• 16 Jun, 2023
• James Clyde
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What is Cognitive Automation? Evolving the Workplace

What is Cognitive Automation and What is it NOT?

His company has been working with enterprises to evaluate how they can use cognitive automation to improve the customer journey in areas like security, analytics, self-service troubleshooting and shopping assistance. Cognitive automation is an extension of existing robotic process automation (RPA) technology. Machine learning enables bots to remember the best ways of completing tasks, while technology like optical character recognition increases the data formats with which bots can interact. Cognitive automation adds a layer of AI to RPA software to enhance the ability of RPA bots to complete tasks that require more knowledge and reasoning.

Also, RPA does not need coding because it relies on framework configuration and deployment. Whereas, cognitive automation relies on machine learning and requires extensive programming knowledge. In most scenarios, organizations can only generate meaningful savings if the last mile of such processes can be handled . Cognitive automation has the ability to mimic human thoughts to manage and analyze large volumes of unstructured data with much greater speed, accuracy, and consistency much like humans or even greater.

The automation solution also foresees the length of the delay and other follow-on effects. As a result, the company can organize and take the required steps to prevent the situation. 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.

This is a branch of AI that addresses the interactions between humans and computers with natural language. Generally speaking, RPA can be applied to 60% of a business’s activities. In banking and finance, RPA can be used for a wide range of processes such as Branch activities, underwriting and loan processing, and more. 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.

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. Cognitive automation can perform high-value tasks such as collecting and interpreting diagnostic results, suggesting database treatment options to physicians, dispensing drugs and more. Cognitive automation offers cognitive input to humans working on specific tasks adding to their analytical capabilities.

This implies a significant decrease in false positives and an overall enhanced reliability of autonomous transaction monitoring. ML-based cognitive automation tools make decisions based on the historical outcomes of previous alerts, current account activity, and external sources of information, such as customers’ social media. 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. Insurance businesses can also experience sudden spikes in claims—think about catastrophic events caused by extreme weather conditions.

The integration of different AI features with RPA helps organizations extend automation to more processes, making the most of not only structured data, but especially the growing volumes of unstructured information. Unstructured information such as customer interactions can be easily analyzed, processed and structured into data useful for the next steps of the process, such as predictive analytics, for example. You can foun additiona information about ai customer service and artificial intelligence and NLP. According to IDC, in 2017, the largest area of AI spending was cognitive applications.

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 what is cognitive automation the majority of FNOL-related tasks, making a prime use case for RPA in insurance. Businesses are increasingly adopting cognitive automation as the next level in process automation. These six use cases show how the technology is making its mark in the enterprise.

Enhancing the human connection

These processes can be any tasks, transactions, or activities unrelated to the software system and required to deliver any solution with a human touch. Itransition offers full-cycle AI development to craft custom process automation, cognitive assistants, personalization and predictive analytics solutions. Processors must retype the text or use standalone optical character recognition tools to copy and paste information from a PDF file into the system for further processing. Cognitive automation uses technologies like OCR to enable automation so the processor can supervise and take decisions based on extracted and persisted information. Karev said it’s important to develop a clear ownership strategy with various stakeholders agreeing on the project goals and tactics. For example, if there is a new business opportunity on the table, both the marketing and operations teams should align on its scope.

As organizations in every industry are putting cognitive automation at the core of their digital and business transformation strategies, there has been an increasing interest in even more advanced capabilities and smart tools. Cognitive automation typically refers to capabilities offered as part of a commercial software package or service customized for a particular use case. For example, an enterprise might buy an invoice-reading service for a specific industry, which would enhance the ability to consume invoices and then feed this data into common business processes in that industry. The healthcare industry deals with streams of unstructured data on a daily basis. Similar to how cognitive automation can boost efficiency in orchestrating a vast amount of data from disparate locations in retail, it can collect and analyze medical data from multiple sources in healthcare as well.

As companies streamline business processes, there’s a significant opportunity to automate cognitive activities. Cognitive automation is an extension of RPA and a step toward hyper-automation and intelligent automation. The process entails automating judgment or knowledge-based tasks or processes using AI. With functionalities limited to structured data and simple rules-based processes, RPA fails to offer a 100% automation solution.

RPA bots can also work around the clock, nonstop, much faster, and with 100% accuracy and precision. 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. Since cognitive automation can analyze complex data from various sources, it helps optimize processes.

Digitizing Your Organization’s Contracting Process

Robotics, also known as robotic process automation, or RPA, refers to the hand work – entering data from one application to another. RPA is the right solution if your process involves structured, large amounts of data and is strictly rule-based. Cognitive automation also improves business quality by making processes more efficient. He focuses on cognitive automation, artificial intelligence, RPA, and mobility.

