US car maker Ford has acknowledged that AI cannot replace decades of engineering experience after the company said it had rehired hundreds of experienced engineers to improve vehicle quality and retrain its automated systems.
Speaking to The Verge last week, Charles Poon, Ford’s Vice President of Vehicle Hardware Engineering, said the company had rehired, newly hired, or promoted around 350 experienced engineers after discovering its AI-driven engineering and quality systems lacked the institutional knowledge needed to deliver the quality improvements it had expected.
“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” said Charles Poon, Vice President of vehicle hardware engineering. “Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.”
According to Poon, rather than a failure of AI technology itself, the problem had been caused by large numbers of ‘greybeard’ engineers leaving the company before the company’s AI systems had been fully trained.
The resulting knowledge gap meant the AI lacked decades of accumulated engineering expertise, causing automated systems to miss defects and design issues that experienced engineers would typically identify.
He said the 350 rehired engineers have been tasked with refining the company’s automated quality systems, improving the data used to train its AI models, and mentoring junior staff.
The company also said it had created a 40-strong software quality assurance team and created an additional 100,000 AI-powered automated tests to revalidate software changes.
Ford reported that after rehiring the experienced engineers, the company ranked top among mainstream brands in the 2026 J.D. Power Initial Quality Survey, a yearly benchmark that measures problems reported by owners during the first 90 days of vehicle ownership – the first time it has achieved that accolade in 16 years.
Ford’s experience is likely to resonate across the manufacturing sector, where AI is being deployed to automate inspection, optimise production, and accelerate product development.
Across manufacturing, AI-powered machine vision systems are increasingly replacing manual quality inspection. By analysing images of components during production, proponents say systems can identify defects far more quickly than conventional inspection methods.
Ford’s experience, however, demonstrates that the accuracy of such systems still depends heavily on the engineering expertise used to train and validate them.
Speaking during a media briefing on the company’s quality improvements, Ford Chief Operating Officer Kumar Galhotra said: ‘We had been relying more and more on automated quality systems and not getting the desired results. We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor.
