A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation

2025-09-19

AI Summary

Existing Robotic Process Automation (RPA) systems exhibit inherent limitations in symbolic processing, hindering the automation of complex, human-centric tasks. Method: Through a systematic literature review, this paper proposes the first taxonomy framework for intelligent RPA, structured around two meta-features—“integration” and “interaction”—and eight dimensions: architecture, capabilities, data, intelligence level, technical depth, deployment environment, lifecycle stage, and human–machine relationship. Contribution/Results: The framework transcends conventional RPA boundaries, explicitly characterizing diverse pathways for deep integration of machine learning and automation. It provides both theoretical foundations and practical guidance for expanding the scope of automatable tasks, thereby addressing a critical gap in the systematic classification of intelligent RPA systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Intelligent User Interfaces

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Robotic process automation (RPA) is a lightweight approach to automating business processes using software robots that emulate user actions at the graphical user interface level. While RPA has gained popularity for its cost-effective and timely automation of rule-based, well-structured tasks, its symbolic nature has inherent limitations when approaching more complex tasks currently performed by human agents. Machine learning concepts enabling intelligent RPA provide an opportunity to broaden the range of automatable tasks. In this paper, we conduct a literature review to explore the connections between RPA and machine learning and organize the joint concept intelligent RPA into a taxonomy. Our taxonomy comprises the two meta-characteristics RPA-ML integration and RPA-ML interaction. Together, they comprise eight dimensions: architecture and ecosystem, capabilities, data basis, intelligence level, and technical depth of integration as well as deployment environment, lifecycle phase, and user-robot relation.

Problem

• Developing taxonomy for machine learning in robotic process automation
• Addressing limitations of rule-based RPA through intelligent automation
• Organizing RPA-ML integration and interaction characteristics into framework

Innovation

• Integrating machine learning with RPA
• Creating taxonomy for intelligent automation
• Defining eight dimensions for RPA-ML interaction