Digitate‘s ignio, a cognitive automation technology, helps with the little hiccups to keep the system functioning. Cognitive automation represents a range of strategies that enhance automation’s ability to gather data, make decisions, and scale automation. It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. This integration leads to a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience.

This creates a whole new set of issues that an enterprise must confront. 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. Generally, organizations start with the basic end using RPA to manage volume and work their way up to cognitive and automation to handle both volume and complexity. It is a common method of digitizing printed texts so they can be electronically edited, searched, displayed online, and used in machine processes such as text-to-speech, cognitive computing and more. Agents no longer have to access multiple systems to get all of the information they need resulting in shorter calls and improve customer experience.

This approach led to 98.5% accuracy in product categorization and reduced manual efforts by 80%. 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. Over time, these pre-trained systems can form their own connections automatically to continuously learn and adapt to incoming data. RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned.

The proliferation of artificial intelligence out there is vast and it’s important to know that not all AI is built the same. Although bots are ‘taught’ their specialisations, they are also all ‘born’ to different things. With this in mind, we thought we would take a moment to distinguish the difference between the more commonly recognised (but probably not understood) AI technology of cognitive automation and the burgeoning RPA intelligence.

Imagine a technology that can help a business better understand, predict and impact the needs and wants of its customers. Well, that technology is cognitive automation because the added layer of AI and machine learning allows it to extend the boundaries of what is possible with traditional RPA. Having emerged about 20 years ago, RPA is a cost-effective solution for businesses wanting to pursue innovation without having to pay heavily to test new ideas. It can also be implemented more quickly than traditional automation systems, freeing up time for employees to tackle an increased number of cognitive and complex tasks.

As we get to the business end of the automation tool, let’s take a quick peek at the application areas where CRPA has shown great promise. Cognitive automation boosts the speed and accuracy of computer-generated responses. Indeed, cognitive processes now account for nearly 20% of service desk interactions. The following factors contribute to cognitive automation being the next significant improvement for enterprise-level operations. When it comes to repetition, they are tireless, reliable, and hardly susceptible to attention gaps. By leaving routine tasks to robots, humans can squeeze the most value from collaboration and emotional intelligence.

This frees up employees to focus on more complex tasks, such as resolving customer complaints. Having delivered significant business outcomes in terms of precision, accuracy, and speed, the automation arena is getting smarter and smarter every day. Do note that cognitive assistance is not a different kind of technology, per se, separate from deep learning or GOFAI. 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.

This could be a crucial advancement in HR processes as the ongoing pandemic has disrupted the routine procedure of onboarding employees. Cognitive automation tools can simplify the onboarding process for new hires that may start their first days outside of the office and provide the support needed for new employees joining the organization. RPA encompasses software that can be easily programmed to perform basic tasks across applications and thus help eliminate mundane, repetitive tasks completed by humans.

Cognitive automation uses specific AI techniques that mimic the way humans think to perform non-routine tasks. It analyses complex and unstructured data to enhance human decision-making and performance. Since cognitive automation encompasses any automation technology, it includes a multitude of skills and highlights such as machine learning, natural language processing, speech synthesis, computer vision, and analytics. The key highlight of cognitive automation is that a cognitive solution could handle more complex problems and inputs.

You can also check out our success stories where we discuss some of our customer cases in more detail. You can check our article where we discuss the differences between RPA and intelligent / cognitive automation. Given the capabilities of both text and speech processing, the ubiquity of RPA in business will only continue to expand and expand rapidly.

Data mining and NLP techniques are used to extract policy data and impacts of policy changes to make automated decisions regarding policy changes. While chatbots are gaining popularity, their impact is limited by how deeply integrated they are into your company’s systems. For example, if they are not integrated into the legacy billing system, a customer will not be able to change her billing period through the chatbot. Cognitive automation allows building chatbots that can make changes in other systems with ease. Additionally, large RPA providers have built marketplaces so developers can submit their cognitive solutions which can easily be plugged into RPA 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.

By fostering curiosity and committing to life-long learning, we can be a valuable part of cognitive automation systems built on AI. Intelligent technologies like artificial intelligence and cognitive automation can help large enterprises coping with modern disruptions thrive. Aera Technology CEO Frederic Laluyaux joins an all-star panel to discuss the future of complex data, analytical solutions and cognitive automation. With supply chain management more complex and unpredictable than ever before, it’s time to move away from RPA and toward intelligent technologies. In particular, it isn’t a magic wand that you can wave to become able to solve problems far beyond what you engineered or to produce infinite returns. We’ve invested about $100B in the field over the past 10 years — roughly half of the inflation-adjusted cost of the Apollo program.

