The automaker is bringing back experienced engineers after AI-driven quality systems failed to meet expectations, reinforcing the value of human expertise.
Ford is bringing veteran engineers back into its product development process after concluding that AI-powered quality systems alone were not delivering the results the automaker expected.
The company has rehired around 350 experienced engineers—some former Ford employees and others recruited from suppliers—to strengthen quality control and engineering oversight, according to executives speaking to journalists, as first reported by Bloomberg.
Ford Chief Operating Officer Kumar Galhotra said the company had become increasingly reliant on automated quality systems but found that technology alone was insufficient to identify potential issues before production.
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Instead, the automaker turned to experienced engineers—internally referred to as “gray beard” engineers—who help identify failure points before parts reach the manufacturing floor.
“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product,” Charles Poon, Ford’s Vice President of Vehicle Hardware Engineering, said.
The move does not signal a retreat from artificial intelligence. Rather, Ford is repositioning AI as a tool that complements human expertise instead of replacing it.
According to the company, the returning engineers are helping train younger employees while refining and improving the AI systems used across engineering and manufacturing workflows.
The strategy is already showing results. CEO Jim Farley said improved product quality has helped reduce warranty claims and recall costs, generating “hundreds and hundreds of millions of dollars” in cost savings for the company.
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Ford also ranked highest among mainstream automotive brands in this week’s J.D. Power Initial Quality Study, suggesting that the combination of experienced engineering talent and AI-assisted processes is beginning to improve manufacturing outcomes.
The company’s approach reflects a broader shift taking place across industries as organizations move beyond early AI experimentation and recognize that human expertise remains essential for complex decision-making, quality assurance, and continuous improvement.
