Ford Rehires Ex-Engineers to Address Flaws in Automated Systems

To mark its achievement as the top contender in JD Power’s initial quality ranking among mainstream automakers, Ford is shedding light on the hurdles it has encountered in recent years, particularly regarding its dependence on automated systems for production and design. It turns out these systems weren’t as reliable as initially thought, prompting Ford to recruit seasoned technicians—sometimes even re-hiring former employees—to rectify mistakes made by the robots.

Ford views AI as a double-edged sword: powerful yet susceptible to errors. The success of AI hinges on the quality of the data used to train its models. Additionally, the company underestimated the importance of the institutional knowledge held by its veteran engineers, who had navigated multiple vehicle development cycles. This oversight contributed to a decline in the quality of 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,” stated Charles Poon, VP of vehicle hardware engineering, during a recent briefing.

Poon noted that some of the company’s most experienced staff left before their valuable knowledge could be fully integrated into Ford’s automated systems. This situation required the return of some employees to help retrain these systems or mentor younger engineers struggling to uphold Ford’s vehicle quality. Over 350 experienced engineers were hired, promoted, or brought back to restore that essential expertise. Their roles include guiding younger engineers and enhancing the data collection and AI training that support 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 explained.

Currently, Ford leads the industry in recalls, and its quality ratings have seen a decline over the past few years. These issues became more evident with the recent challenges surrounding the launches of the Explorer and Aviator, supply chain disruptions during the pandemic, and a noticeable increase in vehicle recalls.

According to Ford’s COO Kumar Galhotra, the automaker realized that its quality approach had become too fragmented. Different departments were working in isolation, relying heavily on a “find and fix” strategy that focused on identifying defects after they occurred and addressing them quickly. While this method could tackle immediate issues, it failed to prevent them from arising 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.”

This transformation goes beyond just vehicle hardware. Software and digital teams are now collaborating more closely with vehicle engineering, manufacturing, and supply chain teams. Ford is striving to merge the speed and flexibility of software development with the rigorous validation standards of automotive engineering.

Historically, Ford discovered software bugs late in the development process because it wasn’t fully utilizing rapid iteration cycles. However, the automaker recognized it couldn’t release software updates as quickly as consumer electronics companies with a “move fast and fix later” mindset. Vehicles operate in a safety-critical environment where customers rely on software to function correctly from the moment they are delivered. To address this, Ford established a dedicated 40-person software quality assurance team focused solely on preventing issues before they arise.

Ford is also committed to integrating AI into more of its processes. The automaker has significantly enhanced its automated testing capabilities, adding over 100,000 new AI-powered tests designed to identify edge cases and stress software systems under various conditions. Thanks to the highly automated testing framework, software changes can be quickly 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 discipline with strict metrics.”