利用生成式AI与基于IDP的自动化智能体实现端到端流程自动化：企业费用处理案例研究
E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
AI总结：
本研究结合生成式AI、IDP与自动化智能体，提出四阶段端到端自动化方法，应用于企业费用处理，显著减少处理时间并提升合规性，验证了复杂流程自动化的可行性。
摘要：
本文在当代数字化转型背景下，提出了一种智能工作自动化方法，通过将生成式AI和智能文档处理（IDP）技术与自动化智能体相结合，实现企业财务费用处理任务的端到端（E2E）自动化。尽管传统的机器人流程自动化（RPA）已被证明在重复性、基于规则的简单任务自动化方面有效，但它在处理非结构化数据、异常管理和复杂决策方面存在局限性。本研究设计并实现了一个四阶段集成流程，包括通过OCR/IDP自动识别收据等支持性文件、基于策略驱动数据库的项目分类、由生成式AI（大语言模型，LLMs）支持的智能异常处理，以及通过自动化智能体进行持续系统学习的人机协同最终决策。该系统应用于韩国一家大型企业（S公司），展示了量化效益，包括纸质收据费用任务处理时间减少超过80%、错误率降低以及合规性提升，同时还有定性效益，如准确性和一致性增强、员工满意度提高以及数据驱动的决策支持。此外，该系统通过从人类判断中学习，逐步改进自动异常处理能力，体现了良性循环。实证上，本研究证实了生成式AI、IDP和自动化智能体的有机集成能够有效克服传统自动化的局限性，并实现复杂企业流程的端到端自动化。研究还讨论了向会计、人力资源和采购等其他领域扩展的潜在可能性，并提出了AI驱动的超自动化发展的未来方向。
英文摘要：
This paper presents an intelligent work automation approach in the context of contemporary digital transformation by integrating generative AI and Intelligent Document Processing (IDP) technologies with an Automation Agent to realize End-to-End (E2E) automation of corporate financial expense processing tasks. While traditional Robotic Process Automation (RPA) has proven effective for repetitive, rule-based simple task automation, it faces limitations in handling unstructured data, exception management, and complex decision-making. This study designs and implements a four-stage integrated process comprising automatic recognition of supporting documents such as receipts via OCR/IDP, item classification based on a policy-driven database, intelligent exception handling supported by generative AI (large language models, LLMs), and human-in-the-loop final decision-making with continuous system learning through an Automation Agent. Applied to a major Korean enterprise (Company S), the system demonstrated quantitative benefits including over 80% reduction in processing time for paper receipt expense tasks, decreased error rates, and improved compliance, as well as qualitative benefits such as enhanced accuracy and consistency, increased employee satisfaction, and data-driven decision support. Furthermore, the system embodies a virtuous cycle by learning from human judgments to progressively improve automatic exception handling capabilities. Empirically, this research confirms that the organic integration of generative AI, IDP, and Automation Agents effectively overcomes the limitations of conventional automation and enables E2E automation of complex corporate processes. The study also discusses potential extensions to other domains such as accounting, human resources, and procurement, and proposes future directions for AI-driven hyper-automation development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
