The auto industry has historically operated under long development cycles.
But amid rapid technological advancements and growing competition from Chinese manufacturers that are bringing new products to market at a lightning pace, automakers are under more pressure than ever to speed things up—without sacrificing quality or cost.
Enter the go-to solution for seemingly every modern-day conundrum: artificial intelligence.
JP Clausen, EVP of global manufacturing at General Motors, recently shared with Tech Brew some of the automaker’s strategies for making the best use of AI tools in manufacturing—and he was quick to caution that AI isn’t a silver bullet.
“Not every problem in this world can be solved by AI. There are some problems that AI and machine learning are good at, but it’s not everything,” said Clausen, an alum of Google, Tesla, and Lego who stepped into the lead manufacturing role at GM last year. “Understanding the problem you’re going to solve first is probably the most important thing of the whole equation.
“Knowing your parameters…is the key point for making an AI system that is successful,” he added. “Because otherwise you’ll just boil the ocean.”
On the factory floor
GM’s implementation of AI in manufacturing is focused on three areas: safety, quality, and efficiency.
The automaker, for example, is putting automated guided vehicles to work in plants, Clausen said, to automate processes that could be cumbersome or unsafe for employees. GM’s leaders have emphasized that they view AI and robots as complementary to workers, not as replacements.
“We want everyone to feel comfortable with the technology. It’s an opportunity, in our mind, to continue to develop our employees and make them a part of the process,” Clausen said. “Robots, AI, and people should work together and make the workplace better for everyone.”
GM is also using robotics and a proprietary AI tool to inspect the welds and paint coats on vehicles to ensure they’re done properly.
And the automaker is leveraging digital twin technology to simulate production lines before they’re up and running, as part of an effort to scale up manufacturing faster and more cost effectively.
“You can build a whole factory. You can build all the production lines. You can build all the different work cells,” Clausen said. “And you can go down and actually simulate all the movements.”
Speed is crucial, he said, because it’s become especially important to bring new products to market quickly in the incredibly competitive EV space.
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Clausen said that GM’s approach of offering a wide variety of electric models across its brands (about a dozen are slated to be on the market by the end of this year), and releasing them at a relatively quick pace, has helped boost its competitiveness. The automaker is now the No. 2 EV maker in the US.
“You can actually go out and scale your production faster if you do it with AI,” Clausen said, “simply because you have less surprises.”
To that end, GM and Nvidia recently announced a new partnership under which the automaker will use Nvidia’s AI chips and software for its autonomous vehicle tech as well as in its plants. Nvidia’s platforms will underpin AI manufacturing models for factory planning, among other initiatives.
“This new collaboration is absolutely key for us to crank more data faster,” Clausen said. “And we definitely believe everything we are doing in manufacturing with robots, simulations, automatic traffic systems, and detecting deviations in quality systems will be further developed with this partnership.”
Looking for anomalies
GM is using another AI tool in plants that integrates data from battery tests, vehicle operations, and service work to train an AI model that’s used to detect voltage anomalies in battery packs before they make their way into an EV, according to Jie Du, senior scientist manager of applied analytics and insights. It’s now in use at all of GM’s battery assembly plants.
The goal is to improve the customer experience, provide more detailed insights to ensure EV battery quality, and help bring down warranty costs.
The idea is that the algorithm can predict voltage anomalies much sooner in the battery pack assembly process, ensuring that packs with defects in them don’t end up in a vehicle before they leave the factory. Since introducing the tool last year, GM has analyzed hundreds of thousands of battery packs and found an anomaly rate of less than 0.1%.
“AI is only as good as the application of it,” Jon Francis, GM’s chief data and analytics officer, whose team leads GM’s battery manufacturing AI work, said in a statement. “At GM, we’re intentional about applying AI in ways that will have a positive impact on the business or our customers. Applying AI to battery manufacturing does exactly that.”
Correction 4/07/2025: This story has been updated to correct JP Clausen's title.