Ford Motor Company recently celebrated a milestone: for the first time in 16 years, it ranked No. 1 in JD Power's initial quality survey among mainstream automakers. But behind that achievement lies a revealing story about the pitfalls of over-relying on automation. The company has admitted that its automated systems were not as robust as assumed, forcing Ford to hire experienced technicians—sometimes bringing back former employees—to fix errors made by its robots.

The Rise and Fall of Automated Design

In recent years, Ford invested heavily in artificial intelligence and automated testing to streamline vehicle design and production. The goal was to use machine learning to optimize every aspect of the manufacturing process, from component design to assembly line quality checks. However, the company soon discovered that AI is only as good as the data it learns from. When veteran engineers left, their institutional knowledge—accumulated over decades of vehicle development cycles—was not fully captured in the automated systems. This gap led to a noticeable drop in quality.

“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,” said Charles Poon, Ford’s vice president of vehicle hardware engineering, in a briefing with reporters. Poon explained that some of the company’s most experienced personnel departed before their knowledge could be transferred into Ford’s automated platforms. That necessitated bringing back those employees to retrain the systems or mentor younger engineers who were struggling to maintain quality standards.

Bringing Back Expertise

According to Poon, Ford hired, promoted, or brought back over 350 experienced engineers to rebuild that layer of expertise. These veterans are now tasked with guiding younger staff and improving the data collection and AI training that underpin Ford’s automated processes. “That’s where some of our most experienced engineers have had experience solving and identifying problems before they creep into the system,” Poon noted.

The decision to rehire former employees reflects a broader recognition within Ford that human judgment remains irreplaceable in complex engineering domains. While AI can accelerate calculations and simulations, it cannot replicate the intuitive understanding that comes from hands-on experience with vehicle dynamics, supplier relationships, and real-world quality issues.

Quality Challenges and Recalls

Ford’s quality issues have been well-documented. The automaker currently leads the industry in the number of recalls, and its quality ratings have slipped over the past several years. These challenges became more pronounced during the launch of the Ford Explorer and Lincoln Aviator, supply-chain disruptions during the COVID-19 pandemic, and a noticeable increase in vehicle recalls. The company realized that its approach to quality had become too fragmented. Different departments operated in silos, and Ford relied heavily on a “find and fix” philosophy—identifying defects after they appeared and correcting them as quickly as possible.

While that approach could address immediate problems, it did not prevent those problems from occurring in the first place. “We’re moving from that find-and-fix mentality to preventing issues before they occur,” said Kumar Galhotra, Ford’s chief operating officer. “We’re focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it.”

Integrating Software and Hardware

The transformation extends beyond vehicle hardware. Software and digital teams now work much more closely with vehicle engineering, manufacturing, and supply-chain teams. Ford is attempting to combine the speed and flexibility of software development with the rigorous validation requirements of automotive-grade engineering. Historically, software bugs were discovered late in the development process because Ford was not fully leveraging rapid iteration cycles. However, the company cannot push updates as fast as consumer electronics companies—vehicles operate in a safety-critical environment where customers depend on software functioning correctly from the moment the vehicle is delivered.

To address this, Ford created a dedicated 40-person software quality assurance team with the sole responsibility of preventing problems before they occur. In addition, the automaker dramatically expanded its automated testing capabilities, adding more than 100,000 new AI-powered tests designed to identify edge cases and stress software systems under a wide range of conditions. Because the testing framework is highly automated, software changes can be rapidly revalidated even late in development, ensuring modifications do not introduce new defects.

“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,” Poon said. “We’ve established software reliability as its own rigorous discipline with strict metrics.”

The Role of AI in Ford’s Future

Despite these challenges, Ford remains committed to integrating AI into its processes. The company believes that artificial intelligence, when properly trained with high-quality data and human oversight, can improve everything from design iteration to manufacturing efficiency. The key lesson learned is that AI cannot replace the deep, experiential knowledge of veteran engineers. Instead, the two must work in tandem: humans provide context and intuition, while machines handle scale and speed.

Ford’s experience mirrors a broader trend across the automotive industry. As automakers rush to adopt digital tools and automated production lines, many are discovering that technology alone does not guarantee quality. The most successful manufacturers are those that balance automation with a strong culture of continuous improvement and a workforce that combines both new and experienced talent.

For now, Ford’s renewed focus on quality appears to be paying off. The JD Power ranking is a significant achievement, but company executives acknowledge that sustaining it will require ongoing vigilance. “We’ve built a foundation, but we can’t rest on our laurels,” Galhotra said. “We have to keep pushing to prevent problems before they ever reach the customer.”

Source: The Verge News