We recently announced the strategic alliance between Jidoka and BigML, where we explained the integration of RPA with other technologies such as Machine Learning. With this integration, Jidoka can provide Machine Learning capabilities in their RPA process automation platform.
To explain the advantages and possibilities offered by this integration, today we present a practical example of the application of both technologies, Jidoka’s RPA and BigML’s Machine Learning: the automation of an e-mail classification process, a use case that will be presented by Jidoka’s CEO, Víctor Ayllón, at the #MLSEV, our first Machine Learning School in Seville, which will be held on March 7-8 in Seville (Spain).
Imagine for a moment that you are responsible for the customer service department of a large company. You and your team receive on a daily basis a very large number of customer emails that are addressed to different departments of the company. You end up spending a lot of time processing these e-mails and redirecting them to the most suitable department to deal with the customer’s request, perhaps using an incident management tool for this task. As it is a process that is performed manually, opening and processing emails one by one, you are conscious of many requests not being dealt with as quickly as would be desirable and you ask yourself the critical question: how can I make this whole process more agile and responsive?
The combination of RPA+ML can be the answer. We explain it to you in the short video below that describes step by step how these two technologies complement each other to automate this process from start to finish, focusing on what each one can “do best”. The video presents the automated process that Jidoka’s CEO, Víctor Ayllón, will present in detail at #MLSEV on March 7-8 in Seville. You cannot miss it!
Jidoka’s software robot takes care of repetitive and mechanical tasks: it opens the mailbox and checks for unread emails, extracts their contents and accesses the Machine Learning tool for analysis. On the other hand, BigML is in charge of processing and interpreting the information contained in the mails, in order to identify, through a predictive model, which department they are related to. But automation is not finished here. Once the target department has been determined, the Jidoka robot resumes the process and uses the BigML prediction to assign a task to the relevant department, using the company’s ticket or incident management platform (in this case Atlassian JIRA).
In this way, using different systems and corporate applications (email manager, task management tool, etc.), RPA and ML complement each other in the execution of the process, and together they make it automatized and faster.