What is Cognitive Automation An Introduction

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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 (FNOL). When it comes to FNOL, there is a high variability in data formats and a high rate of exceptions.

While deterministic can be seen as low-hanging fruits, the real value lies in cognitive automation. “Both RPA and cognitive automation enable organizations to free employees from tedium and focus on the work that truly matters. While cognitive automation offers a greater potential to scale automation throughout the enterprise, RPA provides the basic foundation for automation as a whole. Relates to computers learning on its own from a large amount of data without the need to be specifically programmed. Prediction for doctors, fraud detection in banks, sentiment analysis like favourite movie recommendation on Netflix, surge pricing on Uber are all real-world machine learning application. This technology is behind driverless cars to identify a stop signal, facial recognition in today’s mobile phones.

How Does Cognitive Automation Work?

TalkTalk received a solution from Splunk that enables the cognitive solution to manage the entire backend, giving customers access to an immediate resolution to their issues. Identifying and disclosing any network difficulties has helped TalkTalk enhance its network. As a result, they have greatly decreased the frequency of major incidents and increased uptime. Deliveries that are delayed are the worst thing that can happen to a logistics operations unit. The parcel sorting system and automated warehouses present the most serious difficulty.

A cognitive automation solution is a positive development in the world of automation. A cognitive automation solution for the retail industry can guarantee that all physical and online shop systems operate properly. As a result, the buyer has no trouble browsing and buying the item they want. Aera releases the full power of intelligent data within the modern enterprise, augmenting business operations while keeping employee skills, knowledge, and legacy expertise intact and more valuable than ever in a new digital era.

Traditional RPA usually has challenges with scaling and can break down under certain circumstances, such as when processes change. However, cognitive automation can be more flexible and adaptable, thus leading to more automation. “RPA is a technology that takes the robot out of the human, whereas cognitive automation is the putting of the human into the robot,” said Wayne Butterfield, a director at ISG, a technology research and advisory firm. Data mining and NLP techniques are used to extract policy data and impacts of policy changes to make automated decisions regarding policy changes. Leverage public records, handwritten customer input and scanned documents to perform required KYC checks.

It is frequently referred to as the union of cognitive computing and robotic process automation (RPA), or AI. 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). Cognitive automation describes diverse ways of combining artificial intelligence (AI) and process automation capabilities to improve business outcomes.

AI and ML are fast-growing advanced technologies that, when augmented with automation, can take RPA to the next level. Traditional RPA without IA’s other technologies tends to be limited to automating simple, repetitive processes involving structured data. IA or cognitive automation has a ton of real-world applications across sectors and departments, from automating HR employee onboarding and payroll to financial loan processing and accounts payable.

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. Cognitive automation, or IA, combines artificial intelligence with robotic process automation to deploy intelligent digital workers that streamline workflows and automate tasks. It can also include other automation approaches such as machine learning (ML) and natural language processing (NLP) to read and analyze data in different formats.

Cognitive RPA solutions by RPA companies

Typically, organizations have the most success with cognitive automation when they start with rule-based RPA first. After realizing quick wins with rule-based RPA and building momentum, the scope of automation possibilities can be broadened by introducing cognitive technologies. What’s important, rule-based RPA helps with process standardization, which is often critical to the integration of AI in the workplace and in the corporate workflow. 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.

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 this article, we explore RPA tools in terms of cognitive abilities, what makes them cognitively capable, and which RPA vendors provide such tools. Learn how to optimize your employee onboarding process through implementing AI automation, saving costs and hours of productive time. This article dispels fear and provides tools to control AI-enabled automation.

Therefore, cognitive automation knows how to address the problem if it reappears. With time, this gains new capabilities, making it better suited to handle complicated problems and a variety of exceptions. ServiceNow’s onboarding procedure starts before the new employee’s first work day.

Make automated decisions about claims based on policy and claim data and notify payment systems. While these are efforts by major RPA vendors to augment their bots, RPA companies can not build custom AI solutions for each process. Therefore, companies rely on AI focused companies like IBM and niche tech consultancy firms to build more sophisticated automation services. You can check our article where we discuss the differences between RPA and intelligent / cognitive automation.

The undeniable potential of AI: How will AI transform various businesses?

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. Intending to enhance Bookmyshow‘s client interactions, Splunk has provided them with a cognitive automation solution. Due to the extensive use of machinery at Tata Steel, problems frequently cropped up.

