Ford execs say they made a mistake when they replaced human engineers with AI

Over the past three years, Ford has re‑engaged roughly 350 seasoned engineers—often dubbed “gray‑beard” veterans—to mentor newer talent and revamp diagnostic systems and AI tools that were falling short of quality standards.

Charles Poon, Ford’s VP of vehicle hardware engineering, acknowledged that leadership had underestimated the invaluable expertise of these veterans who have weathered numerous product cycles. He conceded that substituting them with AI alone was a costly misstep, noting that while AI is a powerful asset, its effectiveness hinges on the quality of its training data.

These reinstated experts now lead compulsory sessions to diagnose vehicles and refine automated engineering software and AI tools, catching glitches before they reach production. By pinpointing potential failure modes early, they help avert the costly recalls and defects that once drained billions, supporting Ford’s goal to slash expenses by a billion dollars this year.

Last year’s JD Power Quality Survey—a benchmark that evaluates vehicle quality during the first three months of ownership—placed Ford 10th among mainstream brands, falling short of the industry average.

This year, however, JD Power crowned Ford as the leading mainstream brand, surpassing industry giants such as Toyota and Honda. The automaker credits this dramatic leap to the seasoned engineers’ contributions.

Ford’s recent admission that artificial intelligence alone cannot design and test high‑quality vehicles without seasoned human oversight is merely the tip of the iceberg. A Careerminds study of companies that implemented AI‑driven layoffs found that 35.6% had to rehire more than half of the employees they had previously let go, while 32.7% rehired between a quarter and a half of their workforce.

In 2024, Klarna’s CEO Sebastian Siemiatkowski announced that a new chatbot was performing the work of 700 full‑time customer‑service agents. The claim triggered a hiring freeze and the elimination of hundreds of positions. Yet by mid‑2025 and into 2026, Klarna was scrambling to recruit human agents again because customer satisfaction had plunged. AI excels at answering simple queries—like checking an account balance—but when faced with complex, nuanced issues it often resorts to generic, corporate jargon.

These developments underscore a broader industry lesson: automation can deliver impressive efficiencies, but it also carries the risk of eroding service quality and employee value if not paired with thoughtful human oversight.