Ford rehires 350 veteran engineers after AI failures expose limits of automated quality control

Ford rehires 'gray beard' engineers after AI falls short

Ford executives said they have hired 350 veteran engineers -- some of them were former employees, while others had been working at suppliers -- after artificial intelligence and automated systems failed to deliver the desired quality level. Bloomberg reports the company's chief operating officer Kumar Galhotra told journalists that Ford had been "relying more and more on automated quality systems" with disappointing results. So the company "brought back technical specialists," and those specialists "hunt for failure points before a part ever reaches the plant floor." Charles Poon, Ford's vice president of vehicle hardware engineering, added, "Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product." To be clear, this doesn't mean Ford is abandoning its AI plans entirely. Instead, it's using the rehired employees -- referred to as "gray beard" engineers -- to train younger staff and reprogram AI tools. This rehiring seems to be paying off, with Ford anticipating that it will lead to $1 billion in reduced costs this year. The automaker also claimed the top spot among mainstream brands in the JD Power Initial Quality Survey released this week.

Ford Had to Rehire Veteran Engineers After Its AI Flopped. Other Employers Should Take Notice

North American Car, Truck and SUV of the Year (NACTOY) Awards Juror At a conference last year, Ford CEO Jim Farley said that artificial intelligence is "going to replace literally half of all white-collar workers in the US." Just last week, Ford executives said that the automaker had quietly rehired more than 350 of what it internally calls "gray beard" engineers over the past three years to help fix the AI quality-control systems that weren't getting the job done. Over the last decade, US automakers have cut more than 20,000 jobs, nearly a 20% reduction in workforce between Ford, General Motors and Stellantis combined. While Ford hasn't said for sure how many of these gray beard rehires were originally fired to make way for AI and how many are simply returning retirees, Farley's recent statements on automation-fueled worker replacement certainly paint an awkward picture. Representatives for Ford and the United Auto Workers union did not immediately respond to requests for comment. Not getting the desired results "Artificial intelligence is a fantastic tool, but it's only as good as the information you use to train it," Charles Poon, Ford's vice-president of vehicle hardware engineering, told reporters last week. "Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product." Kumar Galhotra, Ford chief operating officer, was even more blunt about the realities of AI in manufacturing, saying that Ford had been "relying more and more on automated quality systems and not getting the desired results." More than a simple oopsie, automation issues have been costing Ford billions in warranty costs and recalls. A study from iSeeCars, an automotive marketplace and research company, ranked recent Ford models among the most recalled vehicles in the industry. Ford's statements and the rehiring of experienced workers are essentially an admission that moving too quickly into AI was a big mistake. Many major corporations in almost every aspect of tech and manufacturing have been naming artificial intelligence as an excuse for large workforce reductions, often without fully accounting for what gets lost when that human factor walks out the door. Entire industries have been crunching the uncomfortable numbers of replacing human judgment with automated systems, with some even backtracking on their decisions when the true cost of AI proves too high. What happens now? Last week, Ford announced that, for the first time in 16 years, it had captured the number one spot among mainstream brands in JD Power's 2026 Initial Quality Survey, up from tenth last year. The automaker credits the rise, in part, to the contributions of the rehired gray beards. But before you get too excited about the triumph of these modern-day John Henrys over the machines set out to replace them, don't forget what ultimately happened to that folklore hero: He was still replaced by the steam engine. Galhotra said the rehired specialists -- some former Ford employees, others drawn from industry suppliers -- were brought back specifically to "hunt for failure points before a part ever reaches the plant floor." Ford isn't abandoning AI. Instead, the returning gray beards are doing two things: training younger staff who never worked alongside those veterans and helping to rebuild the data pipelines that the AI tools run on. Essentially, they've been brought back to fix and train the automated software systems that replaced them. Ford also said it has built a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases late in development. Technology marches on. Ford just happened to learn the lesson loudly enough to become a case study, but I don't think it will be the last. There may not always be gray beards to call on to save the day.

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."

