Hyperautomation, an evolution of traditional automation

Hyperautomation, is a relatively recent concept in the field of technology, emerging prominently in the late 2010s. It focuses on expanding the capabilities of automation by integrating advanced technologies like artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other digital tools. Below is a historical overview of how hyperautomation evolved:

1. Early Automation (1950s–1990s):

   The concept of automation began with the advent of computers in the mid-20th century, which replaced manual calculations and repetitive tasks with automated systems. Early automation was primarily focused on manufacturing and industrial processes, such as assembly lines, driven by programmable logic controllers (PLCs) and basic software algorithms. These systems were rule-based, with minimal flexibility for dealing with unstructured tasks.

2. Business Process Automation (BPA) and Workflow Automation (1990s–2000s):

   By the 1990s, businesses started automating more complex processes, particularly in administrative and back-office functions. Tools like Enterprise Resource Planning (ERP) systems and workflow automation platforms began handling repetitive tasks like data entry, invoice processing, and document management. These systems improved efficiency but lacked the ability to handle more complex tasks requiring decision-making or unstructured data processing.

3. Robotic Process Automation (RPA) (2000s–2010s):

   RPA emerged in the 2000s as a solution to automate rule-based, high-volume tasks across industries, particularly in sectors like finance, healthcare, and customer service. RPA tools mimicked human interactions with software systems (e.g., logging into applications, entering data), enabling businesses to automate repetitive, manual tasks without overhauling their legacy systems. However, RPA was limited to automating structured tasks with clear, predefined rules and couldn’t handle unstructured data or decision-making processes.

4. AI and Machine Learning Integration (2010s):

   The introduction of AI and ML began to bridge the gap left by traditional automation tools. AI brought capabilities like natural language processing (NLP), image recognition, and predictive analytics, allowing systems to handle unstructured data and make decisions based on patterns learned from historical data. This integration expanded the scope of automation from repetitive tasks to more cognitive and decision-based tasks.

5. The Birth of Hyperautomation (2019):  

   In 2019, Gartner coined the term **hyperautomation** in its list of strategic technology trends for 2020. Hyperautomation refers to the combination of multiple technologies—RPA, AI, ML, process mining, advanced analytics, and intelligent business management software (iBPMS)—to automate as many business processes as possible. It emphasizes not just task automation but the automation of end-to-end processes, from discovery and monitoring to execution and optimization.

   The concept aimed to address the limitations of traditional automation, allowing businesses to automate complex, non-routine processes that required decision-making, learning, and adaptability. Hyperautomation integrates human involvement, where humans can intervene in more complex scenarios while machines handle the bulk of routine work.

6. Expansion of Hyperautomation (2020–Present): 

   Since the COVID-19 pandemic accelerated digital transformation across industries, hyperautomation gained significant traction. Organizations began deploying hyperautomation solutions to ensure business continuity, enhance efficiency, and reduce costs in the face of remote work challenges. Hyperautomation solutions allowed businesses to discover automation opportunities using process mining, automate more tasks by integrating AI, and continually optimize workflows through real-time analytics.

   Major tech companies and automation vendors started offering hyperautomation platforms that combined RPA, AI, low-code/no-code development, and business intelligence tools. Hyperautomation’s ability to automate complex processes across different departments (finance, HR, IT, customer service) made it a crucial part of enterprise digital transformation strategies.

7. Future of Hyperautomation:

   Looking ahead, hyperautomation is expected to evolve further with the development of more sophisticated AI models, better integration of data-driven insights, and the use of advanced robotics. It is anticipated to play a critical role in transforming industries such as healthcare, finance, supply chain, and manufacturing by enabling fully autonomous processes, reducing human intervention, and fostering innovation.

Conclusion:

Hyperautomation represents the convergence of various digital technologies to automate complex business processes. From the basic task automation of the 1950s to the AI-driven hyperautomation of today, it reflects a growing desire for organizations to optimize efficiency, productivity, and decision-making by integrating human expertise with intelligent automation systems. As it evolves, hyperautomation will likely be at the forefront of the next wave of digital transformation.