9 Differnces Between RPA and Cognitive Intelligence?

Leverage public records, handwritten what is cognitive automation input and scanned documents to perform required KYC checks. OCR to automate the capture and processing of new application documents. WorkFusion promotes their bots cognitive capabilities under Smart Process Automation. 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. You now can streamline and automate your business more efficiently and cost-effectively in a time where every company is striving to get lean and mean. With so many unknowns in the market, profitability and client retention are the goals of nearly every business leader right now.


Rive transformation at scale, leveraging advancements in AI and machine learning to solve some of the toughest business challenges with acute accuracy. By helping you optimise your processes and workflow, cognitive automation can address these crucial challenges and deliver real business value. To make an informed decision for investing in AI technologies, it is important to understand the differences of both RPA and cognitive automation. As processes are automated with more programming and better RPA tools, the processes that need higher-level cognitive functions are the next we’ll see automated.

RPA vs Cognitive Automation: Understanding the Difference

As the number of tasks and processes that are candidates for cognitive automation is steadily increasing, the workforce of the future will be required to re-skill workers towards more unique human work (Card & Nelson, 2019). Consequently, organizations will have to adapt structures and organizational practices and align the new technology with a comprehensive strategy regarding the future of work (Zarkadakis et al., 2016). Overall, this shall account for benefitting from the advantages of cognitive automation in a responsible manner. In this vein, we can observe that there are tasks and processes that are neither purely conducted by humans nor purely by cognitive machines. Often, we encounter so-called “hybrid intelligence” (Dellermann et al., 2019) approaches, where the entities – human and machine agent – mutually achieve a higher performance than if they acted separately.

  • Thus, intelligent process mining ensures highly efficient processes consuming less time and lower costs.
  • RPA operates most of the time using a straightforward “if-then” logic since there is no coding involved.
  • TalkTalk received a solution from Splunk that enables the cognitive solution to manage the entire backend, giving customers access to an immediate resolution to their issues.
  • In that, the outcomes of these forms of BPA are deterministic as all business process rules are predefined by a human entity and follow if-then structures.
  • BPA is a part of the field of Business Process Management, which has its roots in the field of WfM (W. M. P. van der Aalst et al., 2018).
  • Unlike traditional unattended RPA, cognitive RPA is adept at handling exceptions without human intervention.

Or, dynamic interactive voice response can be used to improve the IVR experience. It adjusts the phone tree for repeat callers in a way that anticipates where they will need to go, helping them avoid the usual maze of options. AI-based automations can watch for the triggers that suggest it’s time to send an email, then compose and send the correspondence. One example is to blend RPA and cognitive abilities for chatbots that make a customer feel like he or she is instant-messaging with a human customer service representative.

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Furthermore, systems that instantiate cognitive automation operate in and on larger complementary IS ecosystems. 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.

  • Traditional automation requires clear business rules, processes, and structure; however, traditional manpower requires none of these.
  • Cognitive automation is designed to function similarly to human thoughts and subsequent actions to organize and analyze the more complex data with accuracy and consistency.
  • Cognitive automation utilizes data mining, text analytics, artificial intelligence , machine learning, and automation to help employees with specific analytics tasks, without the need for IT or data scientists.
  • They are designed to be used by business users and be operational in just a few weeks.
  • 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.
  • So it is clear now that there is a difference between these two types of Automation.

They perform highly granular tasks such as login, extraction, publishing, updating, deleting, etc. Cognitive Automation — extending human intelligence in complex teams and organizations. Business owners can use 500apps to get accurate, timely data that can help them make decisions better. 500apps aggregates the most accurate data and connects you with decision-makers and their confidants with ease. Cognitive intelligence is like a data scientist who draws inferences from various types and sets of data.

