迈向智能自动化(IA):关于机器人流程自动化(RPA)的发展、挑战和未来趋势的文献综述

Q2 Engineering
Julia Siderska, Lili Aunimo, Thomas Süße, John von Stamm, Damian Kedziora, Suraya Nabilah Binti Mohd Aini
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引用次数: 0

摘要

摘要 机器人流程自动化(RPA)与人工智能(AI)的融合为未来企业自动化和提高生产力提供了巨大潜力。RPA 通过整合先进的技术和能力,超越了简单的任务自动化,从而迅速发展成为智能流程自动化(IPA)。本文旨在确定企业在从 RPA 过渡到 IPA 的过程中所面临的组织、技术和以人为本的挑战。研究过程包括使用 ResearchRabbit AI 工具进行科学文献检索,该工具提供了一系列与所提研究问题相关的参考文献。通过文献综述,作者确定了企业从 RPA 过渡到 IPA 所面临的主要挑战和可能的对策。由此收集的参考科学文章为本研究的内容和实质分析奠定了基础。此外,本研究还确定了人工智能技术和算法,如自然语言处理 (NLP)、机器学习 (ML)、深度学习 (DL)、预测分析等,这些技术和算法可以与 RPA 相结合,促进向 IPA 的过渡。本文还对未来潜在的研究领域提出了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Intelligent Automation (IA): Literature Review on the Evolution of Robotic Process Automation (RPA), its Challenges, and Future Trends
Abstract Robotic Process Automation (RPA) and Artificial Intelligence (AI) integration offer great potential for the future of corporate automation and increased productivity. RPA rapidly evolves into Intelligent Process Automation (IPA) by incorporating advanced technologies and capabilities beyond simple task automation. The paper aims to identify the organisational, technological, and human-centred challenges that companies face in transitioning from RPA to IPA. The research process involved conducting the scientific literature search using the ResearchRabbit AI tool, which provided a set of reference papers relevant to the formulated research questions. As a result of the conducted literature review, the authors identified key challenges and possible countermeasures for companies transitioning from RPA to IPA. The resulting collection of reference scientific articles formed the basis for this study’s content and substantive analysis. Furthermore, this study contributes by identifying artificial intelligence techniques and algorithms, such as Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), predictive analytics, and others, that can be integrated with RPA to facilitate the transition to IPA. The paper also offers insights into potential future research areas.
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来源期刊
Engineering Management in Production and Services
Engineering Management in Production and Services Business, Management and Accounting-Management Information Systems
CiteScore
3.40
自引率
0.00%
发文量
27
审稿时长
7 weeks
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