Enhancing Pega Robotics Process Automation with Machine Learning: A Novel Integration for Optimized Performance

Abstract

The adoption of Pega Robotics Process Automation (RPA) is transforming business operations by streamlining processes and reducing costs. However, evolving business environments necessitate more adaptive and intelligent RPA solutions. This paper presents an innovative model that integrates Machine Learning (ML) algorithms into Pega Robotics to enhance automation performance, improve decision-making, and increase adaptability in dynamic environments. By incorporating dynamic predictive analytics, anomaly detection, and adaptive learning, the proposed model addresses critical challenges such as scalability, flexibility, and efficiency. Empirical validation is provided through case studies and comparative analysis, demonstrating significant improvements in process efficiency, error reduction, and scalability. Theoretical insights and mathematical modeling offer a framework for practical implementation and scalability solutions, providing a comprehensive guide for deploying ML-enhanced RPA systems.