Earlier the concept of using RPA in business was something beyond imagination by many companies. But RPA has stood the test of time and more and more companies are thinking about using Robotic process automation to automate repetitive tasks. Why companies prefer RPA is because implementing RPA does not require a change in the existing system or infrastructure. It can automate the existing system by making use of the existing infrastructure. RPA and AI are often confused to be the same but they differ from each other in many aspects. While RPA is process driven, AI and ML are data-driven. While RPA imitates human actions, AI is associated with the stimulation of human intelligence.
Process Driven RPA
As mentioned earlier RPA is concerned with the automation of repetitive tasks. RPA automates the different processes that exist in a company such as business process, workflow process, IT support process, transaction process and so on. Automating these process eventually means higher efficiency and increased productivity to a business as it eliminates the chances of errors being made by employees by doing repetitive works. RPA is process driven in the sense that it can complete actions based on a specific set of rules. It makes sure that these rules are applied throughout the process so that it achieves a specific result. RPA helps automate the financial process that occurs in the back office, data entry, customer service, and human resources, which are time-consuming and tedious processes.
RPA basically completes processes rather quickly. It digitizes and audits process data thereby improving the work efficiency.
Data Driven Machine Learning and AI
At present, the interest in Artificial Intelligence by many companies is focused on data-driven AI. Extraction of a huge volume of data is now possible with cheap data storage, fast processors, and through similar data-centric techniques. Machine Learning and Artificial Intelligence enabled organizations to shift from data-driven to data-centric. The primary reason for this shift is that for an organization data is everything and it is the life force.
In order to thrive in a competitive age, organizations need assertion of their success by deriving intelligence from data. Data-driven AI works in such a way that it builds a system that is capable of identifying the right answer from a pool of questions or from the training it gets. How this works is based on neural network algorithms. The data-driven AI does not depend on a set of rules described by humans, rather it enables the system to learn on its own based on the training data provided.
Machine Learning also ensures quality data which is quite crucial for business processes. Decisions taken from inaccurate data can hamper the overall progress of the business. Machine learning checks the data quality, the completeness of data and formatting. If any issue arises ML can send alerts to data owners as well as end users and can put forward suggestions to fix the issue and improve the quality of data. Processing of large volumes of data from various sources is now easy with ML. As a result, businesses are made capable of making better decisions.
It is beyond doubt that ML and AI are the next steps of Robotic Process Automation. ML and AI are necessary to take RPA to the next level. When RPA and AI are combined it can handle any type of data, be it structured, semi-structured or unstructured. And when ML is introduced into the scene it can improve the business process by predicting possible scenarios. Thus RPA is just only a step towards intelligent automation.