Transforming Robotic Process Automation with AI and ML

By combining the capabilities of robotic process automation with artificial intelligence, it is possible to transform regular robotic tasks into intelligent processes.


The speed at which technology is evolving today is unprecedented and staying on par with Moore’s law. Since the early 2000s, a lot of attention has been focused on Artificial Intelligence and Machine Learning, and the progress in these areas of tech has been substantial. Today, we see machine learning in many routine processes and probably don’t even realize it (like your email spam filter for example). Similarly, the field of robotics and automation is continually seeing the implementation of AI and Machine Learning to build ‘Intelligent Systems’.


The speed at which technology is evolving today is unprecedented and staying on par with Moore’s law. Since the early 2000s, a lot of attention has been focused on Artificial Intelligence and Machine Learning, and the progress in these areas of tech has been substantial. Today, we see machine learning in many routine processes and probably don’t even realize it (like your email spam filter for example). Similarly, the field of robotics and automation is continually seeing the implementation of AI and Machine Learning to build ‘Intelligent Systems’.
By implementing AI, ML, and NLP to robotic process automation, organizations are further reducing processing time, increasing productivity, and delivering utmost client satisfaction

RPA, or Robotic Process Automation, is the automation of repetitive tasks. While RPA’s major contribution previously was elimination of human error, with the advent and application of AI and machine learning, RPA can perform smart decision making in addition to workflow automation.

Let’s dive in and analyze how intelligent process automation can revolutionize RPA.


Robotic Process Automation and Intelligent Automation


While RPA helps automate repetitive processes, intelligent automation enables RPA systems with the ability of decision making, problem-solving, persuasion, and even creativity. Let’s see how:


1. Machine Learning in RPA

To help you understand the concept, RPA does things, AI thinks, and ML learns. Now, combine all these for intelligent automation. Machine learning utilizes historic data to learn patterns and predict outcomes.

Consider invoice processing for an ecommerce platform, for example.RPA will help you automate fulfilling orders, generating the invoice, adjusting the inventory levels, downloading emails, tracking the order, etc. This is a set repetitive process, hence a prospective area to install RPA. Intelligent Automation, on the other hand, can be used to learn and think to influence the buyer’s actions. It can track the visitor’s path and learn what other products they might be interested in, and present it to them just before billing. This is a common feature you see in most online ecommerce portals today.


2. Process Driven vs Data Driven

RPA is process-driven and Intelligent Automation is data-driven.


In IA data-driven processes, an ML algorithm is selected and then it is trained with various quality data samples. This ensures that the ML algorithm will recognize patterns and learn. ML continues to consume and analyze data throughout its lifecycle to get smarter.


3. Machine Vision

The capability of a machine or software to process and act on visual input is machine vision. Visual input is provided by way of cameras, infrared systems, sensors, etc. AI and ML together provide the capability to process this visual information and then realign RPA processes.


Automated Guided Vehicles (AGVs) are a great example. In a warehouse, AGVs are automated to follow a fixed path. Equipped with Visual receptors and aided by AI, they can be made ‘intelligent’ enough to spot obstructions and then act accordingly (brake, divert path, etc.)


4. Natural Language Processing

Natural Language Processing, like machine vision and machine learning, enhances the capabilities of RPA systems. NLP is the capability of a machine or software to capture and understand voice commands, and then perform actions. While RPA can be used to understand and execute a fixed set of commands, like a voice enabled customer support portal, IA can make it a powerful system, like Siri or Alexa.


How it is Being Utilized


Organizations and businesses utilize intelligent process automation in various ways. Here are some of the most common examples:

  • Fraud prevention in banks and financial institutes.

  • Maintaining compliance in healthcare facilities by monitoring assets, patient flows and hospital staff.

  • Customer service by way of chatbots.

  • Freight tracking for logistics.

  • In event management by tracking attendee locations and activity and assessing events they are likely to attend.

  • In the public sector, to identify potential threats and neutralize them by way of cameras and sensors.

Moving Towards Intelligent Automation


By implementing AI, ML, and NLP to robotic process automation, organizations are further reducing processing time, increasing productivity, and delivering utmost client satisfaction. To implement a similar intelligent automation system in your organization, start by evaluating opportunities and relevant business use cases.


Content credit FourNxt


FourNxt is a Technology firm based the UAE specialized in Intelligent automation, Low code technologies and Process Management. FourNxt is a turnkey IA solutions provider having partnerships with leading platforms such as UiPath, Automation Anywhere, Blueprism and Microsoft. FourNxt provides services ranging from consulting, design, development and MSP models. FourNxt provides services across EMEA and has development centres based in India.


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