Cognitive automation occurs when a piece of software brings intelligence to information-intensive processes. It has to do with robotic process automation (RPA) and fuses artificial intelligence (AI) and cognitive computing together. Using AI, the process extends and improves actions typically correlated with RPA, saving users money and satisfying customers while accurately completing complex business processes that use unstructured information.

RPA is a means to automate business processes using AI or digital workers. Cognitive computing, meanwhile, allows these workers to process signals or inputs.

Other interesting terms…

Read More about Cognitive Automation

While cognitive automation is not machine learning (ML), it does use algorithms and technologies, such as natural language processing (NLP), text analytics and data mining, semantic technology, and ML. Learn about these to know the answer to “What is cognitive automation?”

What Are the Pillars of Cognitive Automation?

The following technologies make cognition-based decisions possible:

  • ML: Improves a system’s performance by learning from real-time interactions even without explicitly programmed instructions.
  • Data mining: Finds meaningful correlations, patterns, and trends from data warehouses and repositories using statistical and mathematical techniques.
  • NLP: Allows computers to communicate with humans in their native language.
  • Cognitive reasoning: Allows systems to imitate human reasoning by engaging in natural dialogs with people.
  • Voice recognition: Transcribes human voice and speech into text or commands.
  • Optical character recognition: Lets devices match patterns to convert scanned documents into corresponding computer text in real-time.
  • Emotion recognition: Allows computers to understand a person’s emotional state during voice- and text-based interactions.
  • Recommendation engine: Provides insights and recommendations based on different data components and analytics.

Why Is Cognitive Automation Important to Enterprises?

Cognitive automation helps organizations automate more processes to make the most of not only structured but also unstructured data. Customer interactions, for instance, are considered unstructured information, and they can be analyzed, processed, and structured easily into useful data for the next step in a business process. It can be prepared for predictive analytics, for example.

Cognitive automation makes processes more efficient and improves business quality. As such, it enables digital and business transformation. A study revealed that companies that use cognitive automation were able to do the following:

  • Automate 50–70% of tasks
  • Cut down data processing time by 50–60%
  • Decrease annual labor expenditures by 20–30%
  • Achieve triple-digit returns on investment (RoIs)

Cognitive Automation and Traditional Automation, What’s the Difference?

Traditional automation is limited to processes that only involve finishing tasks following a rigid ruleset. The decisions made can only follow an “if-then” logic and so do not need any human judgment. This rigidity leads the technology to fail to get meaning from and process unstructured data. The processes that benefit from traditional automation include data entry, automated help desk support, and approval routing.

Cognitive automation or intelligent process automation (IPA), meanwhile, can process both structured and unstructured data to automate more complex processes. It provides AI with cognitive ability and automates processes that use large volumes of text and images.

Cognitive Automation and RPA, Do They Differ?

While cognitive automation and RPA are related, the two have distinct differences primarily in terms of application scope.

RPA uses structured data to do monotonous human tasks more accurately. An RPA system can take over tasks that don’t require analytical skills or cognitive thinking. These activities include answering queries, performing calculations, and maintaining records and transactions.

Cognitive automation goes a step further in that systems endowed with it can analyze even unstructured data. In a sense, cognitive automation systems can use AI to mimic human thinking to perform even nonroutine tasks. These machines learn continuously to make decisions based on context, understanding complex relationships, and engaging in conversations with others.

The two also differ in terms of components.

RPA uses basic technologies like macros (rules or patterns that show how a certain input should be processed to produce a desired result). RPA does not involve much coding and uses an “if-then” approach to processing.

Cognitive automation, meanwhile, uses a knowledge-based approach. It needs more advanced technologies like NLP, text analytics, data mining, semantic technology, and ML to work. Without these, it can’t function as humans would.

How Do You Use Cognitive Automation to Optimize Your Workforce?

For a preview of optimizing your workforce with cognitive automation, watch this video.

With cognitive automation’s help, organizations can improve these processes:

Claims Processing

Processing claims is a labor-intensive task that insurance company employees face every day, but it can be optimized using cognitive automation tools.

Cognitive automation can port customer data from filled-up claim forms into your customer database. It can also scan, digitize, and port over customer data sourced from printed claim forms without requiring a real person to read and interpret it.

Document Processing

As mentioned earlier, using cognitive automation tools can turn unstructured files, such as documents, into structured data. It extracts relevant unstructured data from files and transforms it into a standardized format for the systems’ use.

Cognitive automation tools can also understand and classify different Portable Document Format (PDF) files, allowing users to trigger different actions depending on the document type automatically.

In sum, cognitive automation eases more complicated but repetitive processes to help organizations perform tasks more efficiently.

Key Takeaways