But what happened inside Ford tells a very different story—one that should serve as a warning to every executive convinced software can replace decades of real-world experience.
Ford’s AI Strategy Didn’t Deliver
Beginning in 2020, Ford dramatically reduced its salaried workforce, eliminating more than 5,000 positions.
Many of those leaving were seasoned engineers who had spent decades solving manufacturing problems, improving vehicle reliability, and learning lessons that never appear in a textbook or database.
The expectation was that advanced software, automation, and artificial intelligence would make engineering more efficient.
Instead, Ford learned a painful lesson.
Charles Poon, Ford’s vice president of vehicle hardware engineering, openly acknowledged the company’s mistake.
“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high quality product,” Poon told reporters.
Reality proved otherwise.
Artificial intelligence could analyze enormous amounts of information, but it lacked something critical: the practical knowledge accumulated by engineers who had spent decades building and testing vehicles.
Those hard-earned instincts—the ability to recognize subtle warning signs before problems emerged—simply couldn’t be downloaded into a computer.
The consequences became impossible to ignore.
Ford led all American automakers in recalls this year, issuing 51 separate recalls affecting more than 11 million vehicles.
The technology was impressive.
The results were not.
Ford Changed Course
Instead of doubling down on a strategy that wasn’t working, Ford made an unusual decision in today’s corporate climate.
The company acknowledged its mistake.
Over the last several years, Ford rehired, promoted, or brought back roughly 350 experienced engineers whose knowledge had become impossible to replace.
These veterans weren’t hired back simply to fill office chairs.
They immediately became central to Ford’s effort to rebuild quality.
Engineering teams now conduct detailed design reviews every week, identifying potential failures long before production begins.
Vehicle components are torn apart and examined daily rather than every few months.
Prototype vehicles are pushed to their limits under extreme conditions, with testing designed to simulate approximately 225,000 miles of real-world driving before customers ever take delivery.
Poon explained exactly why the company reversed course.
“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.
That statement doesn’t reject artificial intelligence.
It acknowledges a simple truth: AI performs only as well as the people teaching it.
Without experienced professionals guiding the technology, even sophisticated systems can make costly mistakes.
The Results Speak for Themselves
Ford’s renewed focus on experienced engineering talent has already produced measurable improvements.
According to the newly released 2026 J.D. Power U.S. Initial Quality Study, Ford ranked as the highest-performing mainstream automotive brand in America—its best showing since 2010.
The company also achieved the largest year-over-year improvement among mainstream automakers, reducing customer-reported problems by 41 issues per 100 vehicles.
Its flagship products delivered especially impressive results.
The F-150, Mustang, and Super Duty each earned best-in-segment honors for the second consecutive year.
Even more remarkable, seven of Ford’s ten evaluated models finished among the top three in their respective categories, giving the company one of the strongest performances in the industry.
Ford Chief Operating Officer Kumar Galhotra credited the turnaround to the engineers who drove the company’s renewed commitment to quality, describing them as being “at the heart” of the recovery.
As quality improved, warranty expenses declined, recall costs dropped, and Ford’s financial outlook strengthened.
A Remarkable Reversal
Perhaps the most striking part of Ford’s story is how dramatically it contrasts with Jim Farley’s earlier predictions.
The same executive who confidently declared AI would eliminate half of America’s white-collar jobs now finds his company celebrating the experienced employees whose knowledge proved indispensable.
Ford wasn’t alone in pursuing aggressive workforce reductions.
Across Detroit, General Motors eliminated roughly 11,000 salaried positions, while America’s Big Three automakers collectively cut more than 20,000 white-collar jobs as companies assured investors that artificial intelligence would help bridge the gap.
Ford’s experience suggests that expectation was overly optimistic.
Technology accelerated many processes, but it failed to replace the judgment developed through decades of hands-on work.
The employees once viewed as expensive and outdated ultimately became the key to restoring Ford’s reputation for quality.
That lesson extends far beyond one automaker.
Artificial intelligence remains a powerful tool capable of improving productivity and streamlining countless tasks.
But tools still require skilled professionals who understand how—and when—to use them.
Ford’s remarkable turnaround serves as a reminder that software cannot instantly replicate the knowledge earned through years spent solving real-world problems.
In the end, it wasn’t artificial intelligence that rescued one of America’s most iconic automakers.
It was the people whose experience the machines could never duplicate.
