A bill of materials (BOM) is central to product development and manufacturing because it lists every component, quantity, revision, and sourcing detail needed to build an assembly. This article on the CoLab blog says that despite the importance and rigorous checks, mismatches between BOMs and engineering drawings still slip through reviews and reach suppliers or production, causing delays, rework, scrap, and higher costs. These errors persist even in organizations using CAD and product lifecycle management (PLM) systems.
Part of the problem is the complexity of engineering workflows. BOMs often exist in multiple places: embedded on drawings, stored in CAD models, maintained in PLM systems, and sometimes used differently by manufacturing or procurement. When changes occur in one system, such as a revision update or sourcing change, they don’t always propagate automatically back into the drawing or BOM table. Manual steps such as updating quantities, matching filenames to internal part numbers, or entering revision data create opportunities for inconsistency, especially under schedule pressure.
Traditional tools focus on managing states and approvals but do not interpret drawings or verify intent, leaving engineers with the tedious task of visually reconciling BOM lists with geometry, callouts, and assembly views. Under current practices, reviewers rely on memory and manual cross-checks, which become harder as complexity grows and schedules tighten.
Artificial intelligence presents a practical way to reduce these errors by acting as an assistant during drawing reviews. AI agents can read and interpret drawing packages, cross-reference BOM tables with visual geometry, and flag mismatches early, before drawings are released for quoting or production. These agents do not replace engineers but perform exhaustive checks without fatigue, surfacing inconsistencies that might escape human reviewers.
When integrated into a collaborative review environment that connects CAD, PLM, and feedback history, AI can help teams move from manual checking to a system where errors are caught and resolved earlier, freeing engineers to focus on validating design intent and manufacturability.