AI Didn’t Fail Ford. Ford Failed Their AI. 

Wade Wyant on July 2, 2026

The automaker just gave every business leader in America the most expensive lesson in AI strategy. Here is what you need to learn from it. 

Ford had a problem. Quality was suffering. Cars were going out the door with more issues than they should have had, and the company knew it. So leadership looked at AI and thought: there is the answer. We can automate quality. We can let the machines catch what the humans missed.1 

They were not entirely wrong. But they were wrong in one critical way. And it cost them. 

Before they could teach the AI to do the job, they let the people who knew how to do the job walk out the door. Ford shed roughly 5,300 salaried positions since its 2020 employment peak, part of a broader wave that eliminated more than 20,000 white-collar jobs across Detroit’s automakers. Among those who left: the experienced engineers who had spent decades understanding exactly why a specific design choice would cause a problem three years down the road.2 

Here is the part that should stop every CEO reading this cold. The AI did not fail. The AI amplified exactly what it was given. And what it was given was weak. Without the institutional knowledge of those veteran engineers encoded in the training data, Ford’s AI tools amplified bad inputs instead of catching design flaws. The system was working fine. The fuel going into the system was gone. 

“You cannot train a machine on knowledge that no longer exists.” 

WHAT FORD ACTUALLY DID WRONG 

Charles Poon, Ford’s VP of vehicle hardware engineering, said it directly: Ford mistakenly believed it could swap in AI and still produce a high-quality product. The company underestimated the value of veteran engineers and what they carried in their heads.3 

I want to give Ford credit for saying this out loud. That kind of honesty takes guts, especially from a company as proud as Ford. But let me be equally honest about what the lesson actually is, because the wrong takeaway here is dangerous. 

The wrong takeaway is: AI failed. Do not trust it. 

The right takeaway is: AI is only as good as the intelligence you put into it. If you try to replace your best people before you have captured what they know, you have not built an AI strategy. You have built an expensive way to scale your own ignorance. 

Think about it this way. You have a master technician who has built and fixed the same engine for 30 years. He can hear a problem before it shows up in the data. He knows from experience that a particular combination of tolerances under heat stress will cause a failure six months later, even if every spec on paper looks fine. Now you hand his tools to a machine. The machine can run the same checks. But it never learned why he made certain calls. The machine is perfectly precise and completely blind at the same time. 

That is what Ford built. And they had to spend months and hundreds of hires to fix it. 

WHAT FORD HAD TO DO TO TURN IT AROUND 

Ford’s response was not to abandon AI. The response was to go get the humans back and use them to fix the AI. 

The company rehired, newly hired, or promoted 350 experienced engineers. Those engineers were not brought back to do what they had always done. They were brought back for three specific purposes: to mentor junior staff, to rebuild the data pipelines feeding Ford’s AI training systems, and to refine the automated tools that were originally supposed to replace them. Ford also built a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases and revalidate software changes late in development.4 

The result: Ford topped J.D. Power’s 2026 Initial Quality Study for the first time in 16 years, scoring 152 problems per 100 vehicles, ahead of Nissan and Buick. The F-150, Mustang, and Super Duty each won best in segment for the second consecutive year. The J.D. Power Initial Quality Study measures problems reported by owners in the first 90 days of ownership.5 

Read that again. The fix was not less AI. The fix was human intelligence feeding better AI. That is a very different story than the headlines are telling. 

“The fix was not less AI. The fix was human intelligence feeding better AI.” 

WHY THIS MATTERS FOR YOUR BUSINESS RIGHT NOW 

Ford is a massive company with the resources to hire 350 people to fix a mistake. Most of the leaders I work with do not have that runway. Which means if you make this same mistake at your company, you may not get the chance to correct it. 

Here is what I see happen all the time in mid-market businesses. A leader hears about AI. They get excited. They start thinking about what they can automate, what headcount they can reduce, what costs they can cut. And the first thing they do is let go of the people who carry the institutional knowledge before they have figured out how to capture it. 

That knowledge does not live in your files. It does not live in your org chart. It lives in the heads of your best people: the ones who have seen what works, what fails, and why. The ones who can answer a question before you finish asking it because they have seen that exact situation twelve times before. 

When those people leave, that knowledge walks out with them. And no amount of AI investment will get it back, because there is nothing left to train on. 

WHAT WE DO DIFFERENTLY AT REDEGADES.AI 

At REDEGADES.AI, we have never believed AI replaces people. We have always believed AI surfaces the intelligence that helps your best people make better decisions, faster. 

The first thing we do when we work with a leadership team is capture what they know. The strategy. The judgment. The pattern recognition that lives in the CEO and the C-suite and has never been written down anywhere. We build a private AI environment that reflects your organization’s thinking, not some generic model trained on the whole internet. Your intelligence. Your data. Your people’s expertise, scaled across your company.6 

That is the opposite of what Ford did. Ford tried to skip to the output without building the foundation. We start with the foundation, because we know that is the only way the output is worth anything. 

