Ford has started bringing experienced engineers back to the company after its artificial intelligence (AI) and automated quality systems failed to deliver the expected results.
According to a Bloomberg report, the US carmaker has rehired around 350 veteran engineers over the past three years. The group includes former Ford employees as well as experts from supplier companies.
Ford said it had relied too much on AI and automation while losing the valuable knowledge of experienced engineers who had worked on several generations of vehicles.
Charles Poon, Ford’s Vice President of Vehicle Hardware Engineering, admitted that the company expected too much from AI.
He said AI is a powerful tool, but it can only perform well if it is trained with the right data. According to Poon, Ford did not preserve enough knowledge from its experienced engineers before many of them left the company. As a result, the AI systems were unable to identify many real-world problems during vehicle development.
To solve this issue, Ford brought back more than 350 experienced engineers. Inside the company, they are known as “gray beard” engineers. Their job is to mentor younger employees, improve AI systems with better data, and help identify quality issues before vehicles reach production.
Ford’s Chief Operating Officer, Kumar Galhotra, said the company had depended too much on automated quality systems but did not get the desired results. He said experienced engineers are now leading quality reviews and helping the company prevent problems before they happen instead of fixing them later.
Ford has also improved coordination between its software, manufacturing and supply chain teams. In addition, the company has created a 40-member software quality assurance team to improve software reliability before vehicles are delivered to customers.
The company clarified that it is not moving away from AI. Instead, it is combining AI with human expertise. Ford has introduced more than 100,000 AI-powered validation tests to detect software and vehicle issues under different conditions before production.
The company believes that combining experienced engineers with AI technology will help improve vehicle quality, reduce costs and prevent problems much earlier in the development process.
