Using enterprise intelligent automation for cognitive tasks

In this paper, UiPath Chief Robotics Officer Boris Krumrey delves into the ways RPA and AI can best achieve a powerful digital labor, detailing on implementation and operating challenges. You will also need a combination of driver and irons, you will need RPA tools, and you will need cognitive tools like ABBYY, and you are finally going to need the AI tools like IBM Watson or Google TensorFlow. Reaching the green represents implementing Intelligent Process Automation; the driver is RPA, the irons are the cognitive tools like Abbyy and the putter represents the AI tools like TensorFlow or IBM Watson. Please be informed that when you click the Send button Itransition Group will process your personal data in accordance with our Privacy notice for the purpose of providing you with appropriate information. According to Deloitte’s 2019 Automation with Intelligence report, many companies haven’t yet considered how many of their employees need reskilling as a result of automation.

Because cognitive automation bots are still only trained based on data, these aspects of process automation are more difficult for machines. Cognitive automation works by combining the power of artificial intelligence (AI) and automation to enable systems to perform tasks that typically require human intelligence. This technology uses algorithms to interpret information, make decisions, and execute actions to improve efficiency in various business processes.

Businesses are increasingly adopting cognitive automation as the next level in process automation. “Cognitive automation is not just a different name for intelligent automation and hyper-automation,” said Amardeep Modi, practice director at Everest Group, a technology analysis firm. “Cognitive automation refers to automation cognitive automation tools of judgment- or knowledge-based tasks or processes using AI.” Intelligent automation simplifies processes, frees up resources and improves operational efficiencies through various applications. An insurance provider can use intelligent automation to calculate payments, estimate rates and address compliance needs.

RPA is relatively easier to integrate into existing systems and processes, while cognitive process automation may require more complex integration due to its advanced AI capabilities and the need for handling unstructured data sources. Cognitive process automation starts by processing various types of data, including text, images, and sensor data, using techniques like natural language processing and machine learning. Essentially, cognitive automation within RPA setups allows companies to widen the array of automation scenarios to handle unstructured data, analyze context, and make non-binary decisions.

What is Intelligent Automation: Guide to RPA’s Future in 2024

As mentioned above, cognitive automation is fueled through the use of Machine Learning and its subfield Deep Learning in particular. And without making it overly technical, we find that a basic knowledge of fundamental concepts is important to understand what can be achieved through such applications. 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. Cognitive automation is also starting to enhance operational excellence by complementing RPA bots, conversational AI chatbots, virtual assistants and business intelligence dashboards.

This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed. You can foun additiona information about ai customer service and artificial intelligence and NLP. Most RPA companies have been investing in various ways to build cognitive capabilities but cognitive capabilities of different tools vary of course. The ideal way would be to test the RPA tool to be procured against the cognitive capabilities required by the process you will automate in your company. Even if the RPA tool does not have built-in cognitive automation capabilities, most tools are flexible enough to allow cognitive software vendors to build extensions. “The biggest challenge is data, access to data and figuring out where to get started,” Samuel said. All cloud platform providers have made many of the applications for weaving together machine learning, big data and AI easily accessible.

Cognitive Automation and Medical Supply Chains: Putting Patients First

CIOs will derive the most transformation value by maintaining appropriate governance control with a faster pace of automation. Data mining and NLP techniques are used to extract policy data and impacts of policy changes to make automated decisions regarding policy changes. Processing these transactions require paperwork processing and completing regulatory checks including sanctions checks and proper buyer and seller apportioning. Leverage public records, handwritten customer input and scanned documents to perform required KYC checks. 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.

In addition to simple process bots, companies implementing conversational agents such as chatbots further automate processes, including appointments, reminders, inquiries and calls from customers, suppliers, employees and other parties. Moving up the ladder of enterprise intelligent automation can help companies performing increasingly more complex tasks that don’t always follow the same pattern or flow. Dealing with unstructured data and inputs, fixing and validating data as necessary for context or virtual assistants to help with process development all require more cognitive ability from automation systems.

Also, 32 percent of respondents said they will be implementing it in some form by the end of 2020. Cognitive automation can use AI to reduce the cases where automation gets stuck while encountering different types of data or different processes. For example, AI can reduce the time to recover in an IT failure by recognizing anomalies across IT systems and identifying the root cause of a problem more quickly. This can lead to big time savings for employees who can spend more time considering strategic improvements rather than clarifying and verifying documents or troubleshooting IT errors across complex cloud environments. RPA tools without cognitive capabilities are relatively dumb and simple; should be used for simple, repetitive business processes. IBM Consulting’s extreme automation consulting services enable enterprises to move beyond simple task automations to handling high-profile, customer-facing and revenue-producing processes with built-in adoption and scale.

An infographic offering a comprehensive overview of TCS’ Cognitive Automation Platform. Automation components such as rule engines and email automation form the foundational layer. These are integrated with cognitive capabilities in the form of NLP models, chatbots, smart search and so on to help BFSI organizations expand their enterprise-level automation capabilities to achieve better business outcomes. Conversely, cognitive automation learns the intent of a situation using available senses to execute a task, similar to the way humans learn. It then uses these senses to make predictions and intelligent choices, thus allowing for a more resilient, adaptable system.

