What is Cognitive Automation? How It Can Transform Your Business AI-Powered Automation

The CoE, leveraging RPA tools, identifies and prioritizes processes suitable for automation based on complexity, volume, and ROI potential criteria. Each technology contributes uniquely to cognitive automation, enhancing overall efficiency, reducing errors, and scaling complex operations that combine structured and unstructured data. IA is capable of advanced data analytics techniques to process and interpret large volumes of data quickly and accurately.

It goes beyond automating repetitive and rule-based tasks and handles complex tasks that require human-like understanding and decision-making. By leveraging NLP, machine learning algorithms, and cognitive reasoning, cognitive automation solutions offer a symphony of capabilities that revolutionize how businesses operate. 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.

When there is a sufficient convergence of physical and cognitive symptoms, it leads to the manifestation of physical frailty and/or cognitive decline phenotypes. While physical frailty has been recognized as a clinical entity for some time, the concept of cognitive frailty (CF) is now gaining increasing attention in the geriatrics research community. CF refers to the co-occurrence of physical frailty and cognitive impairment in older adults, which has been suggested as a potential precursor to both dementia and adverse physical outcomes. However, this condition represents a challenge for researchers and clinicians, as there remains a lack of consensus regarding the definition and diagnostic criteria for CF, which has limited its utility. Here, using insights from both the physical frailty literature and cognitive science research, we describe emerging research on CF.

OCR technology is designed to recognize and extract text from images or documents. Intelligent data capture in cognitive automation involves collecting information from various sources, such as documents or images, with no human intervention. 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 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.

“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. When determining what tasks to automate, enterprises should start by looking at whether the process workflows, tasks and processes can be improved or even eliminated prior to automation. There are some obvious things to automate within an enterprise that provide short-term ROI — repetitive, boring, low-value busywork, like reporting tasks or data management or cleanup, that can easily be passed on to a robot for process automation. With disconnected processes and customer data in multiple systems, resolving a single customer service issue could mean accessing dozens of different systems and sources of data.

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. According to IDC, in 2017, the largest area of AI spending was cognitive applications. This includes applications that automate processes that automatically learn, discover, and make recommendations or predictions. Overall, cognitive software platforms will see investments of nearly $2.5 billion this year. 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%.

RPA or cognitive automation: Which one is better?

More sophisticated cognitive automation that automates decision processes requires more planning, customization and ongoing iteration to see the best results. The company implemented a cognitive automation application based on established global standards to automate categorization at the local level. The incoming data from retailers and vendors, which consisted of multiple formats such as text and images, are now processed using cognitive automation capabilities. The local datasets are matched with global standards to create a new set of clean, structured data. This approach led to 98.5% accuracy in product categorization and reduced manual efforts by 80%.

In addition, cognitive automation tools can understand and classify different PDF documents. This allows us to automatically trigger different actions based on the type of document received. It infuses a cognitive ability and can accommodate the automation of business processes utilizing large volumes of text and images. 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.

The Cambridge Analytica scandal showed how social media data can be exploited to develop psychological profiles for political microtargeting. Yet, while destabilizing, traditional disinformation campaigns on social media have still shown mixed results in achieving strategic goals. The ability of AI systems to learn and instantly adapt their messages to their interlocutors will enable a new level of microtargeting and personalized disinformation. These systems require proper setup of the right data sets, training and consistent monitoring of the performance over time to adjust as needed. These technologies are coming together to understand how people, processes and content interact together and in order to completely reengineer how they work together.

From your business workflows to your IT operations, we got you covered with AI-powered automation. For example, an attended bot can bring up relevant data on an agent’s screen at the optimal moment in a live customer interaction to help the agent upsell the customer to a specific product. Cognitive warfare capabilities will only continue to advance and change not only the nature of conflict but how we are able to respond to it. Taking action now might help create a technological landscape that is a benefit, rather than a burden (or worse) to human autonomy and society at large. It’s also important to plan for the new types of failure modes of cognitive analytics applications.

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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. 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.

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 is why robotic process automation consulting is becoming increasingly popular with enterprises.