Employers who laid off workers citing AI are already starting to regret it

Companies are rapidly changing their minds that artificial intelligence can "do it all" by rehiring employees to propel their businesses forward, as investors fret over the longevity of the ongoing AI boom happening in the financial markets. Automaker Ford is one of the latest companies to reverse course. It is reportedly re-employing hundreds of experienced human engineers to work on quality issues automated systems couldn't address. "Artificial intelligence is a fantastic tool, but it's only as good as the information you use to train it," Charles Poon, Ford's vice president of vehicle hardware engineering, told the media. Other companies that have walked back their hiring plans to focus more on human capital include Commonwealth Bank of Australia and software giant IBM. Last year, CBA laid off more than 40 customer service staff and replaced them with an AI voice bot. However, the AI system was unable to cope, which led to an increase in calls, prompting CBA to reverse the job cuts. "Getting CBA to rescind these job cuts is a massive win," Australia's finance sector union said in a statement. According to an ABC report in August last year, CBA admitted it "did not adequately consider all relevant business considerations" when announcing the redundancies and acknowledged "we should have been more thorough in our assessment of the roles required". Similarly, IBM replaced its HR functions with AI that handled around 94% of routine requests but was unable to meet the other 6%, which included ethical dilemmas. IBM then announced plans to triple its U.S. entry-level hiring across all business units in 2026. "If we don't continue to invest in entry-level hires, what happens in 3-5 years?," IBM chief human resources officer Nickle LaMoreaux said at a Charter AI Summit in New York. "There's no pipeline; the well simply dries up," LaMoreaux added. These examples echo views presented by analysts that making employees redundant while using more AI may not necessarily offer the best route to business growth. "Budgeting on 'tech to replace humans' without investing in training or upskilling left teams unprepared to leverage AI," according to a report by Intuition Labs. "Notably, among companies pushing automation, many later 'regretted' layoffs, having cut the very people needed to oversee AI," it added. According to a report by Orgvue, 39% of business leaders made employees redundant due to AI deployment. However, among that number, 55% admit wrong decisions about those redundancies were made. "Where AI outputs are inconsistent, inaccurate, or difficult to apply, companies often need to reintroduce human oversight," said Jessica Zhang, senior vice president of APAC at HR solutions provider ADP. "This can lead to duplicated effort, slower decision-making, and diminished productivity gains," Zhang added. Meanwhile, 32% of U.S. hiring managers said they eliminated a role primarily due to AI and later rehired for the same or a similar position, according to data from Robert Half sent to CNBC. "AI is changing the workplace, but it's becoming clear that organizations are finding more value in building human-AI collaboration versus replacing human work entirely," Capitol Technology University noted.

Ford rehires human engineers after AI fails to match quality checks

Ford says it has hired back some human engineers after AI failed to match their skills and experience. In a bid to reap the benefits of the tech, which developers claim can cut costs and boost productivity, the US carmaker adopted it across some parts of its operations including for quality checks. But, according to Bloomberg, its executives said the firm has rehired more than 300 "veteran" quality inspectors in recent years to make up for the pitfalls of automated systems. "Artificial intelligence is a fantastic tool, but it's only as good as the information you use to train it," Charles Poon, vice president of vehicle hardware engineering, told reporters. "Over prior years, we didn't pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles," he said. The US automaker is among many to have seized on the buzz around AI, particularly amid Wall Street fervour about the tech's potential to increase margins. "AI will leave a lot of white collar people behind," Ford boss Jim Farley said in an interview with author Walter Isaacson last June. In an October earnings call, chief operating officer Kumar Galhotra said the firm was "deploying AI across the entire industrial system". This included rolling out 900 AI-powered cameras in its plants "to detect quality issues at the source and help us mitigate supply disruptions", Galhotra told investors. But Poon told reporters on Wednesday the firm's AI-driven checks had failed to live up to expectations. "Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product," he said. Poon reportedly pointed to automated tools lacking the training and expertise of veteran technicians - many of whom he said had left the company before their knowledge could be used to improve its tech. He said these human workers had since been reintroduced to train up its systems, as well as mentor younger workers. "We recognised that for us to enhance some of our automation and machine learning and artificial intelligence tools we needed to ensure that they were trained by the most experienced individuals," he said, per Bloomberg. Ford's admittance of its AI failings came as it lauded its return to the top of an index used as an industry benchmark to measure vehicle quality. It said it was the number one mainstream automaker in the US JD Power Initial Quality Study - a ranking it has not held since 2010. In a press release marking the news, the company said "reaching best-in-class quality required a significant talent refresh". This involved replacing senior leaders across engineering, supply chain and manufacturing, it said, as well as hiring the roughly 300 veteran engineers "who carry the hard-earned wisdom of decades of design". Sign up for our Tech Decoded newsletter to follow the world's top tech stories and trends. Outside the UK? Sign up here.

