Another company is regretting its decision to lay off employees in favor of AI.
Automotive giant Ford has reportedly hired around 350 veteran engineers, several of whom are former employees, after its AI systems couldn't deliver the results the company anticipated, Bloomberg reported.
Ford COO Kumar Galhotra told Bloomberg that the company had been increasingly relying on "automated quality systems,” and brought back technical specialists to find failure points "before a part ever reaches the plant floor."
Additionally, Charles Poon, the automaker's VP of vehicle hardware engineering, said that the company "mistakenly" thought that introducing AI that ingested design requirements would result in a high-quality product. "We recognized that for us to enhance some of our automation and machine learning and artificial intelligence tools, we needed to ensure that they were trained by the most experienced individuals," he said.
Though Ford is far from abandoning its AI plans, it provides an example of what can happen when tech companies jump the gun on using AI to automate employee work. It's also just the latest signal that AI may be giving the job market some whiplash.
A report from PwC published in mid-June found that companies using AI to augment and supercharge their workforces are seeing more returns than those that simply replace large chunks of their staff with automation. A report from Gartner published earlier this year predicted that more than 50% of the companies that cut staff due to AI, aiming to save on payroll, would end up rehiring employees to perform similar work.
Our Deeper View #
Though there's a lot of talk about how AI will change the nature of work and create new jobs, Ford's decision to bring back staff is a sign that the tech may simply not be as prepared as we anticipated to handle certain tasks. It's why the recipe for success isn't worker replacement, but rather worker augmentation. Both the employees and the AI systems get better working in tandem. Then, when the systems are ready, the employee can take on more meaningful work. Additionally, not all jobs are equally ready for integrating AI. Physical AI systems for blue-collar work have further to go than AI for white-collar work. And, given the potentially dangerous nature of industrial and manufacturing settings, the stakes of getting it right are high.