Ford hired AI and sacked humans. It backfired badly

Ford has admitted to rehiring hundreds of human workers after its aggressive AI adoption strategy backfired.

The US automaker hired over 350 veteran engineers, referred to internally as “gray beards”, over the past three years in order to address mistakes made by automated systems.

The staff will lead quality reviews after the automation issues cost the company billions of dollars, Bloomberg reported, while some workers will also help improve and train the AI systems.

“We had been relying more and more on automated quality systems and not getting the desired results,” said Kumar Galhotra, Ford’s chief operating officer.

“We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor.”

Ford had been increasingly relying on AI-driven inspection systems to streamline production and address quality control issues, however the firm acknowledged that AI lacked the nuanced judgement when it came to complex problems.

After rehiring experienced engineers, Ford experienced a marked improvement in its quality standards.

According to the latest J.D. Power Initial Quality Survey, an annual automotive benchmark that measures the quality of new vehicles, Ford ranked top among mainstream brands – the first time it has achieved that milestone in 16 years.

Ford continues to have quality issues with its older vehicles, and remains the most recalled automaker in the US, though executives blamed this on past issues involving automation, rather than the rehiring of humans.

The company said it would not abandon its use of AI, but plans to now use it in conjunction with human oversight and experience.

“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” said Charles Poon, Ford’s vice president of vehicle hardware engineering.

“Over prior years, we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles.

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