Task mining is recording how people actually use their computer, then analysing those recordings to find which repetitive steps are worth automating. Instead of guessing where time goes, you watch real clicks, copy-paste moves and app switches and let the data point at the biggest bottlenecks. It answers a simple question: what are we doing over and over that a machine could do instead?
Picture a fitness tracker, but for office work. It quietly logs the small actions you repeat all day, then shows you a pattern you never noticed, like the forty minutes spent every morning moving numbers between two spreadsheets. That hidden routine is exactly the kind of thing task automation or a software robot can take over.
The privacy stakes are real, though. You are watching real people work, so a sensible study runs with clear consent, anonymises who did what, and captures patterns rather than the words in someone’s private email. Skip that and the insight is worthless and the trust is gone. A study also needs to run long enough to catch the rhythm of real work, often a couple of weeks, because a single day misses the month-end crunch or the Monday backlog where the worst repetition usually hides.
Task mining looks at the person; its sibling looks at the system. Where task mining studies individual desktop behaviour, process mining reads event logs to map a whole process across departments. Used together they give you both the close-up and the bird’s-eye view before you spend a euro on building. The payoff is a ranked shortlist, so the most expensive, most repeated tasks get automated first.
At TopDevs we use task mining at the start of an automation project so the roadmap is based on what people really do, not on what they think they do.
