ML in Robotic Process Automation (RPA)

ML in Robotic Process Automation (RPA)

Robotic Process Automation (RPA) refers to a technique that automates the business process, resulting in operational efficiencies.  The automation technique emulates human interaction with dynamic robots. The robots refer to application agents that automate the processes, eliminating or reducing human interactions.

By 2020, automation will reduce human intervention in business-shared services by about 65 percent, according to Gartner.

RPA works like a ‘record and plays’ feature where the robot is taught how to carry out commands and that replay the instructions recurrently. The simple yet efficient automation technique can optimize several processes.

Benefits and Shortcomings of RPA

Traditional automation techniques involved the integration of separate systems with enterprise resource planning (ERP) software using verification logic. Although the outcome of the process was similar to RPA techniques, the process was costly as it required additional effort and costs in developing and maintaining different software.

RPA makes the automation process simpler and less costly. A single application is taught to automate the processes that require reduced development and maintenance. The application runs 24/7 similar to a robot and requires less maintenance as compared to traditional automation methods.

But the limitation of RPA is that the decision-making process cannot be automated. User intervention is required whenit’s time to make decisions that the software is not programmed to handle. The solution to the problem is machine learning that creates a knowledge base and uses it to make decisions automatically.

ML Application in RPA

RPA automation does not entirely automate the processes. The techniques split the process into automatable and non-automatable areas.  In other words, maximum automation is not possible in RPA processes. The technique can only automate procedural processes. However, areas that require the application of knowledge and judgment are not automated.

RPA will route the process to a user where human judgment has to be applied. Through applying machine learning to RPA, maximum automation is possible.

Machine learning (ML) technology has matured a lot over the years. Advanced ML algorithms can solve real-life problems that require decision-making and judgment. Extending ML to RPA can help achieve complete automation of processes.

ML models can be applied to RPA workflows for performing tasks that the human brain can perform. The artificial intelligence model can be used to maintain and use the knowledge base for solving problems. The model can be plugged into the business process for maximum automation.  ML in RPA can lead to an optimized and efficient process.

Suppose you are developing an ML model with a knowledge base of past system performance of up to five years. The application of the model in RPA will allow the automated system to predict performance issues and make appropriate decisions. The ML algorithm mimics the decision process of a person who knows how to analyze past performance to solve issues. As a result, decision-making does not need to be routed to the human user. The system makes all decisions for resolving issues and ensuring smooth operations.

Benefits of Extending ML to RPA

The integration of machines and RPA results in various benefits for an organization. Organizations can completely automate processes by injecting artificial intelligence into machine learning.

ML techniques, such as natural language processing and speech recognition, automate the tasks that require perceptual and cognitive capabilities.  The opportunities provided by ML in RPA can best be understood by imagining an employee having to carry on a variety of repetitive tasks. The tasks can become boring for a human user that will result in decreased productivity. Mistakes would also increase, resulting in increased costs to organizations.

ML integration to RPA can automate all tasks even those that require basic cognitive abilities. The employee will then be freed from the less complex tasks to perform tasks that require a greater degree of creativity, thinking and higher-level critical engagement. Since all the monotonous and simple tasks are delegated to a system, employees will be assigned complex and challenging tasks due to which there will be less burnout and stress.

The successful integration of ML into RPA will make automation apps more dynamic. Systems will be capable of performing intelligent tasks like solving exceptions and eliminating duplicate or wrong entries. Robots or applications will be able to deal with unpredicted situations and bring critical system issues to management’s attention. As a result, the integration of ML into RPA will result in improved efficiency and reduced costs.

Different Technologies Integrated Outcomes

ML and RPA are two different technologies. The integration of the technologies results in the desired benefits that have been discussed in this article. The two systems alone will have different outcomes. Robots or automated application won’t be able to achieve a decision-making capability without ML. Similarly, ML won’t provide any value without integrating with robot process automation.

Combining ML with RPA allows the system to evaluate processes and make decisions. Artificial intelligence algorithms expose the robot to a large amount of data. The robots can be programmed to store information, filter relevant data, and use relevant information to make decisions.

The integration of ML with RPA results in powerful automation applications. Many RPA solutions have added this capability, resulting in enhanced outcomes. Vendors, such as UiPath, BluePrism, and Automation Anywhere, have created a robust ecosystem with power ML algorithms.


ML integration with RPA is an emerging field that requires the expertise of professionals with the skills and knowledge required to develop powerful ML models. The process is data-driven where RPA is customized according to the end-user needs.

With the right experts, organizations can truly benefit from the power of ML and RPA integration. RPA solutions with ML capabilities allow organizations to benefit from intelligent systems that provide cost and operational efficiencies.

Partnering with the right professionals is vital for the successful implementation of ML-based solutions. AtWinjit, we have a team of highly experienced professionals who can help you successfully transition to more advanced RPA solutions. We can help you adopt solutions that are designed according to your exact business needs. Let us help you transform your business  operations with innovative technological solutions.