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Ford revealed challenges with its automated production and design systems that led to errors. The company had to rehire experienced engineers, including former employees, to fix these mistakes. This highlights the limitations of AI systems when not properly trained or supervised.
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Ford had to hire back retired engineers to fix quality mistakes made by its automated systems. By rebuilding its human expertise, the automaker is now back on top in terms of vehicle quality.
The automaker was recently named No. 1 in JD Power’s initial quality ranking for the first time in 16 years.
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To celebrate its new status as No. 1 in JD Power’s initial quality ranking among mainstream automakers, Ford is opening up about the challenges it has faced in recent years, especially around its reliance on automated systems in production and design. It turns out that those automated systems were not as robust as previously assumed, requiring Ford to hire experienced technicians — sometimes bringing back former employees — to correct errors made by the company’s robots.
In Ford’s view, AI is both powerful and prone to pitfalls. Its effectiveness depends entirely on the quality of the data used to train the AI models. In addition, the automaker underestimated the value of the institutional knowledge accumulated by its more veteran engineers who had worked through multiple vehicle-development cycles. And this combination of phenomena led to a drop in quality in Ford’s vehicles.
“Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product,” said Charles Poon, VP of vehicle hardware engineering, in a briefing this week with reporters.
“Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product.”