Ford Motor Company has rehired more than 300 experienced engineers after finding that artificial intelligence (AI) alone could not match the expertise required to maintain vehicle quality, highlighting the continued importance of human knowledge in advanced manufacturing.
The move comes after the US automaker expanded the use of AI across parts of its operations, including quality inspections, in an effort to improve efficiency, reduce costs and boost productivity.
However, company executives acknowledged that the technology did not deliver the expected results without the guidance and experience of seasoned engineers.
According to reports, Charles Poon, Ford’s Vice President of Vehicle Hardware Engineering, said AI remained a valuable tool but stressed that its effectiveness depended on the quality of the data and expertise used to train it.
“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” he said.
Poon admitted the company had underestimated the value of its most experienced engineers, many of whom had worked through multiple vehicle development cycles.
He said Ford had not adequately captured their knowledge before some left the company, limiting the effectiveness of its AI-powered quality systems.
“We didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles,” he said.
Ford has been among several global manufacturers investing heavily in AI as companies seek to automate processes and improve productivity.
Last year, Ford Chief Executive Officer Jim Farley predicted that AI would significantly reshape white-collar employment, while Chief Operating Officer Kumar Galhotra said the company was deploying AI across its industrial operations.
As part of that strategy, Ford introduced about 900 AI-powered cameras across its manufacturing plants to identify quality issues early and reduce supply chain disruptions.
Despite those investments, Poon said the AI-driven inspection systems failed to meet expectations because they lacked the practical judgement developed through years of engineering experience.
He noted that the company had mistakenly believed feeding design requirements into AI systems would automatically produce high-quality outcomes.
Instead, Ford found that experienced technicians remained essential in identifying defects and making critical quality decisions that AI systems struggled to replicate.
The company has since brought back veteran engineers to strengthen quality assurance, train AI models with better data and mentor younger employees.
According to Poon, Ford recognised that improving its automation, machine learning and AI capabilities required systems to be trained by the company’s most experienced professionals.
The renewed focus on human expertise appears to be yielding results.
Ford recently returned to the top position among mainstream automakers in the United States in the J.D. Power Initial Quality Study, an industry benchmark that measures vehicle quality. It is the first time the company has achieved the ranking since 2010.
In a statement marking the achievement, Ford said reaching best-in-class quality required a significant talent refresh, including changes to senior leadership across engineering, manufacturing and supply chain operations.
The company also credited the recruitment of about 300 veteran engineers whose decades of design experience have helped improve product quality and strengthen its AI systems.
The development underscores a growing view across industries that while AI can enhance productivity, human expertise remains critical in areas requiring judgement, experience and complex decision-making.
