Process workshops are useful. They capture intent, policy, role knowledge and the reasons people make certain decisions. They are also incomplete by nature. Participants remember typical work better than edge cases. Different teams describe the same handoff differently. Rework is often normalised. Waiting time is easy to underestimate. And the process drawn on a whiteboard can quietly become the process everyone wishes existed.
Process mining adds a different kind of evidence. It reconstructs execution paths from timestamped events in operational systems, allowing teams to compare the designed process with what actually happened.
Workshops tell you why people believe the process works a certain way. Process data can tell you how often reality disagrees.
See variants, not only the happy path
A conventional process map tends to show one primary route with a few exceptions. Real operations often contain dozens or hundreds of variants. Some are legitimate customer or regulatory paths. Others are symptoms of rework, missing information, inconsistent ownership or system friction.
Microsoft’s current Power Automate Process Mining roadmap highlights variant analysis, rework detection, root-cause analysis, custom metrics and process comparison. Its 2026 object-centric process mining capability also addresses a limitation of simple case-based models by allowing events to relate to several objects at once—for example, invoices, orders and shipments—so analysis can preserve more of the real operational context.
Measure where time is actually lost
Teams often focus on handling time because it is visible. Process data can reveal that the larger delay sits between actions: a request waits 18 hours for review, then takes four minutes to process. A workshop may describe the four-minute task in detail while the queue remains invisible.
For commercially important workflows, analyse throughput time, waiting time, rework frequency, handoff count, exception rate and the distribution of cases—not just averages. A process with a respectable average can still contain a damaging long tail.
Find rework people have stopped noticing
Repeated activities are a strong signal. A record moves forward, returns to a previous stage, gets corrected, then moves forward again. Staff may regard this as ordinary because it happens every day. Event analysis can quantify how often it happens and which conditions correlate with it.
That evidence changes the improvement conversation. Instead of debating whether a problem is common, the team can investigate why a specific variant creates repeat handling or delay.
Use workshops and mining together
Process mining is not a substitute for operational knowledge. Event logs do not automatically explain intent, customer context, policy constraints or why people take work outside the system. They can also be misleading when timestamps are incomplete, systems capture only part of the workflow, or important activity happens in email and spreadsheets.
The stronger method combines both. Use data to identify patterns and anomalies. Use workshops, interviews and observation to interpret them. Then test proposed changes against the evidence.
Know when process mining is worth the effort
It is most useful where transaction volume is meaningful, digital event data exists, the process spans several stages or systems, and the organisation cannot reliably explain why outcomes vary. For a small, stable workflow with a handful of cases, direct observation may be faster and cheaper.
Do not buy a process-mining platform simply to produce attractive maps. Start with a commercial question: where is cycle time being lost, why is rework occurring, which route creates failure, or which process variant should be redesigned?
What better looks like
A mature improvement programme treats process design as a hypothesis that can be tested. Teams understand the intended workflow, observe the actual workflow, quantify meaningful variation and use both operational context and execution evidence to decide what to remove, standardise, integrate or automate.
The goal is not perfect conformity. It is knowing which variation creates value and which variation creates avoidable cost.