In a candid reflection on its recent quality struggles, Ford has revealed that it had to bring back over 350 experienced engineers, including many former employees, to rectify mistakes caused by its overreliance on automated systems. The automaker, which recently topped JD Power’s initial quality ranking for the first time in 16 years, acknowledged that its artificial intelligence and robotics were not as robust as initially assumed, leading to a decline in vehicle quality and an increase in recalls.
Ford’s Vice President of Vehicle Hardware Engineering, Charles Poon, explained that the company mistakenly believed that introducing AI and adjusting design requirements would automatically produce high-quality 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,” Poon said during a briefing with reporters. However, the reality was that the automated systems lacked the nuanced understanding that comes from years of hands-on experience, resulting in design flaws and production errors that slipped through the cracks.
The core issue, according to Ford, was the loss of institutional knowledge when veteran engineers retired or left the company. These engineers had worked through multiple vehicle-development cycles and possessed critical insights that were not fully transferred into the AI models. To address this, Ford launched an initiative to hire, promote, or bring back over 350 experienced engineers. These individuals are now tasked with retraining the automated systems, improving data collection, and mentoring younger engineers who struggled to maintain quality standards.
“That’s where some of our most experienced engineers have had experience solving and identifying those problems before they creep into the system,” Poon noted. The automaker also acknowledged that its quality approach had become too fragmented, with departments operating in silos and relying on a “find and fix” mentality. Ford’s Chief Operating Officer, Kumar Galhotra, emphasized a shift toward prevention: “We’re moving from that find-and-fix mentality to preventing issues before they occur. We’re focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it.”
Ford’s quality challenges have been well-documented. The company has led the industry in the number of recalls in recent years, and its quality ratings slipped amid supply-chain disruptions during the pandemic and difficulties with the launches of the Explorer and Aviator models. The automaker now aims to integrate software and digital teams more closely with vehicle engineering, manufacturing, and supply-chain teams. This cross-functional collaboration is designed to combine the speed and flexibility of software development with the rigorous validation required for automotive engineering.
Historically, Ford discovered software bugs late in the development process because it wasn’t leveraging rapid iteration cycles. To fix this, the company created a dedicated 40-person software quality assurance team focused solely on preventing problems before they occur. Additionally, Ford has expanded its automated testing capabilities by adding over 100,000 AI-powered tests that identify edge cases and stress software systems under a wide range of conditions. “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 explained.
Despite these efforts, Ford remains committed to integrating AI into its processes, but with a more balanced approach. The automaker now recognizes that AI is powerful but prone to pitfalls, and its effectiveness depends on high-quality data and human oversight. By rehiring experienced engineers and fostering a culture of prevention, Ford hopes to sustain its quality improvements and avoid repeating the mistakes that led to its recent struggles. The company’s journey serves as a cautionary tale for other industries racing to adopt automation without preserving the human expertise that underpins true innovation.
Source: The Verge News