What is Intelligent Automation?

As enterprises continue to invest and rely on technologies, intelligent automation services will continue to prove powerful additions to the enterprise technology landscape. Still, the enterprise requires humans to choose and apply automation techniques to specific tasks — for now. One area currently under development is the ability for machines to autonomously discover and optimize processes within the enterprise. Some automation tools have started to combine automation and cognitive technologies to figure out how processes are configured or actually operating.

Currently there is some confusion about what RPA is and how it differs from cognitive automation. To manage this enormous data-management demand and turn it into actionable planning and implementation, companies must have a tool that provides enhanced market prediction and visibility. With light-speed jumps in ML/AI technologies every few months, it’s quite a challenge keeping up with the tongue-twisting terminologies itself aside from understanding the depth of technologies. To make matters worse, often these technologies are buried in larger software suites, even though all or nothing may not be the most practical answer for some businesses. This shift of models will improve the adoption of new types of automation across rapidly evolving business functions.

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.

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 cognitive automation tools 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.

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.

Unlocking Business Agility & Future-ready Operations with TCS’ CBO

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 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.

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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.

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.

As Digital Transformation Accelerates, Adoption of Intelligent Technologies On The Rise

RPA is typically programmed upfront but can break when the applications it works with change. Cognitive automation requires more in-depth training and may need updating as the characteristics of the data set evolve. But at the end of the day, both are considered complementary rather than competitive approaches to addressing different aspects of automation. KlearStack is an AI-based platform that achieves intelligent data extraction from unstructured documents. It uses AI algorithms to make intelligent decisions based on the processed data, enabling it to categorize information, make predictions, and take actions as needed.

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.

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.

VIDEO: Embracing the Future of Work In The Era of Cognitive Automation

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.

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.

Cofounder and CEO of Docsumo, a document AI platform that helps enterprises read, validate and analyze unstructured data. When it comes to automation, tasks performed by simple workflow automation bots are fastest when those tasks can be carried out in a repetitive format. Processes that follow a simple flow and set of rules are most effective for yielding immediately effective results with nonintelligent bots. For example, employees who spend hours every day moving files or copying and pasting data from one source to another will find significant value from task automation. Yet the way companies respond to these shifts has remained oddly similar–using organizational data to inform business decisions, in the hopes of getting the right products in the right place at the best time to optimize revenue. The human element–that expert mind that is able to comprehend and act on a vast amount of information in context–has remained essential to the planning and implementation process, even as it has become more digital than ever.

By leaving routine tasks to robots, humans can squeeze the most value from collaboration and emotional intelligence. This is why robotic process automation consulting is becoming increasingly popular with enterprises. Cognitive automation is a win-win situation for many companies looking to elevate customer experiences and team collaboration. Research from Accenture for the retail banking sector indicates that personalization efforts for customers with the help of cognitive automation tools can increase revenue by 6%.

And they are automatically able to suggest and modify processes to improve overall flow, learn from itself to figure out better ways to handle process flow and conduct automatic orchestration of multiple bots to optimize processes. Cognitive automation utilizes data mining, text analytics, artificial intelligence (AI), machine learning, and automation to help employees with specific analytics tasks, without the need for IT or data scientists. Cognitive automation simulates human thought and subsequent actions to analyze and operate with accuracy and consistency.

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.

All of these create chaos through inventory mismatches, ongoing product research and development, market entry, changing customer buying patterns, and more. This occurs in hyper-competitive industry sectors that are being constantly upset by startups and entrepreneurs who are more adaptable (or simply lucky) in how they meet ongoing consumer demand. We won’t go much deeper into the technicalities of Machine Learning here but if you are new to the subject and want to dive into the matter, have a look at our beginner’s guide to how machines learn.

TCS’ Cognitive Automation Platform uses artificial intelligence (AI) to drive intelligent process automation across front- and back offices. It’s a suite of business and technology solutions that seamlessly integrate with existing enterprise solutions and offer easy plug and play features. TCS leverages its deep domain knowledge to contextualize the platform to a company’s unique requirements. Another viewpoint lies in thinking about how both approaches complement process improvement initiatives, said James Matcher, partner in the technology consulting practice at EY, a multinational professional services network. Process automation remains the foundational premise of both RPA and cognitive automation, by which tasks and processes executed by humans are now executed by digital workers.

Spending on cognitive-related IT and business services will be more than $3.5 billion and will enjoy a five-year CAGR of nearly 70%. Deloitte explains how their team used bots with natural language processing capabilities to solve this issue. You can also check our article on intelligent automation in finance and accounting for more examples.

However, there are times when information is incomplete, requires additional enhancement or combines with multiple sources to complete a particular task. For example, customer data might have incomplete history that is not required in one system, but it’s required in another. The ability to capture greater insight from unstructured data is currently at the forefront of any intelligent automation task. 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, 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.

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.

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.

Solutions

This article will explain to you in detail which cognitive automation solutions are available for your company and hopefully guide you to the most suitable one according to your needs. 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. “We see a lot of use cases involving scanned documents that have to be manually processed one by one,” said Sebastian Schrötel, vice president of machine learning and intelligent robotic process automation at SAP. 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. Within a company, cognitive process automation streamlines daily operations for employees by automating repetitive tasks.

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.

Certain tasks are currently best suited for humans, such as those that require reading or understanding text, making complex decisions, or aspects of recognition or pattern matching. In addition, interactive tasks that require collaboration with other humans and rely on communication skills and empathy are difficult to automate with unintelligent tools. 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 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.

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.

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.

• Most RPA companies have been investing in various ways to build cognitive capabilities but cognitive capabilities of different tools vary of course.
• Adopting a digital operating model enables companies to scale and grow in an increasingly competitive environment while exceeding market expectations.
• Organizations adding enterprise intelligent automation are putting the power of cognitive technology to work addressing the more complicated challenges in the corporate environment.
• Some automation tools have started to combine automation and cognitive technologies to figure out how processes are configured or actually operating.
• Automation of various tasks helps businesses to save cost, reduce manual labor, optimize resource allocation, and minimize operational expenses.

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.

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.