We’re all familiar with work that suffers from the three Rs - Routine, Repetitive, and Rules-driven. We’re also aware that this kind of work can not only be boring and demotivating but can also lead to errors that cause bigger problems further down the line.
Enter RPA. Robotic Process Automation is software that aims to automate back office work, eliminating the need for humans to perform mundane tasks like gathering and sorting data and freeing up their time to do more strategic work. By replacing full-time equivalent (FTE) hours with machine-based learning, we can give time back to departments such as HR, financial services, and call centers to focus on getting more strategic and meaningful results from their work.
A Stepping Stone to Machine Learning
In many ways, RPA lays the groundwork for machine learning and more intelligent applications. If the companies we work with have an RPA solution in place, it generally means that they have an automation and data-first mentality and are probably ready for more complex machine learning models to help solve their business problems. It also means that their data is probably in a format that we can use as inputs for machine learning models.
In many cases, once an organization has seen the levels of productivity that machine learning can produce through RPA, many are eager to embrace more complex digital transformation strategies.
RPA and DataRobot
For machine learning to work appropriately, it needs real-world data sets to work from. RPA does this well, enabling users to gather vast quantities of data to get them moving. In fact, it has been said that data is the new oil, fueling a marketplace that is looking for more and more ways to combine RPA and AI. Since data is the fuel that powers AI, then RPA is a natural supplier of that data and a natural complement to the technology.
The process of working with DataRobot is a seamless one. Once the RPA vendor has collected and sorted data into the right format for machine learning, it is put into the DataRobot system, which automatically trains a model or produces a prediction. This can then be fed back into an RPA system so that it can act on that information by doing whatever task is necessary. In order to ensure that you are consistently getting accurate predictions, DataRobot can pass along new data and retrain a model to ensure accuracy.
A good example of this is when a prospect visits your website. That prospect is a lead that can then be inputted in SFDC automatically using RPA. The RPA vendor feeds that lead into DataRobot where we can provide a lead score on whether or not the prospect is a good target. That score is fed back to the RPA tool. If that prospect is a good lead, the RPA tool can create an automated email to the prospect and you can move that lead forward in SFDC.
Ours is a building blocks approach where the ultimate goal is to create a loop like this one:
By automating processes with RPA, companies will achieve quick wins and ROI. However, in today’s competitive environment, companies should seize the opportunity to implement AI to learn from and constantly improve upon their business processes from the start.
It is easy to get started by using our software development kit so that you can deploy at scale. Register for our webinar on February 14th from 12:00-1:00 EST to learn how American Fidelity utilized UiPath and DataRobot to implement intelligent process automation across their organization. Find out how they leveraged both automated machine learning and RPA capabilities to significantly impact the enterprise!
About the Author:
Scott Armstrong works in Business Development at DataRobot. Previously, Scott ran Business Development at Domino Data Lab and worked in Business Development at Cloudera. He enjoys the intersection of Automation, Data Science and AI, and spends time with his wife and kids biking and hiking in the woods of South Carolina and North Georgia. Scott has an MBA from the University of Chicago Booth School of Business.