Julia Siderska, Lili Aunimo, Thomas Süße, John von Stamm, Damian Kedziora, Suraya Nabilah Binti Mohd Aini
{"title":"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","authors":"Julia Siderska, Lili Aunimo, Thomas Süße, John von Stamm, Damian Kedziora, Suraya Nabilah Binti Mohd Aini","doi":"10.2478/emj-2023-0030","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":37237,"journal":{"name":"Engineering Management in Production and Services","volume":"16 1","pages":"90 - 103"},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards Intelligent Automation (IA): Literature Review on the Evolution of Robotic Process Automation (RPA), its Challenges, and Future Trends\",\"authors\":\"Julia Siderska, Lili Aunimo, Thomas Süße, John von Stamm, Damian Kedziora, Suraya Nabilah Binti Mohd Aini\",\"doi\":\"10.2478/emj-2023-0030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":37237,\"journal\":{\"name\":\"Engineering Management in Production and Services\",\"volume\":\"16 1\",\"pages\":\"90 - 103\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Management in Production and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/emj-2023-0030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Management in Production and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/emj-2023-0030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
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.