Comparing RPA vs. cognitive automation is “like comparing a machine to a human in the way they learn a task then execute upon it,” said Tony Winter, chief technology officer at QAD, an ERP provider. Text Analytics API performs sentiment analysis, key phrase extraction, language detection, and named entity recognition on textual data, facilitating tasks such as social media monitoring, customer feedback analysis, and content categorization. Cognitive automation can automate data extraction from invoices using optical character recognition (OCR) and machine learning techniques. 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.

A cognitive automation solution is a positive development in the world of automation. Cognitive automation does move the problem to the front of the human queue in the event of singular exceptions. 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. 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. Future AI models and algorithms are expected to have greater capabilities in understanding and reasoning across various data modalities, handling complex tasks with higher autonomy and adaptability.

Cognitive automation expands the number of tasks that RPA can accomplish, which is good. However, it also increases the complexity of the technology used to perform those tasks, which is bad, argued Chris Nicholson, CEO of Pathmind, a company applying AI to industrial operations. RPA has been around for over 20 years and the technology is generally based on use cases where data is structured, such as entering repetitive information into an ERP when processing invoices. While they are both important technologies, there are some fundamental differences in how they work, what they can do and how CIOs need to plan for their implementation within their organization. This approach empowers humans with AI-driven insights, recommendations, and automation tools while preserving human oversight and judgment.

Cognitive automation is an umbrella term for software solutions that leverage cognitive technologies to emulate human intelligence to perform specific tasks. Besides the application at hand, we found that two important dimensions lay in (1) the budget and (2) the required Machine Learning capabilities. This article will explain to you in detail which cognitive cognitive automation meaning automation solutions are available for your company and hopefully guide you to the most suitable one according to your needs. Thus, cognitive automation represents a leap forward in the evolutionary chain of automating processes – reason enough to dive a bit deeper into cognitive automation and how it differs from traditional process automation solutions.

This proactive approach to patient monitoring improves patient outcomes and reduces the burden on healthcare staff. ML algorithms can analyze financial transactions in real time to identify suspicious patterns or anomalies indicative of fraudulent activity. This accelerates Chat GPT the invoice processing cycle, reduces manual errors, and enhances accuracy in financial record-keeping. All of these create chaos through inventory mismatches, ongoing product research and development, market entry, changing customer buying patterns, and more.

Here is where AIOps simplifies the resolution of issues, even proactively, before it leads to a loss in revenue or customers. Cognitive computing systems become intelligent enough to reason and react without needing pre-written instructions. Workflow automation, screen scraping, and macro scripts are a few of the technologies it uses. Depending on where the consumer is in the purchase process, the solution periodically gives the salespeople the necessary information. This can aid the salesman in encouraging the buyer just a little bit more to make a purchase.

Intelligent automation streamlines processes that were otherwise composed of manual tasks or based on legacy systems, which can be resource-intensive, costly and prone to human error. The applications of IA span across industries, providing efficiencies in different areas of the business. It represents a spectrum of approaches that improve how automation can capture data, automate decision-making and scale automation. It also suggests a way of packaging AI and automation capabilities for capturing best practices, facilitating reuse or as part of an AI service app store.

Using enterprise intelligent automation for cognitive tasks

One potential explanation is that as the damage accumulation underlying frailty occurs, compensatory mechanisms (such as photostatic mechanisms or DNA repair) tend to be hijacked by the frailty status and may allow the AD pathology to progress unchecked. This interaction suggests that addressing frailty in older adults might impact cognitive outcomes, potentially delaying or mitigating cognitive decline. Alternatively, early physical decline and pre-frailty may reflect an early undiagnosed brain pathology. Further longitudinal studies are required to enhance our understanding of the relationship between physical frailty and cognitive function. Cognitive process automation can automate complex cognitive tasks, enabling faster and more accurate data and information processing.

Evidence supports the notion that higher education enhances resilience against cognitive impairment. Indeed, research shows that, given equivalent degrees of brain pathology, individuals with more education display fewer cognitive issues than expected88. Moreover, factors such as higher socioeconomic status, complex occupations, low stress levels and active participation in mental, physical and social activities probably bolster resilience and decelerate cognitive decline89. Research has also investigated dual-task interventions, which involve performing two tasks (for example, walking and memory tests) simultaneously. Aging individuals, especially those with cognitive impairment, may experience a decline in their ability to perform dual tasks. These complex tasks demand more cognitive and motor resources and could potentially prevent or reverse frailty in older adults.

