Ford Returns to Experienced Staff After AI Quality Control Failure

Several months ago, American automaker Ford published a surprising report. It rehired more than 300 experienced engineers and quality control technicians whom it had previously laid off due to the massive expansion of artificial intelligence. The reason was that AI-based systems were unable to meet the standards required by the automaker.

Here again, we can see that the blind obsession with automation had its shortcomings at one of the world’s largest car manufacturers. That is precisely what the company discovered when it tried to eliminate the human factor from production entirely.

Ford’s management initially believed it had a great plan. It installed 900 AI-connected cameras in its factories and distribution centers. Their task was to detect defects at the very beginning of the manufacturing process and prevent problems in supply chains. At the time, Chief Operating Officer Kumar Galhotra proudly told investors that the company was “deploying artificial intelligence throughout its entire industrial system.” It sounded extremely convincing: cost reductions, skyrocketing productivity, and the automation of repetitive tasks. It was believed that this would enable modern industry to operate far more efficiently. But reality is not quite so rosy yet.

Charles Poon, vice president of vehicle hardware engineering, later openly admitted where they had gone wrong: “Artificial intelligence is a great tool, but it is only as good as the data you use to train it.” Management realized that the failure of the AI systems stemmed from one fatal mistake: the company had grossly underestimated the decades of experience possessed by its longest-serving employees. “We mistakenly assumed that all we had to do was introduce artificial intelligence, feed it the design requirements, and it would automatically create a top-quality vehicle on its own,” Poon told reporters.

A very specific management error was behind the failure of AI. Many experienced engineers and technicians with decades of experience left the company before they had a chance to transfer their unique knowledge into the automated systems. This created a critical gap. There is a direct correlation: the more effectively the AI systems absorbed the knowledge of these specialists, the more accurately they would operate. Instead, Ford attempted to achieve quality without them.

The automaker’s management has now realized that this mistake cannot be resolved through digitization alone. Practices shaped over decades of human experience cannot simply be rewritten as source code. Knowledge of what must be done in real-world production, what the unwritten rules are, where hidden defects lurk, and how to prevent them simply cannot be gleaned from numbers and charts.

Ford therefore acted immediately and called hundreds of these veterans back into service. Although their new role differs from the one they had in the past, it is all the more important. They have now been put in charge of training and reprogramming the AI systems themselves. At the same time, they serve as teachers for the younger generation of employees, passing on their craft.

Instead of the machine replacing the human, the human became the machine’s indispensable guide. Charles Poon added: “We realized that to improve our automation, machine-learning tools, and artificial intelligence, we need to ensure that they are trained by the most experienced people.” Moreover, the presence of these veterans has already produced tangible results.

Ford has risen to first place among mass-market brands in the prestigious U.S. Initial Quality Study (IQS) rankings by the renowned company J.D. Power after years of struggle. The automaker had not achieved a similar success since 2010, a full sixteen years ago. Ford’s management unequivocally attributes this triumph to a “significant restoration of experience.” The company was saved not only by personnel reshuffles in manufacturing and supply chain management, but specifically by the return of approximately three hundred engineers who brought with them hard-earned wisdom from previous decades of vehicle development.

Moreover, this case is not an isolated one. While Ford publicly acknowledges that AI failed in quality control without humans, its archrival General Motors is taking precisely the opposite approach. It has laid off more than a thousand employees at its main development and assembly center and is replacing those positions with robots. These two opposing approaches perfectly reflect the profound uncertainty surrounding the role of artificial intelligence in the automotive industry. Ford has bet on quality and believes that human judgment is absolutely essential to achieving it. General Motors is betting on pure automation in pursuit of lower operating costs.

However, the results so far support Ford’s approach. When the automaker commented on its return to the top of the rankings, it clearly stated that achieving best-in-class quality had required a fundamental workforce renewal. In doing so, the company acknowledges that it has gone back to basics: to people who understand their work completely. Yet just a year ago, Ford’s senior management was saying something entirely different. In an interview with writer Walter Isaacson, CEO Jim Farley predicted that “artificial intelligence will leave a lot of white-collar workers far behind.” At the time, the statement sounded visionary. Today, several months later, it sounds more like a warning that came a second too late.

No one today can say with certainty whether this represents a permanent reversal of the trend or merely a temporary solution. In any case, Ford has learned a lesson that AI developers often forget: not every complex problem can be solved without human intuition.

Sources: bbc.com, clashreport.com and livemint.com