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How Does RPA Work & What Can It Do For an Organization?

Robotic process automation (RPA) is a powerful tool that businesses are increasingly turning to to maximize efficiency, reduce operational costs, and improve customer experience. But just how does RPA work?

In this blog post, we dive into the history of RPA, how it works, and its pros and cons. We also explore how RPA can be used in tandem with artificial intelligence (AI) to achieve hyperautomation, and how Conexiom can help bridge the gap. Learn more about RPA and how it can increase your business’s efficiency today.

RPA is a productivity-enhancing, efficiency-boosting software used by business leaders to accelerate and refine business processes. RPA automates the execution of mundane, repetitive, and sequential tasks — typically the kind of time-consuming and error-prone activities your employees dread.

Owing to previously unattainable productivity and efficiency gains, RPA adoption has accelerated in recent years. Factors driving growth include imperatives to optimize business processes. Specifically, to increase productivity, maximize ROI on personnel and tech investments, all while striving to increase business resiliency.

So what exactly is robotic process automation?

RPA is a technology that combines a user interface (UI) with descriptor technologies. It enables users to create and implement scripts, also called bots, that execute pre-defined keystrokes or time-based actions. Typically without a human employee supervising the process.

As a result, RPA bots can copy and scale a sequence of operational tasks (called transactional steps). Typically, teams create RPA bots to execute activities that follow yes/no/either/or logic. These bots are particularly well-suited for repetitive tasks that require high accuracy, or which may take a long time, or represent low operational value.

Also, RPA can integrate with popular enterprise applications, and sits at the center of many digital transformation strategies.

In this article, we’ll be taking a closer look under the hood of RPA, examining how it works in more detail. We’ll also briefly review RPA’s evolution, discuss the business pros and cons, and introduce an effective solution to automate what RPA can’t.

A Brief History of RPA

There’s no consensus on whether RPA is innovative technology, or whether it’s a layer of polish for previous generations of software.

Here, we’ll briefly overview the technological developments near the end of the 20th century, which made today’s RPA possible.

Data scraping

Technologists use data scraping, also known as web or screen scraping, to capture information in one application and move it to another. The most common use case is to gather information from legacy systems and transfer it to a new application. The complexity of its code limits the practicality of screen scraping, and some compatibility issues.

Workflow automation

Workflow automation and management tools use pre-configured rules with the goal of making tasks and business processes operate independently. The concept originated in the 1920s with modern manufacturing, but grew to greater prominence in the 1990s. Workflow automation works by capturing business-critical data from documents and transporting it to a system of records.

AI (Artificial Intelligence)

AI software can accurately execute more nuanced, complex business processes, reliably replicating the decision-making and situational awareness of a human employee. AI tends to require a greater initial investment, but can execute transactional steps in the ‘smartest’, and most efficient, ways.

Under the Hood of RPA: How it Works

In theory, businesses can integrate RPA software with legacy systems and enterprise applications to automatically capture data without the need for human oversight.

These are the front-end capabilities which really define RPA and make it a distinct software solution. With that said, RPA is sometimes capable of accessing databases and applications via back-end connections.

In the words of IBM, RPA technology must include the following capabilities:

  • Low-code capabilities to build automation scripts
  • Integration with enterprise applications
  • Orchestration and administration, including configuration, monitoring and security

Setting up RPA

Process discovery is the initial step of RPA setup. Business leaders support process discovery by working with employees to establish which repetitive, sequential tasks are most suitable for automation. With this clearly defined, an organization can move to begin sourcing the software.

Software engineers working with an organization’s chosen RPA platform create the scripts that execute the steps in a process, as defined by employees. The software selects the most logical and streamlined automation workflow

Attended vs unattended RPA, and front/back-end integrations

As we mentioned earlier, RPA software may connect with your applications through one of two ways: front and back-end integrations. On the back-end, the RPA software accesses systems under the control of a process automation server. Usually, this will be for unattended RPA, where the bots perform tasks with a bare minimum, or ideally zero, intervention from human employees.

For front-end integrations, the picture is a little more complicated.

