Source: The Next Web
Ford's attempt to replace human engineering judgment with AI for vehicle quality assessment created a costly gap between algorithmic confidence and automotive safety standards. The company discovered its models were missing defects that seasoned engineers would catch. This failure exposes a real constraint in AI adoption for high-stakes manufacturing: domain expertise and intuition built over decades cannot be substituted with ML models trained on historical data, especially when quality failures carry legal and reputational risk. Companies automating critical functions need to think about AI as augmentation rather than replacement, at least until the technology matures enough to handle edge cases at scale.