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Ford had to hire back former engineers to fix mistakes made by its automated systems
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. According to Poon, some of the company's most experienced personnel left before all of their accumulated knowledge could be fully transferred into Ford's automated systems. That necessitated bringing back some of those employees to retrain those systems, or in some cases, mentor younger engineers who were currently struggling to maintain Ford's vehicle quality. Poon said that Ford hired, promoted, or brought back over 350 experienced engineers to rebuild that layer of expertise. In addition to guiding younger engineers, they've also been tasked with improving the data collection and AI training that underpin Ford's automated systems. "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. Ford currently leads the industry in the number of recalls, and its quality ratings have slipped over the past several years. Those challenges became more pronounced recently, with the difficulties associated with the launches of the Explorer and Aviator, supply-chain disruptions during the covid pandemic, and the noticeable growth in the number of its 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."
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Ford Made This 1 Miscalculation on AI -- and Then Had to Hire More Humans to Fix It
Artificial intelligence and automation aren't foolproof. It's a lesson that Ford had to learn the hard way when it was forced to hire -- and in some cases rehire -- experienced employees to correct mistakes that undermined the quality of its vehicles. The Detroit-based automaker disclosed the snafu during a briefing following its announcement that it topped the JD Power 2026 U.S. Initial Quality Study, which ranks vehicles by quality. For context, Porsche topped the overall list, whereas Ford topped the list of mass-market brands. "Many doubted that an American company with a huge American workforce could compete with the world's best on quality, let alone reach the top. But we put our heads down and worked together every day to deliver for our customers," Ford president and CEO Jim Farley said in a statement. There may be good reason for the skepticism that Farley alluded to. USA Today reported earlier this month that Ford currently leads U.S. automakers in vehicle recalls in 2026. It is followed by Stellantis (parent of brands like Chrysler and Jeep), General Motors, Hyundai, and Toyota. Ford's chief operating officer Kumar Galhotra, however, says that recalls are a "lagging indicator" of vehicle quality and will likely come down in the future for newer Ford models, according to Bloomberg.
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Ford topped JD Power's initial quality ranking for mainstream automakers, but the victory came after a costly lesson in AI limitations. The company had to hire back over 350 experienced engineers to fix mistakes made by automated systems that lacked the institutional knowledge of veteran employees. The misstep led to increased vehicle recalls and quality issues.

Ford has revealed a significant setback in its reliance on AI and automated systems, admitting the company had to hire back over 350 experienced engineers to correct errors that compromised vehicle quality control
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. The disclosure came as Ford celebrated its top position in JD Power's initial quality ranking among mass-market automakers, with Porsche leading the overall list2
. The achievement masks a troubling period where the Detroit-based automaker struggled with AI and automation failures that undermined manufacturing processes."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, VP of vehicle hardware engineering, admitted during a briefing with reporters
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. The company's AI-driven processes proved inadequate when experienced personnel departed before their institutional knowledge could be fully transferred into automated systems. This created a vacuum that younger engineers couldn't fill, leading to quality degradation across Ford's vehicle lineup.The automaker's struggle highlights a critical lesson about AI effectiveness: it depends entirely on data training quality and cannot replace decades of accumulated human expertise. Ford underestimated the value veteran engineers brought through multiple vehicle-development cycles. When these experienced technicians left, they took with them nuanced problem-solving abilities that AI models hadn't captured. According to Poon, Ford had to bring back former employees to retrain automated systems and mentor younger engineers who were struggling to maintain quality standards
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.The consequences became visible through Ford's quality control struggles. The company currently leads U.S. automakers in vehicle recalls in 2025, followed by Stellantis, General Motors, Hyundai, and Toyota
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. Quality ratings slipped over several years, with challenges becoming particularly pronounced during the launches of the Explorer and Aviator models, compounded by supply-chain disruptions during the pandemic.Ford COO Kumar Galhotra acknowledged that the automaker's approach had become too fragmented, with different departments operating in silos and relying heavily on a "find and fix" philosophy that addressed defects after they appeared rather than preventing them
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. "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 now extends beyond vehicle hardware. Software and digital teams work closely with vehicle engineering, manufacturing, and supply-chain teams. Ford created a dedicated 40-person software quality assurance team with sole responsibility for preventing problems before they occur. The automaker discovered it wasn't fully leveraging rapid iteration cycles available in software development, only finding bugs late in the process. However, Poon noted that Ford couldn't adopt a "move fast and fix later" mentality like consumer electronics companies because vehicles operate in safety-critical environments where software must function correctly from delivery
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Despite setbacks, Ford remains committed to integrating AI into its processes, but with stronger guardrails. The company has expanded automated testing capabilities, adding more than 100,000 new AI-powered tests designed to identify edge cases and stress software systems under various conditions. Because the testing framework is highly automated, software changes can be rapidly revalidated even late in development, ensuring modifications don't introduce new defects
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.Ford president and CEO Jim Farley framed the turnaround as proof that American manufacturing can compete globally. "Many doubted that an American company with a huge American workforce could compete with the world's best on quality, let alone reach the top," Farley said
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. Galhotra maintains that vehicle recalls are a lagging indicator and will likely decline for newer Ford models as the company's restructured quality approach takes hold. The question remains whether Ford's expensive lesson about AI limitations will translate into sustained quality improvements or whether the company's recall numbers will continue reflecting past mistakes.Summarized by
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