Increased reach vs. increased management

When implemented strategically, intelligent automation (IA) can transform entire operations across your enterprise through workflow automation; but if done with a shaky foundation, your IA won’t have a stable launchpad to skyrocket to success. 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. 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. You might even have noticed that some RPA software vendors — Automation Anywhere is one of them — are attempting to be more precise with their language.

The organization can use chatbots to carry out procedures like policy renewal, customer query ticket administration, resolving general customer inquiries at scale, etc. 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. Most businesses are only scratching the surface of cognitive automation and are yet to uncover their full potential. A cognitive automation solution may just be what it takes to revitalize resources and take operational performance to the next level.

Tools such as the Trail-Making Test, Stroop Test or Digit Symbol Substitution Test can be particularly useful in highlighting these specific cognitive domains. For enterprises to achieve increasing levels of operational efficiency at higher levels of scale, organizations have to rely on automation. Organizations adding enterprise intelligent automation are putting the power of cognitive technology to work addressing the more complicated challenges in the corporate environment. Your automation could use OCR technology and machine learning to process handling of invoices that used to take a long time to deal with manually. Machine learning helps the robot become more accurate and learn from exceptions and mistakes, until only a tiny fraction require human intervention.

This approach ensures end users’ apprehensions regarding their digital literacy are alleviated, thus facilitating user buy-in. 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. Given that the majority of today’s banks have an online application process, cognitive bots can source relevant data from submitted documents and make an informed prediction, which will be further passed to a human agent to verify.

Task mining and process mining analyze your current business processes to determine which are the best automation candidates. They can also identify bottlenecks and inefficiencies in your processes so you can make improvements before implementing further technology. 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. 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.

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. 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. Ethical AI and Responsible Automation are also emerging as critical considerations in developing and deploying cognitive automation systems. As AI technologies continue to advance, there is a growing recognition of the complementary strengths of humans and AI systems. Another prominent trend shaping the future of cognitive automation is the emphasis on human-AI collaboration.

It now has a new set of capabilities above RPA, thanks to the addition of AI and ML. Some of the capabilities of cognitive automation include self-healing and rapid triaging. However, if you are impressed by them and implement them in your business, first, you should know the differences between cognitive automation and RPA. The cognitive automation solution looks for errors and fixes them if any portion fails. ServiceNow’s onboarding procedure starts before the new employee’s first work day.

These areas include data and systems architecture, infrastructure accessibility and operational connectivity to the business. Take DecisionEngines InvoiceIQ for example, it’s bots can auto codes SOW to the right projects in your accounting system. This means that businesses can avoid the manual task of coding each invoice to the right project. Currently there is some confusion about what RPA is and how it differs from cognitive automation. Cognitive automation has proven to be effective in addressing those key challenges by supporting companies in optimizing their day-to-day activities as well as their entire business. Explore the cons of artificial intelligence before you decide whether artificial intelligence in insurance is good or bad.

This may help us to subclassify frailty based on specific physical, cognitive and social domains and identify novel biomarkers to understand the mechanisms and relationships between these domains that constitute CF. However, once we look past rote tasks, enterprise intelligent automation become more complex. 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.

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. Find out what AI-powered automation is and how to reap the benefits of it in your own business. 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. RPA is taught to perform a specific task following rudimentary rules that are blindly executed for as long as the surrounding system remains unchanged.

• This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed.
• What should be clear from this blog post is that organizations need both traditional RPA and advanced cognitive automation to elevate process automation since they have both structured data and unstructured data fueling their processes.
• This makes it challenging to differentiate between cognitive impairment caused by physical conditions and cognitive impairment resulting from comorbid physical frailty and early/prodromal AD.
• For successful cognitive automation adoption, business users should be guided on how to develop their technical skills first, before moving on to reskilling (if necessary) to perform higher-value tasks that require critical thinking and strategic analysis.

