Role of Process Discovery and Optimization in Hyperautomation

Hyperautomation is redefining the digital transformation landscape by combining technologies such as Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics to automate end-toend complex business operations. This chapter focuses on two critical enablers of hyperautomation, specifically process discovery and process optimization. Process discovery is quintessential for tools like Celonis, UiPath Process Mining, and IBM Process Mining, which assist in automatically mapping workflows by extracting event logs from enterprise systems, providing actionable insights into process bottlenecks, redundancies, and compliance violations. These insights help organizations identify high-impact automation candidates. This chapter sheds considerable light on process optimization that leverages predictive analytics, AI-based decision modeling, and continuous feedback loops to rationalize workflows, improve resource allocation, reduce operational costs, and enhance customer experience. The chapter discusses realworld applications of platforms such as Automation Anywhere and Microsoft Power Automate, highlighting their roles in driving intelligent automation at scale. Despite rapid adoption, the chapter also identifies critical research gaps, such as the absence of universal standards for process modeling, integration challenges across legacy and modern systems, security and privacy risks in data-driven automation, and the urgent need for upskilling and workforce transition frameworks. Addressing these gaps is essential for building resilient, scalable, and ethically grounded hyperautomation systems in future enterprises.