There are a lot of ways to connect RPA to desktop applications; the goal is for the software to read/write data to execute the process, while also capturing information directly from a UI. By combining the two functions, RPA can more fully replicate the behavior of an employee.

Capture is a key characteristic of attended RPA. It ‘recognizes’ informational elements based on interpretation of properties and technology families, and by auditing structure. Where the information is located on the screen is unimportant.

RPA might also integrate with applications on the front-end by controlled user interface connectivity. The interface makes it possible to activate hidden fields or access unseen controls that are not viewable to other users. Admins can use this functionality to increase security around sensitive information like account numbers or banking info.

Testing and refining

Pilot programs for RPA software are a crucial part of the overall rollout. It’s not ideal to hand over the controls of a business process to bots without extensive testing and tracking the real-world functionality of those bots.

It’s also worth comparing the performance of RPA software with that of staff. Often, this can present key findings and feedback, enabling learnings which an organization can use to really fine-tune the software.

Overcoming scalability shortfalls with AI integration

A key consideration in the functioning of any RPA system should be its ability to scale effectively as operations grow. Scripts that work well at a certain level of operational capacity may prove to be wholly unsuitable for an expanded workload and increased volume.

Similarly, as an organization increases the scope of work for the script, the risk of errors or lack of visibility increases as well.

A clear understanding of systems and standard operating procedures is essential knowledge for an enterprise looking to implement RPA. Knowing what’s under the hood will speed up identifying and resolving issues while scaling bots, and also help maintain compliance with industry regulations.

Though the need for accurate standard operating procedure (SOP) documentation is clear, the production of such documents often returns an organization to a manual process. The technology inherent in RPA is not, by itself, sophisticated enough to produce effective self-maintenance documentation and SOP oversight.

This leads to more time-consuming, labor-intensive work that makes it hard for employees to deliver value. Plus, when opportunities to scale arrive, spotty documentation means critical insights are missing.

The challenges of bot oversight are a good example of where RPA can align with AI to produce true digital transformation. In fact, AI-guided RPA is part of the promise of hyperautomation. While devs can program a bot to handle repetitive sequences, a sophisticated AI can supervise the process and address errors in real-time.

Pros & Cons of Implementing RPA

When dealing with structured data it can easily process, and manipulate, RPA software opens the door to a lot of significant business benefits, including:

  • Increased employee satisfaction
  • A vastly improved customer experience
  • Reduced operational costs
  • More accurate data processing
  • The ability to plug into legacy systems
  • A decrease in technical software code

However, take this with a grain of salt. While RPA is good at dealing with structured data, real-world documents often contain variables that fall outside a predefined process. Because RPA is not adaptive, these unexpected inputs can break automated processes.

In this scenario, RPA stops working, and organizations are left simultaneously trying to pick up the pieces, mitigate shortfalls, and keep operations running.

The business truth is that RPA alone is not enough for a true digital transformation. Without effective integration with complementary software, RPA cannot achieve hyperautomation for a business.

Aside from the problems with unstructured data, which are considerable, there are scalability issues which we’ve already touched on. After automating the lowest hanging fruit, i.e., the most simple and repetitive tasks, organizations tend to falter when attempting to automate more complicated processes. As staff dedicate time to refine and update the software, they must continually test and re-develop it, reducing gains in efficiency and productivity.

Maximizing RPA & Bridging the Gap

With that said, it is ideal to deploy RPA alongside other solutions to maximize its potential.

In many ways, RPA is similar to OCR (optical character recognition); the software itself presents some powerful opportunities and the potential to make real gains. Integrating it with other software that works in harmony is the next step to achieving a real transformation.

When your business needs to process more complex documents or replicate complicated business logic, Conexiom’s more advanced, adaptive AI is a better fit.

By minimizing manual document processing, you create space for more valuable activities like communicating with customers or managing suppliers. What your team can do after automation is where busineses yield value from their investment in solutions like OCR, RPA, and AI.

To find out more about Conexiom Platform and discover how we can help propel your business forward, get in touch today.

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