Cognitive automation maintains regulatory compliance by analyzing and interpreting complex regulations and policies, then implementing those into the digital workforce’s tasks. It also helps organizations identify potential risks, monitor compliance adherence and flag potential fraud, errors or missing information. Cognitive automation has the potential to completely reorient the work environment by elevating efficiency and empowering organizations and their people to make data-driven decisions quickly and accurately. The integration of these components creates a solution that powers business and technology transformation. He focuses on cognitive automation, artificial intelligence, RPA, and mobility. The coolest thing is that as new data is added to a cognitive system, the system can make more and more connections.

You can also check out our success stories where we discuss some of our customer cases in more detail. The way RPA processes data differs significantly from cognitive automation in several important ways. However, if you are impressed by them and implement them in your business, first, you should know the differences between cognitive automation and RPA.

• Thinking about cognitive automation as a business enabler rather than a technology investment and applying a holistic approach with clearly defined goals and vision are fundamental prerequisites for cognitive automation implementation success.
• Prediction for doctors, fraud detection in banks, sentiment analysis like favourite movie recommendation on Netflix, surge pricing on Uber are all real-world machine learning application.
• Cognitive automation can uncover patterns, trends and insights from large datasets that may not be readily apparent to humans.
• “Cognitive automation multiplies the value delivered by traditional automation, with little additional, and perhaps in some cases, a lower, cost,” said Jerry Cuomo, IBM fellow, vice president and CTO at IBM Automation.

If any are found, it simply adds the issue to the queue for human resolution. Cognitive automation involves incorporating an additional layer of AI and ML. 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. The issues faced by Postnord were addressed, and to some extent, reduced, by Digitate‘s ignio AIOps Cognitive automation solution. The automation solution also foresees the length of the delay and other follow-on effects.

Regulatory compliance and risk management

“To achieve this level of automation, CIOs are realizing there’s a big difference between automating manual data entry and digitally changing how entire processes are executed,” Macciola said. Anthony Macciola, chief innovation officer at Abbyy, said two of the biggest benefits of cognitive automation initiatives have been creating exceptional CX and driving operational excellence. In CX, cognitive automation is enabling the development of conversation-driven experiences. He expects cognitive automation to be a requirement for virtual assistants to be proactive and effective in interactions where conversation and content intersect. As the digital agenda becomes more democratized in companies and cognitive automation more systemically applied, the relationship and integration of IT and the business functions will become much more complex. “RPA is a great way to start automating processes and cognitive automation is a continuum of that,” said Manoj Karanth, vice president and global head of data science and engineering at Mindtree, a business consultancy.

This assists in resolving more difficult issues and gaining valuable insights from complicated data. In this situation, if there are difficulties, the solution checks them, fixes them, or, as soon as possible, forwards the problem to a human operator to avoid further delays. Once implemented, the solution aids in maintaining a record of the equipment and stock condition. Every time it notices a fault or a chance that an error will occur, it raises an alert. Attempts to use analytics and create data lakes are viable options that many companies have adopted to try and maximize the value of their available data.

“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 of judgment- or knowledge-based tasks or processes using AI.” 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 could involve the use of a variety of tools such as RPA, AI, process mining, business process management and analytics, Modi said. Traditionally cognitive capabilities were the realm of data analytics and digitization. Robotic Process Automation (RPA) works best if you have a structured process, involves a large volume of data and is rule based. If this process involves complex, unstructured data that requires human intervention then Cognitive automation is the answer. These skills, tools and processes can make more types of unstructured data available in structured format, which enables more complex decision-making, reasoning and predictive analytics. Cognitive automation has a place in most technologies built in the cloud, said John Samuel, executive vice president at CGS, an applications, enterprise learning and business process outsourcing company.

Digitate‘s ignio, a cognitive automation technology, helps with the little hiccups to keep the system functioning. Change used to occur on a scale of decades, with technology catching up to support industry shifts and market demands. Sentiment analysis or ‘opinion mining’ is a technique used in cognitive automation to determine the sentiment expressed in input sources such as textual data. NLP and ML algorithms classify the conveyed emotions, attitudes or opinions, determining whether the tone of the message is positive, negative or neutral.

They should also agree on whether the cognitive automation tool should empower agents to focus more on proactively upselling or speeding up average handling time. 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. In sectors with strict regulations, such as finance and healthcare, cognitive automation assists professionals by identifying potential risks. It ensures compliance with industry standards, and providing a reliable framework for handling sensitive data, fostering a sense of security among stakeholders.

“One of the biggest challenges for organizations that have embarked on automation initiatives and want to expand their automation and digitalization footprint is knowing what their processes are,” Kohli said. 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.

