Ford has revised its vehicle quality control strategy after an over-reliance on automation and artificial intelligence contributed to manufacturing and design shortcomings. Over the past three years, the automaker has brought in 350 experienced engineering specialists—including former employees and personnel from supplier companies—to rebuild data pipelines, retrain machine-learning systems, and correct errors generated by automated design tools.
The shift reflects a growing recognition among executives that automated quality systems were deployed without the depth of human expertise needed to train and validate them effectively. The personnel changes are part of a broader corporate restructuring that has coincided with an improvement in the company’s independent quality rankings.
Limits of Automation in Quality Control
Ford executives said the company rapidly expanded automated quality inspection systems in recent years, including the installation of 900 AI-powered cameras across production facilities to detect defects and manage supply chain disruptions. However, the systems did not consistently meet the performance standards of experienced human engineers.
“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product,” said Charles Poon, vice president of vehicle hardware engineering, Ford.
Corporate reporting suggests that many veteran engineers left the company before their knowledge could be incorporated into training datasets for automated systems. As a result, the models often lacked sufficient grounding in real-world engineering experience, and in some cases reinforced flawed inputs rather than flagging them.
“Artificial intelligence is a fantastic tool, but it is only as good as the information you use to train it. Over prior years, we did not pay as much attention as we should have to the experience of our most knowledgeable engineers who have been with us through many product cycles,” Poon added.
The expansion of automation also coincided with workforce reductions in white-collar roles. Since 2020, Ford has cut 5,300 salaried positions, part of a broader industry-wide reduction that saw Detroit’s three major automakers eliminate roughly 20,000 white-collar jobs. Ford CEO Jim Farley has previously said AI would displace white-collar workers “at massive scale” and “leave a lot of white collar people behind.”
Rebuilding Human Expertise in the Loop
To correct the quality issues, Ford has hired 350 veteran technical specialists—internally referred to as “gray beard” engineers. Their role includes troubleshooting systemic issues, mentoring younger engineers, and retraining machine-learning models with higher-quality engineering inputs.
“We realized that to improve our automation and machine learning systems, we needed to ensure they were trained by the most experienced individuals,” Poon said.
Kumar Galhotra, Ford’s chief operating officer, said the company had increasingly depended on automated quality systems, with inconsistent outcomes. “We brought back technical specialists. They hunt for failure points before a part ever reaches the plant floor,” he said.
Ford has also created a dedicated software quality assurance team of 40 engineers and deployed more than 100,000 AI-driven automated tests designed to detect component and system defects before production.
Impact on Quality and Financial Performance
The increased involvement of experienced engineers has coincided with measurable improvements in cost and quality metrics. CEO Jim Farley said reduced warranty and recall expenses have created “hundreds and hundreds of millions of dollars” in cost benefits for the company.
Ford also improved its position in the JD Power Initial Quality Survey, ranking first among mainstream brands after placing tenth the previous year and below industry average. The result marks the company’s best performance in the survey in 16 years, since 2010.
Despite this improvement, Ford continues to report the highest number of vehicle recalls among major US automakers. Galhotra described recall data as a lagging indicator of past production quality rather than current manufacturing performance, adding that volumes are expected to decline as newer vehicles produced under revised processes enter the market.
Broader Organizational Changes
The quality overhaul is part of a wider restructuring of Ford’s internal operations. After the departure of electric vehicle division head Doug Field, the company consolidated its electric vehicle, digital, and design functions with its global industrial operations.
The resulting division, Product Creation and Industrialization, is led by Galhotra. It is tasked with accelerating product updates across 80% of Ford’s North American vehicle lineup by volume by 2029.