As Digital Transformation Accelerates, Intelligent Tech On The Rise

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. Unlike other types of AI, such as machine learning, or deep learning, cognitive automation solutions imitate the way humans think. This means using technologies such as natural language processing, image processing, pattern recognition, and — most importantly — contextual analyses to make more intuitive leaps, perceptions, and judgments. RPA exists to perform mundane or manual tasks more reliably, quickly and repeatedly compared to their human counterparts. It is a proven technology used across various industries – be it finance, retail, manufacturing, insurance, telecom, and beyond.

Accurate, fast decisions is the heart of cognitive automation – the challenge is less about available technology and more about executive buy-in. Executives from Unilever, Ernst & Young and Aera Technology come together to discuss the future of decisions and cognitive automation. AI and human intelligence are working together for improved data management, decreasing hiring problems, and more. Cognitive automation is a blending of machine intelligence with automation processes on all levels of corporate performance.

Some of the capabilities of cognitive automation include self-healing and rapid triaging. Manual duties can be more than onerous in the telecom industry, where the user base numbers millions. A cognitive automated system can immediately access the customer’s queries and offer a resolution based on the customer’s inputs. A new connection, a connection renewal, a change of plans, technical difficulties, etc., are all examples of queries. Due to the extensive use of machinery at Tata Steel, problems frequently cropped up.

If not, it alerts a human to address the mechanical problem as soon as possible to minimize downtime. The issues faced by Postnord were addressed, and to some extent, reduced, by Digitate‘s ignio AIOps Cognitive automation solution. Let’s see some of the cognitive automation examples for better understanding. IBM Cloud Pak® for Automation provide a complete and modular set of AI-powered automation capabilities to tackle both common and complex operational challenges. Both RPA and cognitive automation allow businesses to be smarter and more efficient.

From hyperautomation to low-code platforms and increased focus on security, learn about the latest developments shaping the world of automation. Niels Van Hove explains how to achieve “lights out” or autonomous supply chain planning. Michael Krigsman talks to GE Digital about the powerful impacts of developing digital twins. Cognitive Automation empowers workers, transforming them into super-humans able to do the work only humans can do.

These tasks can be handled by using simple programming capabilities and do not require any intelligence. Cognitive automation combined with RPA’s qualities imports an extra mile of composure; contextual adaptation. For instance, in the healthcare industry, cognitive automation helps providers better understand and predict the impact of their patients health. It provides additional free time for employees to do more complex and cognitive tasks and can be implemented quickly as opposed to traditional automation systems. 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.

After their successful implementation, companies can expand their data extraction capabilities with AI-based tools. 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. In this case, bots are used at the beginning and the end of the process. 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.

• Cognitive automation uses technologies like OCR to enable automation so the processor can supervise and take decisions based on extracted and persisted information.
• Autonomous delivery service, surveillance of algorithms, AI outperforming humans, and the phone of the future.
• Some popular cognitive automation tools include UiPath, Automation Anywhere, and Blue Prism.
• With Comidor Document Analyser Models, enterprises can scan documents such as invoices and create digital copies.
• RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned.

By augmenting RPA solutions with cognitive capabilities, companies can achieve higher accuracy and productivity, maximizing the benefits of RPA. Cognitive automation solutions are pre-trained to automate specific business processes and require less data before they can make an impact. This data can also be easily analyzed, processed, and structured into useful data for the next step in the business process. Cognitive process automation can automate complex cognitive tasks, enabling faster and more accurate data and information processing.

Intelligent technologies will help create better employee engagement, reduce knowledge loss and increase creativity. Top reads about intelligent technologies and how they’re changing the present—and the future. AI and other intelligent technologies can help precent financial losses across every type of business. The top reads this week about intelligent technologies and how they’re changing the present—and the future.

While Robotic Process Automation(RPA) takes care of paper-intensive tasks, cognitive automation offers intelligence to information-intensive processes by leveraging machine learning algorithms and other technological approaches. The high-end automation technology is a giant leap in the automation journey, extending and improving the range of processes within an organization and thereby gaining cost savings and customer satisfaction in terms of accuracy. Some popular cognitive automation tools include UiPath, Automation Anywhere, and Blue Prism. These tools use AI and machine learning algorithms to identify patterns in data and automate repetitive tasks. By automating routine tasks, cognitive automation helps businesses save time and money, increase productivity, and improve accuracy.

As the robotic software is being integrated with human-like intelligence, the onus of performing a task is moved to the cognitive tools. That being said, the introduction of CRPA does not equate to the negligence of the human workforce. In fact, the truth is advanced automation tools like CRPA compliments the responsibility and demand for human cognition. Despite possessing the utmost sophistication of AI, the technology may fall short to the complexities of the human brain. In short – the onus is on the technology, but the criticality lies in the manual resources. With the amalgamation of Artificial Intelligence and robotic software, cognitive automation, or intelligent automation can perform more complex tasks that fit the bill of the expectations set by the business leaders.