That's absolutely right, and it's a really instructive story! Ford rehired about 300 veteran engineers after AI tools failed to address quality issues, helping the automaker top the JD Power 2026 U.S. Initial Quality Study for the first time since 2010. 

Here are the key takeaways:

The Problem:

Ford's chief operating officer Kumar Galhotra said the company had been "relying more and more on automated quality systems" with disappointing results. The VP of vehicle hardware engineering Charles Poon admitted, "Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high quality product". 

Why It Failed:

The core issue wasn't just that AI couldn't do the work—it was 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 Fix:

Ford hired 350 veteran engineers — some of them were former employees, while others had been working at suppliers — to train younger staff and reprogram AI tools. These engineers now act as quality assurance specialists who catch problems before they reach production. 

The Results:

Ford CEO Jim Farley said the shift is helping improve the company's financial performance, with spending on warranty coverage and recalls coming down. 
It's a fascinating real-world lesson in the tacit knowledge problem—some expertise simply can't be replaced by AI without human input to train it properly.

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