Engineering change management often slows down before a change reaches approval. Leo AI argues that the main bottleneck is impact discovery, where engineers must determine every assembly, drawing, supplier specification, manufacturing instruction, and open order affected by a proposed modification.
A typical change passes through request, impact assessment, approval, document revision, and implementation. During assessment, engineers may need to search several disconnected systems. A seemingly minor modification, such as moving a mounting bracket’s bolt pattern, could affect another product line, supplier tooling, service kits, or work already in progress.
Leo divides impact discovery into five searches. Engineers must identify where a part is used, locate related drawings and models, find supplier and manufacturing documents, examine previous engineering decisions, and identify open work based on the existing design. Much of this information resides in unstructured files or separate systems, making manual discovery time-consuming and vulnerable to missed dependencies.
The article proposes assigning this repetitive retrieval work to an AI agent while leaving decisions to engineers. Given a part number and proposed change, the agent searches connected systems and produces a structured impact summary covering affected assemblies, documents, suppliers, fixtures, historical decisions, and open work. Each result should link to its source so engineers can verify the evidence.
Leo operates as an intelligence layer over existing PDM, PLM, and file systems rather than replacing established change-management workflows. The agent does not approve changes, modify records, or release designs.
For adoption, Leo recommends testing AI against five completed engineering changes, comparing its findings with the original assessments. Teams can then connect one product line, establish an impact-summary format, and run AI alongside manual assessments before wider deployment.
The central idea is straightforward: automate searching, not engineering judgment. Success should be measured by fewer missed dependencies and follow-up changes, rather than search speed alone.
