Cognitive Automation 101 IBM Digital Transformation Blog

Some RPA efforts quickly lead to the realization that automating existing processes is undesirable and that designing better processes is warranted before automating those processes. The emerging trend we are highlighting here is the growing use of cognitive technologies in conjunction with RPA. But before describing that trend, let’s take a closer look at these software robots, or bots. Cognitive Automation has a lot going for it but those benefits can come at a cost, the first of which is an additional financial investment. It also requires more training at the outset and at times that training is in-depth or technical. While the technology is powerful and ever-evolving, it is also worth noting the algorithms for recognising hand-writing are not always perfect and time and resources may be required to make machines ‘read’ hand-written documents.

For instance, if you take a model like StableDiffusion and integrate it into a visual design product to support and expand human workflows, you’re turning cognitive automation into cognitive assistance. Partnering with an experienced vendor with expertise across the continuum can help accelerate the automation journey. Processes require decisions and if those decisions cannot be formulated as a set of rules, machine learning solutions are used to replace human judgment to automate processes. Various combinations of artificial intelligence (AI) with process automation capabilities are referred to as cognitive automation to improve business outcomes. Intelligent/cognitive automation tools allow RPA tools to handle unstructured information and make decisions based on complex, unstructured input.

What Is Cognitive Automation?

Cognitive automation techniques can also be used to streamline commercial mortgage processing. This task involves assessing the creditworthiness of customers by carefully inspecting tax reports, business plans, and mortgage applications. 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. In another example, Deloitte has developed a cognitive automation solution for a large hospital in the UK. The NLP-based software was used to interpret practitioner referrals and data from electronic medical records to identify the urgency status of a particular patient. 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.

One could morally protest robots and automation technologies replacing human jobs with innovative robotic processes. But, practically, a considerable majority of mundane and repetitive tasks presently handled by workers has either been offshored or is being carried out by a workforce who probably would rather be doing something else. With cognitive RPA, decision-making processes are all performed by the robots such that all the long, complex tasks could be automated. Put simply, RPA involves automating menial and repetitive tasks; cognitive automation adds an all-important extra layer of AI and machine learning. RPA encompasses software that can be easily programmed to perform basic tasks across applications and thus help eliminate mundane, repetitive tasks completed by humans.

What Cognitive Automation Can Do

Robots execute these tasks by emulation and interaction with applications and data sources in much the same way as human workers do. Cognitive RPA means Robotic Process Automation (RPA) tools and solutions that leverage Artificial Intelligence (AI), Text Analytics and Machine Learning (ML) to improve the experience of your workforce and consumers. On the other hand, cognitive intelligence uses machine learning and requires the panoptic use of the programming language. It uses more advanced technologies such as natural language processing (NLP), text analysis, data mining, semantic technology and machine learning.

Subex is a leading telecom analytics solution provider and leveraging its solution in areas such as Revenue Assurance, Fraud Management, Partner Management, and IoT Security. Though, assisted RPA should only be applied when real-time human-system interaction is needed. Learn about Deloitte’s offerings, people, and culture as a global provider of audit, assurance, consulting, financial advisory, risk advisory, tax, and related services. From hyperautomation to low-code platforms and increased focus on security, learn about the latest developments shaping the world of automation. All the apps are very handy as we have the best customer success consultants working together with our Sales Director. However, in the long run, Cognitive intelligence produces the most effective for organizations than RPAs.

What is cognitive automation and what it is not?

This dynamic approach enables rapid development and resolution in a production environment. Realizing that they can not build every cognitive solution, top RPA companies are investing in encouraging developers to contribute to their marketplaces where a variety of cognitive solutions from different vendors can be purchased. Given the capabilities of both text and speech processing, the ubiquity of RPA in business will only continue to expand and expand rapidly. To find out how RPA and cognition can help drive your business strategies in the future, Contact Us to begin your journey. Another use case involves cognitive automation helping healthcare providers expedite the evaluation of diagnostic results and offering insights into the most feasible treatment path. With predictive analytics, bots are enabled to make situational decisions.

As new data is added to the cognitive system, it can make more and more connections allowing it to keep learning unsupervised and making adjustments to the new information it is being fed. Helping organizations spend smarter and more efficiently by automating purchasing and invoice processing. By submitting this form, you agree that you have read and understand Apexon’s Terms and Conditions.

What are the uses of cognitive automation?

A combination of the two is best suited for processes that have simple tasks requiring human intervention. Adopting both technologies can provide end-to-end automation solutions for a business. AI and machine learning tools are focused on operationalizing the data science process. Enterprise automation initiatives like iPaaS and RPA continue to focus on accelerating legacy tasks and processes. Along with automating web-based applications, RPA can also automate Windows applications and legacy applications, for which developing IT integration would be a cost-intensive, time-consuming and gargantuan task. RPA can mimic what an end-user does with near-zero errors and without being fatigued, bored, or roguelike humans.

As they get a breather from laborious activities with the help of robotic process automation, they can put their intelligence for improving business-driven efficiency of the processes. In addition to undertaking the three key responsibilities of automation, accuracy, and speed, a cognitive robotic process automation tool drives analytic-based decisions. Cognitive RPA derives its intelligence from the core features of Natural Language Processing (NLP), Optical Character Recognition (OCR), and Machine Learning (ML). These characteristics help this evolved version of RPA to make sense out of volumes of data to extract actionable information. While the simple RPA tool is unable to perform actions that are beyond the scope of its programmed regulations, cognitive RPA employs machine learning to adapt and improve with the changing needs. Many organizations have also successfully automated their KYC processes with RPA.

Natural Language Processing (NLP)

But, interpreting information the way human thinks, and constantly learn, to provide possible outcomes in assisting decision making. However, do note that, bad assumption leads to bad conclusion – no matter how concise a computer is in the process of thinking. Similarly, in the software context, RPA is about mimicking human actions in an automated process. Explore the cons of artificial intelligence before you decide whether artificial intelligence in insurance is good or bad.

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. RPA is rule-based technology, so it is easy to implement using macro scripts and workflow automation using an ‘if-then’ approach which does not involve much coding. Cognitive RPA is a ‘knowledge-based’ technology that can help make sound business decisions.

Insurance

The key of the adoption limitations is the availability of massive data sets, generalised learning, regulation and social acceptance due to potential bias in algorithms. Both RPA and Cognitive Automation have the potential to create business processes smarter and more efficient. Conventional RPA automates repeatable tasks that involve processing highly-structured data. A right candidate for RPA would be one that processes payroll or sends invoices to customers based on standardized data input from applications or forms. For instance, the call center industry routinely deals with a large volume of repetitive monotonous tasks that don’t require decision-making capabilities.

• “Go for cognitive automation, if a given task needs to make decisions that require learning and data analytics, for example, the next best action in the case of the customer service agent,” he told Spiceworks.
• Botpath is an RPA software that increases efficiency and reduces risks by configuring bots to execute tasks accurately and timely.
• It mimics human behavior and intelligence to facilitate decision-making, combining the cognitive ‘thinking’ aspects of artificial intelligence (AI) with the ‘doing’ task functions of robotic process automation (RPA).
• In order to keep up with the increasing demands, some financial and banking organisations have adopted RPA and AI based platforms.

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