Cognitive Automation 101 IBM Digital Transformation Blog

For example, a cognitive automation application might use a machine learning algorithm to determine an interest rate as part of a loan request. 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. 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.

With AI, organizations can achieve a comprehensive understanding of consumer purchasing habits and find ways to deploy inventory more efficiently and closer to the end customer. As the predictive power of artificial intelligence is on the rise, it gives companies the methods and algorithms necessary to digest huge data sets and present the user with insights that are relevant to specific inquiries, circumstances, or goals. The foundation of cognitive automation is software that adds intelligence to information-intensive processes.

How can Cognitive Automation save money, and reallocate it to better uses?

An example of cognitive automation is in the field of customer support, where a company uses AI-powered chatbots to provide assistance to customers. While technologies have shown strong gains in terms of productivity and efficiency, “CIO was to look way beyond this,” said Tom Taulli author of The Robotic Process Automation Handbook. Cognitive automation will enable them to get more time savings and cost efficiencies from automation. “To achieve this level of automation, CIOs are realizing there’s a big difference between automating manual data entry and digitally changing how entire processes are executed,” Macciola said. He observed that traditional automation has a limited scope of the types of tasks that it can automate. For example, they might only enable processing of one type of document — i.e., an invoice or a claim — or struggle with noisy and inconsistent data from IT applications and system logs.

As the digital agenda becomes more democratized in companies and cognitive automation more systemically applied, the relationship and integration of IT and the business functions will become much more complex. Comparing RPA vs. cognitive automation is “like comparing a machine cognitive automation definition to a human in the way they learn a task then execute upon it,” said Tony Winter, chief technology officer at QAD, an ERP provider. Though cognitive automation is a relatively recent phenomenon, most solutions are offered by Robotic Process Automation (RPA) companies.

Regulatory compliance and risk management

Robotic Process Automation (RPA) works best if you have a structured process, involves a large volume of data and is rule based. If this process involves complex, unstructured data that requires human intervention then Cognitive automation is the answer. A digital worker using cognitive automation can use its AI capabilities to deal with unstructured data. Using a digital workforce to handle routine tasks reduces the possibility of human error and can help to streamline workflow. Cognitive automation opens up a world of possibilities for improving your work and life.

With substantial leaps in Machine Learning and AI technologies every few months, it’s pretty challenging to keep up with tongue-twisting terminologies on the other side of understanding the depth of technologies. Even sadder, while not the most practical answer for some businesses, the mistake is often made that these technologies are embedded in larger software packages. CIOs also need to address different considerations when working with each of the technologies.

Cognitive Automation

Given that the majority of today’s banks have an online application process, cognitive bots can source relevant data from submitted documents and make an informed prediction, which will be further passed to a human agent to verify. Upon claim submission, a bot can pull all the relevant information from medical records, police reports, ID documents, while also being able to analyze the extracted information. Then, the bot can automatically classify claims, issue payments, or route them to a human employee for further analysis.

RPA is best deployed in a stable environment with standardized and structured data. Cognitive automation is most valuable when applied in a complex IT environment with non-standardized and unstructured data. You can check our article where we discuss the differences between RPA and intelligent / cognitive automation. IA or cognitive automation has a ton of real-world applications across sectors and departments, from automating HR employee onboarding and payroll to financial loan processing and accounts payable. The integration of these components to create a solution that powers business and technology transformation.

NLP and ML algorithms classify the conveyed emotions, attitudes or opinions, determining whether the tone of the message is positive, negative or neutral. Cognitive automation has the potential to completely reorient the work environment by elevating efficiency and empowering organizations and their people to make data-driven decisions quickly and accurately. Change used to occur on a scale of decades, with technology catching up to support industry shifts and market demands. Corporate transformation was driven by organic customer demand and fulfilled by people who took the time to sift through trends and marketing research, and then used their years of experience to plan out the optimal supply lines and resource allocations. The way RPA processes data differs significantly from cognitive automation in several important ways. It now has a new set of capabilities above RPA, thanks to the addition of AI and ML.

RPA usage has primarily focused on the manual activities of processes and was largely used to drive a degree of process efficiency and reduction of routine manual processing. Traditional RPA usually has challenges with scaling and can break down under certain circumstances, such as when processes change. However, cognitive automation can be more flexible and adaptable, thus leading to more automation.