The next-generation of automation comes to life with AI and machine learning.
Over the decades, business process automation has had a significant positive impact on capital and labor productivity and the standard of living in countries around the world. Now, we are entering a new era for business process automation with the introduction of robotic process automation (RPA).
RPA software uses artificial intelligence and machine learning to automate high-volume, repeatable tasks-including queries, calculations, transactions and maintenance of records-that have been traditionally performed by humans. Unlike traditional automation, RPA can learn from experience and improve the process over time.
RPA is proving to be not only an essential productivity tool but is redefining the standard for automation across many industries, including automotive, aerospace, banking, finance, medical and telecommunication.
According to Gartner, the annual RPA growth rate could exceed 40% by 2020 as more and more companies recognize the business value of shifting from traditional automation tools that need to be programmed by humans to AI/ML-enabled RPA software.
The AI Difference
There are a few important differences between legacy automation and RPA that are important to emphasize, as they are the foundation for dramatic improvements in business process agility, cost and productivity.
Manage more complexity
Because RPA is powered by artificial intelligence, it is capable of replicating not only simple rote tasks but very complex tasks that have multiple decision points that require “judgment calls” and decision-making capabilities at a scale, speed and accuracy than humans are not capable of managing.
RPA achieves this advanced level by applying a few key cognitive technologies, such as natural language processing so it can converse with humans, machine learning so it can learn from experience, and deep learning, which is a subset of machine learning so that it can learn even more from experience. The more interaction RPA software has with humans and data, the more it can refine the decision-making process, which will autonomously boost process efficiency and performance and lower cost.
For example, when RPA bots are deployed to extract field values from unstructured log data, machine learning modules are deployed to classify the log error and invoke the respective RPA intent to rectify the error. And the result is a 10x faster path to automation.
RPA use cases
In the IT environment, network operation centers (NOC) are responsible for monitoring network alarms such as wrong access point error, wrong total area coverage error and other issues that affect network performance. NOC engineers have several duties they need to perform on a regular schedule, such as remote access authentication, hardware configuration and network parameter configuration to ensure the network runs smoothly.
To automate these NOC functions, RPA bots are deployed to access and update the configuration parameters to and from multiple applications. Human intervention is required to identify which parameters should be updated for an incoming alarm. To achieve intelligent automation—and take the human out of the loop—machine learning modules are trained and deployed to identify the root cause of an alarm based on analysis of huge quantities of historical data. Once the root cause is identified, the autonomous RPA bot fixes the issue.
Another important use case of automation is managing the billing process for say, a telecommunications service provider. In this case, the RPA bot is charged with verifying and correcting the bills and the respective call detail records. It is a complex and time-consuming task for humans to identify which billing statements require reverification and correction. But an RPA bot equipped with a machine-learning module can be quickly deployed to detect anomalous billing and carry out the reverification and correction process in a matter of a few seconds. The RPA bot can learn, process, correct and manage the data much faster than traditional automation software and requires no human intervention to execute the task.
These examples give us a glimpse into how AI can broaden the scope of traditional automation software and dramatically extend the capabilities. Taking advantage of AI and widening the sphere of possibilities, RPA can drive digital transformation and unlock many new opportunities.
In our experience with RPA clients, Aricent has learned that delivering rapid automation success requires a methodical approach founded on the objective of creating business value. We recommend companies begin the process by establishing an automation center of excellence that establishes a balance between the technology and staff empowerment. At the end of the day, it is the humans who will be responsible for the transformation.
About the Author
seasoned Machine Learning and Analytics expert
Pramir Sarkar is a seasoned Machine Learning and Analytics expert with 17+ years of industrial experience. He loves big data architecting and drawing co-relations from them in real time, and is proud of his ability to synthesize the ideas to solve business problems. Pramir always enjoys knowledge sharing with technology enthusiasts.