Schedule a Meeting

Return to Enterprise Automation Blog

The shortcomings of Robotic Process Automation (RPA)

July 18 2023

4 min read

When Robotic Process Automation was first introduced, it offered a quick and simple way to automate some of the repetitive back office business processes without the need of APIs. As a result, it came to be viewed as the easiest point-of-entry for many digital transformation initiatives. However, as organizations began implementing RPA solutions and those implementations matured, companies began seeing first-hand the constraints of RPA.

In this article we are going to have a closer look at some of the limitations of Robotic Process Automation.

RPA Struggles with Complex Processes

Business processes are often quite complex with multiple steps, conditions, and decisions to be made before arriving at a final outcome. RPA bots may struggle with such complexity, and as a result, they may not be able to automate the entire process.

For example, business processes frequently change, such as when a software application or even an entire system receives an upgrade. For complex processes with many moving parts, even the slightest change can cause an RPA bot to break.

RPA is Reliant on Templates

For robotic process automation to work with document-centric processes , it requires a new template to be created for each document variance. For documents with a wide range of formats and layouts, this quickly becomes a drain on employee productivity.

Take invoice formats, for example. Before RPA bots can accurately automate invoice processing, a template must be created for each invoice format received, which can easily be in the hundreds or thousands for large organizations. These highly variable documents are harder to standardize, resulting in high maintenance costs and process exceptions that must be handled manually.

RPA Requires Additional Technology

Where robotic process automation fails, additional automation tools such as intelligent document processing (IDP) or optical character recognition (OCR) are needed to translate the unstructured information into structured data that (once a template has been created) can be processed by RPA.

While many RPA vendors already offer these capabilities through 3rd party providers, it’s not proprietary technology, which adds complexity and more costs to existing automation initiatives.

RPA Scales Poorly

Because it relies on predefined rules and templates to automate tasks, robotic process automation struggles to scale when seasonal spikes in customer demand come along. As the number of processes and tasks increase, it becomes increasingly challenging to manage and maintain RPA bots, and there’s also a risk of the rules and templates becoming outdated or inconsistent. When a process grows in complexity and exceptions arise with increasing frequency, it’s a sure sign that an RPA solution is failing to keep up.

RPA Can’t Learn From Its Mistakes

Unfortunately, it’s impossible to program every exception into a solution’s logic. As a result, when an RPA bot encounters a scenario it hasn’t seen before, the bot will simply stop or, even worse, carry on unaware of the mistake it made. If uncaught, this can lead to cascading clerical errors, and even result in more extreme real-world ramifications, such as an individual not receiving their disability benefit or a delayed insurance claim.

The solution: Augmenting RPA with Intelligent Automation

For those relying solely on RPA to carry the burden of today’s complex business processes, the limitations of rules-based technology can’t keep up with modern business. This is where intelligent automation comes in.By adding ML technology to an existing automation stack, organizations can improve their operations and business outcomes.

To discover how intelligent automation and intelligent document processing can improve your organization’s efficiency, watch a demo of the Hyperscience platform today!