Ford rehiring veteran engineers to fix AI-driven quality problems

Ford Motor Co. said it finished No. 1 among mainstream brands in the 2026 J.D. Power U.S. Initial Quality Study after rehiring and promoting more than 350 experienced engineers over the past three years to repair quality problems that automated systems missed. The company internally calls some of those veterans “gray beard” engineers, and it has put them to work mentoring younger staff, reprogramming AI tools, and restoring the product-cycle judgment that Ford says had eroded.

The automaker’s own explanation was blunt. “Mistakenly we thought that by just introducing artificial intelligence ... that would produce a high-quality product,” Ford said, framing the turnaround as a correction to an overreliance on software. Jim Farley has described the effort as a broader culture change rather than a retreat from AI, and Ford still expects automation to help reduce costs, including a projected $1 billion in savings this year.

The quality numbers matter because Ford has spent years dealing with recalls and fit-and-finish problems that have weighed on its reputation in Dearborn, Michigan, and across the U.S. market. Ford said its No. 1 finish among mainstream brands was its first since 2010, ending a 16-year gap at the top of that segment. The ranking gives the company a rare clean data point in an industry where quality scores can move quickly and consumer tolerance for defects is thin.

J.D. Power said the 2026 study, released June 25, was based on responses from 78,514 purchasers and lessees of new 2026 model-year vehicles surveyed after 90 days of ownership. Overall new-vehicle quality improved year over year in nine of 10 categories, but infotainment and connectivity remained the stubborn weak spot. That detail underscores why Ford is leaning back on experienced engineers: the most frustrating problems are often the ones that require human judgment, not just automated checks.

For Ford, the message is less about replacing machines than about correcting the limits of machine-first development. The company is betting that veteran engineers can catch the defects, retrain the staff, and tighten validation fast enough to turn quality into a cost advantage instead of a drag on margins.