Cognitive Automation is the conversion of manual business processes to automated processes by identifying network performance issues and their impact on a business, answering with cognitive input and finding optimal solutions. Addressing the challenges most often faced by network operators empowers predictive operations over reactive solutions. Over time, these pre-trained systems can form their own connections automatically to continuously learn and adapt to incoming data.
This can help an organization more accurately predict deficiency while overcoming obstacles such as oversubscribed network tools, lack of visibility into network performance, and finding potential network bottlenecks. Cognitive automation can anticipate network events, identify the fault locations, pinpoint the root cause, and employ the proper resolution steps‚ all automated and at a fraction of the time. Because it is automated, cognitive automation needs less data impact progress, using predictive analytics and deploying cognitive decision-making to build overall network intelligence.
Cognitive automation utilizes data mining, text analytics, artificial intelligence (AI), machine learning, and automation to help employees with specific analytics tasks, without the need for IT or data scientists. Cognitive automation simulates human thought and subsequent actions to analyze and operate with accuracy and consistency. This knowledge-based approach adjusts for the more information-intensive processes by leveraging algorithms and technical methodology to make more informed data-driven business decisions.
Companies looking for automation functionality will likely consider both Robotic Process Automation (RPA) and cognitive automation systems. While both traditional RPA and cognitive automation provide smart and efficient process automation tools, there are many differences in scope, methodology, processing capabilities, and overall benefits for the business.
What are the key differences between cognitive automation and RPA?
Ease & Usability
- RPA tools perform simple tasks, to reduce the copious amount of tedious, repetitive tasks carried out by humans.
- Cognitive automation is designed to function similarly to human thoughts and subsequent actions to organize and analyze the more complex data with accuracy and consistency.
Scope
- RPA utilizes structured data to execute monotonous human tasks that are rules-based and do not require cognitive thinking (e.g. responding to inquiries, performing calculations, and managing records and transactions).
- Cognitive automation uses artificial intelligence techniques that imitate the way humans think, analyzing intricate, unstructured data to perform non-routine tasks. It enhances human decision-making and involves cognitive abilities such as natural language processing, context-based decision-making, and engaging in conversations.
Methodology & Processing Capabilities
- RPA utilizes basic technologies that are easy to understand and implement. It is rule-based, does not require extensive coding, and employs an ‘if-then’ method to processes.
- Cognitive automation is a knowledge-based approach, using advanced technologies like data mining, text analytics, and machine learning to take complex data and make it easier for humans to make better, more intuitive business decisions.
Benefits
- RPA supports innovation by taking over the repetitive tasks, freeing up employee time for more cognitive tasks. It increases productivity, reduces the cost of testing new ideas, reduces attrition by lessening the amount of tedious work for employees, and ultimately provides for a higher-level customer experience.
- Cognitive automation is pre-trained and needs less data before making an impact. It supplies cognitive input to humans, adding to their own analytical capabilities, and does not require assistance from data scientists or IT. As new data comes in, connections form automatically so the system can continuously learn and adjust to new information.