Robotic process automation: A path to the cognitive enterprise Deloitte Insights

Cognitive automation is using technology similar to artificial intelligence to transform inputs of hearing, text, vision and other human behaviors to provide a human-like output, including decision making. Businesses that adopt cognitive automation will be able to stay ahead of the competition and improve their bottom line. We still have a long way to go before we have freely thinking robots, but research is producing machine capabilities that assist businesses to automate more work and simplify the operations that employees are left with. When it comes to choosing between RPA and cognitive automation, the correct answer isn’t necessarily choosing one or the other.

The cognitive automation can then learn from this process as it goes, which means that the cognitive automation can suggest new work to automate. To make an informed decision for investing in AI technologies, it is important to understand the differences of both RPA and cognitive automation. With cognitive intelligence, you move automation to the next level by technically processing the end products of RPA tasks. 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.

Operations optimization

The next step is, therefore, to determine the ideal cognitive automation approach and thoroughly evaluate the chosen solution. Let’s break down how cognitive automation bridges the gaps where other approaches to automation, most notably Robotic Process Automation (RPA) and integration tools (iPaaS) fall short. Cognitive automation is a summarizing term for the application of Machine Learning technologies to automation in order to take over tasks that would otherwise require manual labor to be accomplished. We got together with UiPath partners for a face-to-face event at Marriott Courtyard on 12th December, 2019 to explore RPA, AI, & Cognitive Automation. If you have an interest in knowing more about our services, feel free to get in touch via email at [email protected] or visit

You can also imagine that any errors are disruptive to the entire process and would require a human for exception handling. A significant part of new investments will be in the areas of data science and AI-based tools that provide cognitive automation. Cognitive automation is a deep-processing and integration of complex documents and data that requires explicit training by a subject matter expert.

The Sustainable Impact of Cognitive Automation

As organizations have found the perfect candidate in CRPA, they are gradually upgrading their automation tools in what will be their stepping stone in experiencing true hyper-automation. If you change variables on a human’s workflow, the individual will adapt and accommodate with little to not training. Cognitive Process Automation brings this level of intelligence to the table while keeping the speed of computing power. Through cognitive automation, enterprise-wide decision-making processes are digitized, augmented, and automated. Once a cognitive automation platform understands how to operate the enterprise’s processes autonomously, it can also offer real-time insights and recommendations on actions to take to improve performance and outcomes. Now, with cognitive automation, businesses can take this a step further by automating more complex tasks that require human judgment.

The solution helps you reduce operational costs, enhance resource utilization, and increase ROI, while freeing up your resources for strategic initiatives. The RPA developer plays a heavy role in selected steps of the RPA SDLC (build, deploy, and improve) by converting business requirements into RPA language. Batch operation is handling transactions in a batch or group, often used for end-of-cycle processing. It is an inherent part of the finance sector for processing bank reports, whether generated at the end of the day, monthly or bi-weekly.

These processes need to be taken care of in runtime for a company that manufactures airplanes like Airbus since they are significantly more crucial. Managing all the warehouses a business operates in its many geographic locations is difficult. Some of the duties involved in managing the warehouses include maintaining a record of all the merchandise available, ensuring all machinery is maintained at all times, resolving issues as they arise, etc. To learn more about the return on investment (ROI) of CRPA, I recommend reading “Understanding RPA ROI” by the Institute for Robotic Process Automation & Artificial Intelligence (IRPAAI).

Bots can evaluate form data provided by the customer for preliminary approval processing tasks like credit checks, scanning driver’s licenses, extracting ID card data, and more. Likewise, technology takes center stage in driving loan processing initiatives or accelerating back-office processing in the banking & financial services sector. In short, the role of cognitive automation is to add an AI layer to automated functions, ensuring that bots can carry out reasoning and knowledge-based tasks more efficiently and effectively. In the retail sector, a cognitive automation solution can ensure all the store systems – physical or online – are working correctly. Thus, the customer does not face any issues with browsing and purchasing the item they like. In case of failures in any section, the cognitive automation solution checks and resolves the issue.

Any other format, such as unstructured data, necessitates the use of cognitive automation. Cognitive automation also creates relationships and finds similarities between items through association learning. RPA is a method of using artificial intelligence (AI) or digital workers to automate business processes.