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 replaced hundreds of engineers with AI -- now it's bringing them back after quality problems

Waking up to another round of layoffs at a major company can be disheartening, especially when it's announced that the latest loss of human workers is due to AI. A slew of AI-driven firings have been seen at massive enterprises -- Amazon, Meta, Oracle, and Salesforce are just a few of the businesses that have released a depressingly high number of their workers as they pursue more AI-focused initiatives. In comparison, it's increasingly rare to see a story on your timeline where a company reverses its decision to rely on AI too heavily and reverts to the human workforce it released or refocuses its efforts to employ new staff. Thankfully, that aforementioned rare occurrence has become a reality, according to a recent Bloomberg report. And that report showcases just how much someone in such a high position at a company is willing to admit the error in their ways when trying to pursue bigger moves in the AI space. Here's a bit of cheerful news for those who've been treated to far too much doom and gloom about AI negatively affecting the job market. On the road back to human ingenuity Ford reportedly adopted AI across several sections of its car-making operations. During an October 2025 earnings call, COO Kumar Galhotra noted that the company was "deploying AI across the entire industrial system." Those plans included a mass rollout of 900 AI-powered cameras in its manufacturing plants that were meant "to detect quality issues at the source and help us mitigate supply disruptions." But it seems like those efforts fell short of expectations, as Charles Poon, Ford's vice president of vehicle hardware engineering, noted how the company's automated tools lacked the training and expertise of its experienced engineers. Ford's executives have seemingly rethought their decision to go all-in on AI, as it's being reported that the company has rehired more than 300 veteran quality inspectors to pick up the slack that those AI tools are responsible for. Galhotra alluded to less-than-stellar results emanating from the company's increased AI usage, which has led to Ford bringing back its best technical specialists to "hunt for failure points before a part ever reaches the plant floor." Those specialists, who have lovingly been referred to as "grey bear" engineers due to their veteran status, have been brought back to train Ford's younger staff and refine its AI tools. So far, that decision has seemingly paid off, as Ford CEO Jim Farley has alluded to the company having lowered warranty and recall costs as of late. Ford isn't the only company that decided to rehire its formerly laid-off employees after deciding to go big on AI. After Klarna CEO Sebastian Siemiatkowski commented that a chatbot was handling the work of 700 customer service reps in 2024, the company began restaffing humans in 2025. And according to a Careerminds survey conducted in February, 32.7% of companies that have conducted AI-led layoffs have rehired between 25-50% of the roles they got rid of beforehand and 35.6% of the companies that were surveyed noted that they rehired for more than half of the roles they initially laid off. Final thoughts Amidst the dark clouds brought on by companies laying off so many of their employees as they continue to invest in AI automation efforts, it's nice to see a slight shining beacon emerge as some businesses reverse their layoffs and bring back the humans they need to do the work AI simply can't master. And to see another major company like IBM, which has simultaneously installed more AI into its systems while also having plans to hire more software developers, is amazing to see. Follow Tom's Guide on Google News and add us as a preferred source to get our up-to-date news, analysis, and reviews in your feeds. Subscribe to Tom's Guide on YouTube and follow us on TikTok.

