AI Integration with RPA (Robotic Process Automation) - ByteScout

AI Integration with RPA (Robotic Process Automation)

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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.

Make Your Robots – Try RPA Tools

Difference between RPA and AI

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.

  • Action-centric knowledge-centric: Simply put, RPA is concerned more about mimicking some human action instead of truly understanding it. AI, on the other hand, is more interested in analyzing that specific human action and mimicking the decision making and learning process (instead of simply copying it).

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.

RPA Integration with AI

In short, RPA is much more action-centric whereas artificial intelligence and machine learning are more knowledge-based.

  • Process-driven data-driven: Another key distinction between RPA and AI lies in their core interest. RPA is mostly process-driven, and it is usually associated with automating bulk, rule-based tasks. Things like AI and ML, then again, are all about quality training data. The higher in quality and quantity it is, the better the final model you can expect.

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.

The necessity of AI integration

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.

Benefits of AI integration

  1. Better outcomes: Focusing personnel on higher valued activities has the potential to improve business metrics. Indeed, some processes add more value to an enterprise compared to the others. These processes are simply more important and usually many other processes depend directly or indirectly on them. Think about the interaction between a consumer and an employee – they are way more important than scraping invoice data. RPA and AI together is an excellent tool fit for these processes that can help automate the process while improving the overall performance and productivity of the organization.
  2. Decreased costs: Although it might sound scary, automation can reduce the number of available jobs. Though it had not been that much of a threat in the past, this can change. Many specialists including Andrew NG support this fact and predict that the risk of this job shrinkage is highest in those sectors that can be replaced by bots. But again, that can be seen from another perspective – by reducing the redundant jobs the organizations can reduce their cost significantly. Experts predict that 30-50% cost reduction is possible thanks to the unification of AI and RPA.

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.

  1. Higher employee satisfaction: There is no need to go through the painstaking task of replicating data manually from one machine to another. Many studies have shown time and time again that job satisfaction is directly related to the type of task that the employees handle and the amount of appreciation that she holds towards it.
  2. Improved internal processes: Even before an organization can implement AI into the already existing RPA pipeline, they need to define certain governance rules. Without the rules, it would only be a matter of time before the system starts to fall apart. Having clear and predefined rules help improve the overall performance metrics of that process. It fast forwards the reporting and onboarding procedure and, in effect, improves the productivity of the organization.
  3. Reuse, not replace: Certainly, the most crucial benefit of including AI in the workflow is that it does not necessarily need you to update your present systems. Rather, it fits into the present system which is indeed a big benefit.

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.