Unearthing RPA Potential: What You Need to Know About Task Mining & UiPath

Regarding business process optimization, RPA (Robotic Process Automation) and other automation technologies have been getting much attention recently, and it’s easy to understand why. Companies that have implemented RPA report multiple benefits:

• increased productivity (reported by 86% of companies),
• improved accuracy (90%),
• higher compliance (92%),
• cost savings (59%), and more.

No wonder increased RPA adoption is one of the predominant current automation trends.

But what many automation enthusiasts don’t talk about is how RPA adopters get to the point where they can benefit from these advantages. Or, more specifically, how they know which tasks to automate. This is a crucial question, as recognizing proper automation candidates can make the difference between an RPA success and an RPA failure.

Luckily, leading-edge RPA platforms offer the tools to support organizations in discovering and selecting the best processes to automate. One of them is task mining. Today, we’ll discuss the task mining capabilities of our RPA tool of choice—UiPath—and how they empower its users to find the tasks that will benefit the most from automation.

Before tackling anything else, let’s look at the definition of task mining.

Task mining automatically captures and analyzes user interactions to uncover and map all steps within the workflows these users perform. In practice, task mining involves recording or logging user behavior, such as mouse clicks, keyboard inputs, and screen transitions, to gain insights into the sequence of actions that make up specific tasks.

And why bother recording all these actions meticulously? Because task mining helps identify patterns, bottlenecks, variations, and inefficiencies in workflows, enabling organizations to streamline processes, improve productivity, and identify areas for optimization.

Indeed, more and more organizations realize that potential, stimulating the growth of the task-mining tools market. In the coming years, the task mining software industry will grow by 75-85%.

1. Data collection from user devices. The first step in task mining is collecting data from users’ machines. The records captured can include keystrokes, clicks, scrolls, user inputs, recordings, screenshots, and more. Task mining tools often use OCR (optical character recognition) and NLP (natural language processing) to extract additional context details from this data.
2. Segmenting and dividing collected data. Next, machine learning algorithms break down this data into specific tasks such as purchase order creation or customer service ticket submission. The data can be combined with event log data to help contextualize performance. This view of the data allows businesses to discover and fix bottlenecks accordingly.

Task mining is particularly beneficial from the standpoint of RPA. Through monitoring user interactions with applications, task mining allows organizations to identify repetitive, rule-based tasks that are the best candidates for automation. With that, businesses can pinpoint and prioritize areas where RPA can be applied most effectively to reduce manual effort and free up valuable human resources for more strategic tasks.

By automating mundane and repetitive tasks through RPA, organizations can significantly boost productivity and reduce error rates. All in all, task mining acts as an automation enabler, guiding organizations toward unlocking the full potential of RPA to drive their digital transformation.

If you’ve heard the term “process mining” before, you’re probably wondering how it differs from task mining. The two are related and often performed together. Still, they aren’t the same.

Process mining focuses on optimizing business processes as a whole, e.g., supply chain or customer service. Task mining, on the other hand, aims to improve individual tasks that may be parts of larger processes.

The two also rely on different metrics. Process mining uses business data pulled from systems like CRM or ERP, while task mining uses desktop activities and interaction data such as clicks, keystrokes, screenshots, and recordings. Put shortly, process mining focuses on the large picture, and task mining is concerned with the individual parts of each process.

We’ve already said that we’ll be explaining task mining using the example of UiPath, which we usually choose to work with for these types of projects. It’s not just a matter of our preference for the tool that makes us go with it most of the time.

Among the top RPA platforms, such as Automation Anywhere, Blue Prism, Power Automate, or Google Cloud Platform, UiPath has consistently been named the industry leader. It’s also the tool we decided to use due to its features and capabilities, both on the side of the developer and the end-user.

When it comes to task mining capabilities, UiPath has its own tool called simply UiPath Task Mining. It’s an AI-based solution that leverages machine learning to investigate user desktop activity and identify automation opportunities.

What is also worth knowing regarding the combination of the UiPath automation platform and task mining technology is that UiPath offers two types of task mining: assisted and unassisted.

• Assisted task mining is concerned with analyzing known tasks for potential improvements. For that, users need to capture the steps that make up the sequence using UiPath’s recorder or build a diagram independently. Then, they can tweak the process by adding, removing, and labeling each step, creating an accurate task mapping.

The collaborative process can be shared with other team members to reflect better any task variations that will later be merged into a top-down workflow overview. Once the overview is complete, users can export it as a document that will inform automation development.

With Task Mining, UiPath users can map all stages of any task into a clear diagram. (image source: UiPath)