Ford just posted its best J.D. Power quality score in 16 years. Getting there meant undoing a decade of bets on automation.
Ford topped the 2026 Initial Quality Study as the No. 1 mainstream brand, beating Toyota and Honda outright. Only two luxury brands, Porsche and Genesis, scored higher across the entire industry.
A year earlier, Ford ranked 10th among mainstream brands, below the industry average. No mainstream brand improved more this year than Ford did.
The turnaround came from a place nobody expected: 350 veteran engineers Ford had to hire, rehire, or promote back into roles automation was supposed to handle.
What Ford actually got wrong
For the better part of a decade, Ford leaned hard on automation, AI-assisted design tools, and algorithmic decision-making to run vehicle development. The pitch was the standard one: let the systems handle scale and consistency, free up the humans for everything else.
Charles Poon, Ford's VP of vehicle hardware engineering, told reporters the company assumed feeding its design requirements into AI tools would be enough on its own to produce a high-quality vehicle. It wasn't.
The problem wasn't the AI itself. It was what the AI never learned.
As senior engineers left, retired, or were pushed out, what they carried in their heads left with them — which suppliers cut corners under deadline pressure, which tolerances had bitten Ford before, which shortcuts looked fine on paper and failed on the line. None of it lived anywhere an algorithm could read it. Ford's automated systems, trained on incomplete data, had no way to compensate.
Poon's point, stripped of the corporate phrasing: a model can't outperform the data it was trained on, and Ford hadn't been capturing what its most experienced people actually knew.
The timing made it worse
This wasn't AI failing in a vacuum. It hit during the troubled launches of the Explorer and Lincoln Aviator, pandemic-era supply chain chaos, and a recall count that's still the highest in the US auto industry.
Ford expects roughly $1 billion in warranty and materials costs this year alone. COO Kumar Galhotra has waved off the current recall numbers as a trailing signal rather than a live one — the fixes are already built into newer vehicles, the backlog just hasn't cleared the system yet.
Bringing the engineers back, specifically
Over the last three years, Ford hired, promoted, or rehired more than 350 veteran engineers. Many were former employees. Some came from suppliers.
Concretely, that means:
Run mandatory meetings that troubleshoot quality problems before they reach production
Reprogram Ford's automated quality-control tools directly, using knowledge those tools were never trained on
In Galhotra's words, they "hunt for failure points before a part ever reaches the plant floor"
Their job wasn't to replace the AI tools. Their job was to become the missing training data themselves — the person who remembers exactly why a coolant fitting got redesigned three programs ago, and catches the new supplier about to repeat the mistake.
AI didn't get fired. It got reassigned.
Here's the part that gets lost in the headline: Ford isn't walking away from AI. It's using the technology where it's genuinely strong, and pulling it back where it wasn't.
On the assembly line, Ford runs AI over the readings each engine produces during hot testing, watching for the kind of microscopic drift a human inspector would never catch by eye. A part can sit fully within spec and still get flagged, if it diverges even slightly from the pattern set by every engine built before it.
That's a narrow, data-saturated, well-defined problem. AI is excellent at exactly that.
On the software side, Ford built a dedicated 40-person quality assurance team over the last 18 months. In Poon's words, it's "a group that didn't even exist before."
Their job is catching defects before launch instead of patching them after a recall.
Backing that team: more than 100,000 new AI-generated tests built to stress software against edge cases automatically, so a late-stage code change gets fully revalidated instead of shipped on faith. Poon told TechSpot the company has treated software reliability as "its own rigorous discipline with strict metrics."
That's the split that held up: automation for scale and edge cases, humans for judgment and the failures nobody had written a test for yet.
The lesson, for anyone running an engineering org
Choosing AI wasn't the mistake. The mistake was assuming AI could absorb judgment nobody had ever written down, then letting the people who held that judgment leave before anyone captured it.
That gap doesn't show up on a roadmap. It shows up eighteen months later, as a recall, or a quiet regression nobody can quite explain.
Ford had the rare luxury of buying that knowledge back. The same engineers it had pushed out were mostly still reachable, still willing, still mostly intact in what they knew.
Most teams that make this trade don't get a clean rehire on the other side of it. Ford turned its gap into a 16-year-best quality score. Whether yours turns into a turnaround story or just the regression depends entirely on how early you notice the gap exists.
Originally published on ZyVOP
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