[Paper Review] Robotic Process Automation -- A Systematic Literature Review and Assessment Framework

Judith Wewerka, Manfred Reichert|arXiv (Cornell University)|Dec 22, 2020

Robotic Process Automation ApplicationsEngineering66 references17 citations

TL;DR

This paper presents a systematic literature review of 63 RPA publications to establish a comprehensive understanding of Robotic Process Automation, including its definitions, differences from related technologies, suitable business processes, effects, implementation methods, and integration with AI. It introduces the ANCOPUR framework for systematically assessing and comparing RPA research, enabling scholars and practitioners to evaluate the novelty and contribution of emerging works in this rapidly growing field.

ABSTRACT

Robotic Process Automation (RPA) is the automation of rule-based routine processes to increase efficiency and to reduce costs. Due to the utmost importance of process automation in industry, RPA attracts increasing attention in the scientific field as well. This paper presents the state-of-the-art in the RPA field by means of a Systematic Literature Review (SLR). In this SLR, 63 publications are identified, categorised, and analysed along well-defined research questions. From the SLR findings, moreover, a framework for systematically analysing, assessing, and comparing existing as well as upcoming RPA works is derived. The discovered thematic clusters advise further investigations in order to develop an even more detailed structural research approach for RPA.

Motivation & Objective

• To map the state-of-the-art in Robotic Process Automation (RPA) research through a systematic literature review of 63 publications.
• To identify and clarify key distinctions between RPA and related technologies such as BPM and cognitive automation.
• To develop a structured, reusable framework—ANCOPUR—for assessing, comparing, and classifying future RPA research publications.
• To analyze the effects of RPA on business processes and employees, focusing on efficiency, cost reduction, and human impact.
• To explore the integration of Artificial Intelligence (AI) with RPA and identify gaps in current methodological approaches for RPA implementation.

Proposed method

• Conducted a systematic literature review (SLR) following Kitchenham’s guidelines to ensure methodological rigor and reproducibility.
• Defined five research questions (RQs) to structure the review: RPA definition and differentiation, RPA-suitable processes and tools, RPA effects, implementation methods, and AI integration.
• Searched academic databases using a refined search string including terms like 'robotic process automation', 'intelligent process automation', and 'AI in business process'.
• Applied inclusion and exclusion criteria to filter publications based on relevance, publication type, and date (up to June 2020).
• Extracted data on business domains, processes, tools used (e.g., UiPath, Blue Prism), and methodological approaches from case studies.
• Developed the ANCOPUR framework—based on criteria such as novelty, contribution type, and alignment with existing research—to enable systematic evaluation of future RPA publications.

Experimental results

Research questions

• RQ1 What is RPA, and how does it differ from related technologies such as BPM and cognitive automation?
• RQ2 Which business processes are suitable for RPA, and which tools are predominantly used in their automation?
• RQ3 What are the observed effects of RPA on business processes and on employees and organizational work life?
• RQ4 Are there established methods to improve the implementation of RPA projects, and if so, in which lifecycle phases are they applied?
• RQ5 To what extent is Artificial Intelligence being combined with RPA, and what are the current use cases?

Key findings

• RPA is primarily applied in business areas like BPO and Shared Services, where repetitive, rule-based processes such as payment receipt generation are common.
• Blue Prism and UiPath are the dominant RPA tools in case studies, though other tools like Automation Anywhere and Pegasystems are also emerging in industry.
• The majority of RPA implementation methods (16 out of 22) were published between 2019 and 2020, with most focusing on the analysis phase of the RPA lifecycle.
• RPA is widely reported to improve process speed, availability, compliance, and quality, while reducing costs and freeing employees from non-value-adding tasks.
• The integration of AI with RPA is still in its infancy, with only four concrete use cases identified—primarily focused on email classification—despite growing interest.
• Quantitative research on RPA remains scarce, and the field is still in its early stages, with a notable trend toward AI-RPA integration and methodological development in 2018–2020.