Sophistication or Simplicity – Traditional Automation vs RPA

As Oracle’s HR cloud approaches its 10th year, HCM has added cutting-edge, if not edgy, new features to deliver more meaningful functionality, improve employee engagement and drive efficiency with AI

As AI-powered “machine learning” robots inch closer to the marketplace, it’s worth considering how the current level of AI-automation (Robotic Process Automation) compares to traditional “Back-end” database automation.  Over the next 5-10 years, RPA business is projected to grow from a $500 million to a 3.1 billion dollar market. 

With such large potential, it becomes prudent to ask – “Don’t we already have tasks we program for automation?  How would RPA automation differ from what we already have?”  The article below will get into the differences between the two kinds of automation, while making a central point clear – while RPA holds immense power and earning potential, there are some examples where, even 20-30 years from now, that traditional automation will be a preferable option.  Let’s get into the key differences below:

RPA and Traditional Automation: Front-end vs. Back-end control

Before RPA, traditional automation always required the presence of a savvy IT professional or coder for a simple reason: traditional automation requires the specific software coding instructions be included in the “back-end” database of a server.  Setting up such tasks can be incredibly time-consuming and technically difficult – they usually take months to set up, and once they are, the IT professional needs to stay present in case any bugs arise, taking them away from other projects that could use their attention.

With RPA, the AI-infused bot works only the “front-end” of the server, interacting through the Graphic User Interface (GUI) instead of through the “back-end” physical database.  This shift frees up the IT professional, and instead makes it so a company could focus on putting an expert of the content in charge of the project.  For example, instead of having to code the automation for a banking service collecting and organizing personal customer information, the RPA-powered bot could allow a bank teller to control the process, making sure the banking expertise can shine through technical difficulties that arise with “back-end” programming. 

The Key Advantage of RPA – seamless use across applications

Because RPA works on the “front-end” GUI of a server, its main advantage over traditional automation manifests in how gracefully it integrates multiple applications to achieve a specific goal or end in mind.  In one example, RPA bots can revolutionize the payroll industry through integrating several applications that complete the “payroll setup” and payout of workers.  At West Monroe Partners, “RPA bots can pull new-hire information from a word document, and in turn use that information to populate ADP payroll, expense and a time-entry system […] It then designs and orders business cards and even schedules building access on an employee’s first day. Traditional automation methods just can’t handle this type of scenario.” 

In other words, RPA integrates all the applications that need to be accessed when setting up a company’s payroll system.  Instead of someone needing to manually open every application and carry the same data across each platform, RPA is the first step in allowing an AI-powered bot to flow across each app platform, populating if with the necessary information that flawlessly sets up payroll for the month.  Traditional automation could maybe organize new-hire info into a single excel spreadsheet, but it would not have the capacity to then access ADP payroll apps, and it certainly could not then schedule an appointment for the employee in question. 

RPA enjoys this advantage because it does not need the company to develop an API that governs how a certain set of data can be applied to different applications.  Instead of requiring the use of an API to allow programs to “talk” to each other, RPA does not require the work needed to set up these APIs – it can bypass this step and begin working for the user right away.  As a result, user-run RPA processes do not require a high degree of technical expertise; instead, it would be more helpful that the user is an expert in the particular field the technology is being employed for (e.g. – banking, health care, etc…).  However, just because RPA holds seamless “front-end” user movement, that does not mean there are certain scenarios where traditional automation maintains its superiority. 

Hold the Phone!  Key Advantages of Traditional Automation

While RPA currently finds its “sweet spot” in situations where an application lacks an API (or access to legacy servers) to connect to a larger server database, traditional automation (controlled through these API’s) can easily outpace RPA should these API’s be in place.  The presence of an experienced software programmer on call can effectively make use of API’s such as JSON, and from there can automate tasks that require less integration.  For example, simple calculations and macros (such as populating an excel spreadsheet) can still be done through traditional automation, and it would be a waste of time, money, and resources to utilize the inter-application flexibility of RPA. 

As Parikshit Kalra sums up, “You don’t need an excavator if a shovel is good enough for the task at hand.”  Simply put, there are specific scenarios where it would be overkill to employ RPA.  The technology is still in its nascent stages, and indiscriminate use of it may lead to a snowballing of tech costs to keep the RPA bot up-to-date. 

Should a high-level software engineer be employed, he or she can code an API that allows the transfer of massive sets of data across servers.  At this point, RPA, working only at the “front-end” user interface level, would not have that level of effectiveness in vast sums of data.  Rather, RPA would be better-suited for specific scenarios where the user can utilize their field of expertise while gliding across various applications.  Should the task has predictable and strict parameters, a software programmer could enter the “back-end” of the server and set up an API that execute those parameters to a high-degree of accuracy.  Put another way, API-based automation still is the preferred choice for large, scalable amounts of data needing to move across servers.  At this point, having an IT professional to set up the proper API can be the superior choice to RPA.  However, it remains unclear if this trend will hold into the future – there may come a day where RPA becomes superior to API automation in every regard.  However, that day has not arrived, and large companies with huge swaths of data are still best-equipped to take the time and build an API specific to their industry’s needs.

Conclusion – The AI future has (Almost) Arrived

In conclusion, RPA may the new and “sexier” option for organizing data across a company’s applications, but that does not mean it has fully replaced traditional, API-based automation.  For simple data calculations carried out over vast sums of information, the scalability brought with constructing a “back-end” API still outpaces RPA.

However, for any situation where the user lacks IT skills or multi-system integration is a requirement, RPA is a revolution – it allows the worker to focus on their field of expertise (banking, health care, design) without worrying about whether they get their coding right.  We’ll see where the future of AI-powered bots goes, but for now, RPA simply serves to help “fill the gaps” that API automation cannot reach.

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