# content
Ford Motor Co. bet big on artificial intelligence to sharpen vehicle quality checks. The results fell short. So the automaker spent the past three years bringing back more than 350 veteran engineers, many of them its own former employees. These “gray beard” experts now train younger staff, rebuild data pipelines, and fine-tune the very automated systems once meant to replace them.

Charles Poon, Ford’s vice president of vehicle hardware engineering, put the reversal in plain terms. “Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” he told BBC News. Poon added that the company had mistakenly believed ingesting design requirements alone would deliver high-quality output. It did not.

The misstep carried real consequences. Ford racked up billions in warranty costs and faced stubborn quality problems that delayed model launches. Recall numbers climbed. Yet after the talent refresh, the company earned the top spot among mainstream brands in J.D. Power quality rankings. The veterans supplied the hard-earned wisdom that algorithms lacked.

COO Kumar Galhotra acknowledged over-reliance on automated systems. The returning engineers did more than inspect. They mentored juniors. They reprogrammed machine-learning models. They rebuilt the training data the AI needed to spot defects humans catch instinctively. Bloomberg first detailed the scale of this quiet course correction in a report that rippled across industry coverage.

Bloomberg reported that Ford hired, promoted, or brought back the 350 veterans over three years. Many came from suppliers or retirement. Their institutional knowledge had walked out the door during earlier cost-cutting. Without it, the automated tools amplified flaws instead of catching them.

But the story does not end with engineering halls. The same company that struggled to replace human judgment with code showed zero tolerance for far smaller errors on the factory floor. Last month Ford fired Kurt Kromm, a 60-year-old UAW member with 11 years at its Kentucky Truck Plant in Louisville. The charge? Stealing a $1.95 chocolate chip cookie.

Kromm is diabetic. Early one Saturday shift his blood sugar dropped. Around 3:30 a.m. he headed to the break room. The first Aramark self-checkout kiosk flashed an error. He moved to a second machine, paid, ate the cookie, and returned to work. A week later security summoned him. A union representative delivered the news. They had him on video stealing the snack.

“First you tell me I’m a thief and then you tell me I’m a liar,” Kromm said as he produced his bank statement. The $1.95 charge appeared clearly. He had paid. Carscoops and the original Shifting Gears newsletter on Substack both chronicled the episode in detail. Kromm earned over $200,000 the previous year working long hours. Why risk termination over a cookie?

He was not alone. Other workers lost jobs over similar kiosk glitches. One electrician described the machines as notorious for failed transactions. Ford’s zero-tolerance theft policy had claimed at least five others. The company later confirmed Kromm’s payment with Aramark. It offered reinstatement and about $28,000 in back pay for five weeks missed. The union had expected more.

Kromm turned down the offer. He found a better-paying role near his hometown in Kenosha, Wisconsin. Ford, for its part, agreed to change policy. Future kiosk disputes would trigger suspension, not immediate firing. A spokeswoman told reporters there are “times when we look into things and realize it could have been handled different.”

The contrast lands heavily. On one side, executives admit AI systems need seasoned humans to supply judgment and data. On the other, the same organization marched out an 11-year employee over less than two dollars before basic verification. Both episodes reveal gaps in how Ford balances technology, policy, and people.

Recent coverage shows the rehiring move is part of a broader pattern. CNBC noted on July 1 that employers who cut workers for AI are now reversing some decisions. Ford stands out because its reversal targets quality inspectors whose expertise directly feeds the algorithms. CNBC highlighted the automaker alongside other firms discovering limits to pure automation.

Forbes framed the decision as evidence of human value in the AI era. The publication reported that Ford’s bet on automation cost three years, billions of dollars, and a quality crisis. Only after rehiring veterans did metrics improve. COO Galhotra and Poon both stressed that experienced technicians provide the nuanced understanding automation still misses on complex problems.

Industry observers point to larger forces. Detroit’s automakers shed thousands of white-collar positions since 2020. Ford alone cut more than 5,300 salaried roles. CEO Jim Farley had predicted AI would displace large numbers of such workers. The quality turnaround complicates that outlook. Rehired engineers now retrain the systems and mentor the very juniors hired to replace retirees.

The Kentucky Truck Plant itself tells part of the tale. It builds Super Duty trucks, Expeditions, and Lincoln Navigators. More than 8,000 workers generate billions in annual revenue. Yet glitchy kiosks and rigid policies created avoidable friction on the shop floor. Kromm averaged 60 hours a week in 2025. His termination, even if later walked back, signals how quickly trust erodes when processes override common sense.

Ford is hardly unique. Other manufacturers have watched automation projects stumble when they underweight tacit knowledge. What separates this case is the public admission. Poon’s comments read like a cautionary note to an industry racing toward greater AI adoption. Data pipelines are only as strong as the experience used to build them. Judgment calls on edge cases still favor the veteran eye.

So the company finds itself in an awkward spot. It celebrates topping quality charts after bringing back the very experts it once let go. At the same time it confronts criticism over a cookie incident that should never have reached termination. Both stories, though different in scale, circle the same question. How does a modern manufacturer value human input when technology promises efficiency?

