AI and digital twins: An automotive revolution or risky bet?  

In the automotive manufacturing space, AI is mainly being discussed as a productivity booster – but there are unrecognized benefits of AI that have been significantly overlooked. From writing code to optimizing design, AI holds the potential to greatly transform the industry.

The pace at which it can happen in a heavily regulated industry is unclear, but one fact remains true: AI is here to stay, and manufacturers need to implement the tech in 2024 to effectively transform and optimize vehicle design processes. But where do manufacturers start, and how far are they able to go in the near future? 

Breaking Down the Types of AI 

When discussing a starting point for AI adoption, manufacturers must understand the difference between generative AI, which creates code, content, and more, and functional AI, which helps to iterate operations for improved efficiency. Each provides different capabilities that help accelerate car manufacturing and maintenance. 

For example, today’s cars often contain smart infotainment systems that require consistent data to feed into the cloud to help the system manage the state of the car – from transmission temperatures to oil change alerts. Manufacturers can leverage both forms of AI: generative AI can write the code that powers these systems, while functional AI can assist with processing data in a timely manner.  

Disrupting Digital Twin Technology  

The automotive industry has already experienced disruption in their manufacturing processes with digital twin technology – a virtual model of a physical object or process. In fact, some of the most well-known automotive manufacturers like BMW and GM already deploy this technology to replicate manufacturing processes, such as simulating crash tests, optimized aerodynamic designs, and more. However, as more organizations adopt AI in 2024, companies will marry these two technologies to optimize efficiency.  

Say a manufacturer runs digital twin simulations to identify areas in the assembly line that typically slow down overall production. Once identified, these manufacturers can leverage these digital twins to run simulations to test different operational efficiencies to maximize production. Now, adding in AI, suddenly the simulations can be iterated on orders of magnitude faster as the AI can be used to automatically redesign and optimize, while the engineers no longer need to spend large amounts of time deciphering which variables to tweak and test.  

Looking Ahead: The Road to Efficiency in 2024 

The digital twin use case is a prime example of how AI will potentially disrupt the automotive industry in 2024. However, given the intensity and amount of regulation the industry already faces, implementing generative AI into the vehicles and the systems they run may be deemed too risky at the current moment. For organizations to truly extract the full potential value from AI, the best strategy is to combine it with digital twin technology.  

And, while most automotive manufacturers have added some form of AI into their processes in the last year, it's likely that they focus on the back-office work and iterating paper processes. In 2024, expect manufacturers to move the focus away from these solutions and integrate AI into their technical processes instead, leveraging digital twin technology to transform automotive production. The hype over AI may be at its peak, but its real potential has only just begun.  

Brad Hart is CTO at Perforce, a maker of enterprise scale development tools.