Ford Motor Company has earned its first top ranking among mainstream automakers in the JD Power Initial Quality Study since 2010 — and the path to that achievement runs directly through the company's admission that replacing veteran engineers with artificial intelligence was a mistake that cost it years of quality, billions in warranty expenses, and an industry-leading recall record it is only now beginning to escape.
Over the past three years, Ford hired, promoted, and rehired approximately 350 experienced engineers and technical specialists — many of them retired employees or veterans from key suppliers — after concluding that its AI and automated quality control systems were delivering inferior results. The problem, as Ford VP of Vehicle Hardware Engineering Charles Poon explained plainly to reporters, was not that AI is inherently incapable. It was that the company let its most experienced engineers walk out the door before their institutional knowledge could be captured and used to train the tools that were supposed to replace them. When experienced engineers left, they took decades of hard-won pattern recognition with them. The AI tools trained on the data that remained produced a systematically lower quality product. Ford moved from 15th to 1st among mainstream brands in the JD Power study — a swing of 41 fewer reported problems per 100 vehicles — scoring 152 problems per 100 vehicles against Nissan's 156 and Buick's 162. Only premium brands Porsche and Genesis ranked higher across all brands studied.
"Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product." — Charles Poon, Ford VP of Vehicle Hardware Engineering
The rehired engineers were not brought back to compete with the AI — they were brought back to fix it. Their three roles were clear: identify failure points that automated systems kept missing, mentor junior engineers who had never worked alongside true veterans, and retrain the AI tools on better, more expert-grounded data. Ford COO Kumar Galhotra described their work succinctly: they hunt for failure points before a part ever reaches the plant floor. The F-150, Ford Mustang, and Super Duty each won their segments for the second consecutive year. Seven of Ford's top ten models ranked in the top three of their respective categories. CEO Jim Farley said the quality improvements have produced hundreds of millions of dollars in cumulative savings from reduced warranty and recall expenses — a financial payoff that directly validates the investment in human expertise.
The lesson Ford learned is one that is being quietly relearned across American industry: artificial intelligence amplifies human expertise. It does not replace it. When companies gut their experienced workforces expecting AI to cover the gap, they discover too late that the gap AI cannot fill is the very judgment, intuition, and institutional memory that makes the technology useful in the first place. Ford's quality turnaround — from 15th to 1st in three years — is a case study in what happens when a company is willing to admit it was wrong and act on that admission at scale.
Despite the JD Power win, Ford remains the most-recalled automaker in the United States in 2026, having issued 51 recalls covering more than 11 million vehicles — more than double the next-closest manufacturer. Company executives have noted that recalls are a trailing quality indicator reflecting vehicles built before the current engineering overhaul, and that the metric should improve as newer, higher-quality vehicles make up a larger share of the fleet in coming years.
Ford's story is the right lesson at the right moment. As businesses across every sector rush to replace human workers with automation, the Detroit giant spent three years learning the hard way what happens when you remove the people who know things the machines don't. The result was billions in warranty costs, industry-leading recalls, and a quality ranking near the bottom of the industry. The fix was not more AI. It was 350 people who had spent careers building cars. That is not a story about technology failing. It is a story about wisdom — and what it costs when companies choose to discard it.