Enterprise Field Notes · Issue #11

Ford Rehired the People AI Was Supposed to Replace

By Ben Pickett · July 2, 2026

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Enterprise Field Notes · Issue #11

Ford hired 350 veteran engineers after its AI fell short, then topped J.D. Power. Why pairing AI with your experts beats replacing them, from Polanyi to Kasparov to your own factory floor.

By Ben Pickett

I sell AI for a living. So, when Ford hired 350 veteran engineers because its AI was not good enough, that is my story to explain, not Ford’s to bury.

Here is the part that should stop you. A year ago, Ford’s CEO stood on a stage in Aspen and said AI would replace “literally half of all white-collar workers in the U.S.” This year, Ford went looking for the humans.

You know a Ray. Thirty years on the job, the one who can hear a bad weld from across the bay and knows the exact gasket that fails in a Michigan February. For a decade, the org chart treated Ray like overhead. Expensive, analog, replaceable. Then the requirements went into the AI, the AI produced the designs, the designs did not hold up, and Ford went and found 350 Rays. Some retired, some at suppliers, all brought back.

They are not there to babysit the robots. They run mandatory weekly design reviews that catch failure points before a blueprint reaches the factory floor. They mentor the young engineers. They retrain the AI that kept missing things. Charles Poon, Ford’s VP of vehicle hardware engineering, said the quiet part out loud: “Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.” Then the line to tape to the wall: “Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it.”

Around the same time, Ford was named the top mainstream brand in J.D. Power’s Initial Quality Study for the first time since 2010. I am not going to claim the engineers caused that. Initial-quality scores mostly measure first-90-day gripes, not the durability failures these veterans were brought in to catch, and the timelines do not line up that cleanly. There was a hard quality push underneath it too, on the back of a punishing stretch of recalls. Read the award as reputational backdrop, not proof. The proof is simpler, and it is in Ford’s checkbook: they paid real money to put veterans back in the room.

The assumption failed, not the AI

Read it fast and it sounds like AI lost. It did not. The assumption lost. The assumption that Ray’s thirty years lived inside the requirements document.

They never did. Sixty years ago the philosopher Michael Polanyi put it in five words: “We know more than we can tell.” Economists now call it Polanyi’s Paradox, and it is why automation keeps hitting a wall. The most valuable thing Ray knows, he cannot fully write down, which means you cannot fully feed it to a model by handing it a spec. The knowledge lives in the doing, in ten thousand small judgments he makes without narrating them. You get it by putting Ray next to the machine, not by asking Ray to type it up.

We already learned this, in chess

After a computer beat Garry Kasparov, he did not quit. He invented a new game where humans and machines play together. In a 2005 open tournament, the winners were not a grandmaster, and not a supercomputer. They were two amateurs from New Hampshire running three ordinary chess programs and a better process for using them. They beat the grandmasters. They beat the supercomputers. Kasparov’s takeaway: “a weak human plus a machine plus a better process was superior to a strong computer alone, and superior to a strong human plus a machine with an inferior process.”

Ford just rediscovered centaur chess on an assembly line. The win was never the model. It was the pairing, plus the process.

Yes, engines eventually got strong enough to beat the centaurs too. But chess is a closed game with fixed rules and perfect information. Your business is not. It is messy, changing, and full of judgment calls no model has trained on, which is exactly the world where the pairing still wins.

It is not just a game

The same result shows up where the stakes are life and death. Across studies in radiology, a doctor reading scans with AI catches more than the AI alone and more than the doctor alone. But there is a catch that proves the whole point: it only works when the human stays in charge. When radiologists lean on the AI and stop thinking, the pairing gets worse, not better. That is Kasparov’s finding restated in a hospital. The pairing wins, and the process, who leads whom, is the entire game.

Why this is urgent, not philosophy

About one in four American manufacturing workers is over 55. In one industry survey, most manufacturing leaders said they expect to lose at least half of their institutional knowledge as their people retire. Your Rays are walking out the door on a clock.

So when you point AI at their jobs to cut cost, you are not trimming overhead. You are setting fire to the one thing your competitors cannot download.

I spent most of my career in reliability engineering, a lot of it at Nike, at a scale where one bad change could take the whole system down. The rule we lived by: when something broke, you did not blame the engineer, you fixed the system that let it through. The system breaking across corporate America right now is the one that treats its Rays as a line item and its AI as a replacement. Both halves of that are wrong.