Ford’s CEO Jim Farley said publicly that AI is going to replace literally half of all white-collar workers in the US. His own company’s quality crisis now complicates that prediction. Human judgment still wins. The right application of AI just makes it sharper.7 

“Human judgment still wins. AI just makes it sharper.” 

THE QUESTION YOU SHOULD BE ASKING 

If something happened to your top three or four people tomorrow, what would your company actually know? Not what would be in your files or your systems or your processes. What pattern recognition, what judgment, what decades of hard-won expertise would walk out the door with them? 

If the answer is most of it, you have the same problem Ford had. You just have not run into the consequences yet. 

The golden window for building a real AI strategy, one where your intelligence is captured before it walks out the door, is right now. Not when you are already in the quality crisis. Not when you are making 350 emergency hires to rebuild what you let go. Now. 

Ford figured it out. They earned the top quality ranking in 16 years as proof. The question is whether you learn from their mistake, or wait until you have to make it yourself. 

Notes 

1. Alina Maria Stan, “Ford had to rehire 350 engineers after its AI got vehicle quality wrong,” The Next Web, June 26, 2026, https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

2. Stan, “Ford had to rehire 350 engineers.” Ford’s salaried workforce reduction since its 2020 employment peak and the broader 20,000-job contraction across Detroit’s automakers are both reported in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

3. Stan, “Ford had to rehire 350 engineers.” Charles Poon’s statement that Ford “mistakenly believed it could swap in AI and still produce a high-quality product” is quoted directly in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

4. Stan, “Ford had to rehire 350 engineers.” The 350-engineer figure, their three assigned roles, the 40-person software QA team, and the 100,000-plus automated tests are all reported in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

5. Stan, “Ford had to rehire 350 engineers.” Ford’s J.D. Power 2026 Initial Quality Study results — 152 problems per 100 vehicles, first-place ranking among mainstream brands for the first time in 16 years, and three segment wins for the F-150, Mustang, and Super Duty — are reported in this article. The J.D. Power Initial Quality Study measures problems reported by owners in the first 90 days of ownership. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

6. REDEGADES.AI, company methodology. For information on REDEGADES.AI’s private AI implementation approach and the Decision Alignment Layer, visit https://www.redegades.ai. 

7. Stan, “Ford had to rehire 350 engineers.” Jim Farley’s statement that AI “is going to replace literally half of all white-collar workers in the US” is quoted in this article. https://thenextweb.com/news/ford-rehired-350-engineers-ai-quality-jd-power. 

You Just Bought a One-Way Ticket. Now What?  

Wade Wyant on June 27, 2026

AI has unlocked a gold rush of software development inside your business. Before you celebrate, answer four questions. 

THE DAM JUST BROKE 

For twenty years, your company did the smart thing. Every time a leader walked in with a problem that needed a software solution, the conversation went roughly the same way. How much will it cost? How long will it take? Who maintains it? The answers were always: more than you want to spend, longer than you want to wait, and somebody we probably have to hire. So you put the problem on the shelf and moved on. 

That shelf got very full. Marc Andreessen, co-founder of Andreessen Horowitz, has argued publicly that the world had far more demand for software than anyone ever realized. The reason we did not see it was simple: the friction of building software had trained everyone to stop asking. Leaders are smart people. They learned to skip the question before it became a headache.1 

Then AI showed up and changed the math completely. 

Today, over 26 percent of all production code is being written by AI, up from 22 percent just one quarter ago.2 What used to take a development team three months now takes an afternoon. What used to require a programmer now requires someone willing to describe a problem in plain English. The shelf is being cleared out at a pace nobody predicted. 

“We did not go from walking to driving. We went straight from walking to flying.” 

Two terms have emerged to describe this new reality. Low code refers to development platforms that reduce manual programming through visual tools and templates. Vibe coding, a term coined by AI researcher Andrej Karpathy in February 2025, takes it further: you describe what you want, and the AI builds it. You are not programming. You are directing. The technical barrier, the one that kept that shelf full for twenty years, is gone. 

THIS IS THE BEST NEWS IN BUSINESS. MOSTLY. 

At REDEGADES.AI, we watch this transformation play out inside our clients every week. Problems that sat unsolved for years are getting fixed in days. Workflows that required three humans and a spreadsheet are being automated. The productivity unlock is real, and we encourage every company we work with to move fast. Build aggressively. The companies that do are pulling ahead of the ones that are still waiting for a pricing page.3 

But here is the part of the story that does not get enough airtime, and I am going to give it some, because I think ignoring it is going to be expensive for a lot of companies in the next two years. 

Flying somewhere on a one-way ticket is great. Right up until you need to come back. 

