Towards Intelligent Automation (IA): Literature Review on the Evolution of Robotic Process Automation (RPA), its Challenges, and Future Trends

TLDR

Robotic Process Automation (RPA) integrated with AI promises significant corporate automation and productivity gains, and is evolving into Intelligent Process Automation (IPA) by incorporating advanced technologies beyond simple task automation. The study aims to identify organisational, technological, and human‑centred challenges in transitioning from RPA to IPA, and to catalogue AI techniques such as NLP, ML, DL, and predictive analytics that can facilitate this shift. The authors conducted a literature search with the ResearchRabbit AI tool, gathering relevant papers that formed the basis for their analysis of the RPA‑to‑IPA transition. The review uncovered key challenges and countermeasures for companies moving to IPA, and highlighted future research directions.

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.