Ford has revealed that it hired, promoted and brought back more than 350 experienced engineers after discovering that its AI-driven design and production systems were not delivering the level of quality the company expected.

The admission comes as Ford celebrates a major milestone. The automaker recently secured the top position in JD Power's Initial Quality Study for the first time in 16 years, a turnaround executives partly attribute to bringing veteran engineering expertise back into the development process.

The company's experience offers a rare glimpse into one of the biggest questions facing businesses today: what happens when organisations rely on AI before fully capturing the knowledge of the people they are trying to replace?

When AI Meets Missing Human Knowledge

According to Charles Poon, Ford's Vice President of Vehicle Hardware Engineering, the company initially believed that introducing AI into its design and engineering processes would naturally lead to better products.

That assumption proved costly.

Many of Ford's most experienced engineers had already left the organisation, taking decades of practical knowledge with them. While Ford's AI systems had access to design requirements and technical specifications, they lacked the institutional experience that veteran engineers accumulated through years of solving real-world manufacturing and product development challenges.

Poon acknowledged that Ford underestimated the importance of that expertise.

The result was a gap between what the AI systems were designed to do and what was required to consistently deliver high-quality vehicles.

To address the problem, Ford brought back experienced engineers and promoted others into leadership roles. Their responsibility extends beyond fixing defects. They are also mentoring younger engineers and helping improve the quality of the data used to train Ford's AI systems.

A New Approach to Quality Control

The company has also introduced broader changes to its development process.

Ford created a dedicated 40-person software quality assurance team focused on identifying issues before they reach customers. The automaker says it has also added more than 100,000 AI-powered automated tests capable of evaluating software updates and identifying edge-case failures before vehicles leave production.

Executives believe the combination of experienced human oversight and automated testing has played a key role in improving quality performance.

Ford Chief Operating Officer Kumar Galhotra said the company is also breaking down organisational silos by encouraging closer collaboration between software, manufacturing, engineering and supply chain teams.

Instead of relying on a traditional "find and fix" approach after problems emerge, Ford is attempting to identify potential failures much earlier in the development cycle.

What Ford's Experience Says About AI

Ford's story challenges one of the more common assumptions surrounding artificial intelligence in the workplace.

The lesson is not that AI failed. In fact, Ford is expanding its use of AI-powered testing and automation. What failed was the assumption that AI could deliver optimal results without the expertise that helped build the systems in the first place.

The company ultimately discovered that technology alone could not replace decades of engineering knowledge.

For businesses rushing to automate processes and reduce headcount, Ford's experience serves as a reminder that AI is only as effective as the data, processes and human expertise behind it.

As organisations across industries invest heavily in artificial intelligence, the companies that benefit most may not be those that replace people the fastest, but those that find the right balance between automation and experience.