Description: Hyperautomation refers to the use of advanced technologies, such as artificial intelligence (AI) and machine learning, to automate processes and tasks beyond traditional automation. This approach not only aims to automate repetitive and manual tasks but also integrates the ability to analyze data and make real-time decisions. Hyperautomation combines robotic process automation (RPA) tools with AI, enabling organizations to optimize complex workflows and improve operational efficiency. Key features of hyperautomation include the ability to learn and adapt to new situations, integration of multiple systems, and continuous improvement of automated processes. Its relevance lies in the growing need for companies to be more agile and competitive in a constantly changing business environment, where speed and accuracy are essential. Hyperautomation not only reduces costs and errors but also frees employees from mundane tasks, allowing them to focus on higher-value strategic activities.
History: The term ‘hyperautomation’ was popularized by Gartner in 2019, although process automation has existed for decades. The evolution of automation began with the automation of simple tasks in manufacturing during the 20th century, and over time expanded to robotic process automation (RPA) in the 2000s. The integration of artificial intelligence and machine learning into these processes has led to hyperautomation, which seeks not only to automate tasks but also to optimize and continuously improve business processes.
Uses: Hyperautomation is used across various industries to improve operational efficiency, reduce costs, and increase agility. Its applications include business process automation, supply chain management, customer service, human resources management, and data analytics. Organizations implement hyperautomation to digitally transform their operations and quickly adapt to market demands.
Examples: An example of hyperautomation is the use of RPA combined with AI in the banking sector to process loan applications. AI analyzes customer information and makes decisions about approval, while RPA manages documentation and workflow. Another example is in the healthcare sector, where AI-powered chatbots are used to answer patient questions and automate appointment scheduling.
