Ford rehired – Ford executives say AI couldn’t solve the company’s quality-control problems. With quality issues tied to how AI tools were trained, the automaker points to rehiring its most experienced engineers—bringing back 350 “gray beard” veterans over three years—as a k
On a press call last week, Ford executives delivered a confession that doesn’t fit neatly into the AI hype cycle.
The company said its quality-control problems could not be resolved with artificial intelligence.
The admission lands at a tense time for the automaker. Ford logged a record number of recalls in 2025. and it has already issued 51 recalls to date this year—significantly more than its mass-market peers. Ford did not deny the pressure. Instead, it pointed to a different scoreboard: an annual survey that measures initial vehicle quality.
That survey put Ford well ahead of its mass market competitors, moving the automaker up from 10th place last year.
Ford’s explanation for the jump was as specific as it was human. The company said it improved quality by bringing back some of its most tenured engineers.
“Artificial intelligence is a fantastic tool. but it’s only as good as the information you use to train it. ” Ford VP Charles Poon told reporters. per a Bloomberg report. “Over prior years. we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles.”.
Ford described a recruitment effort that has less to do with novelty than with institutional memory. Over the last three years, the automaker reportedly hired or brought back 350 “gray beard” engineers. The company said it drew from both its own pool of former employees and engineers who had worked with suppliers.
Ford credits those veteran engineers with transferring knowledge to younger workers and improving its AI-powered quality tools—an effort tied directly to the company’s own stated weakness.
“Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product,” Poon said.
Bloomberg reported that part of the reason Ford’s AI tools had not been effective was that the company had not infused them with institutional knowledge and expertise from its most seasoned technicians. Poon added that Ford needed to make sure its machine learning and artificial intelligence systems were trained using its most experienced individuals.
“We recognized that for us to enhance some of our automation and machine learning and artificial intelligence tools we needed to ensure that they were trained by the most experienced individuals,” Poon said.
The story is an uncomfortable reminder for a corporate world that has often treated AI as a shortcut to efficiency—something companies rush to advertise to shareholders. It’s also a rarer kind of admission: Ford is saying that the information quality matters as much as the technology itself. and that the missing ingredient wasn’t an engineering tool. It was experience.
That point sits awkwardly in a labor market where many employers are eager to attract young talent and workers with AI fluency. Some companies. Ford’s situation suggests. also treat AI and automation as a way to solve problems created by an aging workforce—without reckoning with what older employees can leave behind when they depart: headcount. yes. but also deep expertise.
In an excerpt from his new book, author Dan Pontefract recently wrote that the demographic shift isn’t something businesses can simply wish away.
“Older workers are not optional,” Pontefract wrote. “They are the scaffolding holding up skills transfer, institutional memory, and cultural continuity across every workplace on the planet.”
Ford’s latest numbers and its explanation are now pulling those ideas into the open—at the exact moment when the company is trying to push quality higher while facing scrutiny over recalls. The improvement in its initial-vehicle-quality ranking may point to what worked. The pushback against AI as a standalone fix points to what still doesn’t.
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