Task Mining transformed: driving operational excellence in 2025

Sure enough, it was always like this. The technology itself emerged before the term Task Mining was invented.  Specialists researched the area of user activity but for different purposes. They used user interaction data for usability testing and IT monitoring. At the time, people didn’t think of using user interaction data for different purposes. So, Task Mining has lived its eras of development. Let’s see what transformations it has experienced. 

In the early 2010s, Task Mining that we know today, was in its nascent stages, evolving from academic research and early user activity monitoring tools. The major focus used to be on specialized use cases like software usability testing or simple keystroke logging. While these tools could capture user actions, the analysis was largely manual and lacked the sophisticated AI to automatically discover and group tasks at an enterprise scale. The challenge wasn't a lack of data capture, but a lack of scalable, intelligent analysis to turn that data into operational insights.

By 2017, the first generation of commercial Task Mining tools became more common. The approach to Task Mining changed. They showed a significant step forward, moving from standard screen recording to capture a richer stream of user interaction data, including clicks, keystrokes, and application usage. However, these early solutions often relied on OCR and rule-based algorithms to make sense of the data. While powerful, this approach had its own limitations. The final analysis they got was often time-consuming as it took too long to collect data, configure and interpret it. All of it made analytics of that time too long and prevented rapid and dynamic process optimization.