FORD has rehired hundreds of experienced engineers and quality specialists after conceding that artificial intelligence (AI) and automated checking systems fell short of expectations.

The US auto giant has brought back, hired or promoted around 350 veteran engineers over the past three years, using their experience to identify design and manufacturing problems earlier in the vehicle development process.

Ford executives said the move followed an over-reliance on AI-led systems that were expected to improve quality but failed to match the judgement of experienced human inspectors.

Ford vice-president of vehicle hardware engineering, Charles Poon

Ford vice-president of vehicle hardware engineering, Charles Poon, said: “Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product”.

The engineers, described internally as “grey beard” specialists, are now being used not only to find faults before vehicles reach production, but also to train younger staff and improve the AI tools themselves.

The shift comes as Ford continues to work through long-running quality issues, including warranty costs and recall activity that have weighed heavily on earnings in recent years.

Rather than abandoning AI, Ford says it is now using the technology in combination with human oversight, including more than 100,000 AI-powered software tests designed to validate vehicle systems under different conditions.The revised approach appears to be delivering early gains.

Ford recently ranked as the top mainstream brand in the 2026 JD Power US Initial Quality Study, its best result in the survey since 2010.

The lesson for Ford – and the broader car industry – is that artificial intelligence may accelerate development and testing, but it cannot yet replace decades of accumulated engineering experience.

For an industry racing to cut costs, shorten development cycles and manage increasingly complex software-defined vehicles, Ford’s experience suggests the most effective quality solution may still require people who know exactly where to look.