Measuring cognitive workload in automated knowledge work environments: a systematic literature review Cognition, Technology & Work

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. 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. From your business workflows to your IT operations, we got you covered with AI-powered automation. From your business workflows to your IT operations, we’ve got you covered with AI-powered automation. “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.

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. It can carry out various tasks, including determining the cause of a problem, resolving it on its own, and learning how to remedy it. 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.

AI can help RPA automate tasks more fully and handle more complex use cases. RPA also enables AI insights to be actioned on more quickly instead of waiting on manual implementations. Additionally, both technologies help serve as a growth-stimulating, deflationary force, powering new business models, and accelerating productivity and innovation, while reducing costs.

The biggest challenge is that cognitive automation requires customization and integration work specific to each enterprise. This is less of an issue when cognitive automation services are only used for straightforward tasks like using OCR and machine vision to automatically interpret an invoice’s text and structure. 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.

Document processing automation

Cognitive automation promises to enhance other forms of automation tooling, including RPA and low-code platforms, by infusing AI into business processes. These enhancements have the potential to open new automation use cases and enhance the performance of existing automations. Though cognitive automation is a relatively recent phenomenon, most solutions are offered by Robotic Process Automation (RPA) companies. Check out our RPA guide or our guide on RPA vendor comparison for more info. You can also learn about other innovations in RPA such as no code RPA from our future of RPA article. Cognitive automation does move the problem to the front of the human queue in the event of singular exceptions.

However, this rigidity leads RPAs to fail to retrieve meaning and process forward unstructured data. Advantages resulting from cognitive automation also include improvement in compliance and overall business quality, greater operational scalability, reduced turnaround, and lower error rates. All of these have a positive impact on business flexibility and employee efficiency. For instance, at a call center, customer service agents receive support from cognitive systems to help them engage with customers, answer inquiries, and provide better customer experiences. According to experts, cognitive automation is the second group of tasks where machines may pick up knowledge and make decisions independently or with people’s assistance. For instance, Religare, a well-known health insurance provider, automated its customer service using a chatbot powered by NLP and saved over 80% of its FTEs.

Through cognitive automation, it is possible to automate most of the essential routine steps involved in claims processing. These tools can port over your customer data from claims forms that have already been filled into your customer database. It can also scan, digitize, and port over customer data sourced from printed claim forms which would traditionally be read and interpreted by a real person. 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.

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. When implemented strategically, intelligent automation (IA) can transform entire operations across your enterprise through workflow cognitive automation definition automation; but if done with a shaky foundation, your IA won’t have a stable launchpad to skyrocket to success. To reap the highest rewards and return on investment (ROI) for your automation project, it’s important to know which tasks or processes to automate first so you know your efforts and financial investments are going to the right place.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Cognitive automation tools can handle exceptions, make suggestions, and come to conclusions. He suggested CIOs start to think about how to break up their service delivery experience into the appropriate pieces to automate using existing technology. The automation footprint could scale up with improvements in cognitive automation components. RPA tools were initially used to perform repetitive tasks with greater precision and accuracy, which has helped organizations reduce back-office costs and increase productivity. While basic tasks can be automated using RPA, subsequent tasks require context, judgment and an ability to learn.

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. The adoption of cognitive RPA in healthcare and as a part of pharmacy automation comes naturally. In such a high-stake industry, decreasing the error rate is extremely valuable. Moreover, clinics deal with vast amounts of unstructured data coming from diagnostic tools, reports, knowledge bases, the internet of medical things, and other sources.

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

(IDC, 2019) Cognitive automation mimics human behaviour and is applied on task which normally requires human intelligence like interpretation of unstructured data, understand patterns or make judgement calls. 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. Within a company, cognitive process automation streamlines daily operations for employees by automating repetitive tasks. It enables smoother collaboration between teams, and enhancing overall workflow efficiency, resulting in a more productive work environment.

• “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.
• Therefore, cognitive automation knows how to address the problem if it reappears.
• RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned.
• 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.

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.

CIOs also need to address different considerations when working with each of the technologies. 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.

Cognitive automation can use AI techniques in places where document processing, vision, natural language and sound are required, taking automation to the next level. 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 value of intelligent automation in the world today, across industries, is unmistakable.

