Ford just admitted that it scrambled to rehire former employees and find new technicians after its AI systems simply weren’t good enough.

“Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product,” the automaker’s VP of vehicle hardware engineering Charles Poon told reporters, per The Verge.

It’s a catastrophically naive blunder that plenty of other arrogant bosses have been making. But seemingly Ford thinks it can come out looking better if it owns up to it and frames it as a cautionary tale — fresh off of earning the number top spot in JD Power’s initial quality ranking for the first time in over nearly two decades.

The way Poon tells it, though, AI wasn’t exactly the problem. Instead, it all went wrong because its experienced workers left before Ford could get them to transfer their valuable knowledge to Ford’s AI systems and help refine the tech intended to obviate them. So of course they had to bring them back to train the AI systems and the hapless new employees. They were also asked to improve the AI training behind these systems.

Poon is being vague about why those experienced employees left, but Ford has been gradually cutting down its workforce, with over 5,000 fewer workers than it had in 2020. Meanwhile, its CEO Jim Farley has declared that AI “going to replace literally half of all white-collar workers in the US.”

In all, Poon says Ford rehired, newly hired, or promoted 350 experienced engineers to fix the AI fallout. That’s not a lot in the grand scheme of things, but the true cost was the reputational damage it suffered in the meantime. As The Verge notes, it’s recalled cars more often than any other automaker in the US this year, and has slipped in dependability rankings.

If you thought imagined the automaker’s leadership would have turned against AI over the whole episode, think again — per The Verge, it’s added more than 100,000 new AI-powered tests to identify edge cases and stress software systems.

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