Return of the 'greybeards': AI backfired - so Ford had to rehire humans

The US motor company found that the hundreds of AI cameras being used for design and manufacturing checks were prone to pitfalls Age: There's a clue in the name. I'm thinking old, and probably male? Most likely. Certainly human, that's the main thing. Are ZZ Top back on the road? No ... Actually, yes! But this isn't about them. Who is it about then? Veteran engineers, working for Ford Motor Company in the US. Oh dear, I think I know how this story goes: hundreds of longstanding workers get laid off because of automation and artificial intelligence ... That kinda was how the story was going; the company has 5,000 fewer workers than it did in 2020. Recently, though, there's been an unexpected twist. Ooh, I love those, go on. Over the past three years, the company has hired 350 veteran engineers - known as "greybeards" (or "graybeards" if you're reading this in the US) - made up of former Ford employees and workers from suppliers. Excellent news! Why, though? I'm guessing it's not because - despite the threat from the massive acceleration going on in the Chinese automotive industry - Ford has suddenly discovered its charitable side? No. It's more about doing the things that AI proved to be a bit rubbish at. AI replaced with human beings, man bites dog! Go on! Not quite replaced. But they discovered that the hundreds of AI-powered cameras they were using, including for design and manufacturing checks, were prone to pitfalls. Because? To quote Ford's vice president of vehicle hardware engineering, Charles Poon: "Artificial intelligence is a fantastic tool, but it's only as good as the information you use to train it." Hmm, now who would have that kind of knowhow and experience, I wonder? "Over prior years, we didn't pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles," Poon said. "Who have been with us through many product cycles", Mr Poon - they're people, remember. True. With facial hair to prove it. So the AI gets chucked on the scrapheap and the "greybeards" come back through the factory gates, singing, like elves ... In the fairytale version maybe. And in the real version? A combination of the two. Ford said that AI is very important to quality gains, "and that, in tandem with deep technical expertise, is what's needed". Yeah, until all that expertise has been successfully transferred to the machines. And the human becomes redundant. Not just from work, but existentially. Argggghhh! Do say: "Pssst! Yes, you, comrade greybeard. Let's teach this one wrong, introduce a few glitches, so there may even be a few jobs left for our kids ..." Don't say: "Wait, who are these cars of the future even for? Our robot overlords?"

Ford realized AI wasn't capable of taking human jobs years ago -- and hired 350 'gray beard' engineers to steer its program | Fortune

