Ford rehires veteran engineers after AI quality systems fall short

Ford executives have hired 350 veteran engineers, some of whom are former employees and others who worked at suppliers, after artificial intelligence and automated systems failed to deliver the desired quality level. Bloomberg reports that the company's chief operating officer, Kumar Galhotra, told journalists that Ford had been 'relying more and more on automated quality systems' with disappointing results. So the company 'brought back technical specialists,' and those specialists 'hunt for failure points before a part ever reaches the plant floor.' Charles Poon, Ford's vice president of vehicle hardware engineering, added, '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.' This doesn't mean Ford is abandoning its AI plans entirely.

Instead, it's using the rehired employees — referred to as 'gray beard' engineers — to train younger staff and reprogram AI tools. This rehiring seems to be paying off, with Ford anticipating that it will lead to $1 billion in reduced costs this year. The automaker also claimed the top spot among mainstream brands in the JD Power Initial Quality Survey released this week.

The move highlights the limitations of relying solely on AI and automation in manufacturing. While these technologies can bring efficiency and cost savings, human expertise and judgment are still essential in ensuring quality and identifying potential failure points. For Ford, reincorporating veteran engineers has helped bridge the gap between AI-driven processes and real-world results.

This approach may serve as a model for other manufacturers and industries that are grappling with the challenges of integrating AI into their production workflows. As companies continue to invest in AI and automation, the need for skilled workers who can complement these technologies will only grow. The success of Ford's strategy will depend on its ability to strike a balance between AI-driven innovation and human expertise.