With its staggering potential for growth in the next couple of years, cognitive applications stand to become one of the most widespread and fastest growing emerging technologies seen in recent times. Cognitive Automation relies on analytics and the intelligence encapsulated in the latest AI/machine learning and multivariate models to make real-time recommendations. With access to harmonized data, the process to create and train models is accelerated. A platform must also make these models available to any open development environment. There are many more applications of automation for structuring processes, including process strategy, modeling, implementation, execution, monitoring and control, and continuous process improvement.

  • But as RPA accomplish that without any thought process for example button pushing, Information capture and Data entry.
  • If your process involves structured, voluminous data and is strictly rules-based, then RPA would be the right solution.
  • Public Safety – By the help cognitive technology and RPA, better insights are exported to obtain better conditional awareness.
  • Even a minor change will require massive development and testing costs.
  • This amplifies the capabilities of automation from simply “if this, then that” into more complex applications.
  • It represents a spectrum of approaches that improve how automation can capture data, automate decision-making and scale automation.

Think about the incredible amount of data flow running through a financial services company for a moment. As companies are becoming more digital daily, we will use the example of a structured, accurate, online form. Organizations with millions in their innovation budget can build or outsource the technical expertise required to automate each individual process in an organization. It can take anywhere from 9-12 months to automate one process and only works if the process and business logic stays the exact same.

Key Difference Cognitive Automation and Robotic Process Automation

Innovation has helped ease the pain of implementing automation and getting the workforce back to the root of what they’re trying to accomplish. 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. In the banking and finance industry, RPA can be used for a wide range of processes such as retail branch activities, consumer and commercial underwriting and loan processing, anti-money laundering, KYC and so on.

What is the advantage of cognitive automation?

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.

With such extravagant growth predictions, cognitive automation and RPA have the potential to fundamentally reshape the way businesses work. The coolest thing is that as new data is added to a cognitive system, the system can make more and more connections. This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed. RPA is simple to manage, while cognitive automation requires additional management overhead.

RPA & Computer Vision: 5 Examples of Intelligent Automation

Cognitive technology utilizes a knowledge base to handle complex tasks. The technology examines human-like conversations and behaviors and uses it to understand how humans behave. In a context of increasing data complexity and growth, the automation of operation processes is becoming more and more important to tackle volume and provide relevant and timely insights.

Network Operation processes are typically standard or tendentially standardised and have a high degree of predictability making them candidates for automation. The pace of CSPs’ automation levels can be increased by leveraging the insights brought by cognitive technologies. Automation, modeling and analysis help semiconductor enterprises achieve improvements in area scaling, material science, and transistor performance. Further, it accelerates design verification, improves wafer yield rates, and boosts productivity at nanometer fabs and assembly test factories.

Integration of Cognitive Automation and RPA

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. Automating decision-making to reduce manual decision-making, mitigate bias and speed business processes that may have stalled with human decision-makers. Cognitive intelligence is dynamic and progressive and can extend the nature of the data it can interpret.


RPA is a technology used to mimic repetitive human tasks with more precision and accuracy by using software robots. RPA is ideal for those processes that do not require decision-making or human intervention. However, there are going to be plenty of situations that do require human decision-making, and when there is voluminous data involved, it can become very challenging for the human workforce to make the right decisions. Nonetheless, cognitive automation is reaching out to provide capabilities of understanding, reasoning, learning and interacting. These systems understand unstructured data, images and language and virtually operationalize structured and unstructured data.

Demystifying the two technologies: Three key differences

You can also check our article on intelligent automation in finance and accounting for more examples. Although Artificial Intelligence is often used as a buzzword in the technology space, it’s commonly misused. Rather, it is Machine Learning that is used in Intelligent Process Automation that makes it, well… intelligent. what is cognitive automation Machine Learning helps Robotic Process Automation recognize patterns and improve through experience. This enables Intelligent Process Automation to take on more complex and advanced processes than Robotic Process Automation alone. The next breed of Business Process Automation is Intelligent Process Automation .

  • Cognitive automation is based on algorithms and technological approaches such as natural language processing, text analytics, data mining, semantic technologies, and machine learning.
  • Once assigned to the project, our team is first trained to configure the solutions as per your needs.
  • Benefits RPA supports innovation by taking over the repetitive tasks, freeing up employee time for more cognitive tasks.
  • Cognitive robotic process automation is the form of business process automation technology using AI and ML.
  • They are designed to be used by business users and be operational in just a few weeks.
  • You can check our article where we discuss the differences between RPA and intelligent / cognitive automation.

What we know today as Robotic Process Automation was once the raw, bleeding edge of technology. Compared to computers that could do, well,nothingon their own, tech that could operate on its own, firing off processes and organizing of its own accord, was the height of sophistication. However, that this was only the start in an ever-changing evolution of business process automation.

Robotic vs cognitive: The two ends of Intelligent Automation continuum

AIMultiple informs hundreds of thousands of businesses including 55% of Fortune 500 every month. 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 in how they meet ongoing consumer demand.

what is cognitive automation