Purchase Order Processing

The insurance sector is just one vertical segment that’s taking advantage of CRPA technology to expedite the claims process. One company we’re working with told us their agents were making more than 650,000 outbound calls per year in their attempts to close short-term disability claims. These agents were making, on average, six call attempts to reach a claimant to get the required information needed to close the claim. Insurance intake teams and operations teams have, in the last few years, used RPA software to run the structured parts of the intake and claims process. Specifically, these teams would organize incoming data and then feed that data to back-end software bots.

Our approach places business outcomes and successful workforce integration of these RCA technologies at the heart of what we do, driven heavily by our deep industry and functional knowledge. Our thought leadership and strong relationships with both established and emerging tool vendors enables us and our clients to stay at the leading edge of this new frontier. As for ElectroNeek it seamlessly integrates RPA and cognitive automation, such as OCR and machine learning to carry out regular business processes. Currently, organizations usually start with RPA and eventually work up towards implementing cognitive automation. Considering factors like technology cost and data type helps find the optimal mix of automation technologies to be implemented. Essentially, organizations that leverage both technologies can provide the best outcomes for customers and the overall business.

Cognitive Process Automation

Traditional RPA is essentially limited to automated processes that need fast, repetitive actions (which may or may not include structured data) without dealing with too much contextual analysis or contingencies. On the other hand, the automation of business processes provided by them is primarily determined by completing tasks within a strict set of rules. For this reason, some people refer to RPA as "click bots," although most applications today go far beyond that.

Cognitive automation has proven to be effective in addressing those key challenges by supporting companies in optimizing their day-to-day activities as well as their entire business. However, if you are impressed by them and implement them in your business, first, you should know the differences between cognitive automation and RPA. One of the most important parts of a business is the customer experience. The cognitive solution can tackle it independently if it’s a software problem. If not, it alerts a human to address the mechanical problem as soon as possible to minimize downtime. Deliveries that are delayed are the worst thing that can happen to a logistics operations unit.

The differences between RPA and cognitive automation for data processing are like the roles of a data operator and a data scientist. A data operator's primary responsibility is to enter structured data into a system. Whereas, a data scientist's responsibility is to draw inferences from various types of data. The data scientist then presents them to management in a usable format so that they can make informed decisions. RPA is a technology that uses software robots to mimic repetitive human tasks with great precision and accuracy.

The way our programs are built, the Machine Learning component ensures that it keeps learning from its mistakes and continuously improves its ability to learn. The major differences between RPA and cognitive automation lie in the scope of their application and the underpinning technologies, methodology and processing capabilities. The nature and types of benefits that organizations can expect from each are also different. Machine learning is an application of artificial intelligence that gives systems the ability to automatically learn and improve from experience without being programmed to do so. Machine learning focuses on developing computer programs that access data and use it to learn for themselves.

• Want to understand where a cognitive automation solution can fit into your enterprise?
• Our process automation using AI helps to considerably decrease cycle times by automating most business processes.
• Using DocAcquire, the efficiency and quality of your business will increase manifolds.
• You can check our article where we discuss the differences between RPA and intelligent / cognitive automation.

It is possible to use bots with natural language processing capabilities to spot any mismatches between contracts and invoices. When these are found, you are alerted to the issue to make the necessary corrections. Both RPA and cognitive automation allow businesses to be smarter and more efficient. Robotics, also known as robotic process automation, or RPA, refers to the hand work – entering data from one application to another. Cognitive automation refers to the head work or extracting information from various unstructured sources. By eliminating the opportunity for human error in these complex tasks, your company is able to produce higher-quality products and services.

Findings from both reports testify that the pace of cognitive automation and RPA is accelerating business processes more than ever before. As a result CIOs are seeking AI-related technologies to invest in their organizations. 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. Another way businesses can minimize manual mental labor is by using artificial intelligence (AI) to set up and manage robotic process automation (RPA).

• Here, in case of issues, the solution checks and resolves the problems or sends the issue to a human operator at the earliest so that there are no further delays.
• By understanding the two main options better, we can dive deeper into realizing which automation process is suited to different businesses.
• In another example, Deloitte has developed a cognitive automation solution for a large hospital in the UK.
• RPA operates most of the time using a straightforward “if-then” logic since there is no coding involved.
• In short, the role of cognitive automation is to add an AI layer to automated functions, ensuring that bots can carry out reasoning and knowledge-based tasks more efficiently and effectively.

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