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Accumulated experience has regained space at Ford after automated systems did not deliver the expected result in critical quality tasks. With the support of veteran engineers, the automaker is trying to combine artificial intelligence, human review, and technical knowledge to reduce failures, recalls, and costs in the United States.
Ford Motor Co. has turned to about 350 veteran engineers to tackle quality failures that its automated systems and artificial intelligence tools could not solve on their own in the United States.
Adopted over the past three years, the initiative placed experienced professionals at the center of technical reviews, training younger teams, and improving systems used to anticipate vehicle defects.
The change comes amid an important contrast for the automaker, which has become a leader among mass brands in the JD Power initial quality study of 2026, released in June.
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Even so, Ford remains pressured by recalls in the United States and by costs related to warranties, materials, and corrections of vehicles already sold, one of the company’s main points of concern.
According to Bloomberg, these professionals are internally called “gray beard” engineers, an expression used to identify specialists with long experience in product development, testing, and solving industrial problems.
Part of this group returned to the company and part came from suppliers, in an attempt to recover accumulated knowledge over various vehicle cycles and apply this experience to project review and AI training.
“Artificial intelligence is a fantastic tool, but it is only as good as the information used to train it,” said Charles Poon, Ford’s vice president of vehicle hardware engineering, in a conference call with journalists, according to Bloomberg.
In the same conversation, Poon said that the company had not paid enough attention to the experience of the most qualified professionals, especially those who have followed different generations of vehicles and recurring engineering problems.
Veteran engineers gain a central role at Ford
In practice, the specialists started to act before the problem reaches the factory, analyzing possible failure points in parts and projects still in the early stages of development.
They also participate in mandatory reviews, guide younger engineers, and help adjust automation tools, machine learning, and artificial intelligence used by the company in critical quality decisions.
Kumar Galhotra, Ford’s Chief Operating Officer, said that the automaker had been increasing its reliance on automated quality systems but was not achieving the expected result in reducing failures.
With the reintroduction of technical specialists, according to the executive, the company began to look for failures in earlier stages of development, before a part was sent to the production line.
The case exposes an important limit of AI in complex industrial processes, where technical data, failure history, and practical knowledge need to be combined to avoid incomplete decisions.
Instead of abandoning automated systems, Ford began to treat them as tools that depend on context, human validation, and qualified information to work better in engineering and quality control.
“Mistakenly, we thought it would be enough to introduce artificial intelligence and feed the system with the design requirements we had to obtain a high-quality product,” said Poon, according to Bloomberg.
From this assessment, the automaker began to reinforce the training of tools with the support of more experienced professionals, precisely to transform accumulated knowledge into useful data for automated systems.
Ford surpasses Toyota and Honda in initial quality
The progress appeared in the 2026 JD Power Initial Quality Study, which measures problems reported by owners in the first 90 days of using new vehicles.
Collected between June 2025 and May 2026, the survey combines consumer responses with repair data and serves as a reference followed by automakers, dealerships, and suppliers.
Among mass-market brands, Ford ranked first, with 152 problems per 100 vehicles, ahead of Nissan, with 156, and Buick, with 162.
In the overall ranking, Porsche led with 138 problems per 100 vehicles, followed by Genesis, with 151, and Ford itself, which was above traditional quality benchmarks.
This performance placed the automaker ahead of Toyota and Honda in the most recent survey cited by the company and Bloomberg, after years of pressure on its reliability image.
A Reuters reported that it was the first time in 16 years that Ford led the initial quality among new vehicles in the United States, a significant result for the brand’s recovery strategy.
Three models from the automaker also won their respective segments in the JD Power evaluation, which reinforces the improvement in core products for the company’s image and commercial performance.
The list includes the Ford F-150 pickup, the Ford Super Duty truck, and the Ford Mustang sports car, vehicles that have strategic importance for the company in the North American market.
Recalls still pressure Ford in the United States
Despite the improvement in initial quality, recalls remain a sensitive point for Ford, which still needs to turn recent advances into a consistent reduction in correction campaigns.
Reuters reported that the automaker led the automotive sector in the United States in 2026, with 51 recalls registered by June 25, well above Stellantis, which followed with 19 campaigns.
For the company itself, recalls function as a lagging indicator, as many problems appear in vehicles designed years before changes were made in engineering processes.
Galhotra stated that the newer models already show progress compared to vehicles developed between 2013 and 2020, although he did not present a specific date for a consistent drop in these numbers.
The financial effect of the quality and warranty history also weighs on Ford, which has cost the company billions in recent years and increased the pressure for more rigorous controls.
By anticipating problems still in the vehicle development phase, the automaker seeks to reduce future expenses, instead of correcting flaws after the sale or through recall campaigns.
The strategy adopted by Ford does not turn AI into a villain but reinforces that automated systems depend on qualified supervision when dealing with safety, durability, and integration between engineering, manufacturing, and suppliers.
At the automaker, experience has returned to being treated as part of the innovation process, especially when accumulated knowledge helps to correct flaws that automated systems cannot identify on their own.