In healthcare, these AI co-workers can revolutionize patient care by processing vast amounts of medical data, assisting in accurate diagnosis, and even predicting potential health risks. In finance, they can analyze complex market trends, facilitate intelligent investment decisions, and detect fraudulent activities with unparalleled accuracy. The applications are boundless, transforming the way businesses operate and unlocking untapped potential. One of their biggest challenges is ensuring the batch procedures are processed on time.

With cognitive automation powering intuitive AI co-workers, businesses can engage with their customers in a more personalized and meaningful manner. These AI assistants possess the ability to understand and interpret customer queries, providing relevant and accurate responses. They can even analyze sentiment, ensuring that customer concerns are addressed with empathy and understanding. The result is enhanced customer satisfaction, loyalty, and ultimately, business growth. Mundane and time-consuming tasks that once burdened human workers are seamlessly automated, freeing up valuable resources to focus on strategic initiatives and creative endeavors. This not only enhances the overall speed and effectiveness of operations but also fuels innovation and drives organizational success.

ML-based automation can assist healthcare professionals in diagnosing diseases and medical conditions by analyzing patient data such as symptoms, medical history, and diagnostic tests. ML algorithms can analyze historical sales data, market trends, and external factors to predict future product or service demand accurately. A key aspect is establishing an Automation Center of Excellence (CoE), a centralized hub for managing automation initiatives across an organization. They analyze vast data, consider multiple variables, and generate responses or actions based on learned patterns.

Welcome to the world of AI-led Cognitive Process Automation (CPA), a groundbreaking concept that holds the key to unlocking unparalleled efficiency, accuracy, and cost savings for businesses. At the heart of this transformative technology lies the secret to empowering enterprises into navigating the future of automation with confidence and clarity. In this article, we embark on a journey to demystify CPA, peeling back the layers to reveal its fundamental principles, components, and the remarkable benefits it brings.

The way RPA processes data differs significantly from cognitive automation in several important ways. 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.

Additionally, modern enterprise technology like chatbots built with cognitive automation can act as a first line of defense for IT and perform basic troubleshooting when end users run into a problem. 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. Cognitive automation can also help businesses minimize the amount of manual mental labor that employees have to do.

There is a general agreement among experts that the co-occurrence of cognitive decline and physical weakness in older adults is likely to be the result of a complex interplay of biological, environmental and psychosocial factors. However, evidence for direct causality remains lacking, despite the many causes that have been reported in the literature. You can foun additiona information about ai customer service and artificial intelligence and NLP. Several biological mechanisms have been proposed to underlie CF, including chronic inflammation (CI), oxidative stress, neurodegeneration and vascular factors.

Cesari et al. recently highlighted the rationale behind this new construct as promoting a holistic approach to the assessment and management of cognitive impairment in older adults and recognizing frailty as a multidimensional phenomenon30. The past few decades of enterprise automation have seen great efficiency automating repetitive functions that require integration or interaction across a range of systems. Businesses are having success when it comes to automating simple and repetitive tasks that might be considered busywork for human employees. Just about every industry is currently seeing efficiency gains, with various automation tasks helping businesses to cut costs on human capital and free up employees to focus on more relevant or higher-value tasks. By automating these more complex processes, businesses can free up their employees to focus on more strategic tasks.

Cognitive automation plays a pivotal role in the digital transformation of the workplace. It is a form of artificial intelligence that automates tasks that have traditionally been done by humans. By automating these tasks, businesses can free up their employees to focus on more important work.

Cognitive automation can use AI techniques in places where document processing, vision, natural language and sound are required, taking automation to the next level. 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. However, cognitive automation extends the functional boundaries of what is automated well beyond what is feasible through RPA alone. Cognitive automation performs advanced, complex tasks with its ability to read and understand unstructured data. It has the potential to improve organizations’ productivity by handling repetitive or time-intensive tasks and freeing up your human workforce to focus on more strategic activities.

