This article discusses how AI coding tools struggle with legacy code due to missing context and institutional knowledge. It highlights the productivity challenges faced by engineers when using AI on outdated systems compared to new projects. The piece also outlines strategies for improving AI effectiveness through better documentation and restructuring.

This article discusses how AI will reshape engineering by enhancing prototype development, improving documentation quality, and increasing compliance focus. It emphasizes the need for strong data practices as engineers leverage AI to streamline workflows and tackle complex challenges more efficiently.