Seamstress --> Sewing Machines --> Garment Factories --> Computer Controlled Stitching Machines
Propeller Planes --> Jet Engine Planes --> Instrument Landing --> AutoPilot --> Drones
Punch cards --> Green Terminals --> Rich clients --> Web UI --> Bots
In each of the above cycles of human progress, what started off as an individual, human skill-driven activity that produced a certain amount of output/throughput got mechanized. Over a period of time, those activities were vastly automated in a fashion the original practitioners could never have dreamt of. The biggest advantage of these automation cycles was the enormous scaling up of the throughput/output of these processes, along with a high level of output predictability. As the scale and predictability went up, the level of human involvement during these processes became more exception-driven than need-oriented. Along the way, the measurement of these processes and their throughputs also matured, leading to an exceptional, data-driven ability to spot anomalies and introduce corrections over time.
Welcome to Robotic Process Automation. Business processes that have historically been data entry intensive—with regular “handshakes” across different computer systems—can now go through the same cycle of human progress mentioned in the previous examples. Once these processes are cookie-cuttered into Standard Operating Procedures with a series of well-defined steps and tasks along the way, they can be easily automated, measured, and improved over time using RPA software. The benefits are exactly the same as mentioned for the above cycles of human progress: an enormous scaling up of the throughput/output of these processes, and a high level of output predictability.
How can RPA get better over time with analytics?
●The more you measure, the more you can assess, correct, and fine tune for better performance. This applies to any software, and software bots are no exception
●By logging every step of any process performed by the bots (ex: mortgage loans processing, insurance claims handling etc.) along with the data that flows through the bots, we can lay the foundation for a robust analytics platform
●Benchmarking for their performance against process targets (volumes, unit processing time etc.) can help large companies do 2 things: simplify the process steps or add more bots to scale up and handle more volumes
●As the bots get better over time, the next phase of analytics can typically include recommendations provided by the analytics software that can make the bots leaner, smarter, and hungrier!
Our largest customers started small but over time have been able to grow their capacity for their processes multi-fold with a direct impact on their business topline. The digital workforce represents yet another inexorable march of human progress to doing bigger things with their creativity.
By Jay Bala, Senior Director of Products, Automation Anywhere
Jay’s extensive experience in big data analytics and two decades of high tech business leadership are integral to his role at Automation Anywhere where he leads the products team in building an analytics platform to measure the effectiveness of robotic process automation in large enterprises. Before joining Automation Anywhere, Jay was VP, Big Data Solutions and Information Management at Nexient, was co-founder of Claritics (later acquired by Nexient), and held product management leadership roles at companies such as Informatica, Influence, and Ramco Solutions. Jay has an MBA from the Indian Institute of Management, Calcutta, as well as a B.Tech from the Indian Institute of Technology, Madras.