Ford CEO Jim Farley knows AI won’t leave blue-collar workers untouched. But at a backstage media roundtable during Ford Pro Accelerate, he argued the technology will arrive in factories, repair bays, and skilled-trades workplaces principally as a “companion”—a tool to help people perform more complex tasks, build expertise faster, and help employers address an acute shortage of technical labor.
“I think most of these jobs will be both using AI and also protected from it,” Farley said at the roundtable, which also included Linda Hubbard, president and CEO of Carhartt, and Chris Nelson, CEO of Stanley Black & Decker. But “if you work in finance doing spreadsheets, or you’re in a call center, or you’re an entry-level programmer—those jobs are definitely going to be changed and eliminated with at least this first inning of AI.”
Farley’s distinction was straightforward: AI is likely to disrupt routine, screen-based, and standardized knowledge work more quickly than it can replace electricians, technicians, mechanics, and factory skilled-trades workers. Those jobs will be transformed by AI, automation, and software, he said, but they will also remain dependent on people who can diagnose failures, apply practical judgment, and work safely around complex physical systems.
At Ford, Farley said, that change is already underway. The company has more than 10,000 skilled-trades workers, or roughly 20% of its 56,000 UAW workers. The work is moving beyond traditional maintenance of conveyors and other mechanical systems, toward repairing robots, handling fiber, maintaining automated equipment, and working with increasingly digital manufacturing operations.
The new factory trades
Farley said the line separating skilled trades from engineering is increasingly hard to see inside newer auto plants.
“The visible line now, it’s kind of hard to tell when an engineer or a manufacturing engineer stops, and the skilled trade starts in these newer type operations,” he said.
In Ford’s newer manufacturing operations, skilled-trades workers may maintain large robotic casting systems, configure digital manufacturing processes, or troubleshoot machinery used in battery production. Farley said some battery-equipment maintenance is closer to work found in semiconductor fabrication than in a traditional auto plant.
Ford is also using AI and augmented reality to support technicians repairing vehicles. For example, he cited an engine removal from a Ford Super Duty truck, a job that can take two days and require extensive disassembly. Ford Pro is using AI to help dealers who haven’t done that before get it done much quicker.
“If you want to take apart a Super Duty, it’s like a two-day job to take the engine out. The whole truck is disassembled,” he explained. “A lot of people don’t have that skill.”
“We are using AI for them to say, ‘Okay, do this, do that,’” he added. “They’re good mechanics, they just don’t know. They’ve never done it before.”
A labor-force multiplier
Nelson, of Stanley Black & Decker, described AI and robotics as tools for confronting a shortage of construction and industrial workers—a godsend instead of a nightmare.
He said tradespeople often see large volumes of work their employers cannot complete because they lack the labor capacity. Technology that helps people work more efficiently could allow companies to finish their current projects and take on new ones.
When asked if there’s a stigma or backlash to AI in the trades, Nelson said he hasn’t seen that at all.
“I think anything that you can bring to them that says: This will make you better at what you’re doing, quicker, safer, and you’ll get more done in a given day, they’re pretty all-in because they’ve got more work than they can do right now. They see a backlog.”
Expanding on the bottleneck that AI is actually solving in manufacturing, he added: “They see a backlog of future revenue that they cannot access because they can’t get through what they need to do right now,” he said. “They want to access that backlog, and anything that can get them through it is well worth it.”
When asked if workers want AI to slow down instead of speed up, Nelson demurred: “I think you’re getting into some larger questions there.”
Farley jumped in to say that “it’s very important, I think, obviously, to build trust in those moments.” Ford has a lot of vision systems using AI in the background, for instance, to help make decisions on things like dimensional control of a panel or whether doors fit correctly or not.
“If they don’t trust that the data’s going to be used the right way, somehow against them or somehow in a not-so-nice way, it’s going to be a problem,” he said. “They’re not going to want to use the system. They’ll want to turn it off.”
Nelson described an autonomous downward-drilling robot developed with customers working on data-center construction. Data-center construction requires drilling tens of thousands of holes to mount racks, install computing equipment, and run cabling, he said. The robot can be programmed to handle the repetitive drilling while skilled workers move on to more complex tasks.
“It is a companion,” Nelson said. “It is hand-in-hand. It’s not a replacement.”
The age-old tension
The executives’ argument amounted to a different AI story from the one often told about white-collar automation. In their view, the central problem in manufacturing, construction, and infrastructure is a shortage of qualified workers. AI could make the existing workforce more productive, reduce time spent on repetitive tasks and help inexperienced workers become useful more quickly.
Farley stressed that the next generation of skilled-trades workers will need more familiarity with data, software, programming, and automation. But it could change what training looks like, allowing workers to draw on AI systems when they encounter unfamiliar equipment or procedures.
Farley compared the concern to the skepticism that workers have historically shown toward time-and-motion studies. Management may view a system as an efficiency tool; employees may see surveillance, work intensification or a threat to their jobs.
The Ford CEO recalled an experience earlier in his career, when he was doing time-and-motion studies—an older industrial-management practice built around timing workers’ tasks to identify efficiency gains. During a break, he recalled, a worker confronted him after noticing he was being timed.
“Some guy who was three years older than me [was] saying, ‘What, are you trying to take my job?’ That’s the age-old efficiency tension we’ve always had,” he said. “I don’t think it’s really that different.”
For this story, Fortune journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.