Run the centaur play

• Capture judgment before the exit interview, not after. Turn design reviews, post-mortems, and defect analyses into records the model can learn from. Poon’s most expensive sentence was that some of Ford’s best engineers left before their knowledge was ever captured.
• Put your experts on the model, not around it. Not the fallback for when it breaks. The people who review it, catch what it missed, and log every correction. Concretely: a veteran flags in a design review that a bracket will crack under thermal cycling. That correction, with his reasoning, becomes a labeled example the next version of the model trains on. Enough of those and the model stops making the mistake. The corrections are the asset. A model that learns from your best people compounds. A model that learns from a spec plateaus.
• Pay for the process honestly, and keep paying the people. It is slower than “just let the AI do it,” and your best people will resent training their supposed replacement. So do not drain the Ray and move on. The person who trains your model should be the best-retained, best-paid, most-mentored hire in the building, not the one you optimize out next quarter. Name that cost, or the whole thing stalls.

This is not a Ford quirk. Klarna handed hundreds of service jobs to AI, watched quality slide, and rebuilt a human-plus-AI model. Gartner predicts that by 2027, half the companies that cut customer-service staff citing AI will rehire, often for the same work under new titles.

Treat AI as a replacement for expertise and you get a cheaper version of worse. Treat it as an amplifier and the expertise you already paid for becomes the one thing your competitors cannot copy.

One thing to do this week

We are reading this the week of America’s 250th, so one line of optimism, the honest kind: every wave this country rode created more work than it destroyed, but never smoothly and never for everyone, and who came out ahead was a leadership choice about whose knowledge got kept. Adaptation is not a virtue the displaced failed at. It is a path leaders build, or do not.

So, this week: find the retirement closest on your calendar, the veteran whose knowledge would hurt the most to lose. Start with one team, one process, not the whole floor. Put that person on the model, not around it, and log what they correct. That is the whole play, begun.

Ford spent a decade treating its Rays like overhead, then paid to bring 350 of them back. Your Ray is still in the building. For now.

Who is your Ray, and are you capturing what they know before the exit interview? I read every reply.

Header artwork generated with Swa.

References

1. Ford on why it hired 350 “gray beard” engineers: you need their mentorship, and to drive huge AI productivity gains. Fortune, June 29 2026. link
2. Ford CEO Jim Farley: AI will replace half of all white-collar workers (remark at the Aspen Ideas Festival, June 27 2025). Fortune, July 5 2025. link
3. Ford Named Top Mainstream Brand for New Vehicle Quality in J.D. Power 2026 U.S. Initial Quality Study for First Time Since 2010. BusinessWire / Ford, June 25 2026. link
4. Michael Polanyi, “The Tacit Dimension” (1966); David Autor, “Polanyi’s Paradox and the Shape of Employment Growth” (NBER working paper 20485, 2014). link
5. Garry Kasparov on “centaur” / advanced chess and the 2005 PAL/CSS Freestyle tournament won by amateurs Steven Cramton and Zackary Stephen (“ZackS”), from “The Chess Master and the Computer” (New York Review of Books, 2010). link
6. Human plus AI in diagnostic imaging can outperform either alone, but degrades under automation bias when clinicians defer to the model (Tschandl et al., Nature Medicine, 2020). link
7. Gartner Predicts Half of Companies That Cut Customer Service Staff Due to AI Will Rehire by 2027. Gartner, February 3 2026. link
8. Klarna CEO Reverses Course by Hiring More Humans, Not AI. Entrepreneur, 2025. link
9. Manufacturing workforce aging and “brain drain”: retirement and institutional-knowledge loss (Deloitte and The Manufacturing Institute skills-gap research). link

About the author

I'm Ben. I write Enterprise Field Notes, and by day I'm COO at Swa, after years running reliability, data protection, and database operations at Nike. The lesson that keeps proving itself: anything you cannot run without, and cannot walk away from, is a risk you have not priced yet. What is yours?

Header artwork generated with Swa.

Read more of Ben's Enterprise Field Notes at benpickett.com.

© 2026 Ben Pickett · Enterprise Field Notes