Ford Over-Automated, Rehired 350 Engineers, Won Quality

Ford is the No. 1 mainstream automaker in JD Power's 2026 initial quality ranking — and the story of how it got there is a case study in what happens when a company assumes AI can replace accumulated human expertise. For several years, Ford leaned heavily on automated systems to handle production decisions and vehicle design tasks that previously required experienced engineers. The systems turned out to be far less reliable than expected, quality dropped, and the company found itself in the uncomfortable position of having to track down retired engineers and convince them to come back.

Charles Poon, Ford's VP of vehicle hardware engineering, put it plainly in a briefing with reporters this week: "Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product." It did not. The automated systems lacked the institutional knowledge that veteran engineers carry — the kind built over multiple vehicle development cycles, knowing which edge cases bite you, which supplier quirks matter, which warnings are noise and which aren't. Before all of that knowledge could be fully transferred into the AI systems, many of the engineers who held it had already left the company.

350 engineers, and what they actually did

Ford's response was to hire, promote, or bring back more than 350 experienced engineers specifically tasked with fixing the gap. Some mentored younger engineers who were struggling to maintain quality standards. Others worked directly on improving the data collection and training processes that underpin the automated systems. In parallel, Ford created a dedicated 40-person software quality assurance team focused entirely on catching problems before they reach production — a deliberate shift away from what COO Kumar Galhotra called the old "find and fix" philosophy, where defects were caught after they appeared. "We're moving from that find-and-fix mentality to preventing issues before they occur," Galhotra said.

Ford also added more than 100,000 new AI-powered tests designed to identify edge cases and stress systems under a wide range of conditions. Because the testing framework is highly automated, software changes can be rapidly re-validated even late in development, catching problems before they compound into recalls. Ford currently leads the industry in the number of recalls — a record it is actively trying to change — and quality slippage accelerated during the pandemic supply-chain disruptions and the difficult launches of the Explorer and Aviator.

The lesson that applies well beyond car factories

The Ford story maps neatly onto a tension that shows up everywhere AI is being deployed at scale: the model is only as good as the data and context it receives, and domain expertise is hard to encode without the people who hold it. Ford's automated systems made errors not because AI is inherently unreliable, but because the systems were trained on incomplete data and deployed without enough human review. The fix wasn't to abandon AI — Ford explicitly said it remains committed to automation and has dramatically expanded its AI-powered testing. The fix was to put experienced humans back in the loop as trainers and validators, not as replacements for the technology.

That hybrid outcome — AI handles volume and speed, experienced humans handle judgment and edge cases — is increasingly the pattern that actually works in high-stakes environments. Ford just spent several painful years and a quality ranking to prove it.