Ford Rehired Former Engineers to Fix AI Automation Errors

Ford's recent achievement of ranking first among mainstream automakers in JD Power's initial quality study came after the company confronted a difficult reality: its automated systems and AI tools had introduced quality problems that required human expertise to resolve.

The automaker brought back former employees and hired over 350 experienced engineers to address errors that emerged when institutional knowledge failed to transfer adequately to automated systems, according to Charles Poon, Ford's VP of vehicle hardware engineering.

The automation miscalculation

"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 told reporters during a briefing this week, as first reported by The Verge.

The problem stemmed from two converging factors. First, Ford's most experienced personnel departed before their accumulated knowledge could be fully captured in automated systems. Second, the effectiveness of AI depends entirely on training data quality—and Ford's data proved insufficient.

The company's quality ratings had declined over recent years, with particularly pronounced challenges during the Explorer and Aviator launches, pandemic-era supply chain disruptions, and a growing number of recalls. Ford currently leads the industry in total recalls.

Rebuilding expertise layers

Ford's solution involved more than just fixing immediate problems. The rehired and promoted engineers now mentor younger staff while improving the data collection and AI training processes that support 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.

COO Kumar Galhotra described the company's previous approach as too fragmented, with departments operating in silos and relying on a "find and fix" philosophy that addressed defects after they appeared rather than preventing them.

Software integration challenges

The quality issues extended beyond hardware. Ford discovered software bugs late in development cycles because it wasn't leveraging rapid iteration methods effectively. However, the company couldn't adopt the consumer electronics industry's "move fast and fix later" mentality due to automotive safety requirements.

Ford created a 40-person software quality assurance team dedicated to prevention. The company also expanded automated testing capabilities, adding more than 100,000 new AI-powered tests designed to identify edge cases and stress-test software under varied conditions.

Why it matters

Ford's experience reveals a critical tension in manufacturing automation: AI and automated systems can enhance efficiency, but they cannot fully replace the institutional knowledge accumulated through years of hands-on problem-solving. Companies pursuing aggressive automation strategies may need to reconsider how they preserve and transfer expertise across generations of workers. The case also demonstrates that quality improvements in complex manufacturing require balancing automation benefits with human judgment—a lesson relevant across industries adopting AI tools.

The Verge first reported these details from Ford's briefing with reporters this week.