Robotic Process Automation (RPA) is one of the most prevalent technologies of this era. Countless businesses and organizations from multiple industries have implemented it into their workflow. But in itself, RPA is not ‘smart’ enough to tackle the complexity of many processes.
Although not yet very mainstream, many organizations have already started thinking about uniting the powers of RPA and AI in order to create a new tool, usually called Intelligent Process Automation, that would be able to overcome the limitations that the RPA has.
Okay, let’s first understand the difference between Robotic Process Automation and Artificial Intelligence. There are barely any similarities between these technologies, to be honest.
Let’s consider our classic example – invoice processing. Say you run the most popular restaurant in town and you receive tons of invoices each week from the suppliers. Had your finance department been in charge of manually checking those, it would simply take forever. Instead, an RPA bot can be employed to extract crucial information like date, content, and charges from them. But an RPA bot can only do that much – scraping information. There is no room for learning new things like processing a new type of invoice.
In short, RPA is much more action-centric whereas artificial intelligence and machine learning are more knowledge-based.
For our case, we are concerned about coming up with a sufficiently large training dataset, guaranteeing our examples are of good quality. The only task left after that is to train a model capable of handling the complexity of the task.
So RPA, in essence, is a process-driven technology. On the contrary, Artificial Intelligence and Machine Learning are much more data-driven.
As organizations try to squeeze as much value as they can from Robotic Process Automation, it is crucial that we have a clear understanding of what it can or cannot achieve.
Even though an RPA bot is somewhat smart, one of the most essential things to keep in mind is that the actual workhorses of any RPA framework are the bots. And they are in general NOT intelligent in the sense that they cannot learn.
They are also deterministic, meaning that they will do exactly what they are programmed to do, what the hard-coded rules tell them to do. The organizations want to see exactly what – a framework that performs deterministically, instead of probabilistically which is much harder to maintain, and especially debug.
This failure to learn over time is the biggest limitation of RPA, which can be overcome by using AI which adopts a probabilistic strategy. The strict requirement of structured or semi-structured data is one of the biggest limitations of RPA. That is precisely why RPA in conjunction with AI, can be a great tool for handling non-structured, free-flowing data such as emails and texts.
So it’s inevitable that some positions are going to become redundant. The possible solution to the above-mentioned issue is not anything groundbreaking – you need to upskill the human resources in order to keep them stay relevant. Another slightly different rout can be to relocate the employees to some processes that are not that easy to automate and involves complex decision making.
AI and automation together help make the internal processes and the whole workflow much more fluid and flexible. As a result, the human workforce can be shifted from low-value, repetitive processes to high-value, administrative tasks.