RPA, ML and AI – Explained

Robotic Process Automation (RPA) is an inclining way of Business Process Automation technology. It means, technology-enabled automation of complex and repetitive business processes. In traditional workflow automation, a software engineer will write a set of commands/action list and interface to the back-end using API or scripting language. In contrast, in RPA develops the set of commands/action list by watching the user carrying out the task in the application’s Graphical User Interface (GUI). Then, instead of changing the back-end, it performs the automation by repeating the task directly in GUI. This helps the company to implement the automation quickly and efficiently and with limited requirement of back-end technology expertise. RPA helps an organisations to automate Routine tasks, complex processes, queries, calculations, maintenance and transactions.

To implement RPA, it is best to use the software with Artificial Intelligence (AI) and Machine Learning capabilities. AI, in a summary, is the simulation of Human Intelligence processes by machine. This includes learning, reasoning and self-correction by machine itself, without human interference. Most common example of AI is Apple Siri. Machine Learning, on the other hand, is a set of algorithms that allows software applications to improve its predictions and output as the new data comes. ML also detect the patterns in data and adapt the system accordingly. Thus, RPA, in fusion with AI and Machine Learning can provide a significant edge to an organisation.

AI and ML empowers RPA as they add a human touch in the system. RPA alone promises to run 24 x 7 with no stops, no breaks, no vacation, no omitting, no forgetting, no misunderstanding. This sounds promising, but it is a theoretical assumption. It would be difficult to meet this level of service. There will always be exceptions, unexpected events, corner cases and incorrect input data. This is where the AI and ML becomes more important. A synergy of RPA, AI and ML tends to automate even the emotional-based and judgement-based processes. AI and ML integrate a human response into their workflow. This means, that the machine will take a decision which is much closer to how a human would take the decision. AI and ML also take advantage of heterogeneous (text, voice, natural language) and data-dumps (unstructured data) to create rules and pattern thus taking productivity to the next level.

A clear difference between just RPA and RPA+AI+ML is:

RPA: Mimics the human activity in GUI and creates a set of commands in a non-intrusive way. It can handle structured and semi-structured data. The possible actions are determined by the predetermined rules and the behaviour is deterministic.

RPA + AI + ML: Mimics human activity by pattern detection, speech recognition, activity on GUI, reasoning and self-correction. It can handle structured, semi-structured, unstructured and heterogeneous data. Machine learning let the system learn to process data, make decision and improve process.

Even though a fusion on RPA+AI+ML sounds like the only way to implement automation, we should not forget that the costing increases with application of Automation. As different organisations have different requirements, budget and data collection, the automation should be selected carefully before applying. Some of the top applications of RPA are Customer Service, Accounting, Finance Service, Health Care, Human Resource, Supply Chain Management.