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Thus, researchers and practitioners alike will face the challenge of developing strategies on how to seamlessly integrate these systems internally while balancing the spectrum of lightweight and heavyweight IT. For instance, research calls for further investigating how to process execution exceptions caused by software robots by routing them to business process management systems for further handling (König et al., 2020). In addition to this technology management perspective, organizations also need to make strategic decisions on whether to build up AI and ML skills and knowledge within the organization vs. outsourcing the latter. Robotic Process Automation is helping companies reduce costs and improve on quality and productivity by automating some of their most time consuming, rule-based and replicable business processes.

human intelligence

Cognitive automation is responsible for monitoring users’ daily workflows. It identifies processes that would be perfect candidates for automation then deploys the automation on its own, Saxena explained. “Both RPA and cognitive automation enable organizations to free employees from tedium and focus on the work that truly matters. While cognitive automation offers a greater potential to scale automation throughout the enterprise, RPA provides the basic foundation for automation as a whole. In the era of digital acceleration, you can no longer depend on the processes and technologies that brought you to this point. Your organization and enterprise systems were built with different assumptions for a different era of business.

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Learn how industry leaders are transforming their businesses to overcome global challenges and thrive with intelligent automation. RPA provides quick ROI, while cognitive automation requires more time to set up the infrastructure and workflows. RPA automates repetitive actions, while cognitive automation can automate more types of processes. Automating decision-making to reduce manual decision-making, mitigate bias and speed business processes that may have stalled with human decision-makers. Basic cognitive services are often customized, rather than designed from scratch. This makes it easier for business users to provision and customize cognitive automation that reflects their expertise and familiarity with the business.

Therefore, substantiated empirical, real-world facts, methods and tool-support are needed to guide the formation of the right cognitive automation strategy. On the other hand, cognitive intelligence uses machine learning and requires the panoptic use of the programming language. It uses more advanced technologies such as natural language processing , text analysis, data mining, semantic technology and machine learning. It uses these technologies to make work easier for the human workforce and to make informed business decisions. Cognitive automation utilizes data mining, text analytics, artificial intelligence , 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.

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For example, millions of hours of driver decision-making had to be modeled to make self-driving cars feasible. And still, sometimes the car can be autonomous, and sometimes it still does not know what to do. Similarly, training a cognitive automation system requires mapping human decisions in context. Helpfully, cognitive neuroscience provides a guide for cognitive automation in the enterprise. Nonetheless, cognitive automation is reaching out to provide capabilities of understanding, reasoning, learning and interacting.

  • Marcello is the VP, Publishing where he is responsible for directing products, strategy and marketing activities covering the European publishing market.
  • As the number of tasks and processes that are candidates for cognitive automation is steadily increasing, the workforce of the future will be required to re-skill workers towards more unique human work (Card & Nelson, 2019).
  • In the remainder of this paper, we first elaborate on the constituting concepts of cognitive automation to shed light on its grounding.
  • 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.
  • It can take the burden of simple data entry off your team, leading to improved employee satisfaction and engagement.
  • Addressing the challenges most often faced by network operators empowers predictive operations over reactive solutions.

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. Our consultants identify candidate tasks / processes for automation and build proof of concepts based on a prioritization of business challenges and value. It enables chipmakers to address market demand for rugged, high-performance products, while rationalizing production costs.


It seeks to find similarities between items that pertain to specific business processes such as purchase order numbers, invoices, shipping addresses, liabilities, and assets. In this paper, we focus on ML-facilitated BPA, which we refer to as the most prevalent instantation of the phenomenon of cognitive automation. BPA uses process and task descriptions for guiding the performance of business activities (Hofstede et al., 2010). BPA is a part of the field of Business Process Management, which has its roots in the field of WfM (W. M. P. van der Aalst et al., 2018). Thus, we subsumed RPA as one form of deterministic automation approaches in the same vein as WfM, which instantiate the phenomenon that we call “rule-based automation” in this paper.

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RPA helps businesses support innovation without having to pay heavily to test new ideas. It frees up time for employees to do more cognitive and complex tasks and can be implemented promptly as opposed to traditional automation systems. It increases staff productivity and reduces costs and attrition by taking over the performance of tedious tasks over longer durations. The foundation of cognitive automation is software that adds intelligence to information-intensive processes. It is frequently referred to as the union of cognitive computing and robotic process automation , or AI. (Ultram) Our state-of-the-art AI/ML technology can improve your business processes and tackle those complex and challenging tasks that are slowing your productivity.

What is robotic and cognitive automation?

Cognitive RPA is a term for Robotic Process Automation (RPA) tools and solutions that leverage Artificial Intelligence (AI) technologies such as Optical Character Recognition (OCR), Text Analytics, and Machine Learning to improve the experience of your workforce and customers.

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