Another way to answer this is to ask if the current manual process has people making decisions that require collaboration with each other, if yes, then go for cognitive automation. It is used to streamline operations, improve decision-making, and enhance efficiency through the integration of AI technologies, leading to optimized workflows, reduced manual effort, and a more agile response to dynamic market demands. Automation of various tasks helps businesses to save cost, reduce manual labor, optimize resource allocation, and minimize operational expenses. This cost-effective approach contributes to improved profitability and resource management.

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. 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. The foundation of cognitive automation is software that adds intelligence to information-intensive processes.

Its systems can analyze large datasets, extract relevant insights and provide decision support. Intelligent automation simplifies processes, frees up resources and improves operational efficiencies through various applications. An insurance provider can use intelligent automation to calculate payments, estimate rates and address compliance needs. 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. Automation technology, like RPA, can also access information through legacy systems, integrating well with other applications through front-end integrations.

However, it is likely to take longer to implement these solutions as your company would need to find a capable cognitive solution provider on top of the RPA provider. Only the simplest tools, initially built in 2000s before the explosion of interest in RPA are in this bucket. 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.

For example, they might only enable processing of one type of document — i.e., an invoice or a claim — or struggle with noisy and inconsistent data from IT applications and system logs. 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. RPA is best deployed in a stable environment with standardized and structured data. Cognitive automation is most valuable when applied in a complex IT environment with non-standardized and unstructured data. 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.

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. 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 integration leads to a transformative solution that streamlines processes and simplifies workflows to ultimately improve the customer experience. 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.

As a result CIOs are seeking AI-related technologies to invest in their organizations. By augmenting RPA with cognitive technologies, the software can take into account a multitude of risk factors and intelligently assess them. 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. RPA is referred to as automation software that can be integrated with existing digital systems to take on mundane work that requires monotonous data gathering, transferring, and reformatting.

Intelligent process automation demands more than the simple rule-based systems of RPA. You can think of RPA as “doing” tasks, while AI and ML encompass more of the “thinking” and “learning,” respectively. It trains algorithms using data so that the software can perform tasks in a quicker, more efficient way. Cognitive automation tools such as employee onboarding bots can help by taking care of many required tasks in a fast, efficient, predictable and error-free manner. These tasks can range from answering complex customer queries to extracting pertinent information from document scans. Some examples of mature cognitive automation use cases include intelligent document processing and intelligent virtual agents.

AI combines cognitive automation, machine learning (ML), natural language processing (NLP), reasoning, hypothesis generation and analysis. In order for RPA tools in the marketplace to remain competitive, they will need to move beyond task automation and expand their offerings to include intelligent automation (IA). This type of automation expands on RPA functionality by incorporating sub-disciplines of artificial intelligence, like machine learning, natural language processing, and computer vision. 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. Essentially, cognitive automation within RPA setups allows companies to widen the array of automation scenarios to handle unstructured data, analyze context, and make non-binary decisions.

“RPA handles task automations such as copy and paste, moving and opening documents, and transferring data, very effectively. However, to succeed, organizations need to be able to effectively scale complex automations spanning cross-functional teams,” Saxena added. Claims processing, one of the most fundamental operations in insurance, can be largely optimized by cognitive automation.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

“This makes it possible for analysts, business users, and subject matter experts to engage with automated workflows, not just traditional RPA developers,” Seetharamiah added. As CIOs embrace more automation tools like RPA, they should also consider utilizing cognitive automation for higher-level tasks to further improve business processes. Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization.

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 automation solutions are available for your company and hopefully guide you to the most suitable one according to your needs. Processing claims is perhaps one of the most labor-intensive tasks faced by insurance company employees and thus poses an operational burden on the company. Many of them have achieved significant optimization of this challenge by adopting cognitive automation tools. By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions.

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. In this domain, cognitive automation is benefiting from improvements in AI for ITSM and in using natural language processing to automate trouble ticket resolution.

This allows the automation platform to behave similarly to a human worker, performing routine tasks, such as logging in and copying and pasting from one system to another. While back-end connections to databases and enterprise web services also assist in automation, RPA’s real value is in its quick and simple front-end integrations. RPA combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.