The critical difference is that RPA is process-driven, whereas AI is data-driven. RPA bots can only follow the processes defined by an end user, while AI bots use machine learning to recognize patterns in data, in particular unstructured data, and learn over time. Put differently, AI is intended to simulate human intelligence, while RPA is solely for replicating human-directed tasks. While the use of artificial intelligence and RPA tools minimize the need for human intervention, the way in which they automate processes is different. RPA primarily deals with structured data and predefined rules, whereas cognitive automation can handle unstructured data, making sense of it through natural language processing and machine learning. “The ability to handle unstructured data makes intelligent automation a great tool to handle some of the most mission-critical business functions more efficiently and without human error,” said Prince Kohli, CTO of Automation Anywhere.

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. 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 automation can also use AI to support more types of decisions as well. For example, a cognitive automation application might use a machine learning algorithm to determine an interest rate as part of a loan request.

Cognitive automation tools are relatively new, but experts say they offer a substantial upgrade over earlier generations of automation software. Now, IT leaders are looking to expand the range of cognitive automation use cases they support in the enterprise. According to IDC, AI use cases that will see the most investment this year are automated customer service agents, sales process recommendation and automation and automated threat intelligence and prevention systems.

Use case 3: Attended automation

In 2020, Gartner reportedOpens a new window that 80% of executives expect to increase spending on digital business initiatives in 2022. In fact, spending on cognitive and AI systems will reach $77.6 billion in 2022, according to a report by IDCOpens a new window . Findings from both reports testify that the pace of cognitive automation and RPA is accelerating business processes more than ever before.

We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research. Robotic process automation is often mistaken for artificial intelligence (AI), but the two are distinctly different.

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. Cognitive automation is the current focus for most RPA companies’ product teams.

RPA automates routine and repetitive tasks, which are ordinarily carried out by skilled workers relying on basic technologies, such as screen scraping, macro scripts and workflow automation. RPA performs tasks with more precision and accuracy by using software robots. But when complex data is involved it can be very challenging and may ask for human intervention. Cognitive process automation can automate complex cognitive tasks, enabling faster and more accurate data and information processing. This results in improved efficiency and productivity by reducing the time and effort required for tasks that traditionally rely on human cognitive abilities.

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 is an umbrella term for software solutions that leverage cognitive technologies to emulate human intelligence to perform specific tasks. Key distinctions between robotic process automation (RPA) vs. cognitive automation include how they complement human workers, the types of data they work with, the timeline for projects and how they are programmed. Deloitte explains how their team used bots with natural language processing capabilities to solve this issue.

Like our brains’ neural networks creating pathways as we take in new information, cognitive automation makes connections in patterns and uses that information to make decisions. IBM Cloud Pak® for Automation provide a complete and modular set of AI-powered automation capabilities to tackle both common and complex operational challenges. 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. 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.

He sees cognitive automation improving other areas like healthcare, where providers must handle millions of forms of all shapes and sizes. Employee time would be better spent caring for people rather than tending to processes and paperwork. Many organizations have also successfully automated their cognitive automation definition KYC processes with RPA. KYC compliance requires organizations to inspect vast amounts of documents that verify customers’ identities and check the legitimacy of their financial operations. RPA bots can successfully retrieve information from disparate sources for further human-led KYC analysis.

• The automation footprint could scale up with improvements in cognitive automation components.
• Change used to occur on a scale of decades, with technology catching up to support industry shifts and market demands.
• It ensures accurate responses to queries, providing personalized support, and fostering a sense of trust in the company’s services.

Cognitive automation may also play a role in automatically inventorying complex business processes. Employee onboarding is another example of a complex, multistep, manual process that requires a lot of HR bandwidth and can be streamlined with cognitive automation. “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.

The organization can use chatbots to carry out procedures like policy renewal, customer query ticket administration, resolving general customer inquiries at scale, etc. 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. Intelligent virtual assistants and chatbots provide personalized and responsive support for a more streamlined customer journey. These systems have natural language understanding, meaning they can answer queries, offer recommendations and assist with tasks, enhancing customer service via faster, more accurate response times.

According to experts, cognitive automation falls under the second category of tasks where systems can learn and make decisions independently or with support from humans. Other than that, the most effective way to adopt intelligent automation is to gradually augment RPA bots with cognitive technologies. In an enterprise context, RPA bots are often used to extract and convert data. 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.

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. 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. An example of cognitive automation is in the field of customer support, where a company uses AI-powered chatbots to provide assistance to customers.

It handles all the labor-intensive processes involved in settling the employee in. These include setting up an organization account, configuring an email address, granting the required system access, etc. Cognitive automation represents a range of strategies that enhance automation’s ability to gather data, make decisions, and scale 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. 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.