GM executives are racing each other on AI adoption

You wouldn't expect senior auto executives to gamify software adoption, but that's exactly what's happening at General Motors. CFO Paul Jacobson revealed that Mary Barra's top leaders compare monthly AI-usage figures across every function and department, calling it "a healthy competition among all of Mary's senior leaders". Jacobson admitted his own finance team is currently "kind of in the middle of the pack."

A GM spokesperson echoed Jacobson's language, describing the rivalry as a "friendly competition" rather than a mandate. Still, the fact that usage gets tracked and shared monthly across departments suggests GM wants visibility into who's actually adopting the tools, not just who says they are.

GM has previously detailed how AI tools now offer design recommendations for concept cars and flag potential supply chain disruptions before they become costly.

Chief product officer Sterling Anderson has said his team's explicit goal is compressing the timeline between design and production, a priority that ties directly into the competitive pressure Jacobson described.

Speaking at JPMorgan's US All Stars Conference in London, Jacobson connected GM's AI urgency to intensifying competition from Chinese automakers, who are expanding globally with cheaper, longer-range EV technology.

He described the US as "a little bit of a safe haven", with manufacturers redirecting capacity here to avoid the sharpest edges of that competition elsewhere.

Despite the internal contest, Jacobson was candid that GM's AI strategy remains in its "infancy", with the company still working out how the technology will reshape vehicle features, quality control, and manufacturing.

His warning was blunt: companies that don't become more efficient will fall "further and further behind".

Within his own department, Jacobson's ambition is specific, flipping how analysts spend their time. Today, he said, about 80% of their hours go toward explaining past results, with just 20% spent anticipating what's next.

He wants AI to invert that ratio entirely, freeing analysts to focus on prediction rather than retrospective reporting, since, in his words, "AI's going to do that so much faster than any of us can."