WIth all the discussion about the AI bubble, AI hype and mass automation displacement, Ford Motor Company has a message for the U.S. economy: human experience matters. Over the last three years, the company has hired 350 veteran engineers -- dubbed "gray beards" internally and made up of both former Ford employees and workers from suppliers -- to help train junior staff and reprogram ineffective artificial intelligence tools. It's because the company realized what AI is and isn't good for. "Artificial intelligence is a fantastic tool, but it's only as good as the information you use to train it," Charles Poon, Ford's vice president of vehicle hardware engineering, told reporters last week. "Over prior years, we didn't pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles." By mid-2024, recalls were costing Ford $4.8 billion per year. Last July, the company notched the superlative as the automaker with the most recalls ever issued in a single year with 90, including an estimated $570 million charge for nearly 700,000 crossover vehicles. Since then, the company has made a concerted effort to improve quality control efforts and now ranks No. 1 among mainstream brands in the most recent JD Power Initial Quality Survey published on Thursday. Last year, the company ranked 10th for quality. The company attributes increases in quality to a "culture change" emphasizing the role of human workers. "We have AI tools for vision systems," Ford CEO Jim Farley told Bloomberg TV. "But most of all, it's just old-fashioned hard work of our team members all working together to pay attention to the very small details that will make a difference between a perfectly built Ford and an OK-built Toyota. It's just an incredible attention to every single detail." AI has increasingly shown it can increase productivity of certain activities, but is only making meaningful gains for companies if they are able to articulate a clear vision around how it should be deployed and augment the work of human employees. Moreover, tech executives like Bryan Catanzaro, Nvidia's vice president of applied deep learning, has said the cost of AI still far exceeds that of human labor, suggesting that even as companies continue to deploy automation, the role of human workers to both guide that technology -- and ensure its proliferation -- are more important than ever. This has been a struggle across the Fortune 500 since the influential (and contested) MIT study in 2025 that only 5% of companies were seeing a meaningful return on investment from generative AI pilots. Other surveys have found similar splits between the rate of AI adoption and the meaningful results from it. It's a point Ford has been making for years: If you want automation to be successful, hire smart humans first. Ford's repeated calls for more human workers Farley has long warned about the dearth of blue-collar workers creating a crisis in the "essential economy," essentially slowing down the buildout of key industries, such as the automotive industry, as well as the expansion of AI infrastructure. He previously said the country is short 600,000 factory workers and 500,000 construction workers right now, attributing the slimming labor force to a lack of awareness of a shortage. "On the surface, this looks like a people problem, and most are," Farley told Axios last year. "But it's actually not that simple. It's an awareness problem. It's a societal problem." The CEO has advocated for policy changes to incentivize greater blue-collar job fulfillment, including greater investments in vocations training and apprenticeships, as well as pro-trade polities that grow the "essential economy." "If we are successful -- when we are successful -- we'll take on bigger, higher-class problems," he said. "Right now, the problems we're trying to solve are pretty practical: I need 6,000 technicians in my dealerships on Monday morning." However, as Ford and the automotive industry blend automation and human workforce together, there may be other challenges ahead. Earlier this month, UAW, among the largest unions in North America representing auto workers, expressed concern for the future of humans in the industry amid waves of automation. UAW President Shawn Fain argued at the union's conference that workers should share in the financial gains automakers reap in automation-related productivity gains. "We need to be clear about this: We are in a fight for humanity," Fain said. "The fruits of our labor have multiplied like never before, but workers aren't reaping the harvest. And if AI continues to be used as an accessory to that crime, it has to be stopped. It doesn't have to be this way; in a just society, when workers create more value, they see more of the benefit." Ford did not immediately respond to Fortune's request for comment. How Ford orchestrated its quality turnaround According to Ford COO Kumar Galhotra, the veteran engineers responsible for its quality problem turnaround "hunt for failure points before a part ever reaches the plant floor," he said at last week's press meeting. Today, the technical specialists run mandatory meetings to address quality concerns, as well as reprogram AI tools to counter glitches before they occur. To be sure, Ford continues to encounter issues with recalls, expecting more than $1 billion in warranty and material costs this year. These costs are a lagging metric, according to Galhotra, and are expected to decrease over time. The company also hopes to save $1 billion in costs this year. "Because we're doing more to prevent issues upfront, we believe these recall numbers are going to steadily come down with the newer vehicles," he said. "I can't give you a very specific date on when the number will turn." Farley said Ford is already making up some of the money previously lost because of quality issues. "We're seeing our warranty coverages come down. We're seeing our recall costs come down," he said. "These are all contributing to literally hundreds and hundreds of millions of dollars of a tailwind for Ford on cost."

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.

