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What is Robotic Process Automation (RPA)?

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A great deal of the work that people do every day doesn’t involve any of their creativity or unique skills, being highly tedious and simple tasks like categorizing emails and messages, updating spreadsheets, processing transactions, and more. Robotic Process Automation (RPA) is an emerging technology that often leverages aspects of artificial intelligence to automate these tasks, with the goal of enabling workers to devote their attention to more important tasks. RPA can be accomplished with a variety of different techniques, tools, and algorithms, and the corrected applications of RPA can bring organizations many benefits.

What is Robotic Process Automation (RPA)?

Despite having the name “robot” in it, Robotic Process Automation has nothing to do with physical robots. Rather, the robots referred to in RPA are software bots, and RPA systems are essentially just a collection of bots that carry out specific, often tedious tasks. RPA bots can run on either physical or virtual machines, and they can be directed to carry out tasks by the software’s user. RPA interfaces are intended to allow even people unfamiliar with the construction of the bots to define a set of tasks for the bot to perform.

As previously mentioned, the main purpose of an RPA is to automate the many repetitive, mundane tasks that people often have to do in a workplace. Saving time and resources is the goal of RPA. The tasks that RPA is used to carry out need to be fairly simple, with a concrete series of steps to follow to accomplish this task.

Benefits of Robotic Process Automation (RPA)

When properly utilized, RPA technology can free up timer, personnel, and resources, letting them be applied to more important tasks and challenges. RPA can be used to enable better customer service by handling the first interactions with customers and directing them to the right customer service agent. RPA systems can also be used to improve how data is collected and handled. For instance, when transactions occur they can be digitized and automatically entered into a database.

RPA systems can also be used to ensure that the operations of a business comply with established standards and regulations. RPA can also meaningfully reduce human error rates and log actions taken so that if there if the system does produce an error, the events that led to the error can easily be identified. Ultimately, the benefits of RPA apply to any situation where a process can be made more efficient by automating many of the steps needed to complete that process.

How Robotic Process Automation (RPA) Works

The exact methods RPA platforms and bots use to carry out their task vary, but they often employ some machine learning and AI algorithms, as well as computer vision algorithms.

Machine learning and AI techniques may be employed to let the bots learn which actions are correlated with the goals the operator has defined. However, RPA platforms often carry out most of their actions according to rules, therefore acting more like traditional programs than AI. As a result, there is some debate regarding whether or not RPA systems should be classified as AI systems.

Even so, RPA often works in concert with AI technologies and algorithms. Deep neural networks can be used to interpret complex image and text data, enabling the bots to determine what actions need to be carried out to handle this data in the manner the user has specified, even if the actions the bot takes is strictly rules-based. For instance, convolutional neural networks can be used to allow a network to interpret images on a screen and react based upon how those images are classified.

What Processes Can Be Handled By RPA?

Examples of tasks that can be handled by RPA systems include basic data manipulation, transaction processing, and communicating with other digital systems. A RPA system could be set up to collect data from specific sources or clean data that has been received. In general, there are four criteria that a task must fulfill to be a good candidate for automation with RPA.

First, the process must be rule-based, with very specific instructions and ground facts that can be used to determine what to do with the information the system encounters. Secondly, the process should occur at specific times or have a definable start condition. Thirdly, the process should have clear inputs and outputs. Finally, the task should have volume, it should deal with a sizable amount of information and require a fair amount of time to complete so that it would make sense to automate the process.

Based on these principles, let’s examine some potential use cases for RPA.

One way that RPA could be used is to expedite the process of handling customer returns. Returns are typically a costly, time-intensive endeavor. When a return is requested, the customer service agent has to send a number of messages that confirm the return and how the customer would like their money refunded, update current inventory in the system, and then after making the payment to the customer update the sales figures. Much of this could be handled by an RPA that ascertains which items are being returned and how the customer wants their refund dispersed. The RPA would just use rules that take as an input the product being returned and the customer’s information and output a complete refund document that the agent would just have to glance at and approve.

Another potential use case for RPA is for retailers who would like to automate aspects of their supply chain management. RPA could be used to keep items in stock, checking inventory levels whenever an item is sold and when the stock falls below a certain threshold orders for replacements can be made.

Drawbacks To Robotic Process Automation (RPA)

While RPA systems have the potential to save companies who use them time, money, and effort, they are not suited to every task. RPA implementations may often fail due to the constraints of the system they operate in. If not properly designed and implemented, RPA systems can also exacerbate currently existing problems as they operate on rules that may cease to be applicable as situations evolve. For example, if an RPA system is instructed to order replacements of items whenever a stock falls too low, it may not be able to adjust to fluctuations in demand and continue ordering large batches of products even as the overall demand for those products declines. Scaling RPA platforms up across a company also proves to be difficult, as the more rules-based a system-becomes the more inflexible it becomes.

Additionally, the act of installing thousands of bots across a system might be much more time-intensive and costly than expected, potentially costly enough that the savings the RPA system brings don’t offset the costs of installation. The economic impacts of RPA systems can be difficult to predict and the relationship between automation and cost reduction is not a linear one. Automating 30% of a task will not necessarily reduce a company’s costs by 30%.

Blogger and programmer with specialties in Machine Learning and Deep Learning topics. Daniel hopes to help others use the power of AI for social good.