Artificial intelligence (AI) will be a crucial part of the German carmaker’s plans to reduce costs and speed development, The Wall Street Journal (WSJ) reported Wednesday (Sept. 30) from a BMW investor event.
The company told investors AI will allow it to become agile and effective, which will mean the need for fewer management layers and a reorganization of business units.
The number of divisions and management roles will be cut by 20% over the coming months, with “a comparable reduction at the organizational levels below,” the company said.
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BMW executives have already reached agreements with labor representatives for a major staff restructuring that could impact as many as 8,000 white-collar workers in Germany, the report said, citing a source familiar with the matter. The company had just under 155,000 employees at the end of 2025.
“We are improving our structures and cost base so we can meet the increasingly fierce competition that will define this industry in the coming years,” CEO Milan Nedeljkovic said in a statement from the event. “The workforce restructuring program is an important lever for this.”
The WSJ report notes that automakers in Germany are stepping up cost cutting efforts amid increasing competition and a shrinking market in China. These companies are also dealing with rising energy costs and U.S. tariffs, the report added.
Against this backdrop, BMW said at the Wednesday event that it must greatly increase speed and efficiency companywide, as it works towards improving its earnings before the end of the decade, the report said.
PYMNTS wrote in June that BMW now runs more than 600 AI use cases across its business, per figures from AWS.
This includes engineers using AI to conduct crash simulations without building physical prototypes, and procurement teams use the technology to analyze supplier contracts and generate tender documents. It’s part of a broader trend among European car companies, as covered here in May.
“The pressure behind that movement is specific,” that report said. “Order-to-delivery flows across disconnected systems, multiple suppliers and dozens of handoffs. By the time teams learn about a delay, it has already hit the schedule. Inventory builds in the wrong place, small disruptions cascade and manual tracking fails at scale. AI fixes that by monitoring workflows in real time and triggering interventions before delays reach the customer.”
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