The fundamentals of robotic process automation combined with machine learning capabilities to robotize the mundane tasks, plus learning to do a job even better, is what intelligent process automation all about.
We have evolved from room-sized mainframes to laptops, from using stick shifts to autonomous vehicles, from personal assistants to virtual assistants, and to so much more in just the blink of an eye. The fast-paced technology-driven world has made our lives extremely convenient now. We see breakthroughs happening in our lives with technological applications doing the heavy lifting most of the time. This level of sophistication and ease is only possible because of industries becoming digitized. The ultimate aim - to have increased efficiency, enhanced accuracy, improved customer satisfaction, and optimized workflows - has engaged organizations to invest in automation and operating tools. One such automation tool that is widely used today is robotic process automation.
Designed to take up low-quality jobs, robotic process automation has helped organizations reduce human errors significantly. Low to no manual errors have led to increased productivity, ultimately profiting the industries in business and revenue growth. Hence, organizations across the world are largely deploying robotic process automation, which is why its market size is expected to hit 3.11 billion dollars by 2025. Despite the revolutionary benefits, robotic process automation lacks certain features. Due to this downside, organizations are augmenting technological capabilities to rule-based robotic process automation, giving rise to a new automation tool - intelligent process automation.
Why intelligent process automation?
Before we dig deeper here, let's first understand what intelligent process automation actually is. In simple terms, intelligent process automation is an advanced version of robotic process automation when it comes to comprehension, intelligence, and precision. This newly evolved automation tool analyzes prior decisions and actions, learns over time and then gets smarter and more intelligent to make decisions. The need for such an evolution arose because:
Robotic process automation tools are applications that run predetermined codes to carry out a specific set of tasks. For the tool to provide accurate results, it should be fed with the right inputs. Inputs should be in an understandable manner, which means the data should be in a structured format. If the tool is fed with unstructured data, the tool will not understand how to analyze the data in the first place. To add to the complexity, most of the data that organizations collect is in an unstructured format. Hence, to make the most out of all the data gathered, organizations are opting to make automation tools capable of not only handling workflows smartly but also making decisions.
How is it different from robotic process automation?
Fundamentally, intelligent process automation tools augment these key technologies:
ML or big data analytics: With ML, robotic process automation will move beyond basic automation, thereby increasing productivity significantly. By analyzing and learning from historical data actions, ML-powered robotic process automation tools will detect patterns to deal with sentimental, perceptual, and conceptual inputs and predict better.
Smart workflow management tool: Smart workflow software is a tool that takes care of the end-to-end processes executed by both employees and automated systems. Such a management tool enhances visibility in the process.
Natural language generation: These AI-powered engines are capable of creating narratives from the collected data. RPA, when coupled with NLG, cannot only replace manual jobs but also can present a better story for analysts to make decisions.