Ford stated that after the artificial intelligence and automated quality systems had not met expectations, the company had recruited 350 senior engineers to enhance vehicle development and parts quality clearance. Some of the personnel had served in Ford and others had previously worked in the vendor system.

AI failed to independently complete the examination

According to Bloomberg, Mr. Kumar Galhotra, Chief Operator of Ford, told journalists that the company had been relying increasingly on automated quality control systems for some time, but the actual results were not ideal. To this end, Ford re-introduces technical experts to screen potential failure points before spare parts enter the plant line.

Charles Poon, Vice President of the Ford Vehicle Hardware Engineering Project, stated that the company had previously considered that it could produce high-quality products by introducing AI and entering existing design requirements, but that that judgement was not valid.

Senior engineer to correct tool

Ford did not give up the AI plan, but adjusted the way it was used. Rehired senior engineers will have two tasks: training of young staff and helping to reprogram and correct the AI tool to bring it closer to actual manufacturing needs.

According to Ford's statement, the company is using this group of qualified engineers, known as “gray board engineers”, as an intermediate link between manufacturing experience and automated systems, rather than simply returning to the old model of total reliance on labour.

  • Re-employed 350
  • Personnel sources include former staff and vendor engineering Division
  • The focus is on early detection of failure points for spare parts

Ford's projected savings of $1 billion.

According to Ford, this round of re-employment has begun to produce results and the company expects to reduce costs by $1 billion this year. At the same time, Ford states that it ranked first in the mainstream brand in its initial quality survey of the new J.D. Power vehicle, which was released this week.

This adjustment also reflects the fact that, as the manufacturing industry advances AI, it is still more realistic in the short term to allow experienced engineers to work with automation tools, rather than relying entirely on models and systems for quality control.