Besides conventional yet effective approaches to use case identification, some cognitive automation opportunities can be explored in novel ways. Upgrading RPA in banking and financial services with cognitive technologies presents a huge opportunity to achieve the same outcomes more quickly, accurately, and at a lower cost. Technological and digital advancement are the primary drivers in the modern enterprise, which must confront the hurdles of ever-increasing scale, complexity, and pace in practically every industry. Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity.

RPA and Cognitive Automation differ in terms of, task complexity, data handling, adaptability, decision making abilities, & complexity of integration. Cognitive automation enhances the customer experience by providing accurate responses, round-the-clock support, and personalized interactions. This results in increased customer satisfaction, loyalty, and a positive brand image, ultimately leading to business growth and a competitive advantage in the market.

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. Processing claims is perhaps one of the most labor-intensive tasks faced by insurance company employees and thus poses an operational burden on the company. Many of them have achieved significant optimization of this challenge by adopting cognitive automation tools.

Cognitive automation, therefore, marks a radical step forward compared to traditional RPA technologies that simply copy and repeat the activity originally performed by a person step-by-step. 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.

Document processing automation

First, you should build a scoring metric to evaluate vendors as per requirements and run a pilot test with well-defined success metrics involving the concerned teams. For the next step, enable your team with a comprehensive training and adoption module. When considering a purchase, security and compliance are some factors to keep in mind. For example, in accounts payables, SOX, or the Sarbanes-Oxley Act, ensures that proper attention is paid to the issues affecting accounts payable, including payables risk management.

The COVID-19 pandemic has only expedited digital transformation efforts, fueling more investment within infrastructure to support automation. Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks. While RPA systems follow predefined rules and instructions, cognitive automation solutions can learn from data patterns, adapt to new scenarios, and make intelligent decisions, enhancing their problem-solving capabilities. Claims processing, one of the most fundamental operations in insurance, can be largely optimized by cognitive automation. Many insurance companies have to employ massive teams to handle claims in a timely manner and meet customer expectations.

• Processing these transactions require paperwork processing and completing regulatory checks including sanctions checks and proper buyer and seller apportioning.
• This knowledge-based approach adjusts for the more information-intensive processes by leveraging algorithms and technical methodology to make more informed data-driven business decisions.
• Traditional RPA is mainly limited to automating processes (which may or may not involve structured data) that need swift, repetitive actions without much contextual analysis or dealing with contingencies.
• This digital transformation can help companies of various sectors redefine their future of work and can be marked as a first step toward Industry 5.0.
• Automated process bots are great for handling the kind of reporting tasks that tend to fall between departments.
• Cognitive automation expands the number of tasks that RPA can accomplish, which is good.

Over time, these pre-trained systems can form their own connections automatically to continuously learn and adapt to incoming data. Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization.

In this article, we explore RPA tools in terms of cognitive abilities, what makes them cognitively capable, and which RPA vendors provide such tools. Make your business operations a competitive advantage by automating cross-enterprise and expert work. Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce. Automation will expose skills gaps within the workforce and employees will need to adapt to their continuously changing work environments.

TCS’ Cognitive Automation Platform (see Figure 1) helps BFSI organizations expand their enterprise-level automation capabilities by seamlessly integrating legacy systems, modern technologies, and traditional automation solutions. The platform leverages artificial intelligence (AI), machine learning (ML), computer vision, natural language processing (NLP), advanced analytics, and knowledge management, among others, to create a fully automated organization. However, simply automating rote tasks is not sufficient to deal with the continuous changes those enterprises face. In order to provide greater value, these automation tools need to step up the ladder of cognitive automation, incorporating AI and cognitive technologies to see increased value. Intelligent/cognitive automation tools allow RPA tools to handle unstructured information and make decisions based on complex, unstructured input. Cognitive automation (also called smart or intelligent automation) is an emerging field that augments RPA tools with artificial intelligence (AI) capabilities like optical character recognition (OCR) or natural language processing (NLP).

Automating time-intensive or complex processes requires developing a clear understanding of every step along the way to completing a task whether it be completing an invoice, patient care in hospitals, ordering supplies or onboarding an employee. By enabling the software bot to handle this common manual task, the accounting team can spend more time analyzing vendor payments and possibly identifying areas to improve the company’s cash flow. It’s an AI-driven solution that helps you automate more business and IT processes at scale with the ease and speed of traditional RPA. IBM Cloud Pak® for Automation provide a complete and modular set of AI-powered automation capabilities to tackle both common and complex operational challenges. What seems like the simplest litmus test of customer service revealed a massive failure on every index that matters to customers (response, response time, response information). The cognitive automation solution is pre-trained and configured for multiple BFSI use cases.

