Ford has rehired hundreds of experienced engineers after discovering that artificial intelligence and automated quality-control systems were unable to deliver the improvements the company had expected on their own.

The US carmaker said the move is already producing results, helping it improve vehicle quality, reduce costs and secure the top position among mainstream brands in the latest JD Power Initial Quality Survey. The company has brought back around 350 veteran engineers over the past three years, including former employees and specialists from supplier firms.

Ford Reassesses Reliance On Automation

According to a Bloomberg report, Ford’s decision followed concerns that the company had become too dependent on automated systems while failing to preserve decades of engineering knowledge accumulated by long-serving employees.

Charles Poon, Ford’s vice-president of vehicle hardware engineering, said the company had overestimated the ability of artificial intelligence to independently deliver high-quality outcomes.

“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product,” Poon told Bloomberg.

While describing AI as “a fantastic tool”, Poon stressed that “it’s only as good as the information you use to train it”.

He acknowledged that Ford had not adequately captured the expertise of many experienced engineers before they left the company, limiting the effectiveness of AI systems that relied on historical knowledge to identify potential problems during vehicle development.

Veteran Engineers Return To Mentor Teams

To address the gap, Ford recruited more than 350 experienced engineers, including former employees and experts from supplier companies.

Internally known as “gray beard” engineers, the returning specialists have taken on mentoring roles, helping younger engineers develop skills while also contributing to the retraining of AI systems.

The engineers are also involved in identifying design and manufacturing issues early in the development process, reducing the likelihood of defects reaching production lines.

Ford Chief Operating Officer Kumar Galhotra said the company had been “relying more and more on automated quality systems” without achieving the desired outcomes.

He described the experienced engineers as being “at the heart” of Ford’s quality-improvement efforts, leading mandatory reviews and helping the company focus on preventing issues rather than correcting them later.

“We’re moving from that find-and-fix mentality to preventing issues before they occur,” Galhotra said. “Stop admiring the problem and start solving it.”

Quality Efforts Expand Beyond Vehicle Hardware

Ford said the changes extend beyond traditional engineering functions. Software, manufacturing and supply-chain teams now work more closely together to identify potential issues earlier in the vehicle development cycle.

The company has also established a dedicated software quality assurance team comprising 40 specialists tasked with improving software reliability before vehicles are delivered to customers.

According to Ford, this broader collaboration is helping improve product quality across both hardware and software systems.

Company Continues Investing In AI Tools

Despite acknowledging the limitations of its earlier approach, Ford said it is not stepping away from artificial intelligence. Instead, the company is focusing on improving AI performance by training systems with better data and insights provided by experienced engineers.

Ford said it has introduced more than 100,000 AI-powered validation tests designed to identify unusual scenarios and stress-test vehicle software under a wide range of operating conditions.

The company added that its automated testing framework allows engineers to rapidly revalidate software whenever late-stage modifications are made, helping detect issues before vehicles reach customers.

By combining human expertise with AI-driven testing, Ford believes it can improve product quality while maintaining the efficiency benefits offered by automation and advanced software tools.

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