Ford rehires 350 engineers after AI fails at quality

Ford has rehired more than 300 engineers, some former employees, after AI failed to provide the same quality, according to information first reported by Bloomberg. 

“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 during a media call, according to Bloomberg. 

Ford told Motor 1 in a statement that it moved its vehicle engineering, manufacturing, supply chain, and quality teams under one organization and leader in 2023. The automaker added that the reorganization allowed the company to look at the entire lifecycle of a vehicle, from software development to suppliers on the paint floor. 

“At the same time, we have rallied the whole company around a clear vision: Quality Comes First,” Ford told Motor 1. “We’ve built a culture of relentless problem-solving and recognizing our teams when they prevent issues from reaching customers.”

The company uses a number of tools including AI, Ford said. It adds that AI is powerful for catching potential quality issues but it is only as good as the people using it. 

“This is one reason why we have added through internal promotions or new talent more than 350 experienced tech specialists to work alongside newer team members,” Ford says in the statement. “By combining AI’s processing power and pattern recognition with decades of human engineering experience, we’re identifying potential issues and designing quality into our vehicles from day one while teaching the next generation to prevent problems before they ever start.”

Motor 1 notes that the change in quality control comes as Ford set the record for the most recalls ever in 2025. 

Yet, Ford landed on the top of the JD Power 2026 U.S. Initial Quality Study (IQS) for mainstream brands for the first time since 2010 partly because of the rehiring. 

Fox Business reports that this is partially because of the company’s switch back to human engineers.  

“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 says in the Fox Business report. 

He added that AI Tools lacked the training and experience that veteran technicians have. 

“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,” Poon said. 

CNBC says Ford is just the most recent company to decide to rehire humans after laying off for AI. 

Other recent companies include IBM and Commonwealth Bank of Australia, the news outlet says. 

As some industries seem to be reversing their AI decisions, the insurance industry seems to be leaning into AI. 

Most recently, State Farm changes its compensation and benefit package for 19,000 agents in May. 

The reports came soon after State Farm CEO Jon Farney described the insurance company’s transformation into “A Next Gen Good Neighbor” in a blog post.  

A following press release describes the transition in more detail. It talks about faster, simpler claims service, more competitive pricing, and data-driven underwriting. It calls the approach “Human + Digital.” 

The release states that State Farm is making the end-to-end claims process faster, from first notice of loss to payment delivered. 

The collision industry has been vocal in recent months about changes it has seen from State Farm, including moving to centralized auto claim audit teams and cutting labor rates. 

Michael Bradshaw, vice president of K&M Collision in Hickory, voiced concerns about State Farm during a Society of Collision Repair Specialists (SCRS) open board meeting and Collision Industry Conference meeting in April. 

He described how centralized internal review teams have been removing operations that State Farm appraisers have identified. He added that the operations are removed without discussion with the body shop or any proper documentation or explanation.

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