How Cognitive Automation backed by Artificial Intelligence, Machine Learning, and RPA upgrade industries?

  • Hannah
  • July 2, 2020
  • Comments Off on How Cognitive Automation backed by Artificial Intelligence, Machine Learning, and RPA upgrade industries?

Cognitive automation is a new buzzword in the technology and industrial world. It exists at the crossroads between Artificial Intelligence (AI) and Cognitive Computing, with the assistance of Robotic Process Automation (RPA). In other words, it is a type of software that brings intelligence to information-intensive processes. By employing artificial intelligence and machine learning, it takes over tasks that would otherwise require manual labor to be accomplished.

According to a McKinsey report, businesses that adopted cognitive automation tools were able to:

• Automate approximately 50 to 70 percent of tasks.

• Cut down data processing time by 50 to 60 percent.

• Decrease annual labor expenditure by 20 to 30 percent.

• Achieve a triple-digit ROI.

At present, the white-collared automation, i.e., commonly known as Robotic Process Automation (RPA), is limited to structured and relatively simple processes where little to no interpretation is required. This is where integrating cognitive capabilities into RPA can benefit us. These capabilities include speech recognition, natural language processing, and machine learning to automate perceptual and judgment-based tasks that were only done by humans earlier. Two widely famous examples are IBM Watson and Blue Prism.

In real-life scenarios where data primarily exists in a semi-structured or unstructured format, the machine learning aspect of Cognitive automation can mine patterns and make predictions about process outcomes. It infuses a cognitive ability and can accommodate the automation of business processes utilizing large volumes of text, speech, audio, and visuals.

Thanks to cognitive automation and cognitive RPA, enterprises can now go beyond basic automation to deliver business outcomes such as higher customer satisfaction, lower churn, and increased revenues. They also enable companies to break existing trade-offs between speed, cost, and quality. Further, companies can discover new ways to boost the efficiency of their business practices with minimal error, better machinery judgment, and save on expenses.

Let us illustrate a few examples of how important this technology is:

1. Virgin Trains has deployed cognitive RPA to refund customers for late running trains automatically. As customer emails arrive, a natural language processing tool reads them, understands meaning and sentiment, categorizes, and then recognizes critical information in the text to serve the customer quickly and cleanly.

From discerning the customer’s complaint with cognitive computing to actively issuing the refund with software bots, the entire process has been automated. The cognitive automation solution has reduced daily processing time and manual labor involved in dealing with customer emails by 85 percent.

2. In October 2016, Swedish bank, Skandinaviska Enskilda Banken (SEB), purchased cognitive robotic process automation software, Amelia, from one of the leaders in the industry, IPsoft, for improving its customer service. Launched in 2014, Amelia can speak 20 languages and understand the semantics of languages. In case, Amelia is not able to solve the problem; it passes the query to the human operator. It observes the interaction to improve its knowledge for handling further such cases on its own.

By introducing Amelia, not only does the bank offer customers an opportunity to get an immediate response to their queries, but it also ensures that call volume for live human agents is concentrated on the highest value support areas.

Other than these, the potential applications of this automation are significant. In HR, employers can use cognitive automation to filter candidates for recruitment, predict the likelihood of resignations, analyze employee satisfaction, and much more. In healthcare, cognitive automation can ease maintenance of the inventory database that keeps the record of supply levels of every inventory item, including medicines, gloves, and needles, among others. It can also feed the digital database with patient details without any human intervention. In the insurance sector, too, it can automate most of the essential routine steps involved in claims processing, validate data, process payments, reconciliation, and others. So, the scope of cognitive automation, along with cognitive RPA, will evolve as an integral part of industries in the future.

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