Answers are still forming. Ford now positions its rehired engineers as trainers and mentors. Their role is to close the loop between human insight and machine learning. The policy shift on kiosk disputes suggests some learning at the plant level too. Yet the initial decisions, whether on AI deployment or snack-machine enforcement, reveal how easily organizations can undervalue experience until the costs mount.

Recent social media chatter on X reflects the skepticism. Users noted the irony of firing humans for AI only to hire them back when systems falter. Others recalled past rounds of cuts tied to vaccine policies or cost savings. The rehiring of 350 veterans offers a partial counter-narrative. It shows at least one automaker discovered the limits of its earlier assumptions.

Executives insist the move strengthens Ford’s position. The JD Power result gives them data to point to. Warranty expenses have stabilized. New model launches face fewer delays. Still, the billions already spent and the workers who left permanently serve as reminders. Progress sometimes requires stepping back.

Kromm, for his part, has moved on. He plans one final trip to Louisville to collect tools left behind. The episode cost him five weeks of uncertainty but delivered a higher-paying job. For Ford the price was higher. Reputation, back pay, policy changes, and a very public lesson about the value of its people.

The automaker’s experience may foreshadow challenges elsewhere in manufacturing. As more companies embed AI into quality control, design validation, and assembly oversight, they will confront the same gap. Algorithms trained on incomplete data produce incomplete results. The fastest path forward often runs through the people who already know the work intimately.

Ford’s reversal does not reject artificial intelligence. It reframes it. The technology remains a powerful tool. But veterans supply the training data, the edge-case knowledge, and the mentoring that make the tool effective. Ignore that link and the costs accumulate. Recognize it and quality climbs. The past three years have driven that point home at considerable expense.

# contentMap.en
Ford Motor Co. bet big on artificial intelligence to sharpen vehicle quality checks. The results fell short. So the automaker spent the past three years bringing back more than 350 veteran engineers, many of them its own former employees. These “gray beard” experts now train younger staff, rebuild data pipelines, and fine-tune the very automated systems once meant to replace them.

Charles Poon, Ford’s vice president of vehicle hardware engineering, put the reversal in plain terms. “Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” he told BBC News. Poon added that the company had mistakenly believed ingesting design requirements alone would deliver high-quality output. It did not.

The misstep carried real consequences. Ford racked up billions in warranty costs and faced stubborn quality problems that delayed model launches. Recall numbers climbed. Yet after the talent refresh, the company earned the top spot among mainstream brands in J.D. Power quality rankings. The veterans supplied the hard-earned wisdom that algorithms lacked.

COO Kumar Galhotra acknowledged over-reliance on automated systems. The returning engineers did more than inspect. They mentored juniors. They reprogrammed machine-learning models. They rebuilt the training data the AI needed to spot defects humans catch instinctively. Bloomberg first detailed the scale of this quiet course correction in a report that rippled across industry coverage.

Bloomberg reported that Ford hired, promoted, or brought back the 350 veterans over three years. Many came from suppliers or retirement. Their institutional knowledge had walked out the door during earlier cost-cutting. Without it, the automated tools amplified flaws instead of catching them.

But the story does not end with engineering halls. The same company that struggled to replace human judgment with code showed zero tolerance for far smaller errors on the factory floor. Last month Ford fired Kurt Kromm, a 60-year-old UAW member with 11 years at its Kentucky Truck Plant in Louisville. The charge? Stealing a $1.95 chocolate chip cookie.

Kromm is diabetic. Early one Saturday shift his blood sugar dropped. Around 3:30 a.m. he headed to the break room. The first Aramark self-checkout kiosk flashed an error. He moved to a second machine, paid, ate the cookie, and returned to work. A week later security summoned him. A union representative delivered the news. They had him on video stealing the snack.

“First you tell me I’m a thief and then you tell me I’m a liar,” Kromm said as he produced his bank statement. The $1.95 charge appeared clearly. He had paid. Carscoops and the original Shifting Gears newsletter on Substack both chronicled the episode in detail. Kromm earned over $200,000 the previous year working long hours. Why risk termination over a cookie?

He was not alone. Other workers lost jobs over similar kiosk glitches. One electrician described the machines as notorious for failed transactions. Ford’s zero-tolerance theft policy had claimed at least five others. The company later confirmed Kromm’s payment with Aramark. It offered reinstatement and about $28,000 in back pay for five weeks missed. The union had expected more.

Kromm turned down the offer. He found a better-paying role near his hometown in Kenosha, Wisconsin. Ford, for its part, agreed to change policy. Future kiosk disputes would trigger suspension, not immediate firing. A spokeswoman told reporters there are “times when we look into things and realize it could have been handled different.”

The contrast lands heavily. On one side, executives admit AI systems need seasoned humans to supply judgment and data. On the other, the same organization marched out an 11-year employee over less than two dollars before basic verification. Both episodes reveal gaps in how Ford balances technology, policy, and people.

