Overcoming Challenges in Hyper Automation

Hyper automation is the next great digital transformation leap, going beyond legacy automation by uniting a rich set of next-generation technologies. It brings together Robotic Process Automation (RPA) to automate rule-based activities, Artificial Intelligence (AI) and Machine Learning (ML) to make decisions and predict outcomes, Natural Language Processing (NLP) to understand human language, and Process Mining to discover, monitor, and optimize actual business processes. This allows both basic and sophisticated processes to be automated across an enterprise, from back-office to front-office. What gives hyper automation on top of these automation methods, its past reconstructions is the power of doing all those things that the previously mentioned automation types were unable to achieve, continuously monitor for intelligence, react according to changing events, learn from results to augment it better, perhaps faster, smarter, and larger. It enables businesses to create a digital workforce that is capable of interacting with human employees, improving productivity, and reducing operating costs to help deliver services. This chapter discusses such complex problems and thoroughly analyzes their causes and consequences. Besides, it also focuses on successful models, strategic interventions, and best practices that an organization needs to adopt to address such challenges and leverage the opportunities of hyper automation.