Everyone from CEOs to politicians has been talking about the likelihood of AI-related job loss, and several companies have already let people go in anticipation that AI can do their work. Ford Motor Company is the latest to rehire those workers when AI proved inadequate for the job. Elsewhere, many of the managers who have let people go regret their decisions, and some companies are revising their hiring plans. To remedy the chaos, Neville and Shel discuss the importance of strategy and knowledge management systems, among other things.

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Raw Transcript:

Shel Holtz
Hi everybody, and welcome to episode number 521 of For Immediate Release. I’m Shel Holtz

Neville Hobson
And I’m Neville Hobson. Here’s a story that should make every one of us pause before we get too comfortable handing things over to AI. Ford, the automaker, has just rehired somewhere between three hundred and three hundred and fifty veteran engineers. Note the word rehired. The company had let them go in recent years as it leaned into AI-driven quality checks. Ford calls them greybeard engineers. That’s not a throwaway nickname. It’s the whole point of the story. These are the people with decades of experience across multiple product cycles, and Ford let a lot of them go only to discover it needed them back because the AI wasn’t working the way Ford expected. We’ll look into what happened right after this Charles Poon, Ford’s vice president of vehicle hardware engineering, put it plainly on a call with reporters. Here’s what he said: “Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.” Think about that for a moment. Ford didn’t skip a step. They fed the AI everything that was written down, every design requirement, every documented specification. It still wasn’t enough. And it wasn’t just one system. Ford had installed around nine hundred AI-assisted cameras on the production line specifically to catch quality issues. Nine hundred cameras, and still they couldn’t replace the trained eye of an experienced technician who knows what a problem looks like before it becomes a visible defect. Ford’s chief operating officer, Kumar Galhotra, added more context. He said the company had been leaning more and more on automated quality systems, and the results were disappointing. Teams across software, hardware, manufacturing, and supply chain had also been working in isolation from each other, which meant defects were being caught late and fixed under pressure rather than prevented early. Galhotra described this as a find and fix mentality that Ford is now trying to move away from towards genuinely preventing problems before they start. The returning engineers sit right at the center of that shift. They now run mandatory weekly quality and design reviews, hunting for failure points before a single part reaches the factory floor And here’s the part I think matters most for us. A lot of the people who held that hard-won judgment had already walked out the door to suppliers, to retirement before anyone at Ford thought to capture what was in their heads. Poon admitted as much. “Over prior years, we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers who have been with us through many product cycles,” he said. So Ford had to buy that expertise back three years into this process at real cost. Was it worth it? By Ford’s own numbers, yes. The company has just topped the J.D. Power Initial Quality Survey for mainstream brands for the first time since twenty-ten. That’s sixteen years. CEO Jim Farley says the rehired engineers are already contributing what he called literally hundreds and hundreds of millions of dollars in savings, largely through reduced warranty and recall costs. Ford’s even projecting around a billion dollars in cost reduction this year on the back of this quality push. Now, here’s a tension worth sitting with. This is the same Jim Farley who said publicly on other occasions that AI is gonna replace rough-roughly half of all white-collar jobs. And yet here’s his own vice president standing in front of journalists explaining that Ford’s entire quality turnaround depended on bringing back the very human expertise the company thought it could do without. To be clear, this isn’t really a story about AI failing and humans winning. Ford isn’t walking away from AI. Those returning engineers aren’t just doing inspections. They’re training junior staff, and they’re reprogramming the AI tools themselves, feeding them the judgment that design requirements alone couldn’t capture. It’s a hybrid fix, not a retreat. But for anyone in our line of work, comms, knowledge management, anyone thinking about where AI fits into institutional expertise, there’s a sharp lesson underneath all of this. Documentation isn’t the same as judgment, and once the people carrying that judgment are gone, you don’t get it back for free or quickly or easily. I think there’s a bigger question here, too, about how organizations are handling this handover between human expertise and automation, and whether Ford’s experience is a one-off or a warning sign for a lot more companies than just car manufacturers. Shel

Shel Holtz
Yeah, I think it is a warning sign. But I, I don’t think it’s a trend necessarily, the idea that AI AI layoffs are being reversed everywhere. Yeah I’d just be careful about overstating that. There’s really only a handful of well-documented company examples of this. I think it’s easier to say that the way many organizations overestimated how quickly AI could substitute for the experienced judgment of their staff, I think that’s a reasonable way to look at it. Increasing number are recalibrating toward AI human collaboration rather than there are examples though. IBM has reversed course. They didn’t rehire the same people, but they’ve really reversed course on this whole replacement idea. An AI system deployed to take over HR work handled about 94% of incoming requests, but the 6% it couldn’t resolve including situations in- involving ethical judgment, really revealed the limits of all of this into the hands of a large language model. And then the company announced that it planned to triple its US entry-level hiring this year. That’s a pretty significant reversal. Klarna’s the one that … that’s the poster child for all of this. They were one of the first to announce that they were going to replace their customer service with AI. A year later their CEO publicly reversed course, admitting that customer experience had gone down the tank, quality had fallen the company had over-prioritized cost savings which most companies seem to be doing. They’re looking at cost savings and not other ways AI could really improve things or even help the organization grow and that human customer service remained essential. So they went back to hiring customer service representatives, and they expanded their human support. They la- even reassigned engineers and marketers into customer support roles while they were busy rebuilding the support organization that they had decimated. CEO, I think it was he who was quoted saying, “Cost, unfortunately, seems to have been too predominant evaluation factor.” that said I think it is worth noting that according to one research organization, I hadn’t heard of them before, but OrgView, 39% of busin...