Ford turns to human engineers after AI fell short, recruits 350 professionals

Ford has revealed that it was forced to bring experienced engineers back into its workforce after artificial intelligence failed to deliver the level of vehicle quality the company expected, offering a cautionary tale as businesses increasingly turn to AI to streamline operations.

Speaking to reporters, Charles Poon, Ford's vice-president of vehicle hardware engineering, said the company had underestimated the value of decades of engineering experience when it expanded the use of AI in its development process.

The comments, first reported by The Verge, come shortly after Ford topped JD Power's 2026 Initial Quality Study among mainstream automotive brands for the first time in 16 years.

According to Poon, the issue was not that the AI technology itself was incapable. Instead, the systems lacked the benefit of institutional knowledge because experienced engineers had left before their expertise could be incorporated into the company's AI models and development tools.

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Ford recruits 350 engineers to replace AI

To address the problem, Ford rehired, recruited or promoted around 350 experienced engineers. Their role extended beyond traditional engineering work, with the company tasking them to mentor younger employees, improve the quality of training data used by AI systems and refine the automated tools that had originally been expected to reduce the need for human intervention.

Poon suggested that AI performed only as well as the information it received. Without years of practical engineering judgement embedded into its training process, the technology struggled to identify weaknesses in vehicle design and, in some cases, reinforced flawed assumptions instead of correcting them.

Although Poon did not explain why so many experienced employees had left, the development follows several years of workforce reductions across the US automotive industry. Since reaching its employment peak in 2020, Ford has eliminated roughly 5,300 salaried positions as part of a broader restructuring effort. Across Detroit's major carmakers, more than 20,000 white-collar jobs have reportedly disappeared during the same period.

The revelation also comes after Ford chief executive Jim Farley previously argued that AI "is going to replace literally half of all white-collar workers in the US", a prediction that now appears more nuanced in light of the company's own experience.

AI remains central to Ford's strategy

Rather than abandoning AI, Ford has chosen to strengthen the systems supporting it. Alongside rebuilding its engineering teams, the company established a dedicated software quality assurance group comprising 40 specialists and introduced more than 100,000 AI-powered automated tests designed to identify edge cases and validate software changes before vehicles reach customers.

The changes appear to have produced measurable improvements. In JD Power's 2026 Initial Quality Study, which tracks the number of problems reported by owners during the first 90 days of vehicle ownership, Ford ranked first among mainstream manufacturers with 152 problems per 100 vehicles. Its F-150, Mustang and Super Duty models also retained their positions as segment leaders for a second consecutive year.

Even so, the company's broader quality record remains mixed. Ford has issued 51 recalls so far in 2026, affecting more than 11 million vehicles, making it the leading US automaker by recall volume this year and more than doubling the total of its nearest competitor.

Ford's experience reflects a wider debate unfolding across industries as organisations seek to balance automation with human expertise. Technology companies including OpenAI, Anthropic, Amazon and Microsoft recently backed RAISE US, a US$500 million non-profit initiative led by former US Commerce Secretary Gina Raimondo to help prepare workers for an AI-driven economy.

For Ford, however, the lesson appears to be less about replacing employees with AI than recognising which expertise cannot easily be replicated. The company's turnaround suggests that while artificial intelligence can accelerate development, its effectiveness still depends heavily on the knowledge and judgement of the people behind it.