Ford’s rise to No. 1 in JD Power’s initial quality ranking among mainstream automakers comes with a cautionary story about automation. The company says its push into AI and automated systems exposed a gap that software alone could not close: hard-won engineering experience.
Automation did not replace judgment
Ford had counted on artificial intelligence, automated production systems, and revised design requirements to help deliver better vehicles. But executives now say that approach was too optimistic when it was not paired with enough veteran oversight and strong enough training data.
Charles Poon, VP of vehicle hardware engineering, described the mistake plainly in a briefing this week with reporters. “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,” he said.
The problem was not that Ford is abandoning AI. The issue was that the company expected automated systems to carry knowledge that had not been fully captured. In Ford’s view, AI can be powerful, but only when the data behind it is strong and the people shaping it understand the problems it must prevent.
Ford rebuilt a layer of expertise
Some of Ford’s most experienced personnel left before their knowledge from multiple vehicle-development cycles had been transferred into automated systems. That created a practical quality problem. The systems could assist, but they did not fully reflect the judgment of people who had already seen recurring issues across vehicle programs.
Ford’s response was to rebuild that human layer. Poon said the company hired, promoted, or brought back over 350 experienced engineers. Their work includes mentoring younger engineers, improving data collection, and helping retrain the AI models behind Ford’s automated systems.
That matters because vehicle quality is not only about detecting defects after they appear. It also depends on recognizing patterns early enough to stop problems from entering the system. As Poon put it, “That’s where some of our most experienced engineers have had experience solving and identifying those problems before they creep into the system.”
From find-and-fix to prevention
Ford’s quality challenges have been visible. The company currently leads the industry in the number of recalls, and its quality ratings have slipped over the past several years. The source article points to difficulties tied to the launches of the Explorer and Aviator, supply-chain disruptions during the covid pandemic, and growth in vehicle recalls.
According to Ford COO Kumar Galhotra, the company eventually saw that its quality work had become too divided across departments. Teams were operating in silos, and the company leaned heavily on a “find and fix” mindset. That approach can correct defects, but it does not necessarily stop them from happening.
Galhotra said Ford is changing that operating model. “We’re moving from that find-and-fix mentality to preventing issues before they occur,” he said. “We’re focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it.”
The shift is organizational as much as technical. Software and digital teams now work more closely with vehicle engineering, manufacturing, and supply-chain teams. Ford is trying to bring together the speed associated with software development and the discipline required in automotive-grade engineering.
Software quality gets its own discipline
Ford also identified a software problem: bugs were being found too late in development. Poon said the automaker had not been fully using the rapid iteration cycles available in software work. But he also made clear that vehicles cannot be treated like consumer electronics, where a company can “move fast and fix later.”
Cars operate in a safety-critical environment. Customers depend on software functioning correctly from the moment the vehicle is delivered. To address that, Ford created a dedicated 40-person software quality assurance team focused on preventing problems before they reach customers.
At the same time, Ford is expanding its use of AI in testing. The company says it has added more than 100,000 new AI-powered tests to find edge cases and stress software systems under many conditions. Because the framework is highly automated, Ford says late software changes can be revalidated quickly without allowing new defects to slip in.
Poon said, “Because these tests are highly automated, even if we have a late change in the software, we can rapidly run back through the entire validation process to guarantee it works perfectly well before it reaches the customer.” He added, “We’ve established software reliability as its own rigorous disciplines with strict metrics.”
The lesson for automotive AI
Ford’s experience shows that AI in vehicle development is not a shortcut around expertise. It can help test, validate, and improve systems, but it depends on the quality of the data, the structure of the process, and the judgment of engineers who know where failures tend to emerge.
The company’s quality rebound does not read as a rejection of automation. It reads as a reset. Ford is still using AI, but it is pairing that technology with veteran engineers, stronger cross-team coordination, and a prevention-first view of quality.