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.