AI Assisted Workflow Optimization and Automation

๐Ÿ“… 2026-09-13
๐Ÿ“ˆ Citations: 0
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๐Ÿค– AI Summary
็ ”็ฉถ้’ˆๅฏนไผไธšๅˆ่ง„ๅทฅไฝœ็š„ๆ•ˆ็އๅ’Œๅ‡†็กฎๆ€ง้œ€ๆฑ‚๏ผŒ้€š่ฟ‡ๆต็จ‹ๅ†้€ ใ€็ณป็ปŸๅปบๆจกๅ’ŒๆŠ€ๆœฏ้›†ๆˆ็ญ‰ๆ–นๆณ•๏ผŒๅนถ็ป“ๅˆRPAใ€่ง„ๅˆ™ๅผ•ๆ“Žๅ’Œ่ฏญไน‰่ฏ†ๅˆซๆŠ€ๆœฏไผ˜ๅŒ–่พ…ๅŠฉๅˆ่ง„ๆต็จ‹ใ€‚
๐Ÿ“ Abstract
Against the backdrop of digital transformation and stricter regulation, enterprise compliance work demands higher efficiency and accuracy. The auxiliary compliance process has become an important entry point for optimizing the compliance system due to its strong transactional nature and high degree of repetition. This study focuses on the process characteristics of auxiliary compliance work, sorts out its structural composition and organizational mechanism, proposes an optimization path with process reengineering, system modeling, and technology integration as the core, and focuses on exploring the collaborative application of key technologies such as RPA, rule engine, and semantic recognition in process automation. Research suggests that the systematic optimization and intelligent upgrading of auxiliary processes will help build a modern compliance operation system that is responsive, efficient, structurally clear, and risk controllable.
Problem

Research questions and friction points this paper is trying to address.

compliance
efficiency
accuracy
digital transformation
regulation
Innovation

Methods, ideas, or system contributions that make the work stand out.

RPA
rule engine
semantic recognition
process reengineering
system modeling
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Zhen Zhong
Senior Data Engineer, Graduate School of Arts & Sciences, Georgetown University, Washington, DC 20001, United States