{"id":383481,"date":"2026-07-01T02:41:56","date_gmt":"2026-07-01T00:41:56","guid":{"rendered":"https:\/\/prostartup.it\/ford-brings-back-veteran-engineers-after-ai-quality-setback\/"},"modified":"2026-07-01T02:41:56","modified_gmt":"2026-07-01T00:41:56","slug":"ford-brings-back-veteran-engineers-after-ai-quality-setback","status":"publish","type":"post","link":"https:\/\/prostartup.it\/ru\/ford-brings-back-veteran-engineers-after-ai-quality-setback\/","title":{"rendered":"Ford Brings Back Veteran Engineers After AI Quality Setback"},"content":{"rendered":"
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Ford is leaning on veteran engineers again after AI did not deliver the quality results the company expected.<\/p>\n

The automaker has rehired about 350 experienced engineers to help improve quality checks, train younger workers, and refine its AI tools. The move shows that even as companies adopt more automation, experienced people still play a key role in high-stakes work where overlooked issues can affect costs, recalls, and customer trust.<\/p>\n

Why Ford brought veteran engineers back<\/h2>\n

Ford had adopted AI and automated systems across parts of its operations, including quality checks, the BBC reported. The company later brought back hundreds of veteran quality inspectors and engineers after executives found that automated systems could not fully replace the judgment of experienced workers.<\/p>\n

\u201cArtificial intelligence is a fantastic tool, but it\u2019s only as good as the information you use to train it,\u201d Charles Poon, Ford\u2019s vice president of vehicle hardware engineering, told reporters, according to the BBC.<\/p>\n

TechCrunch reported that Ford hired 350 veteran engineers, including former employees and specialists who had been working at suppliers. Ford COO Kumar Galhotra said the company had been \u201crelying more and more on automated quality systems\u201d but did not get the quality level it wanted.<\/p>\n

The specialists, sometimes called \u201cgray beard\u201d engineers, now help younger employees find design and manufacturing risks earlier. They also help retrain and reprogram AI tools so the systems can learn from workers with decades of product knowledge.<\/p>\n

Poon said Ford had made the wrong assumption about what AI could do on its own.<\/p>\n

\u201cMistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product,\u201d he said, according to TechCrunch.<\/p>\n

Experienced workers still shape AI results<\/h2>\n

Ford\u2019s experience points to a common issue for companies using AI in quality control. Data can show defects, requirements, and past failures, but it may not capture the judgment that comes from watching parts fail across several product cycles.<\/p>\n

For manufacturers, that gap can affect warranty costs, recalls, supply chains, and customer trust.<\/p>\n

In other industries, the same issue can appear in compliance reviews, software testing, customer support, or security operations, where automated systems can repeat mistakes if experienced workers are not part of the review process.<\/p>\n

Ford\u2019s course correction appears to be helping. Entrepreneur noted that Ford topped J.D. Power\u2019s Initial Quality Study among mainstream brands for the first time in 16 years, with the F-150, Mustang, and Super Duty ranking first in their categories.<\/p>\n

Ford CEO Jim Farley also said the quality turnaround helped lower warranty and recall costs, contributing to \u201chundreds and hundreds of millions of dollars\u201d in cost tailwinds, per TechCrunch.<\/p>\n

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What companies can take from Ford\u2019s AI reset<\/h2>\n

Ford\u2019s AI reset is a reminder that automation works best when people with deep knowledge help shape it. The company is still using AI, but it is pairing the technology with workers who understand how design choices, manufacturing limits, and field failures connect.<\/p>\n

For companies using AI in quality control, compliance, software testing, or other high-stakes work, the takeaway is clear. AI adoption plans should include expert review, knowledge transfer, and measurable quality checks, not only productivity targets.<\/p>\n

Cost savings may still come, but Ford\u2019s example shows they are more likely when automation strengthens experienced teams instead of replacing their judgment.<\/p>\n

Also read: Gartner says AI-driven customer service cuts may reverse by 2027 as firms rebalance automation and human support.<\/strong><\/p>\n

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