Insurance businesses can also experience sudden spikes in claims—think about catastrophic events caused by extreme weather conditions. It’s simply not economically feasible to maintain a large team at all times just in case such situations occur. This is why it’s common to employ intermediaries to deal with complex claim flow processes. Both cognitive automation and intelligent process automation fall within the category of RPA augmented with certain intelligent capabilities, where cognitive automation has come to define a sub-set of AI implementation in the RPA field. As confusing as it gets, cognitive automation may or may not be a part of RPA, as it may find other applications within digital enterprise solutions. Other than that, the most effective way to adopt intelligent automation is to gradually augment RPA bots with cognitive technologies.

AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade.

Middle management can also support these transitions in a way that mitigates anxiety to make sure that employees remain resilient through these periods of change. Intelligent automation is undoubtedly the future of work and companies that forgo adoption will find it difficult to remain competitive in their respective markets. Built using a cloud-first approach, TCS’ platform is API-enabled and available on hyperscalers.

These AI-based tools (UiPath Task Mining and Process Mining, for example) analyze users’ actions and IT systems’ data to suggest processes with automation potential as well as existing gaps and bottlenecks to be addressed with automation. This approach ensures end users’ apprehensions regarding their digital literacy are alleviated, thus facilitating user buy-in. 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. This way, agents can dedicate their time to higher-value activities, with processing times dramatically decreased and customer experience enhanced.

It streamlines operations, reduces manual effort, and accelerates task completion, thus boosting overall efficiency. Cognitive automation is the strategic integration of artificial intelligence (AI) and process automation, aimed at enhancing business outcomes. Cognitive automation techniques can also be used to streamline commercial mortgage processing.

Vendors claim that 70-80% of corporate knowledge tasks can be automated with increased cognitive capabilities. To deal with unstructured data, cognitive bots need to be capable of machine learning and natural language processing. A large part of determining what is effective for process automation is identifying what kinds of tasks require true cognitive abilities. While machine learning has come a long way, enterprise automation tools are not capable of experience, intuition-based judgment or extensive analysis that might draw from existing knowledge in other areas.

For example, in an accounts payable workflow, cognitive automation could transform PDF documents into machine-readable structure data that would then be handed to RPA to perform rules-based data input into the ERP. By automating tasks that are prone to human errors, cognitive automation significantly reduces mistakes, ensuring consistently high-quality output. This is particularly crucial in sectors where precision are paramount, such as healthcare and finance. It can seamlessly integrate with existing systems and software, allowing it to handle large volumes of data and tasks efficiently, making it suitable for businesses of varying sizes and needs. Once the system has made a decision, it automates tasks such as report generation, data entry, and even physical processes in industrial settings, reducing the need for manual intervention.

Only the simplest tools, initially built in 2000s before the explosion of interest in RPA are in this bucket. “The problem is that people, when asked to explain a process from end to end, will often group steps or fail to identify a step altogether,” Kohli said. To solve this problem vendors, including Celonis, Automation Anywhere, UiPath, NICE and Kryon, are developing automated process discovery tools. TCS’ vast industry experience and deep expertise across technologies makes us the preferred partner to global businesses. 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. CIOs also need to address different considerations when working with each of the technologies.

• To solve this problem vendors, including Celonis, Automation Anywhere, UiPath, NICE and Kryon, are developing automated process discovery tools.
• For example, Digital Reasoning’s AI-powered process automation solution allows clinicians to improve efficiency in the oncology sector.
• Cognitive automation can use AI to reduce the cases where automation gets stuck while encountering different types of data or different processes.
• Cognitive process automation starts by processing various types of data, including text, images, and sensor data, using techniques like natural language processing and machine learning.

Itransition offers full-cycle AI development to craft custom process automation, cognitive assistants, personalization and predictive analytics solutions. Automated process bots are great for handling the kind of reporting tasks that tend to fall between departments. If one department is responsible for reviewing a spreadsheet for mismatched data and then passing on the incorrect fields to another department for action, a software agent could easily manage every step for which the department was responsible. “The governance of cognitive automation systems is different, and CIOs need to consequently pay closer attention to how workflows are adapted,” said Jean-François Gagné, co-founder and CEO of Element AI. 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.

Guy Kirkwood, COO & Chief Evangelist at UiPath, and Neil Murphy, Regional Sales Director at ABBYY talk about enhancing RPA with OCR capabilities to widen the scope of automation. Cognitive automation can act as a shield against compliance risks, which has recently become a huge factor. It enables quick and accurate analysis of vast data by identifying patterns and anomalies within the datasets across industries.

Companies want systems to automatically perform reviews on items like contracts to identify favorable terms, consistency in word choice and set up templates quickly to avoid unnecessary exceptions. They are designed to be used by business users and be operational in just a few weeks. For example, Digital Reasoning’s AI-powered process automation solution allows clinicians to improve efficiency in the oncology sector. With the help of deep learning and artificial intelligence in radiology, clinicians can intelligently assess pathology and radiology reports to understand the cancer cases presented and augment subsequent care workflows accordingly. 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. Addressing the challenges most often faced by network operators empowers predictive operations over reactive solutions.