Ford learned the hard way that AI can't replace experienced engineers
SMRTR summary
Ford thought artificial intelligence would transform its engineering process. It did, just not in the way the company hoped.
After expanding AI use in design and manufacturing, Ford discovered its automated systems were only as good as the data feeding them. And that data had a serious blind spot: decades of institutional knowledge that walked out the door with retiring engineers, undocumented and unrecorded.
"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," said Charles Poon, Ford's VP of vehicle hardware engineering.
The fix? Ford brought back more than 350 seasoned engineers, not just to mentor, but to actively rebuild the data foundation those AI systems depend on.
The company also established a dedicated 40-person software quality team and added over 100,000 AI-driven edge-case tests. The goal, says COO Kumar Galhotra, is simple: "Stop admiring the problem and start solving it."
SMRTR provides this summary for quick context. The original article belongs to TechSpot.
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