Recent coverage shows the rehiring move is part of a broader pattern. CNBC noted on July 1 that employers who cut workers for AI are now reversing some decisions. Ford stands out because its reversal targets quality inspectors whose expertise directly feeds the algorithms. CNBC highlighted the automaker alongside other firms discovering limits to pure automation.

Forbes framed the decision as evidence of human value in the AI era. The publication reported that Ford’s bet on automation cost three years, billions of dollars, and a quality crisis. Only after rehiring veterans did metrics improve. COO Galhotra and Poon both stressed that experienced technicians provide the nuanced understanding automation still misses on complex problems.

Industry observers point to larger forces. Detroit’s automakers shed thousands of white-collar positions since 2020. Ford alone cut more than 5,300 salaried roles. CEO Jim Farley had predicted AI would displace large numbers of such workers. The quality turnaround complicates that outlook. Rehired engineers now retrain the systems and mentor the very juniors hired to replace retirees.

The Kentucky Truck Plant itself tells part of the tale. It builds Super Duty trucks, Expeditions, and Lincoln Navigators. More than 8,000 workers generate billions in annual revenue. Yet glitchy kiosks and rigid policies created avoidable friction on the shop floor. Kromm averaged 60 hours a week in 2025. His termination, even if later walked back, signals how quickly trust erodes when processes override common sense.

Ford is hardly unique. Other manufacturers have watched automation projects stumble when they underweight tacit knowledge. What separates this case is the public admission. Poon’s comments read like a cautionary note to an industry racing toward greater AI adoption. Data pipelines are only as strong as the experience used to build them. Judgment calls on edge cases still favor the veteran eye.

So the company finds itself in an awkward spot. It celebrates topping quality charts after bringing back the very experts it once let go. At the same time it confronts criticism over a cookie incident that should never have reached termination. Both stories, though different in scale, circle the same question. How does a modern manufacturer value human input when technology promises efficiency?

Answers are still forming. Ford now positions its rehired engineers as trainers and mentors. Their role is to close the loop between human insight and machine learning. The policy shift on kiosk disputes suggests some learning at the plant level too. Yet the initial decisions, whether on AI deployment or snack-machine enforcement, reveal how easily organizations can undervalue experience until the costs mount.

Recent social media chatter on X reflects the skepticism. Users noted the irony of firing humans for AI only to hire them back when systems falter. Others recalled past rounds of cuts tied to vaccine policies or cost savings. The rehiring of 350 veterans offers a partial counter-narrative. It shows at least one automaker discovered the limits of its earlier assumptions.

Executives insist the move strengthens Ford’s position. The JD Power result gives them data to point to. Warranty expenses have stabilized. New model launches face fewer delays. Still, the billions already spent and the workers who left permanently serve as reminders. Progress sometimes requires stepping back.

Kromm, for his part, has moved on. He plans one final trip to Louisville to collect tools left behind. The episode cost him five weeks of uncertainty but delivered a higher-paying job. For Ford the price was higher. Reputation, back pay, policy changes, and a very public lesson about the value of its people.

The automaker’s experience may foreshadow challenges elsewhere in manufacturing. As more companies embed AI into quality control, design validation, and assembly oversight, they will confront the same gap. Algorithms trained on incomplete data produce incomplete results. The fastest path forward often runs through the people who already know the work intimately.

Ford’s reversal does not reject artificial intelligence. It reframes it. The technology remains a powerful tool. But veterans supply the training data, the edge-case knowledge, and the mentoring that make the tool effective. Ignore that link and the costs accumulate. Recognize it and quality climbs. The past three years have driven that point home at considerable expense.

# name
Ford’s Costly AI Lesson: Rehiring Veterans After Automation Failures and a $1.95 Cookie Firing

# nameMap.en
Ford’s Costly AI Lesson: Rehiring Veterans After Automation Failures and a $1.95 Cookie Firing

# preview.content
Ford Motor Co. bet big on artificial intelligence to sharpen vehicle quality checks. The results fell short. So the automaker spent the past three years bringing back more than 350 veteran engineers, many of them its own former employees. These “gray beard” experts now train younger staff, rebuild data pipelines, and fine-tune the very automated systems once meant to replace them.

Charles Poon, Ford’s vice president of vehicle hardware engineering, put the reversal in plain terms. […]

# summary
Ford Motor Co. bet big on artificial intelligence to sharpen vehicle quality checks. The results fell short. So the automaker spent the past three years bringing back more than 350 veteran engineers, many of them its own former employees. These “gray beard” experts now train younger staff, rebuild data pipelines, and fine-tune the very automated systems once meant to replace them.

Charles Poon, Ford’s vice president of vehicle hardware engineering, put the reversal in plain terms. […]

# summaryMap.en
Ford Motor Co. bet big on artificial intelligence to sharpen vehicle quality checks. The results fell short. So the automaker spent the past three years bringing back more than 350 veteran engineers, many of them its own former employees. These “gray beard” experts now train younger staff, rebuild data pipelines, and fine-tune the very automated systems once meant to replace them.

Charles Poon, Ford’s vice president of vehicle hardware engineering, put the reversal in plain terms. […]