RPA to Cognitive Automation: When Do You Make the Shift?

For many companies, leapfrogging over RPA and starting with cognitive automation might seem like trying to run before you can walk. Rather than trying to emulate the success stories you see overnight, your business should have a well-thought-out, long-term strategy for RPA and cognitive automation in order to maximise your ROI. Upon receiving invoice files, the Account Receivable specialist’s first step is to classify the documents by type, such as recurring, pro forma, or commercial invoices.

First, a bot pulls data from medical records for the NLP model to analyze it, and then, based on the level of urgency, another bot places the patient in the appointment booking system. A self-driving enterprise is one where the cognitive automation platform acts as a digital brain that sits atop and interconnects all transactional systems within that organization. This “brain” is able to comprehend all of the company’s operations and replicate them at scale.

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Then, the bot can automatically classify claims, issue payments, or route them to a human employee for further analysis. This way, agents can dedicate their time to higher-value activities, with processing times dramatically decreased and customer experience enhanced. For example, one of the essentials of claims processing is first notice of loss . When it comes to FNOL, there is a high variability in data formats and a high rate of exceptions. Customers submit claims using various templates, can make mistakes, and attach unstructured data in the form of images and videos. Cognitive automation can optimize the majority of FNOL-related tasks, making a prime use case for RPA in insurance.

Also, cognitive intelligence’s level of technology helps it learn on the job. If it meets an unexpected scenario, the AI can either resolve it or file it out for human intervention, and an RPA robot would have broken down. With the advent of cognitive intelligence, AI aims to adapt the technology so humans can interact with it naturally and daily. They aim to develop a machine that can listen and speak, understand grammatical context, understand emotion and feelings and recognize images. Unfortunately, things have changed, and businesses worldwide are looking for automation for clerical and administrative tasks. RPA enables organizations to drive results more quickly, accurately, and tirelessly than humans.

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At the same time, Cognitive Automation is powered by both thinkings and doing which is processed sequentially, first thinking then doing in a looping manner. RPA rises the bar of the work by removing the manually from work but to some extent and in a looping manner. But as RPA accomplish that without any thought process for example button pushing, Information capture and Data entry. RPA resembles human tasks which are performed by it in a looping manner with more accuracy and precision. Cognitive Automation resembles human behavior which is complicated in comparison of functions performed by RPA. Cognitive Process Automation learns from observing Claims Adjusters and creates its own algorithms for approving or denying claims.

Your organization and enterprise systems were built with different assumptions for a different era of business. In the incoming decade, a significant portion of enterprise success will be largely attributed to the maturity of automation initiatives. 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).

What is Cognitive Process Automation?

Also, when large amounts of data are there, it can be difficult for the human workforce to make the best decisions. Cognitive automation is also a subset of AI that mimics human behavior. Moreover, this is far more complex than the actions and tasks mimicked by RPA processes. Cognitive Automationsimulates what is cognitive automation the human learning procedure to grasp knowledge from the dataset and extort the patterns. It can use all the data sources such as images, video, audio and text for decision making and business intelligence, and this quality makes it independent from the nature of the data.

In a hospital setting, RPA can count the number of patients in a ward or with a particular diagnosis. While cognitive analysis can diagnose ailments, prescribe medications and monitor the health of patients. Knowledge-driven automation techniques streamline design verification and minimize retest, while enhancing design and quality. Clearly, the people who take the assessment quickly identify the gaps they have against the best practices and build a road map to close the gaps. Right now, the velocity of the Virtuous Circle is increasing…better software, increased enterprise value propositions, and another round of investments. The speed of evolution in this industry segment is almost without precedent.

Incremental learning enables automation systems to ingest new data and improve performance of cognitive models / behavior of chatbots. State-of-the-art technology infrastructure for end-to-end marketing services improved customer satisfaction score by 25% at a semiconductor chip manufacturing company. A company that wants to realize much value from implementing RPA must invest in the capability to drive automation. It requires process redesign, navigating the different stakeholders that have purview (security, IT, audit compliance, etc.) and navigating the business unit with the problem. Often the opportunities and problems span multiple business units, which requires coordinating and focus on multiple units and departments. Furthermore, tools such as Intelitics identifies business processes and determines and performs the corresponding processes.

What makes this such an exciting story is that RPA doesn’t apply to just one aspect of the enterprise – it applies anywhere human resources are being deployed for labor-intensive services. Of course, it doesn’t hurt that enterprises have already captured most of the potential value from offshore labor arbitrage. MarketsWe help enterprises navigate complexities of each market, providing invaluable expertise when making critical, strategic decisions related to using and delivering services.

In the case of Data Processing the differentiation is simple in between these two techniques. RPA works on semi-structured or structured data, but Cognitive Automation can work with unstructured data. So now it is clear that there are differences between these two techniques. The cognitive automation can then learn from this process as it goes, which means that the cognitive automation can suggest new work to automate. The expertise required is large, and although you can outsource it, the algorithms require vast amounts of maintenance and change management.

Network Operation processes are typically standard or tendentially standardised and have a high degree of predictability making them candidates for automation. The pace of CSPs’ automation levels can be increased by leveraging the insights brought by cognitive technologies. Our state-of-the-art AI/ML technology can improve your business processes and tackle those complex and challenging tasks that are slowing your productivity. There are many more applications of automation for structuring processes, including process strategy, modeling, implementation, execution, monitoring and control, and continuous process improvement.