Ford's AI car crash - human experts get back in the driving seat

On the face of it, here's a salutary story for our times. As enterprise after enterprise cuts their workforces, cheerily citing AI as a satisfactory replacement, they might do well to cast a wary eye in the direction of Ford Motor, a firm that was one of the early AI-enabled job slashers but which has found that this turned out to something of a strategic car crash. The US automotive giant had shed hundreds of skilled roles, laying off some of its most experienced engineers, only to find that those people were doing a job that AI hasn't been able to match in terms of quality standards, and those who had been replaced had not been able to pass on their experience to those humans who remained. Top Ford executives have admitted that errors were made. The company had introduced advanced AI-powered inspection and validation tools designed to detect assembly issues and improve manufacturing accuracy, but quality issues continued. Ford VP of Vehicle Hardware Engineering Charles Poon told media outlets: Artificial Intelligence is a fantastic tool, but it's only as good as the information you use to train it...Mistakenly, we thought that by just introducing Artificial Intelligence and adjusting the design requirements that we had, that would produce a high-quality product. But in fact, according to Ford Chief Operating Officer Kumar Galhotra: We had been relying more and more on automated quality systems and not getting the desired results. We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor." Over the past three years, the US automaker has hired around 350 experienced engineers, many of them former Ford employees, to step back into their roles. The returnees now mentor younger engineers, execute design reviews, and help train up the company's AI-driven quality systems. That reversal may be paying off. According to the latest J.D. Power Initial Quality Survey, an annual automotive benchmark that measures the quality of new vehicles, Ford has regained the top slot among mainstream brands for the first time in 16 years. The study looks at 227 issue across ten vehicle categories, including infotainment, features, and driving assistance. J.D. Power's power's report said Ford recorded 152 problems per 100 vehicles, a steep improvement on the prior year. AI ongoing Ford is not backing away from AI here, just modifying its approach. CEO Jim Farley last year expressed strong views on the role of the tech within the firm and across the wider automative sector as a whole. At the Aspen Ideas Festival, he explained: I believe that AI and new technologies have an asymmetric impact on our economy. That means a lot of things are helped a lot, and a lot of things are hurt. When you look at these openings in our economy, it is very clear that all the technology we've seen has left a lot of people behind. AI will leave a lot of white collar people behind, and we have to acknowledge that these new technologies are great- they'll make a lot of people's lives better, even people in the central economy - but what are we going to do as a society for the people that it leaves behind that are valuable humans ? For our society we have to have a plan for sustainment, and we don't have that plan today. At the time Farley talked up the importance of training and re-training, which makes a lot of sense given the way things turned out: I'm always amazed at what can happen when you re-train someone. I'll go to Van Dyke, our transmission plant that's now making electric motors. You know, those people went from from making automatic transmissions to complete digital motors. It took a lot of training and a lot of technology, and they are so excited about the future, but it required the company to invest heavily. It's not just about enabling them with AI - it's making different things, so you have to have a plan to transition the team. He's also a great believer in the value of workers with specific trades and skills outside of technology and AI: We need to have a society that doesn't look down on people like that..companies like Ford, other companies, academic, our governments have to get really serious about investing in trade schools and skilled trades. If you go to Germany, every one of our factory workers has an apprentice starting in junior high school. Every one of those jobs has a person behind it for eight years that are trained. That apprentice system is amazing. Is there a way we could do that? Yes. It's hard, but yes, you can do it, and we're starting. But Ford can't be the only one to do it. We need other companies, [and] our country to get serious about this kind of problem. We need government programs, we need high schools to get falling in love with finding companies like Ford that could give these great apprentice jobs. These are the kind of jobs that aren't going to be easily supplanted by AI, he suggested: AI will certainly help with, you know, billing with lots of menial tasks...I think robotics will will substitute quite a few jobs, but that will not fill the gap in what we're talking about. We're talking about millions and millions of people openings for these jobs. A factory job is complicated, it's difficult. So far, maybe 10% of our operations can be roboticized, with the humanoid robot maybe it's 20%, but it's not going to be 80%. And it will take a long time to get to that because the cost of robotics is expensive. Human intelligence is vital, he concluded: Humans are amazing. I see humans do things in our plant that a robot can't do. I remember at one of our plants in Germany we had to figure out how to close the tailgate and one of the workers on their weekend went out and bought a bicycle tire and wheel with a wooden slat, and with the kinetic force of the car going down the line, they just used the tire to close the rear tailgate. We could do that with a robot, but [could it have] the creativity of someone, I'm not sure. My take When you have a brand like Ford and there's a new technology, you have to be really careful. Ford's story serves as a very timely reminder that the most successful AI transformation strategies are not going to be a binary choice between human beings and AI, but an empowering co-existence between the two. Striking that elusive balance is going to the competitive differentiator that sets apart the successful from the merely aspirational.

Ford rehired veteran engineers after AI quality systems fell short

Ford has hired 350 veteran engineers, including former employees and individuals from suppliers, in response to failures in artificial intelligence and automated systems to meet quality standards. Kumar Galhotra, Ford's chief operating officer, stated that the company increasingly relied on automated quality systems, which yielded disappointing results. To address this, Ford is employing technical specialists to identify failure points before parts reach the production floor. Charles Poon, Ford's vice president of vehicle hardware engineering, acknowledged that there was a misconception about artificial intelligence's role in ensuring high product quality based solely on design inputs. He emphasized that the introduction of AI alone would not guarantee the production of high-quality products. Ford clarified that it is not abandoning its AI initiatives. Instead, the hired engineers, referred to as "gray beard" engineers, will assist in training younger staff and improving AI tools. This strategy is anticipated to contribute to $1 billion in cost reductions for Ford in the current year. In addition to these measures, Ford achieved the top ranking among mainstream brands in the JD Power Initial Quality Survey released this week. This recognition highlights the company's efforts to enhance product quality.

