Many reliability engineers struggle with overwhelming data, delayed insights, and reactive maintenance. Our case study explores how the Industrial AI Assistant (IAIA), a self-service AI tool, is reshaping this landscape. We applied IAIA to a KHS Filler and an industrial compressor, helping engineers detect anomalies early, predict failures, and take preventative actions. By putting advanced analytics in the hands of frontline engineers, this tool bridges the gap between data and action—without relying on data scientists. This approach is accelerating digital transformation and asset reliability across industries.

• Business problem: data overload and delayed insights

• Introducing the IAIA: simple, self-service AI for engineers

• Case studies: KHS Filler and industrial compressor

• Key outcomes: prediction, prevention, and increased reliability