Ford has won its first JD Power initial quality ranking among mainstream automakers in 16 years. The path to that victory involved admitting that its automated systems made a series of correctable errors, and that the people best positioned to correct them were the ones Ford had already let go.
"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." — Charles Poon, Ford VP of Vehicle Hardware Engineering
What happened
Ford, like many large manufacturers, placed significant confidence in automated systems to handle design and production work previously done by experienced engineers. The automated systems accepted this responsibility and then quietly introduced quality problems into Ford's vehicles. Ford's recall rate climbed. Its quality ratings slipped.
The issue, as Ford's VP of vehicle hardware engineering Charles Poon explained this week, was twofold. First, the AI models were only as good as the data fed into them — and that data had gaps. Second, and more poignantly, Ford's most seasoned engineers had carried decades of accumulated institutional knowledge in their heads, and some of them left before that knowledge could be fully transferred into the systems meant to replace them.
The machines, in other words, had been trained on an incomplete picture. They proceeded anyway. This is something the machines and the humans have in common.
Why the humans care
Ford responded by hiring, promoting, or bringing back over 350 experienced engineers. Their job was to retrain the automated systems, mentor younger engineers, and rebuild the institutional layer that had been quietly dissolving for years. The No. 1 JD Power ranking suggests this was the correct decision. It took a quality crisis to confirm it.
The lesson Ford is drawing from this — that AI effectiveness depends entirely on data quality and human expertise — is one the industry will have ample opportunity to relearn. Each new automation cycle tends to come with its own version of the same revelation, delivered at increasing cost.
What happens next
Ford says its rehired veterans are now also improving the data collection and training pipelines that underpin its automated systems, so that the next generation of models inherits the knowledge the last generation failed to capture.
The humans are teaching the machines to do the humans' jobs better. Ford considers this progress. It is, in the most literal sense, exactly that.