The foundation of cognitive automation is software that adds intelligence to information-intensive processes. It is frequently referred to as the union of cognitive computing and robotic process automation (RPA), or AI. By utilizing AI technology, cognitive automation broadens and enhances the set of tasks normally associated with RPA, resulting in cost savings, increased customer satisfaction, and increased accuracy in intricate business processes involving unstructured data. Various combinations of artificial intelligence (AI) with process automation capabilities are referred to as cognitive automation to improve business outcomes. Cognitive automation represents a range of strategies that enhance automation's ability to gather data, make decisions, and scale automation. It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. Machine learning is not cognitive automation.

Cognitive automation is a summarizing term for the application of machine learning technologies to automation in order to take over tasks which would otherwise require manual labor to be accomplished. As a result, businesses can streamline their workflows beyond the scope of current automation technologies, and accomplish a next level of operational efficiency. Given its potential, companies are starting to embrace this new technology in their processes. According to a 2019 global business survey by Statista, around 39 percent of respondents confirmed that they have already integrated cognitive automation at a functional level in their businesses. Also, 32 percent of respondents said they will be implementing it in some form by the end of 2020.

Creates deep visibility and understanding of the landscape by learning context. Eliminates noise, predicts emergent conditions and triages issues by managing alerts. It develops and maintains a single-source-of-truth about Enterprises' IT landscape by creating an end-to-end view that connects business, applications, technology platforms and infrastructure. It filters, correlates and prioritizes alerts based on inferred risk and business impact, and aggregates and resolves alerts autonomously. It rapidly identifies the root cause of incidents, honors context-specific business policies and constraints, and prescribes the best action. It notifies stakeholders to obtain necessary consent to execute actions autonomously.

Anyone who has been following the Robotic Process Automation (RPA) revolution that is transforming enterprises worldwide has also been hearing about how artificial intelligence (AI) can augment traditional RPA tools to do more than just RPA alone can achieve. You might even have noticed that some RPA software vendors -- Automation Anywhere is one of them -- are attempting to be more precise with their language. Rather than call our intelligent software robot (bot) product an AI-based solution, we say it is built around cognitive computing theories. But is that any clearer? Let's try and dispel some of it.