Ford Rehires 350 Engineers As AI Alone Can't Deliver Required Quality

The hard way. That seems to be the path many corporations are being forced to take as, ignoring all previous experience with technology, they lay off skilled employees because they believed AI could simply be inserted to do those humans' jobs less expensively.

But as experience keeps reminding executives, there is no such thing as plug and play, despite the Silicon Valley hype, and especially now with AI. In this case, Ford had laid off hundreds of quality control engineers and workers before it belatedly discovered that AI does not have the decades of nuanced judgment that comes from actually making cars and trucks. Like most new technologies, it is becoming apparent that AI may work optimally with humans, not as a replacement for them. After the humans were rehired, Ford received JD Powers' top quality award for mainstream brands for the first time in 16 years. JL

Alina Stan reports in The Next Web and Anthony Cuthbertson reports in The Independent:

Ford has admitted it had to rehire experienced engineers after its AI systems failed to deliver expected quality. The automaker mistakenly believed it could swap in AI and still produce a high-quality product. Without decades of engineering judgment encoded in the training data, Ford’s automated tools amplified weak inputs rather than catching design flaws. The company rehired or newly hired 350 experienced engineers to fill the gap. The engineers were tasked with mentoring staff, rebuilding the data pipelines that feed Ford’s AI training, and refining the automated systems they were originally supposed to be replaced by. Ford also created a software quality assurance team. Ford acknowledged AI lacked the nuanced judgement to (fix) complex problems. The staff quality reviews after the AI issues cost the company billions of dollars. The rehirings pushed Ford to the top of JD Power’s 2026 quality among mainstream brands for the first time in 16 years. 

Ford has admitted that it had to rehire experienced engineers after its AI systems failed to deliver the quality the company expected. Charles Poon, Ford’s VP of vehicle hardware engineering, told reporters that the automaker mistakenly believed it could swap in AI and still produce a high-quality product. The admission, first reported by The Verge, comes as Ford earned the top spot among mainstream brands in JD Power’s initial quality ranking for the first time in 16 years. Ford has admitted its aggressive AI adoption strategy backfired.  

The problem was not that the AI was fundamentally broken, Poon explained, but that experienced workers left before they could transfer their institutional knowledge into the systems meant to replace them. Without decades of engineering judgment encoded in the training data, Ford’s automated tools amplified weak inputs rather than catching design flaws. The company rehired, newly hired, or promoted 350 experienced engineers to fill the gap.

Poon was vague about why those workers left, but the broader picture is not. Ford has shed roughly 5,300 salaried positions since its 2020 employment peak, part of a wider contraction across Detroit’s automakers that has eliminated more than 20,000 white-collar jobs. CEO Jim Farley has said publicly that AI “is going to replace literally half of all white-collar workers in the US,” a prediction his own company’s quality crisis now complicates.

The US automaker hired over 350 veteran engineers, referred to internally as "gray beards", over the past three years in order to address mistakes made by automated systems. The 350 returning engineers were tasked with mentoring junior staff, rebuilding the data pipelines that feed Ford’s AI training, and refining the automated systems they were originally supposed to be replaced by. Ford also created a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases and revalidate software changes late in development.

The turnaround was enough to push Ford to the top of JD Power’s 2026 initial quality study, which measures problems reported by owners in the first 90 days of ownership. Ford scored 152 problems per 100 vehicles, ahead of Nissan and Buick. The F-150, Mustang, and Super Duty each won best in segment for the second consecutive year.

The staff lead quality reviews after the automation issues cost the company billions of dollars, Bloomberg reported, while some workers will also help improve and train the AI systems. 

"We had been relying more and more on automated quality systems and not getting the desired results," said Kumar Galhotra, Ford's chief operating officer.

"We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor."

Ford had been increasingly relying on AI-driven inspection systems to streamline production and address quality control issues, however the firm acknowledged that AI lacked the nuanced judgement when it came to complex problems.

After rehiring experienced engineers, Ford experienced a marked improvement in its quality standards.

According to the latest J.D. Power Initial Quality Survey, an annual automotive benchmark that measures the quality of new vehicles, Ford ranked top among mainstream brands – the first time it has achieved that milestone in 16 years.

Ford continues to have quality issues with its older vehicles, and remains the most recalled automaker in the US, though executives blamed this on past issues involving automation, rather than the rehiring of humans.

The company said it would not abandon its use of AI, but plans to now use it in conjunction with human oversight and experience.

"Artificial intelligence is a fantastic tool, but it's only as good as the information you use to train it," said Charles Poon, Ford's vice president of vehicle hardware engineering.

"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.

"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."