Combining Machine Learning and RPA
Over the years many companies have realized the potential of RPA and implemented it to automate mundane, repetitive tasks. The benefits of implementing RPA are numerous, but when it is combined with AI and ML the results are terrific. Artificial Intelligence and Machine Learning are two different concepts. They perform different functions also. AI takes care of the generation of intelligent thoughts, but ML makes predictions based on large volumes of data. ML can handle massive amounts of data and analyze them to make predictions. Voluminous information such as census/survey information, customer data, buyer information and a lot more are analyzed by ML to make predictions about future behavior. When ML comes across large amounts of data it translates the data into some kind of mathematical model which is later applied to solve problems by applying knowledge. Because of its ability to predicts patterns and information from the set of given data, ML finds its application to a number of problems. It has the ability to adapt to different situations and make predictions accordingly.
So, what happens when ML and RPA are combined? It helps RPA to overcome its limitations. A knowledge base is built from all the given data and these are used to make predictions. Just like you get recommendations on a video watching platform based on your previous watch history and preferences.
Role of AI in decision making
AI has now become an indispensable part of many industries, which has helped businesses take on the path of automation and digital transformation. While RPA can handle only structured data, AI has the ability to handle unstructured data. Businesses that deal with a lot of unstructured data can benefit from AI. Artificial Intelligence can help RPA in areas where it lacks and can serve complementary to RPA. Accounting, customer service inquiry, inventory management, and risk assessment are some of the areas where AI can empower RPA. All these tasks function efficiently when AI and RPA are combined. But there are instances which require the use of cognitive technology. In order for AI and RPA to automate emotional and judgment processes, cognitive capacities such as natural language processing, speech recognition, and ML need to be integrated. These automated processes learn from human actions and take decisions accordingly.
How RPA can function better
When RPA is combined with AI and ML far greater results are achieved. It can be used to detect any fraudulent activity in a financial transaction, breaches in network security and so on. It is also advantageous in process mining, which shows the path taken for a process. And when AI and ML are applied to automation, the results are Cognitive automation and Intelligent automation. Intelligent automation involves document verification, customer segmentation, sentiment analysis, and service. Cognitive automation covers fraud detection, market predictions etc.
RPA, ML, and AI are all complementary to each other automating different tasks and each of this automation requires different pre-requisites. RPA powered by AI can handle complex tasks which require decision making abilities at levels and scales that humans cannot perform.
RPA, when it interacts more with humans and data can make intelligent decisions and refine the whole process of decision making itself. This will help reduce the overall cost, increase efficiency and boost the productivity of a business.