Process Mining
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Process mining reconstructs how a process ran from the event logs that systems recorded. It is usually bought as the discovery phase of a larger program: find the inefficiencies, then redesign, automate, and measure.
What Process Mining actually means
The method is well established and works as designed. Structured systems such as ERP and CRM platforms write timestamped event records. Process mining reads those records, orders them by case, and reconstructs the paths cases actually took, including the loops and delays that a documented process does not show. For processes that live substantially inside one system of record, it produces a genuinely useful picture.
Its constraint is architectural rather than a question of product maturity. It can only see work that a system logged. Work performed in spreadsheets and email, exceptions handled by hand, project work that does not follow a repeatable path, and anything in an application without usable logging is not partially visible, it is absent. In most enterprises that unlogged portion is the majority of execution.
The absence is also silent, which is the harder problem. A missing step does not appear in a log as a gap; the process simply looks continuous. A workflow held together by two spreadsheets and a weekly call can be reported as a clean three-step path, and nothing in the analysis flags the omission.
There is also a latency question. Reconstruction happens after the fact, on a data extract, so the picture describes a past state and refreshing it is a project rather than a property of the system. For compliance reporting and periodic process review that is often acceptable. For targeting AI deployment and then verifying whether the deployment worked, a picture that is both partial and dated is a weak foundation.
Examples
Where process mining is strong
A purchase-to-pay process running almost entirely inside one ERP. The event log is complete for the steps that matter, and mining surfaces the approval loops and the delay concentrated at one authorization stage.
Where it silently under-reports
An invoice process shows three clean steps in the log. In practice a spreadsheet sits between two of them, reconciling systems that do not integrate. The spreadsheet work is invisible, so the process appears more efficient than it is.
Exceptions counted as exits
Cases that leave the standard path are recorded as exits from the process. The manual work those exceptions generate downstream is not in the log, so the analysis under-counts the workload it is supposed to be sizing.
Frequently asked questions
Related terms
Task Mining
Task mining records how individuals perform work at the desktop, capturing the steps that system logs miss. It runs for a fixed observation window and then stops, so the picture it produces is accurate on delivery and degrades from then on.
Unstructured Work
Unstructured work is the execution that happens outside the systems that log it: ad-hoc work in spreadsheets and email, project work that is not a linear process, exceptions handled manually, and legacy applications with no modern logs.
Work Intelligence
Work intelligence is the operational layer that provides a current, evidence-based picture of how work actually moves through an enterprise. It is used to target AI deployment, verify post-deployment outcomes, and continuously update the baseline as the business changes.
