Ford is marking its rise to No. 1 among mainstream automakers in J.D. Power’s initial quality rankings by taking a candid look at the missteps that helped drag down its quality performance in recent years. A key issue, company officials say, was an overconfidence in automated tools used across production and design. Those systems did not prove as dependable as Ford had expected, forcing the automaker to lean again on seasoned technicians and engineers — including, in some cases, former employees — to fix problems the machines had missed or introduced.
The lesson, Ford now says, is that artificial intelligence can be a valuable tool, but only when it is built on strong data and guided by people who know what to look for. The company also acknowledged that it underestimated the importance of institutional knowledge held by veteran engineers who had spent years moving vehicles through multiple development cycles. As that expertise thinned out, gaps emerged in the systems meant to help maintain vehicle quality.
Charles Poon, Ford’s vice president of vehicle hardware engineering, said during a briefing with reporters this week that the company had placed too much faith in technology alone. “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,” he said.
“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’s VP of vehicle hardware engineering
Poon said some of Ford’s most experienced employees departed before their know-how had been fully captured in the company’s automated systems. To close that gap, Ford hired, promoted, or brought back more than 350 experienced engineers. Their role has been twofold: help younger engineers spot and solve quality issues more effectively, and improve the data collection and AI training processes that support Ford’s automated tools.
“That’s where some of our most experienced engineers have had experience solving and identifying those problems before they creep into the system,” Poon said.
The push comes after a difficult stretch for Ford’s reputation on quality. The company currently leads the industry in recalls, and its ratings have weakened over the past several years. Those troubles were amplified by the challenging launches of the Explorer and Aviator, pandemic-era supply-chain disruptions, and a sharp increase in vehicle recalls.
According to Ford’s COO Kumar Galhotra, the automaker eventually concluded that its approach to quality had become too fragmented. Different departments operated in silos, and the company relied heavily on a “find and fix” philosophy that focused on identifying defects after they appeared and correcting them as quickly as possible. While that approach could address immediate problems, it did not prevent those problems from occurring in the first place.
“We’re moving from that find-and-fix mentality to preventing issues before they occur,” Galhotra said. “We’re focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it.”
The transformation extends beyond vehicle hardware. Software and digital teams now work much more closely with vehicle engineering, manufacturing, and supply-chain teams, executives said. And Ford is now attempting to combine the speed and flexibility associated with software development with the rigor and validation requirements of automotive-grade engineering.
Historically, this wasn’t always the case. Ford was only discovering software bugs late in the process because it wasn’t fully leveraging the rapid iteration cycles available, Poon said. That said, the automaker couldn’t push out software updates as fast as consumer electronics companies with the mentality that it could “move fast and fix later,” Poon said. Vehicles, unlike smartphones, operate in a safety-critical environment where customers depend on software functioning correctly from the moment the vehicle is delivered. To fix this, Ford created a dedicated 40-person software quality assurance team with the sole responsibility of preventing problems before they occur.
But don’t think that Ford isn’t dedicated to integrating AI into more of its processes. The automaker says it has dramatically expanded its automated testing capabilities, adding more than 100,000 new AI-powered tests designed to identify edge cases and stress software systems under a wide range of conditions. Because the testing framework is highly automated, software changes can be rapidly revalidated even late in development, ensuring that modifications do not introduce new defects.
“Because these tests are highly automated, even if we have a late change in the software, we can rapidly run back through the entire validation process to guarantee it works perfectly well before it reaches the customer,” Poon said. “We’ve established software reliability as its own rigorous disciplines with strict metrics.”