The Symphony of Cognitive Process Automation

Implementing cognitive automation involves various practical considerations to ensure successful deployment and ongoing efficiency. This article explores the definition, key technologies, implementation, and the future of cognitive automation. Cognitive automation’s significance in modern business operations is that it can drastically reduce the need for constant context-switching among knowledge workers. Irrespective of the concerns about this technology, cognitive automation is driving innovation and enhancing workplace productivity. Disruptive technologies like cognitive automation are often met with resistance as they threaten to replace most mundane jobs. The human brain is wired to notice patterns even where there are none, but cognitive automation takes this a step further, implementing accuracy and predictive modeling in its AI algorithm.

AI-powered chatbots can automate customer service tasks, help desk operations, and other interactive processes that traditionally require human intervention. RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned. But combined with cognitive automation, RPA has the potential to automate entire end-to-end processes and aid in decision-making from both structured and unstructured data. 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.

Although these age-influenced biological changes may vary in their effect, they collectively play a part in the decline. The culmination of these factors leads to a condition known as CF, which underscores the interplay between cognitive and physical deterioration. Despite physical and cognitive decline potentially evolving separately, these shared influences can shape their progression concurrently. If the system picks up an exception – such as a discrepancy between the customer’s name on the form and on the ID document, it can pass it to a human employee for further processing.

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. “A human traditionally had to make the decision or execute the request, but now the software is mimicking the human decision-making activity,” Knisley said. Face API detects and recognizes human faces in images, providing face detection, verification, identification, and emotion recognition capabilities.

By using AI to automate these processes, businesses can save employees a significant amount of time and effort. Now, with cognitive automation, businesses can make a greater impact with less data. For example, businesses can use machine learning to automatically identify patterns in data. 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.

The ability to capture greater insight from unstructured data is currently at the forefront of any intelligent automation task. In its most basic form, machine learning encompasses the ability of machines to learn from data and apply that learning to solve new problems it hasn’t seen yet. Supervised learning is a particular approach of machine learning that learns from well-labeled examples. Companies are using supervised machine learning approaches to teach machines how processes operate in a way that lets intelligent bots learn complete human tasks instead of just being programmed to follow a series of steps. This has resulted in more tasks being available for automation and major business efficiency gains. What should be clear from this blog post is that organizations need both traditional RPA and advanced cognitive automation to elevate process automation since they have both structured data and unstructured data fueling their processes.

Beyond the Fried frailty phenotype, several other methods are available for evaluating frailty. One of these methods is the accumulation of deficits approach, also known as the Frailty Index. This approach determines frailty by quantifying the proportion of potential health deficits that an individual exhibits, encompassing symptoms, signs, diseases, disabilities and laboratory abnormalities49.

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. Using more cognitive automation, https://chat.openai.com/ companies can experience a significant boost in performance-related business outcomes, consolidate dozens of systems into just a handful of coordinated processes and accelerate customer service response times tenfold. 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.

Today’s customers interact with your organization across a range of touch points and channels – chat, interactive IVR, apps, messaging, and more. When you integrate RPA with these channels, you can enable customers to do more without needing the help of a live human representative. “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. One organization he has been working with predicted nearly 35% of its workforce will retire in the next five years. They are looking at cognitive automation to help address the brain drain that they are experiencing.

Most importantly, this platform must be connected outside and in, must operate in real-time, and be fully autonomous. It must also be able to complete its functions with minimal-to-no human intervention on any level. 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. Anyone who has been following the Robotic Process Automation (RPA) revolution that is transforming enterprises worldwide has also been hearing about how artificial intelligence (AI) can augment traditional RPA tools to do more than just RPA alone can achieve.

• There are some obvious things to automate within an enterprise that provide short-term ROI — repetitive, boring, low-value busywork, like reporting tasks or data management or cleanup, that can easily be passed on to a robot for process automation.
• By uncovering process inefficiencies, bottlenecks, and opportunities for optimization, process mining helps organizations identify the best candidates for automation, thus accelerating the transformation toward cognitive automation.
• “The whole process of categorization was carried out manually by a human workforce and was prone to errors and inefficiencies,” Modi said.

RPA plus cognitive automation enables the enterprise to deliver the end-to-end automation and self-service options that so many customers want. Predictive analytics can enable a robot to make judgment calls based on the situations that present themselves. Finally, a cognitive ability called machine learning can enable the system to learn, expand capabilities, and continually improve certain aspects of its functionality on its own. 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. 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.