AI offers automotive companies the promise of significant gains in customer experience, quality, supply chain resilience, manufacturing optimization and financial performance. Through technologies like machine learning, digital twins, computer vision, and natural language processing, AI supports smarter in-vehicle systems, optimized production processes, and intelligent tools aligned with evolving industry needs.
Some examples of AI applications in the automotive sector include vehicle design, development and testing, predictive maintenance, supply chain, plant design and simulation, manufacturing quality optimization, and in-vehicle customer experience. With the rapid advancement of software-defined, connected and autonomous vehicles, in-vehicle AI applications are also being implemented at an accelerating pace.
Although AI is gaining traction in the automotive industry, it is estimated that only 10 percent or less of the industry has adopted AI at scale and is maximizing its operational and strategic benefits. There is, therefore, a significant opportunity for further AI-driven business benefits in the industry.
Types of AI
There are three distinct types of AI:
- Applied AI – where AI is deployed to solve specific real-world problems –
for example, predictive maintenance in a plant. - Generative AI (GenAI) creates (generates) various types of new content and is often used to provide a starting point for a project, which a human being can then develop further. For example, automakers use it to create initial product designs.
- Agentic AI is where an AI ‘agent’ acts autonomously to achieve specified goals, such as re-routing a shipment due to a supply chain issue.
Generative AI and Agentic AI are increasingly seen in applications such as product and shop floor design, quality control, crash testing, voice assistants, customer service and supply chain optimization. In these and other areas, AI offers vast untapped potential for transforming the industry and delivering order-of-magnitude improvements in competitive advantage.
What’s Next for AI?
With global economies constantly shifting, AI – and intelligent automation in general – will play an increasingly important role in the automotive industry. For example, a stated objective of the recent U.S. tariff strategy is to reshore manufacturing. However, given the disparity in wage rates between developed and developing countries, any reshored manufacturing will likely have to be heavily automated and AI-enabled to produce competitively priced products.
Two key challenges in a data-driven digital transformation are data quality and systems integration. The smartest AI is only as good as the underlying data – low data quality leads to unreliable results at best, and serious business impact at worst. Disconnected, siloed systems limit decision velocity and business agility.
A successful transformation must address these foundational requirements, and those vendors and technology platforms that include strong data capabilities and the ability to integrate with existing legacy systems should rise to the top in any selection process. In addition, a multi-tenant cloud architecture is highly recommended in order to maximize scalability and business agility and reduce IT total cost of ownership.
Beyond technology considerations, decision makers should set specific process improvement objectives to achieve long-term, sustainable gains in effectiveness, efficiency, and cost savings. Built-in process mining and robotic process automation (RPA) should be included in key vendor and platform selection criteria.
Furthermore, to accelerate the realization of business benefits, auto companies should seek out solutions that offer pre-built, industry-specific processes that hasten time to value, minimize configuration needs, reduce project costs and risks, and improve user adoption.
The Future of AI-Driven Transformation in Automotive
The CASE-driven (Connected, Autonomous, Software-Defined and Electrified) transformation of the automotive industry is creating unrelenting pressure to act quickly, decisively and proactively in an environment of endemic uncertainty. From optimizing manufacturing capacity, volume and mix for various powertrain combinations (ICE/BEV/hybrid) to managing costs while still funding innovation, improving supply chain resilience, meeting stakeholder and customer expectations and increasing business agility and scalability, challenges and opportunities abound.
Data-driven, AI-enabled digital transformation is an imperative the industry needs to embrace in order not just to navigate the current uncertainty, but also to strengthen its competitive position for the next century of the automobile.
Peter Maithel is the Global Automotive Industry Strategy Lead at Infor and is responsible for the automotive industry strategy which includes the alignment of Infor’s solutions and go-to-market approach with the voice of the customer and overall industry trends.