Several articles have been dedicated already to discussing the pros and cons of Robotic Process Automation, RPA in short. This new technology has, in many ways, revolutionized the way enterprises used to handle their internal processes. In this article, we will be looking at some of the real-life use cases of RPA; industries where either RPA has been implemented successfully or are in the process of planning and testing.
Healthcare is one of the many sectors where RPA has been employed extensively around the globe. Healthcare is a huge industry and RPA has been employed in almost every aspect of this business including scheduling, patient management, regulation compliance, and claim management.
It is one of the few industries where manual and repetitive work is abundant, but with the implementation of RPA, the Healthcare industry can overcome all its inefficiencies and provide more resources for the improvement of the healthcare system. RPA can eliminate the possibilities of human error in healthcare management systems.
The benefits of RPA in healthcare include reduced cost of running the industry since the automated bots would require fewer resources to run than a human employee. Also, the possibilities of human error would be removed from the management systems. The scheduling and appointment procedures will be conducted faster and more efficiently, allowing more patients to get medical care.
Various financial institutions, such as banks and hedge funds, make use of software bots quite often to get rid of the boring, repetitive paperwork. Some of the advantages of employing software bots for these works are lower latency, higher efficiency, and fewer errors.
It helps improve productivity while lowering the cost and streamlining the workflow. Also, the bots are less prone to making errors, which is crucial for financial institutions, as errors can often result in substantial economic losses.
The following are some of the use cases for RPA.
The benefits of RPA in Finance include higher productivity because bots can work 24/7 and make zero mistakes. It also reduces the cost of employment of human staff, and the teams can use resources for more strategic work rather than the routine processes. RPA also ensures data safety and lowers the risks for financial operations by providing improved metrics.
Automation bots can help make the law industry much more transparent as well as efficient. Ask any legal institution or a law firm and they would tell you that a major part of their time is spent getting the paperwork done the right way. By reducing the time that legal advisors and lawyers spend doing all the paperwork and formalities, software bots enable law professionals to spend more time on what matters more – solving the legal issue.
The significance of Robotic Process Automation in enhancing end-user satisfaction is immense. First, by reducing the time spent doing the paperwork you inadvertently reduce the time spent resolving the procedure. Second, as structured data becomes more and more accessible, the whole advisory process becomes more transparent. And third, time equates to money. Less time spent resolving an issue implies the cost would be lower as well.
Utility corporations are at the top of the hierarchy – from east to west, everywhere. And there is no secret behind it – they serve some of the very basic needs of the citizens, be it electricity, gas, or water. RPA has played a significant role in automating many of the client-side processes for the utility corporations including billing, complaint resolution, and record management.
Before employing automation in a business, it is crucial to understand what processes need to be automated and how automation can help the process. A non-robotic process might suffer from the application of automation.
If your organization is thinking about automating some of the internal processes, you need to have certain checkboxes ticked. Robotic Process Automation is not suitable for every task. Knowing which processes can and should be automated using RPA (or any kind of automation, in general) needs experience. These are some scenarios when you should NOT be using RPA.
Although automation bots have now become somewhat intelligent compared to what they used to be, strictly speaking, they are not used for decision making. RPA bots are best used in cases of repetitive and routine tasks rather than thinking processes or decision making.
Therefore these packhorses can only do whatever they are explicitly programmed to do, nothing less, nothing more. And that’s what makes them different from AI-powered tools. Despite various advancements, AI-driven solutions are still not very widely used mainly for two reasons: either they are not very predictable or they are simply not marketable yet.
Let’s take an example: say, you run Guido’s – the hottest Italian restaurant chain in the town. Your suppliers send you the invoice over email at the end of every week. Needless to say, it would be a really tedious and time-consuming job to go through each of the mails, download the invoice, keep a record of them and create a bill.
Instead, if you decide to employ an AI-powered bot, it can not only automate all these steps, it can even create a gist of the mail or send the manager an alert if it spots some anomaly or inconsistency in the numbers. But an RPA bot can not perform these advanced cognitive tasks.
Another thing you need to make sure of is that the task is well-defined. A well-defined task can be decomposed into meaningful granular micro-tasks so that there’s no scope for ambiguity.
Robots need to be programmed to carry out a task, but if the task is not defined correctly, there might be room for errors while the task is processed. Also, RPA cannot take its own decisions, so the developer needs to feed the entire task into it.
Say, Guido’s has decided to give some of its loyal employees who have been there for a long time a bonus. So, ‘append the name of the employee into the incentive list if he has been working here for over 3 years is a clear task, whereas ‘call the supplier to fix a meeting in the next week’ is clearly not. First of all, it is simply way too complex for a software bot. While this might be a walk in the park for a human employee, perceiving human emotions is a terribly tough job even for the most sophisticated AI agents.