When to Use RPA and When RPA is Not Working - ByteScout

When to Use RPA and When RPA is Not Working

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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.

When to Use RPA

  1. When to use RPA?
  2. RPA in Healthcare
  3. RPA in Finance
  4. RPA in Law
  5. RPA in Utilities
  6. When RPA is not working
  7. Thinking and decision making
  8. Complex and ill-defined tasks

When to use RPA?

RPA in Healthcare

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.

  • Request management: A large part of the tasks related to patient request management, patient record handling, and grievance redressal involve repetitive and dry paperwork. By assigning such time-consuming yet necessary jobs to software bots you can streamline the workflow and reduce human intervention.
  • Task scheduling: Robotic Process Automation can help manage the schedule and streamline the workflow of an enterprise. Starting from managing patient appointments and upcoming test reports to keeping track of the doctors and medical staff on leave, automated RPA robots can handle everything.
  • Monitoring: RPA bots make sure that the patients do not need to wait for long before they are attained by the medical staff. Automated monitoring can help track patients who need special attention or have deviated from the needed care.

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.

Make Your Robots – Try RPA Tools

RPA in Finance

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.

  • Fight money laundering: Most countries have strict laws and regulations against money laundering. RPA helps identify and prevent potential acts of such crime. They can also make sure all the KYC details furnished by the customers are up to date and comply with all the rules.
  • Account opening and management: Most banks provide instant account opening facilities these days. Chances are, some kind of automation is taking place behind the scene taking care of tasks like verifying client details, past credit scores, or address proof.
  • Accounting and consistency maintenance: Automation robots have been employed successfully for maintaining accounting information over the years. They can perform cliche and mundane jobs like updating customer details, account specifications, and maintaining consistency of the data. And the best thing about them is that bots tend to be much, much less error-prone in comparison to a human employee.

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.

RPA in Law

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.

  • Data manipulation: It’s no secret that computers are much more efficient and flawless at handling mass data. And the judicial system is all about historical records. Artificial bots are especially great at maintaining and restructuring data making it much easier to access them in the future.
  • Automated paperwork: Another potential use case of Robotic Process Automation is in automating or resolving not so trivial legal matters. One such situation might be drafting legal contracts. As these tasks can be interpreted in terms of hard-coded rules, software robots would be a good fit for getting them done without any human expert intervention.

RPA in Utilities

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.

  • Client handling: As the utility industry is a consumer-based sector, serving the consumers immediately, or in the worst case, making them wait as little as possible, is the only rule of thumb. So, although it is still a somewhat new technology in the energy sector, Robotic Process Automation is one of the go-to solutions for many organizations. Tasks like billing, record management, transfer request, complaint lodging, grievance redressal, etc. can easily be addressed with the help of software bots; in fact, they are more efficient at it compared to human employees.
  • Feature scaling: Another reason why Robotic Process Automation is still a very prevalent option to the utility corporations is the ease of scaling features. But the organization needs to be ultra-careful as even the slightest disruption in service while scaling can have a major societal impact. But fortunately, among the other options, RPA is one of those that needs a minimum amount of structural modification.

When RPA is not working

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.

Thinking and decision making

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.

Complex and ill-defined 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.