What is task mining?

Task mining is a technique for understanding how work actually gets done by capturing the user-level actions performed during it — mouse clicks, keystrokes, field entries, and switches between applications or browser tabs — and assembling those observed actions into an ordered record of the task as it was really carried out, independent of what any underlying system happens to log.

Task mining software runs alongside the person doing the work — as a browser extension, a desktop agent, or similar — and records the interface-level events as they happen: which element was clicked, what was typed, which page or application became active, and when each of these occurred. That raw stream of interface events is then grouped into meaningful steps, so the output is not just a log of clicks but a reconstructed sequence of what the person actually did, in order, with timing attached to each step.

Because task mining observes the interface directly rather than depending on a system to log an event, it captures work that never produces a record anywhere — copying a value between two unrelated tools, checking something in a spreadsheet before continuing in a web form, or working entirely inside an application that keeps no usable audit trail. That is exactly the kind of manual, cross-application work that makes up a large share of real office processes and that log-based process mining cannot observe.

Common uses include building an accurate baseline of how a task is actually performed today, before trying to standardize or automate it; measuring how long a task really takes and where the time and variation come from; and identifying steps that are repetitive enough to be strong robotic-process-automation or AI-automation candidates. In each case, the value comes from observing the work directly instead of relying on a description of how it is supposed to be done.