A significant portion of the software being built right now with AI tools is exactly that: a one-way ticket. It solves the problem in front of you today. It does what you need it to do right now. And then it lands somewhere, and the question of what happens next has not been fully answered. According to a recent survey of technology leaders, 75 percent expect moderate to severe technical debt by 2026 as a direct result of rapid AI-assisted development.4 That debt is already accumulating. The question is whether you see it before it comes due. 

THE FOUR QUESTIONS YOUR IT TEAM IS HOPING YOU WON’T ASK 

Here is the honest version of where most companies are right now. They are building fast, deploying aggressively, and solving problems they could not touch two years ago. All of that is good. What is not good is that four critical questions are going unanswered. Most leaders do not ask them because they feel like IT questions. They are not. They are business continuity questions. 

The first question is: where does this software actually live? 

A tool built inside a chat interface, a link shared with the team, a deployment dropped into a temporary environment: none of those are infrastructure. They are convenient right now. Licensing terms change. Platforms evolve. The tool your sales team relies on every morning may not exist in the same form twelve months from now, and if nobody owns it, nobody will fix it when it breaks. 

The second question is: is it secure? 

AI can write functional code very quickly. What it does not automatically produce is hardened, production-grade code. Research from Veracode found that between 40 and 45 percent of AI-generated code contains vulnerabilities mapping to the OWASP Top 10, the industry’s standard list of critical security risks.5 AI-generated code has a 2.7 times higher vulnerability density than human-written code. And 58 percent of developers admit they trust AI output without testing it. That is not a developer problem. That is a leadership problem. 

The third question is: who maintains it? 

Software built to solve a problem today will eventually run into a change, whether that change comes from inside your business or from a shift in the underlying technology powering the build. When that happens, who fixes it? This is not rhetorical. It needs a real owner, a real answer, and a real plan, before the break happens, not after. 

The fourth question is: who troubleshoots it when something goes wrong? 

This still requires a human in the loop with enough context to know what they are looking at. If everyone who understood what the tool was supposed to do has moved on to the next project, that context is gone. Gone, as in, the tool sits broken while someone tries to re-explain its entire purpose to an AI, hoping to reconstruct something that should have been documented from the start. 

“75 percent of tech leaders expect moderate to severe technical debt by 2026. The clock is already running.” 

THE GOOD NEWS: THIS IS FIXABLE RIGHT NOW 

None of these questions are reasons to slow down. Let me be clear about that. The era of being able to build software fast, at low cost, without a team of developers, is one of the most significant competitive advantages in the history of business. The companies that lean into it are going to look very different from the ones that wait. I am not here to pump the brakes. 

I am here to say: build fast, and build with a plan. 

That means deploying every tool you build into a real environment you own or control. It means running a basic security review before anything touches your data or your customers. It means assigning an owner, not just a builder. And it means documenting what the thing is supposed to do while someone still knows. 

This does not require a large IT team. It does not require a budget overhaul. It requires a checklist and the discipline to use it. At REDEGADES.AI, we help companies build that infrastructure around their AI development so the velocity stays high and the debt does not pile up quietly in the background. 

THE RETURN FLIGHT IS PART OF THE TRIP 

I thought about Y2K when I was putting this together. We spent years worrying about what would happen when the calendar rolled over to the year 2000, and when it did, the lights stayed on. This could be a Y2K situation. Maybe AI tools improve fast enough that maintenance and troubleshooting become trivially easy and the four questions answer themselves. That is possible. 

But the cautionary part of a cautionary tale is not that the outcome is certain. It is that the outcome is avoidable. Build fast. Solve every problem on that shelf. Clear it out. Just make sure someone has thought about the return flight before the plane takes off. 

The shelf is empty. The plane is in the air. Book the return. 

____________________________________________________________ 

CITATIONS 

1  Marc Andreessen, The Joe Rogan Experience, Episode #2234, November 26, 2024, https://open.spotify.com/episode/2JDW5u8BlKHM5C5wInOT4u. See also: Marc Andreessen, “Why AI Will Save the World,” Andreessen Horowitz, June 6, 2023, https://a16z.com/ai-will-save-the-world/. 

2  “AI Coding Assistant Statistics & Trends [2025],” Second Talent, https://www.secondtalent.com/resources/ai-coding-assistant-statistics/. AI-authored code share figure as of mid-2025. 

3  REDEGADES.AI client observation. 

4  “Vibe Coding Hit 84% Adoption. 45% Has Vulnerabilities,” Pixelmojo, https://www.pixelmojo.io/blogs/vibe-coding-technical-debt-crisis-2026-2027. Technical debt projection figure drawn from survey of technology leaders, 2025-2026. 

5  Veracode, “AI-Generated Code Security Risks: What Developers Must Know,” https://www.veracode.com/blog/ai-generated-code-security-risks/. OWASP Top 10 vulnerability mapping figure and 2.7x vulnerability density comparison. Developer trust figure: Faros AI, “The AI Productivity Paradox Research Report,” https://www.faros.ai/blog/ai-software-engineering.