Rule-Based Systems Often Outperform AI for Simple, Structured Tasks

While AI adoption is accelerating across industries, experts warn that applying it to simple, well-defined tasks can be unnecessarily costly and less effective than traditional rule-based approaches. A recent software demo illustrated this by using the YOLO object-detection model to extract a Bill of Materials from Piping and Instrumentation Diagrams, a solution that faces limitations around line tracing, multilayer complexity, and the need to retrain the model for new equipment. The same problem can be solved more efficiently using Python libraries like ezdxf to parse DXF drawing files as graph structures, extracting equipment data layer by layer and exporting it directly into required templates. This rule-based method eliminates training overhead, reduces computational costs, and delivers reliable results for structured, repetitive tasks. Decision-makers are urged to carefully evaluate each use case — guided by frameworks such as Kaggle's AI ethics course — before defaulting to AI as the go-to solution.