Ford rehired 350 engineers after AI systems failed to replace decades of expertise

Ford learned the hard way that AI can't replace experienced engineers

Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. The takeaway: Ford's push to modernize its engineering and production systems with artificial intelligence did not initially deliver the gains the company expected. Instead, it exposed a gap that technology alone could not fill: the loss of hard-earned engineering judgment built over decades. It is a shift that comes as Ford returns to the top of J.D. Power's initial quality rankings among mainstream brands. The improvement reflects changes not only in its processes but also in how the company uses AI - and where it draws the line between automation and human expertise. In recent years, Ford expanded its use of AI in design and manufacturing, leaning on automated systems to speed decisions and simplify development. But those systems proved less resilient than anticipated, particularly when fed incomplete or insufficiently nuanced data. "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 (via The Verge). The problem, according to Ford executives, was not simply technical. As experienced engineers left the company, much of their institutional knowledge - often undocumented and built through repeated product cycles - never made it into the datasets training those AI systems. That left gaps in how issues were identified and prevented. To address that, Ford brought back and promoted more than 350 seasoned engineers. Their role extends beyond mentorship. They are now actively shaping how data is collected, interpreted, and fed into the company's AI models, effectively rebuilding the foundation on which those systems depend. "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 has faced declining quality ratings in recent years and currently leads the industry in recalls. High-profile vehicle launches, including the Explorer and Aviator, revealed execution challenges, while pandemic-era supply chain disruptions added further strain. Ford has had to issue recalls for several high-profile models, including the Aviator Executives say those issues were compounded by structural inefficiencies. Different teams - spanning software, hardware, manufacturing, and supply chain - often worked in isolation. That fragmentation reinforced a reactive approach to quality, where defects were identified late and corrected under pressure. "We're moving from that find-and-fix mentality to preventing issues before they occur," said COO Kumar Galhotra. "We're focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it." A key part of that shift involves integrating software development practices more tightly with traditional automotive engineering. In the past, Ford frequently discovered software defects late in the development cycle. At the same time, it could not adopt the rapid-release mindset common in consumer tech, where issues are often resolved after deployment. That's because vehicles operate under different constraints - software must function correctly from the outset, given the safety implications. To close that gap, Ford established a dedicated 40-person software quality assurance team focused entirely on early-stage validation and defect prevention. AI still plays a central role at Ford, but the company is using it within clearer limits. The company has added more than 100,000 AI-driven tests that target edge cases and push the system under a wide range of conditions. The tests run in an automated system that lets engineers quickly recheck software after changes, even late in development. The aim is to catch any new defects without slowing down the process. "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." Ford's experience points to a wider challenge for companies using AI in complex industrial systems. Automation can speed up work and broaden testing, but it still depends on solid data and the people who know how to use it. In Ford's case, the plan is to rely on a more balanced setup in which AI supports engineers rather than replacing them. It now wants its systems to reflect not only computing power, but also the practical knowledge it has built up over years of making vehicles.

Ford rehired 350 engineers to fix what its AI systems got wrong

Ford rehired 350 engineers after AI failed to replicate veteran expertise, then hit No 1 in JD Power quality for the first time in 16 years. Ford has admitted that it had to rehire experienced engineers after its AI systems failed to deliver the quality the company expected. Charles Poon, Ford's VP of vehicle hardware engineering, told reporters that the automaker mistakenly believed it could swap in AI and still produce a high-quality product. The admission, first reported by The Verge, comes as Ford earned the top spot among mainstream brands in JD Power's initial quality ranking for the first time in 16 years. The problem was not that the AI was fundamentally broken, Poon explained, but that experienced workers left before they could transfer their institutional knowledge into the systems meant to replace them. Without decades of engineering judgment encoded in the training data, Ford's automated tools amplified weak inputs rather than catching design flaws. The company rehired, newly hired, or promoted 350 experienced engineers to fill the gap. Poon was vague about why those workers left, but the broader picture is not. Ford has shed roughly 5,300 salaried positions since its 2020 employment peak, part of a wider contraction across Detroit's automakers that has eliminated more than 20,000 white-collar jobs. CEO Jim Farley has said publicly that AI "is going to replace literally half of all white-collar workers in the US," a prediction his own company's quality crisis now complicates. The 350 returning engineers were tasked with mentoring junior staff, rebuilding the data pipelines that feed Ford's AI training, and refining the automated systems they were originally supposed to be replaced by. Ford also created a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases and revalidate software changes late in development. The turnaround was enough to push Ford to the top of JD Power's 2026 initial quality study, which measures problems reported by owners in the first 90 days of ownership. Ford scored 152 problems per 100 vehicles, ahead of Nissan and Buick. The F-150, Mustang, and Super Duty each won best in segment for the second consecutive year. The quality win does not erase a rougher track record. Ford has led US automakers in recalls this year, issuing 51 so far in 2026 covering more than 11 million vehicles, more than double the next-closest manufacturer. It also joins a growing list of companies discovering that removing human judgment from AI-driven workflows creates problems the technology cannot fix on its own. The episode lands at a moment when AI companies and policymakers are scrambling to figure out what the transition means for workers. OpenAI, Anthropic, Amazon, and Microsoft this week backed RAISE US, a $500 million nonprofit led by former commerce secretary Gina Raimondo to retrain American workers for the AI economy. Ford's experience suggests the harder problem is not retraining but knowing which workers you cannot afford to lose in the first place.

Ford Scrambled to Rehire Engineers After Sabotaging Itself With AI

Can't-miss innovations from the bleeding edge of science and tech Ford just admitted that it scrambled to rehire former employees and find new technicians after its AI systems simply weren't good enough. "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," the automaker's VP of vehicle hardware engineering Charles Poon told reporters, per The Verge. It's a catastrophically naive blunder that plenty of other arrogant bosses have been making. But seemingly Ford thinks it can come out looking better if it owns up to it and frames it as a cautionary tale -- fresh off of earning the number top spot in JD Power's initial quality ranking for the first time in over nearly two decades. The way Poon tells it, though, AI wasn't exactly the problem. Instead, it all went wrong because its experienced workers left before Ford could get them to transfer their valuable knowledge to Ford's AI systems and help refine the tech intended to obviate them. So of course they had to bring them back to train the AI systems and the hapless new employees. They were also asked to improve the AI training behind these systems. Poon is being vague about why those experienced employees left, but Ford has been gradually cutting down its workforce, with over 5,000 fewer workers than it had in 2020. Meanwhile, its CEO Jim Farley has declared that AI "going to replace literally half of all white-collar workers in the US." In all, Poon says Ford rehired, newly hired, or promoted 350 experienced engineers to fix the AI fallout. That's not a lot in the grand scheme of things, but the true cost was the reputational damage it suffered in the meantime. As The Verge notes, it's recalled cars more often than any other automaker in the US this year, and has slipped in dependability rankings. If you thought imagined the automaker's leadership would have turned against AI over the whole episode, think again -- per The Verge, it's added more than 100,000 new AI-powered tests to identify edge cases and stress software systems.