Unveiling the Power of Cognitive Automation in RPA

The market size of Robotic Process Automation (RPA) is estimated to reach US$2,567 million by 2022, at a CAGR of 30.14% between 2017- 2022.

Robotics Process Automation has become an integral part of operations amongst different sectors as the technologies are permeating into the industry. It involves automating tasks that are repetitive, mundane, and time-consuming. A report by the markets and market research forecasted that the market size of Robotic Process Automation (RPA) is estimated to reach US$2,567 million by 2022, at a CAGR of 30.14% between 2017- 2022.

However, one of the most valuable and critical steps in the process of RPA is cognitive automation. Unlike RPA, which requires a pre-requisite set of rules, cognitive automation performs tasks that require critical thinking and human intelligence. Cognitive automation is one of the automation processes and is often known as intelligent automation.

Understanding Cognitive Automation

Cognitive automation can be defined as the process that utilizes artificial intelligence technologies to perform tasks such as sifting through the data, segregating unstructured data, and handling complex, unstructured and data-laden tasks for performing tasks with efficiency accuracy. This technology is designed to imitate human intelligence. Cognitive automation includes machine learning, Natural Language Processing, and Optical Character Recognition (OCR), as the processes through which it can perform the tasks.

By integrating natural language processing with cognitive automation, the tasks involving contracts and customer service gets automated. Moreover, with this AI technology, the system understands the human language and senses the meaning of the human language.

With the help of Optical Cognition Recognition, the images of typed or handwritten or printed text are converted into the machine-encoded text from a scanned document. It is one of the most common methods for digitizing texts so that they can be digitally and electronically.

The Machine learning process in cognitive automation allows the system to learn and improve according to experiences, without human intervention. They also include computing programs for accessing data and getting meaning insights.

Applications of Cognitive Automation 

Due to its capability to think critically and mimic human intelligence, cognitive intelligence has been utilized in almost every industry sector.

1)Healthcare- In healthcare, a huge amount of data is generated regarding patients' diagnosis, history, business reports, diagnostic tools, and other processes that either require investment for healthcare purchases directly or are somehow linked with the healthcare sector's functioning. This data is unsegregated as it gets harnessed from multiple sources. Medical professionals can not invest their valuable time sifting through the data to identify insightful information. Thus, with the help of cognitive automation and its associated processes, namely Natural Language Processing (NLP), the text can be analyzed, thus depicting meaningful insights about the diseases. It also helps in making an informed decision while sifting through the unstructured data.

2) Retail– Retail industry involves harmonizing data from various stores and departments, but this process becomes tricky and challenging due to pre-existing silos amongst the departments. It also becomes difficult to arrange and maintain data from multiple sources and according to different sources' operational procedures manually. With the help of cognitive automation, the data can be streamlined according to the requirements of operations. Moreover, with the help of AI tools of cognitive automation, valuable insights can be drawn from the data gathered about the product providers, partners, and retailers, thus ensuring that operations' efficiency and accuracy are not thwarted.

3) Banking– In banking, an arranged set of information and data is imperative, so that analysis can be made about the customers who are in urgent need of either loan or other banking processes. However, as the data generated in banking is enormous and often received from multiple sources, the manual segregation of data becomes repetitive and time-consuming. By integrating the KYC (Know Your Customer) details with cognitive-automation processes, the banking tasks can be accelerated. By leveraging AI tools for assessing the public records, scanning documents, and processing local and international transactions, reduces paperwork, helps get valuable insights from the given customer's information, and helps make informed and intelligent decisions about the customers who are in urgent requirement of loans. By getting insights with cognitive automation, potential fraud or scams can also be thwarted.

Conclusion

As mentioned earlier, cognitive automation as technology provides an optimistic outlook in the future. If all the AI-tools are leveraged correctly, this technology will be a next-frontier of robotic process automation(RPA).