AI couldn’t fix quality problems. So Ford rehired its most experienced engineers

At a moment when companies are clamoring to embrace AI—in no small part to capture the attention of shareholders—Ford executives made a rather surprising confession. On a press call last week, the automaker admitted that its issues with quality control could not be resolved with AI. Ford logged a record number of recalls in 2025, and the company has already issued 51 recalls to date this year, significantly more than its peers. But the company highlighted its performance in an annual survey that measures initial vehicle quality, which put Ford well ahead of its mass market competitors—up from its 10th place ranking just last year. The key to this improvement in quality, according to Ford? Hiring 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.” In the last three years, Ford reportedly hired or brought back 350 “gray beard” engineers—as the company described them—drawing on both its own pool of former employees and those who worked with suppliers. Ford claims those veteran engineers had imparted their knowledge to younger workers and improved upon the AI-powered quality tools that the company had adopted, crediting those employees for its quality advances in recent years. “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. Part of the reason Ford’s AI tools had not been effective, according to Bloomberg, was because the company had not infused them with institutional knowledge and expertise from its most seasoned technicians. “We recognized that for us to enhance some of our automation and machine learning a