For decades, automation in computer science was synonymous with Robotic Process Automation (RPA)—a technology designed to handle repetitive, rule-based tasks through structured data. However, as the digital landscape has become increasingly saturated with unstructured data and complex decision-making requirements, the limitations of traditional RPA have become apparent. This has led to the rise of Intelligent Process Automation (IPA) , a transformative framework that integrates Artificial Intelligence (AI) and Machine Learning (ML) into the classic automation stack. Unlike its predecessor, IPA is not merely about following a script; it is about learning from experience and handling non-routine tasks that require human-like cognition.