Ford replaced experienced engineers with automated systems, watched recalls pile up across 11 million vehicles, then quietly hired 350 veterans back to fix what the machines got wrong.
There is a version of the Ford story that the company would prefer you focus on: in June 2026, Ford topped JD Power's initial quality study for the first time in 16 years, beating Nissan and Buick with a score of 152 problems per 100 vehicles. The F-150, Mustang, and Super Duty each won best in segment for the second consecutive year. It is a genuine turnaround, and the company is understandably proud of it.
The version that tells you more is what happened before that headline. Ford became the most-recalled automaker in the United States, issuing 51 recalls covering more than 11 million vehicles in the first half of 2026 alone, more than double the next-closest manufacturer. Its largest single recall affected 4.4 million trucks, including the F-Series, over a software defect that could cause trailer brakes and indicators to fail. The company's own executives now describe the period leading up to that crisis in terms that are remarkably blunt for a major automaker: they made a mistake, and it cost them billions.
Charles Poon, Ford's VP of vehicle hardware engineering, put it plainly. The company introduced AI and ingested its design requirements, and simply assumed that combination would produce a high-quality vehicle. It didn't. What Ford had underestimated was not the software. It was the people who left before anyone thought to ask them what they knew.
The engineers Ford lost weren't carrying checklists. They were carrying years of hard-earned pattern recognition: the specific fastener that always loosened on a particular platform, the supplier whose tolerances drifted at high volume, the failure mode that only showed up under a certain combination of temperature and load. That kind of knowledge doesn't sit in a design specification document. It lives in the judgment of someone who has seen what goes wrong, and it transfers slowly, through mentorship and proximity, not by ingesting a spec sheet into a model.
Ford's COO Kumar Galhotra said the company had been relying more and more on automated quality systems and simply was not getting the desired results. So Ford reversed course. Over three years, it hired more than 350 experienced engineers, some new, some rehired, who now run mandatory quality reviews specifically designed to hunt for failure points before a part ever reaches the plant floor. They've also reprogrammed the AI tools to catch the glitches those same veterans identified from experience. The machines are still there. They're just no longer running unsupervised.
Galhotra's framing of the recall numbers is worth noting. He calls them a lagging indicator, meaning the vehicles now rolling off the line reflect the new approach, not the failed one, and the recall numbers for newer models should come down. That's probably true. It also doesn't change what the period of automation cost in warranty expenses, reputation damage, and the considerable friction of unwinding a workforce strategy that looked efficient on paper.
What other manufacturers should take from this
Ford is not an outlier in the direction it was heading. The pressure to reduce engineering headcount by substituting AI into quality and design workflows is industry-wide, and Ford's experiment is the clearest public case study of what happens when that substitution moves faster than the validation of what the AI can actually do.
The math that made the original decision look attractive inverts quickly when recalls start accumulating. A single recall affecting millions of vehicles is not a rounding error. It is a logistics operation, a legal exposure, a customer relationship problem, and a media event, all at once. The engineers Ford let go were a line item. The recalls that followed were not.
Don't misread the JD Power result as proof that the story ended well. It is evidence that the reversal is working, which is a different thing. Ford is now spending money it didn't have to spend, hiring people it had already paid to leave, to fix problems that experienced engineers might have caught before they became recalls. The JD Power trophy is real. So is the detour it took to get back there.
For any manufacturer currently running the numbers on how many engineers an AI platform can replace, Ford's H1 2026 recall list is the more useful document. Galhotra and Poon are telling you directly: the assumption was wrong. The quality came back when the gray beards came back. That's not an argument against AI in manufacturing. It's an argument for validating what AI can actually replace before you hand it the job.
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