Let us examine the RPA – Robotic Process Automation Pitfalls and Risks. Robotic Process Automation also referred to as RPA for short, is a subset of IT that lets anyone configure computer software (or a “robot”) to mimic the actions of a human within the context of a digital system, all to execute a specific business process easier and more efficiently than ever before. Using RPA it’s possible to interpret data, trigger responses and even communicate with other systems – all to perform a wide range of different repetitive tasks so that employees are free to focus on those matters that truly need them.
RPA brings with it a wide range of different benefits – chief among them being an almost immediate cost savings, high quality and more accurate work, and even enhanced cycle times. But at the same time, it’s not without its potential challenges. All told, there are a number of different potential pitfalls of Robotic Process Automation that you should be aware of moving forward. Your key to mitigating risk from these issues involves understanding them as much as possible.
Robotic Process Automation Pitfalls
RPA Pitfall #1: Robotic Process Automation is Not a Silver Bullet
One of the biggest mistakes that many organizations make when embracing Robotic Process Automation comes down to choosing the wrong processes to automate to begin with. They don’t make an effort to understand the natural limitations of RPA and assume that anything can be automated. Then, they don’t end up with the results they were after and they blame the technology rather than the decisions that led them there.
The best candidates for RPA are those processes that are A) impactful, B) simple and C) that do not require high level cognitive tasks. There’s a reason why RPA is about supporting and empowering your human employees, not replacing them.
One example of a process that relies on high-level cognitive tasks involves reading an email. Contained inside that email may be a number of different smaller jobs that would be easy for a human employee to accomplish. But defining a subjective quality like “what makes a product good” or “what visual design is most pleasing” are not well-defined and, as a result, are not ideal candidates for Robotic Process Automation.
A similar issue is that of process complexity – meaning that the conditions of the process change far more frequently than the RPA “bots” can keep up with. If the process changes depending on the answer to someone’s question, for example, RPA isn’t necessarily going to be able to “understand” what needs to be done. If a user on your website is interacting with a chatbot and needs to be connected to the right department, there may come a point in the conversation when a totally different department needs to step in. This would begin a completely different process from the one the chatbot was following when the conversation started. You can’t necessarily expect a chatbot to move from one process flow to another as scenarios change, which is why this is the type of situation where process complexity becomes a major pitfall of RPA by its nature.
Something like invoice capture, on the other hand, is a perfect chance to embrace Robotic Process Automation because even though it requires high-level cognitive abilities, RPA tools can still extract data from those documents and process them in the right way. Deep Learning plugins can be used to further add accuracy to the equation. This is because the format of the data, and the information contained within the invoice, doesn’t change significantly over time.
RPA Pitfall #2: The Problem With Too Much Unstructured Data
Another example of a common pitfall that organizations encounter when working with Robotic Process Automation has to do with unstructured data and the problems it often brings with it.
To be clear, structured data would be any information that is highly organized and easily understood by computers. Most of the time this information is found in relational databases and examples of structured data types can include but are not limited to ones like geolocation information, contact information, the names of customers, purchase histories and more.
Unstructured data, on the other hand, is just about anything else. This is information that cannot be processed and analyzed using traditional tools – and Robotic Process Automation is the perfect example of this.
In addition to audio and visual data, examples of unstructured data can include satellite images, activity on social media sites like Facebook and Twitter and more.
The problem here is that the first step of deploying Robotic Process Automation involves training the bot to identify data within documents and classify those points accordingly, all using unsupervised Machine Learning plugins. When that data is unstructured, it becomes too complicated and the RPA tool won’t be able to accurately tell one piece of data from another.
If you’re trying to automate data extraction in your law firm with RPA, for example, your tool will have a difficult time processing custom documents with no repeating format that is primarily made up of big blocks of words and nothing else. It would have a much easier time extracting insightful data from a structured document like an invoice, which is made up of tables with numbers and a bare minimum amount of text.
This is again why it’s always important to choose your document types carefully when selecting when and where to deploy Robotic Process Automation.
RPA Pitfall #3: Failing to Include “Exceptions” When Training Your Bot
Finally, one of the most important things to understand about Robotic Process Automation (or any automation, really) is that all of these tools need exceptions to function properly.
Every so often, an RPA bot will find itself in a situation that it wasn’t “trained” to handle. In those circumstances, progress will stop and an error will occur. Sometimes, this happens for reasons that you could have predicted (like the bot doesn’t have access to the data it needs to keep going). Other times, it’s because the data it needs is incorrect or missing entirely. Regardless, an exception is like a type of automation that lets the RPA bot continue on in those situations, all while saving the details of the error for further analysis from developers.
Even the most structured documents will encounter exceptions now and again. Therefore, when you initially train your RPA bot, you need to include these exceptions in the training set that you’re using so that it “knows” what to do when it encounters them in the real world. Not only will this help avoid a seemingly endless stream of errors, but it will also cut down on the total number of documents that you’ll eventually need to process manually.
In the end, Robotic Process Automation brings with it a wide range of benefits that most businesses cannot afford to ignore. The ability to complete menial tasks faster, with greater accuracy and much more cost-effectively alone is more than worth the investment for most people. But it’s also not magic – it has its limitations, the same as any other type of technology. The key to mitigating risk – and generating the results that you’re after – involves understanding as much about these pitfalls as possible so that you don’t fall into one yourself.