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.

Ford Tried to Fix Its Quality Problems With AI. It Didn't Work. So They Brought Back a Squad of Human 'Gray Beards.'

Here's a story that should make you feel optimistic about humanity. For years, Ford leaned on AI and automated systems to fix its quality problems. The cars kept getting recalled anyway -- Ford is the most recalled automaker in America. Frustrated, the company tried something different: it rehired 350 veteran engineers called "Gray Beards" to mentor younger staff and retrain the AI tools that weren't delivering, according to Bloomberg. The company's VP of vehicle hardware engineering admitted that Ford mistakenly believed AI alone could produce high-quality vehicles. Turns out the machines are "only as good as the information you use to train it," he said. The Gray Beards came through. Ford just topped JD Power's Initial Quality Survey among mainstream brands for the first time in 16 years, surpassing Toyota and Honda. Three models -- the F-150, Mustang and Super Duty -- each ranked No. 1 in their categories. The company expects the turnaround to generate hundreds of millions of dollars in cost savings this year. That should take some gray out of the beard.

Ford 'Mistakenly' Thought It Could Produce 'High Quality' with AI, Now Its Reportedly Rehiring 350 Vetera

Ford Brings Back Veteran Engineering Talent The hiring push included some former Ford employees and others who had been working at suppliers. The goal was to bring back experienced engineers who could spot failure points before parts reached factories, mentor younger workers and improve the AI tools Ford still plans to use. "Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that would produce a high-quality product," Ford Vice President of Vehicle Hardware Engineering Charles Poon told reporters, according to The Verge. Ford Chief Operating Officer Kumar Galhotra said the company had been "relying more and more on automated quality systems," but the results were not good enough, according to Bloomberg. Quality Gains Help Lower Ford's Costs The shift has started to show up in Ford's costs. CEO Jim Farley told Bloomberg TV last Thursday that the veteran-engineer effort helped reduce warranty and recall costs, "contributing to literally hundreds and hundreds of millions of dollars of a tailwind for Ford on cost." The move comes as Ford last week secured the top mass-market spot in J.D. Power's 2026 U.S. Initial Quality Study, the first time it has done so in 16 years. J.D. Power said Ford recorded 152 problems per 100 vehicles, improving sharply from the prior year. The rebound also arrives after years of quality problems. Ford has led the U.S. industry in recalls this year, though many involved older models. AI Workforce Comments Add Larger Context Ford reported first-quarter 2026 results on April 29, posting adjusted earnings of 66 cents per share, well above estimates of 19 cents. Revenue rose 6% year-over-year to $43.3 billion, and Ford raised its full-year adjusted EBIT outlook to $8.5 billion to $10.5 billion. According to Benzinga Pro data, Ford's next earnings report is unconfirmed but projected for July 29. Benzinga Edge Rankings show that Ford stock offers Satisfactory Momentum and a favorable Price Trend in the Short, Medium and Long term. Price Action: Ford stock closed 0.14% higher at $14.13 on Friday, dropping 0.12% to $14.11 in after-hours trading. Photo Courtesy: Tada Images on Shutterstock.com Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Ford Rehires Engineers After AI-Led Quality Issues Cost Billions

Ford has rehired veteran engineers after AI-led quality issues contributed to billions in warranty costs. The automaker now says artificial intelligence should support engineers, not replace decades of human expertise. Ford Motor has begun bringing back veteran engineers after quality issues and soaring warranty costs exposed the limits of overreliance on artificial intelligence. The automaker's renewed focus on experienced talent comes after years of investing in AI-led quality control that failed to deliver the expected results. This turnaround highlights how the company's expensive mistake showed them that, even if AI could do things faster and analyze huge amounts of data, it could never substitute for years of engineering experience. Following significant , Ford has switched back to the old scheme, in which AI only assisted the engineers.