Difference between RPA and Cognitive Automation
Digital Transformation RPA Technology

Understanding the Difference between RPA and Cognitive Automation

For a long time, organizations who were contemplating implementing automation technologies such as RPA or Cognitive Automation managed to dodge implementing these technologies with the assumption that they can drive just as favorable outcomes without it.

However, that isn’t the case anymore. As businesses across the world look to automate their clerical and administrative tasks, RPA is enabling them to drive results more quickly, accurately, and tirelessly than humans. It allows them to focus on tasks that leverage human capabilities such as emotional intelligence, reasoning, judgment, interaction, and more.

Reports expect the RPA market to grow to $25.66 billion by 2027.

By automating a large part of day-to-day activities, organizations can drive accuracy, improve employee morale and productivity, and ensure reliability and consistency of operations.

However, to drive the intended benefits from their automation initiatives, organizations need to understand the difference between various types of automation technologies such as RPA and Cognitive Automation.

So, what do these two terms mean? How are they similar, and what makes them different? Let’s find out!

Introduction to RPA

RPA, a type of business process automation, enables organizations to automate mundane and repetitive tasks such as data entry, order processing, reporting, onboarding, etc. to improve their accuracy and timeliness and reduce error. Although RPA helps in reducing the manual stress of administrative tasks, it requires human intervention for handling exceptions.

Read: How Small Companies Can Benefit from Robotic Process Automation

RPA cannot, on its own, rectify a wrong data format, include missing information, or make critical business decisions. To enable these tasks, it needs humans to pitch in and do what is necessary. However, when complex or voluminous data is involved, it can become very challenging even for humans to make the right decisions.

Read: Be Aware of These Challenges of Robotic Process Automation Implementation

Introduction to Cognitive Automation

Cognitive Automation is a subset of enterprise automation that uses AI and ML to enhance the power of RPA and automate complex tasks while improving the accuracy, consistency, and reliability of day-to-day operations.
It has the capability to build its own rules, find patterns, and build relationships between different data entities while assisting humans in making intelligent business decisions.

Using features such as Optical Character Recognition, Natural Language Processing, Text Analytics, and more, it takes RPA a step further by bringing in the element of human cognition into the automation workflow and paving the way for learning, reasoning, and self-correction.

Unlike unattended RPA, Cognitive Automation can handle exceptions – with minimal or no human intervention.
When used correctly, Cognitive Automation can make RPA even better. For instance, using NLP and other forms of analytics, it can convert unstructured data into structured data while improving its quality. Furthermore, Cognitive Automation can make autonomous, cognitive-based decisions on its own, freeing humans from the task of comprehending complex data sets while significantly reducing the probability and impact of inaccurate decision-making.

Differences between RPA and Cognitive Automation

Both RPA and Cognitive Automation are helping organizations overcome the challenges of manual task execution and bring in accuracy and speed into business operations.

However, several aspects make these technologies different. Here’s looking at some of these differences:

RPA Cognitive Automation 
Purpose To automate mundane, everyday tasks  To bring AI and ML technology into the automation workflow and assist humans in decision-making
Type  Process-oriented Knowledge-based 
Automation Level Simple day-to-day tasks  More complex tasks
Implementation Time Quick and straightforward to implement Time-consuming and relatively complex to implement
ROI Almost immediate  Takes time 
Technology Foundation Uses basic technologies such as screen mapping, automation, etc.  Uses advanced technologies such as NLP, data mining, semantic analysis, etc. 
Data Requirement  Requires data to be structured  Can work with unstructured data to spot trends and patterns 
Coding  Does not require any coding Requires high levels of programming knowledge
Human Intervention Requires human intervention for handling exceptions Can handle exceptions on its own and requires no human intervention
Decision-Making Capability Cannot take decisions Can take decisions with little or no human support. 
Rules Definition  Requires humans to define rules  Defines its own rules 
Use Cases  Data entry, claims processing, resume scanning, order processing.  Trend analysis, customer service interactions, behavioral analysis, email automation, etc. 

As businesses look to embrace the world of enterprise automation, RPA and Cognitive Automation bring a lot to the table: On one hand, where RPA can perform repetitive tasks based on a specific set of rules, Cognitive Automation can simulate human thought process to discover, learn, and make predictions.

By performing more complex, judgment-based tasks, Cognitive Automation helps organizations establish context and quickly make accurate decisions that would otherwise require days of effort from a handful of expert data scientists or business analysts.

What technology will benefit your organization more depends on the kind of tasks you wish to automate as well as the type of outcomes you need.

If you want a system that automates rules-based tasks, RPA can help drive results quickly and efficiently. However, if you want to deal with highly complex and unstructured data and require accurate decisions to be made, Cognitive Automation, although more complex and time-consuming, can help you achieve context as well as clarity.

You could also use a combination of both technologies to automate day-to-day tasks and enable automated business decision-making.

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