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The article explores the challenges of automating mechanical drawing generation in CAD systems, emphasizing that the primary bottleneck lies not in technical execution but in managing exceptions during batch processing. While generating individual drawings is increasingly automatable, scaling this to hundreds of parts introduces complexities when variations like extra holes or altered datum faces disrupt standardized rules. The core issue is grouping parts with shared geometric features while maintaining human oversight for exceptions. A proposed solution involves an AI-assisted system that clusters parts based on features like bounding-box ratios, hole patterns, and wall thickness, then propagates drawing rules only after human validation of representative parts. The system prioritizes traceability, ensuring exceptions (e.g., unmatched holes, missing tolerances) are flagged for review rather than auto-corrected. This approach shifts the value proposition from 'AI generates drawings' to 'engineers review fewer sheets without losing control of critical exceptions.' The article also highlights existing tools like Autodesk Inventor API and DraftAid, which provide foundational capabilities for drawing automation. However, the key innovation lies in creating a review system that balances automation with human expertise, reducing redundant checks while maintaining quality. The author stresses that success depends on measurable outcomes like reduced full reviews, early exception detection, and transparency in rule application.