THE TAKEAWAY: Ford's push to modernize its engineering and production systems with artificial intelligence did not initially deliver the gains the company expected. Instead, it exposed a gap that technology alone could not fill: the loss of hard-earned engineering judgment built over decades.
It is a shift that comes as Ford returns to the top of J.D. Power's initial quality rankings among mainstream brands. The improvement reflects changes not only in its processes but also in how the company uses AI – and where it draws the line between automation and human expertise.
In recent years, Ford expanded its use of AI in design and manufacturing, leaning on automated systems to speed decisions and simplify development. But those systems proved less resilient than anticipated, particularly when fed incomplete or insufficiently nuanced data.
"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 (via The Verge).
The problem, according to Ford executives, was not simply technical. As experienced engineers left the company, much of their institutional knowledge – often undocumented and built through repeated product cycles – never made it into the datasets training those AI systems. That left gaps in how issues were identified and prevented.
