Cognitive Robotic Process Automation: Concept and Impact on Dynamic IT Capabilities in Public Organizations SpringerLink

In this domain, cognitive automation is benefiting from improvements in AI for ITSM and in using natural language processing to automate trouble ticket resolution. 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. Cognitive automation describes diverse ways of combining artificial intelligence (AI) and process automation capabilities to improve business outcomes. Beyond automating existing processes, companies are using bots to implement new processes that would otherwise be impractical.

• They are looking at cognitive automation to help address the brain drain that they are experiencing.
• Therefore, companies rely on AI focused companies like IBM and niche tech consultancy firms to build more sophisticated automation services.
• Intelligent automation streamlines processes that were otherwise comprised of manual tasks or based on legacy systems, which can be resource-intensive, costly, and prone to human error.
• As the predictive power of artificial intelligence is on the rise, it gives companies the methods and algorithms necessary to digest huge data sets and present the user with insights that are relevant to specific inquiries, circumstances, or goals.
• RPA is best deployed in a stable environment with standardized and structured data.
• The adaptability of a workforce will be important for successful outcomes in automation and digital transformation projects.

In the contemporary landscape of business operations, organizations are increasingly turning to advanced technologies to streamline and enhance their processes. This abstract explores the transformative potential of integrating Robotic Process Automation (RPA) and Artificial Intelligence (AI) to achieve optimal efficiency in business processes. The synergy between RPA and AI promises to revolutionize traditional workflows by automating repetitive tasks and infusing intelligent decision-making capabilities. Robotic Process Automation, characterized by its ability to mimic human actions in software-based environments, provides a foundation for automating rule-based, routine tasks. Concurrently, Artificial Intelligence, with its cognitive capabilities, empowers systems to learn, adapt, and make informed decisions. The amalgamation of RPA and AI fosters a harmonious ecosystem where machines not only execute tasks at unprecedented speeds but also possess the capacity to analyze data and make nuanced decisions.

Cognitive Automation Solution Providers

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. 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.

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, cognitive process automation structured data. This approach led to 98.5% accuracy in product categorization and reduced manual efforts by 80%. In contrast, Modi sees intelligent automation as the automation of more rote tasks and processes by combining RPA and AI.

What’s the Difference Between RPA and Cognitive Automation?

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. Another benefit of cognitive automation lies in handling unstructured data more efficiently compared to traditional RPA, which works best with structured data sources.

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. While chatbots are gaining popularity, their impact is limited by how deeply integrated they are into your company’s systems. For example, if they are not integrated into the legacy billing system, a customer will not be able to change her billing period through the chatbot. Cognitive automation allows building chatbots that can make changes in other systems with ease.

«As automation becomes even more intelligent and sophisticated, the pace and complexity of automation deployments will accelerate,» predicted Prince Kohli, CTO at Automation Anywhere, a leading RPA vendor. 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. 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. Figure 2 illustrates how RPA and a cognitive tool might work in tandem to produce end-to-end automation of the process shown in figure 1 above. Leading companies automate both business and IT to free up employees to focus on what they do best.

That means your digital workforce needs to collaborate with your people, comply with industry standards and governance, and improve workflow efficiency. 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). 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.

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It also helps organizations identify potential risks, monitor compliance adherence and flag potential fraud, errors or missing information. By automating cognitive tasks, organizations can reduce labor costs and optimize resource allocation. Automated systems can handle tasks more efficiently, requiring fewer human resources and allowing employees to focus on higher-value activities. 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. «Ultimately, cognitive automation will morph into more automated decisioning as the technology is proven and tested,» Knisley said.

Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research. The value of intelligent automation in the world today, across industries, is unmistakable. With the automation of repetitive tasks through IA, businesses can reduce their costs as well as establish more consistency within their workflows. 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. Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce.

This type of automation expands on RPA functionality by incorporating sub-disciplines of artificial intelligence, like machine learning, natural language processing, and computer vision. 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. 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. Robotic process automation (RPA) is considered as a significant aspect of modernizing and digitally transforming public administration towards a higher degree of automation.

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.

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This enables organizations to gain valuable insights into their processes so they can make data-driven decisions. And using its AI capabilities, a digital worker can even identify patterns or trends that might have gone previously unnoticed by their human counterparts. If your organization wants a lasting, adaptable cognitive automation solution, then you need a robust and intelligent digital workforce.

When introducing automation into your business processes, consider what your goals are, from improving customer satisfaction to reducing manual labor for your staff. Consider how you want to use this intelligent technology and how it will help you achieve your desired business outcomes. Training AI under specific parameters allows cognitive automation to reduce the potential for human errors and biases. This leads to more reliable and consistent results in areas such as data analysis, language processing and complex decision-making. By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions. Its systems can analyze large datasets, extract relevant insights and provide decision support.

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. Additionally, large RPA providers have built marketplaces so developers can submit their cognitive solutions which can easily be plugged into RPA bots. Cognitive automation maintains regulatory compliance by analyzing and interpreting complex regulations and policies, then implementing those into the digital workforce’s tasks.