This six-part blog series will guide readers through the latest concepts in multichannel document capture, with an emphasis on how AI has transformed what's possible in making your documents and data work for you – and not against you. The emergence of Robotic Process Automation (RPA) was a watershed in helping businesses cost-effectively acquire and tap into the value of data. Thanks to software robots, massive quantities of electronic information were no longer trapped in internal and external systems, and businesses could now harness the power of RPA to access, manage and use this data in powerful ways. At its core, RPA solves problems associated with data-centric manual tasks and remains ideal as a tool that interacts with websites, business and desktop applications, databases and people to execute repetitive work. Simply put, RPA is the "handwork" of processing electronic data.

To try providing anchor points to answer these questions, my colleagues and I have built a simple and very useful framework. In this article, we will describe this framework and provide the keys to understand it. In our next article, we will explain how to use it by providing use cases, and explaining how to build a successful strategy and implementation approach. Please give us your comments, and / or "like" the article; we would love to hear your views and get your reactions. Building on the "future of RPA" as described in my previous article, below are the descriptions of the different generations of robots presented in this framework: The traditional RPA generation includes robots which can perform transactional, repetitive, rule-based actions in a digitalized environment ("dumb robots").