Ford's Humanism: Strengthened Role of Experienced Engineers at the Expense of AI

Ford has decided to once again strengthen the role of human experience in its development processes after finding that AI-based tools and automated quality control systems had failed to meet their goals. Over the past three years, the automaker has reinstated about 350 veteran engineers tasked with supporting younger staff and improving the operation of automation technologies.

Charles Poon, Vice President of Vehicle Hardware Engineering at Ford, explained that while AI is an extremely valuable tool, its effectiveness directly depends on the quality of the information used during training. According to the executive, in the past, the company had not sufficiently valued the wealth of knowledge accumulated by more experienced engineers, gained through numerous product development cycles.

Even COO Kumar Galhotra emphasized how these specialists played a crucial role in improving vehicle quality. Today, they coordinate mandatory meetings dedicated to in-depth analysis of potential critical issues and have contributed to reprogramming AI tools to identify defects before they even manifest during production.

Human Contribution at the Center: Ford's New Strategy

Galhotra also admitted that Ford had gradually increased its reliance on automated quality control systems without achieving the desired results. The return of technical specialists has instead allowed for more effective identification of potential weaknesses in components before they reach the assembly line.

Ford's experience represents a case that deviates from the increasingly prevalent idea that AI is destined to replace many highly skilled professions. The company has recognized that automated systems cannot replicate the skill set built up over the years by the most experienced engineers.

Poon explained that initially, it was thought sufficient to introduce AI tools fed with design requirements to automatically achieve high-quality products. Subsequently, Ford understood that automation, machine learning, and AI could only improve if trained with the knowledge of the more experienced personnel.