Ford Ceo Says Ai Is Taking Over Spreadsheet Jobs. It Still Needs Humans To Fix Robots.

• Ford's CEO said the automaker still needs a lot of human workers to fix its robots.
• Spreadsheet workers? They're having a tough time in the "first inning" of the AI boom, he said.
• It comes as executives sound the alarm that America isn't producing enough blue-collar workers.

Building the spreadsheet will soon be AI's job. But fixing the robots? The company still needs a human for that.

On Wednesday, Ford CEO Jim Farley told reporters during Ford's Accelerate the Essential Economy conference in Detroit that the "first inning" of the AI boom has had very different effects on vocational work and informational work.

"If you work in finance doing spreadsheets, or you're in a call center, or you're an entry-level programmer," he said, "those jobs are definitely going to be changed, eliminated with at least this first inning of AI."

Meanwhile, "AI as more of a companion to our skilled trade," Farley said.

The automaker has more than 10,000 skilled-trades workers in the US, representing roughly 20% of the company's 56,000 UAW employees. Those workers are increasingly repairing robots, working with software, and maintaining automated machinery, he said.

Automation is a central focus for Ford. It's invested heavily in robotics while racing to remake its lineup of EVs. Farley said the line between manufacturing engineers and hourly skilled-trades workers is becoming "completely blurred" in those newer operations.

Ford's massive megacasting machines for its coming Fathom electric pickup, for example, require workers with software and fluid-dynamics expertise, Farley said. Its battery plants also need workers who can maintain specialized equipment. New plants have a closer resemblance to semiconductor fabrication factories than legacy auto assembly lines, he said.

Ford also employs thousands of blue-collar workers at dealerships across the US — and they're working alongside AI, too, Farley said. He pointed to the example of a rural technician working for the first time on an engine in a Super Duty, Ford's largest pickup truck. The company is developing AI guidance to walk that technician through the steps, he said.

Other executives at the conference made a similar case for AI in the physical workforce. Chris Nelson, CEO of Stanley Black & Decker, said electricians have become a bottleneck for data-center construction, and said his company has developed an autonomous drilling robot that can handle the repetitive drilling needed to mount racks and cables in data centers.

"The task is not going to change," Nelson said. "How swiftly, safely, and productively you can accomplish it in concert with AI is going to be, I think, a big part of the formula."

Ford and several large employers used the conference to promote a report from the Alliance for America's Skilled Trades, a group cofounded by Ford, BlackRock, Google, and Carhartt. The group's report said the US will need to fill about 1.7 million skilled-trades openings annually through 2035, while formal training pathways are producing 55 workers for every 100 needed.

The pipeline for that talent is very leaky, the report found. Only 48 of every 100 apprenticeship starters complete their program. Transportation, childcare, and income pressures were leading contributors for why people didn't finish their training.

Farley also said that AI will only help those workers if they trust the tech. Ford employees already have access to AI-powered vision systems to inspect vehicle panels and other work, but workers may resist or ignore the systems if they believe the data could be used against them.

That tension has shadowed workplace technology for decades, Farley said, including when he worked as a time-motion-study expert, clocking how long it took for assemblyline workers to complete tasks (a job, he said, nearly got him into physical altercations with angry staff).

The difference now is that the machines workers are being asked to trust may also be the ones those workers are needed to repair.