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Four Key Differences between RPA and Cognitive Automation

Ramakrishna Mashetty

Head of Business Development, Intelligent Automation, EPAM APAC

In 2017, IDC reported that the highest spend within the artificial intelligence (AI) space would be on cognitive applications, predicting that global corporate spending on cognitive and AI systems will grow into a $46 billion market by 2020. With its staggering potential for growth in the next couple of years, cognitive applications stand to become one of the most widespread and fastest growing emerging technologies seen in recent times.

This rapid growth also creates confusion, as many organizations are unable to determine the right technology to invest in with all the swirling nomenclature around AI-related technologies like robotic process automation (RPA) and cognitive automation. We often hear questions such as:

  • Are we doing the right thing by investing in RPA?
  • Should we invest in cognitive automation instead?
  • How do we decide between investing in RPA and cognitive automation?

Thus, we want to demystify both technologies and explain their differences to help organizations make a more informed investment decision.

Difference in Application

RPA is a technology used to mimic repetitive human tasks with more precision and accuracy by using software robots. RPA is ideal for those processes that do not require decision-making or human intervention. However, there are going to be plenty of situations that do require human decision-making, and when there is voluminous data involved, it can become very challenging for the human workforce to make the right decisions.

This is where cognitive automation comes to the rescue. As a subset of AI, cognitive automation mimics human behavior, which is in many ways more complex than the actions and tasks mimicked by RPA processes. One notable example is how doctors leverage cognitive automation with AI techniques to analyze a patient’s condition to determine a diagnosis.

Difference in Technology

RPA relies on basic technologies, such as screen scraping, macro scripts and workflow automation. Cognitive automation, on the other hand, uses more advanced technologies, such as natural language processing (NLP), text analytics, data mining, semantic technology and machine learning, to make it easier for the human workforce to make informed business decisions. RPA does not require coding, as it depends more on the configuration and deployment of frameworks, whereas cognitive automation uses machine learning and requires the extensive use of programming knowledge.

Difference in the Method of Automation

RPA is rules-based and works on the ‘íf-then’ principle. It is a process-oriented technology, which is often used to work on time-consuming tasks that were previously performed by offshore teams. Cognitive automation is a knowledge-based technology. Here, the machine goes through several human-like conversations and behaviors to understand how humans talk or behave and define its own rules.

Difference in Data Processing

Let’s look at the roles of a data operator and a data scientist to demonstrate the differences between RPA and cognitive automation for data processing. The key role of a data operator is to enter structured data into a system, while a data scientist has to draw inferences from various types of data and present it in a consumable format to management to make informed decisions. RPA and cognitive automation work within the same role-based parameters.

RPA is like the data operator and works on standardized data. It can process the data only when it is available in a structured or semi-structured format. Any other format, such as unstructured data, requires the help of cognitive automation to build relationships and find similarities between the items by learning from association.

For example, if an organization has thousands of unstructured invoices and purchase orders sitting in a database, cognitive automation tools can build relationships between the entities by asking questions such as:

  • Have I seen this quality before?
  • How was it used earlier?
  • How is it connected to what was seen earlier?

And so on. By asking these questions, the tool can interpret and process data with minimal or no human supervision.

Which One is Right for Your Organization?

The choice of technology depends on the nature of your process. If your process involves structured, voluminous data and is strictly rules-based, then RPA would be the right solution. However, if you deal with complex, unstructured data that requires human intervention, then cognitive automation would be more apt for your organization.

While RPA provides immediate ROI, cognitive automation often takes more time as it involves learning the human behavior and language to interpret and automate the data. However, if your process is a combination of simple tasks and requires human intervention, then you can opt for a combination of RPA and cognitive automation.

The best way to develop a solution that works for your organization is by partnering with a software vendor who understands the evolution from RPA to cognitive automation and the importance of combining the two technologies to fortify RPA tools with cognitive automation to provide an end-to-end automation solution. Chances are, you will probably need to utilize both technologies sooner than later. So, for now, understanding how they work is critical to making the right investments at the right times.

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