When I discuss robotic process automation (RPA) with Ventiv’s clients and prospective clients, I’m often asked how RPA differs from machine learning (ML) and artificial intelligence (AI). A November 2017 article from CIO.com offers a good starting point: “RPA can include ML or AI, but it is governed by set business logic and structured inputs, and its rules don't deviate, whereas ML and AI technologies can be trained to make judgments about unstructured inputs.”
This blog post in 30 seconds:
Conventional wisdom holds that for most organizations, robotic process automation (RPA) has value but is only a stopgap on the march toward artificial intelligence (AI) and machine learning (ML).
Conventional wisdom overlooks the great expense and upheaval usually associated with deploying AI and ML. RPA is cost-effective and deploys easily.
In terms of business need, RPA integrates legacy systems. These systems serve important purposes but often can’t be linked to other, newer systems by modern means of transferring information (like API and web services).
All things being equal, why wouldn’t everyone choose artificial intelligence and machine learning and their ability to learn and make judgments? In reality, all things are rarely equal, and deploying AI and ML is usually a very disruptive, costly process. Moreover, you have to ask what you’re trying to achieve; RPA, I’d argue, strives for more modest, yet imminently more attainable, goals.