RPA is revolutionizing the enterprise software market by automating repetitive tasks and leveraging machine learning and AI. Leading organizations, including Telefonica and Xchanging, have turned to Robotic Process Automation (RPA) to streamline business processes and boost efficiency. As the fastest-growing segment in the enterprise software market, RPA has earned its reputation by automating repetitive, time-consuming, and error-prone tasks, resulting in significant cost savings and improved process performance. By mimicking human interaction with graphical application interfaces, RPA blurs the line between day-to-day software and cutting-edge technology.
RPA is an umbrella term for tools that interact with the user interface of computer systems, emulating human actions. While not a new standalone class of techniques, RPA relies on various AI-based enabling technologies to perform its functions. The key question is what tasks can be automated effectively?
Implementing RPA is suitable for processes that are infrequent enough for traditional process automation to be unprofitable but repetitive enough to be formalized into an RPA process. Opportunities for RPA can be identified through logs of interactions between workers and web/desktop applications or by adopting frameworks like Value-driven RPA.
Value-driven RPA, part of process-led digital transformation management, leverages Business Process Management (BPM) capabilities to maximize the value of digital initiatives quickly and with minimal risk. Intelligent Process Automation (IPA) in some sense represents the intersection of RPA with machine learning and AI. For companies to implement RPA-based solutions, they must first build the right datasets for learning. While this requires initial internal effort, these datasets can later be used across similar industries to bootstrap learning agents. RPA's primary goal is to replace human labor, so data collection should focus on activities typically performed by humans, such as UI logs (clickstreams, keylogs, etc.). For reinforcement learning-based solutions, events should be recorded at a more granular level, to reconstruct state features and action probabilities later.
RPA faces hurdles in handling exceptions during automated processes and managing process automation at an organizational level. In the context of Process Analytics, RPA aims to automate business processes involving human-software interaction and provide decision support for resources engaged in process execution, offering exciting opportunities for business process improvement.
Reinforcement learning has the potential to replace or automate knowledge worker decision-making, but these systems are still in their infancy. Currently RL based systems are being used as as recommender systems that offer decision support to knowledge workers.
Overall RPA represents an interesting shift, one that takes a stab at automating part of business processes which consist of humans interacting with day to day software (e.g giving them transferring data from an ERP system to a web application form). The ability to curate enterprise knowledge and use to reason, plan and learn however are some of the bigger challenges, that still need to be addressed.
References:
[1] Integrating Robotic Process Automation into Business Process Management
[2] Robotic Process Mining: Vision and Challenges
[3] How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in Business Process Management?
[4] VALUE-DRIVEN ROBOTIC PROCESS AUTOMATION (RPA) ENABLING EFFECTIVE DIGITAL TRANSFORMATION