RPA provides a quick method of automation, however, is has its own limitations. While RPA works well in situations where processes and decision-making are clearly defined, it’s difficult for RPA alone to be smart. In any RPA-based automation, it is important to split the larger processes into two categories: Silent Automation and Human Intervention.
There are many instances where it is not practical to automate a big process fully. Most processes require a human component to make decisions and continue to the next stage. In such cases where a vast amount of knowledge cannot be effectively defined into algorithms, RPA is not conducive to the process automation, and human intervention is necessary. That’s when Machine Learning (ML) comes into play to solve the “knowledge problem.”
Machine Learning is an emerging technology and has matured to a degree where it can be applied to solve real-life problems. ML essentially works on the principle of encapsulating large amounts of data (or knowledge) into some form of mathematical model. The model can then be utilized to apply this knowledge for solving complex problems through automation.