Why AI Still Can’t Replace Analysts: A Predictive Maintenance Example

Despite impressive achievements in processing data and generating forecasts, AI models still have significant limitations in specialized analytical areas like industrial predictive maintenance. AI cannot fully replace human analysts due to a lack of sufficient training data for diverse and rare equipment failures, an inability to grasp crucial operational context, and problems with poor data quality from sensors. For example, the amount of data needed to train a neural network to reliably detect a single bearing defect across all operating conditions, load levels, and stages of development is immense. The most effective current approach is a Human in the Loop (HITL) system, where algorithms perform initial analysis and human experts provide context, verify alerts, and train the models. This combination of machine speed and expert experience yields the best results in predictive maintenance today.