Use task mining when you need evidence about how a task is performed across a desktop. Use process mining when you need to analyze the paths recorded in system events. Map the process with people when you need to understand its purpose, rules, exceptions and intended design.
Often the best answer combines them. The right method depends on the question and the evidence available, not which category sounds most advanced.
This is a guide to methods, not a ranking of vendors. Products can combine these capabilities, and their collection and privacy controls differ. Check the specific implementation rather than assuming every product in a category behaves the same way.
What each method contributes
| Method | Typical evidence | Particularly useful for | Important limitation |
|---|---|---|---|
| Task mining | Captured desktop interactions or recorded task traces, depending on the system | Repeated steps, app switching, variations and candidate improvements | Observed activity alone may not explain the business reason or correctness |
| Process mining | Event data representing activities, times and the cases or objects they relate to | Process paths, delays, loops and differences across cases | Analysis depends on event completeness and what those events mean |
| Manual mapping | Workshops, interviews, walkthroughs, documents and sampled cases | Decision rules, responsibilities, exceptions and designing a better process | Memory and documented procedure may differ from normal practice |
Microsoft's documentation describes task mining in terms of desktop task steps and recorded actions. Its process-mining material also explains how process evidence and human-led process management can inform improvement. Those capabilities can exist within a broader automation platform; assessment and implementation do not have to be separate purchases. Task mining overview, process mining overview
The original academic definition of process mining emphasizes extracting knowledge from information-system event logs. Contemporary implementations can also work with richer relationships and combine event data with other evidence. Avoid treating one simple event-log design as the limit of every platform. Process mining overview paper
Worked example: one access request, three views
Imagine a hypothetical request for access to an approved application. The following timings are invented to show why evidence needs interpretation.
A ticket opens at 09:00. Approval is recorded at 13:00. Fulfillment is recorded at 13:20, and the ticket closes at 13:25.
The event history establishes a four-hour, 25-minute elapsed path between opening and closing. It suggests a four-hour interval before approval. That interval is not automatically four hours of wasted analyst labor, and the event time may be the time a record was updated rather than the exact real-world action.
A reviewed task trace identifies 12 minutes of active analyst work: checking details, opening an admin console, applying the approved setting, checking the result and documenting it. That helps identify a possible fulfillment improvement. It still does not establish that the requested access was appropriate.
In a walkthrough, the team explains that requests missing a manager cost center cannot be approved. Analysts chase that information through email, which is not consistently reflected in the ticket history. The team also explains which access types need an additional approver.
Each method contributed something different:
| Question | Most useful evidence in this example | Possible intervention |
|---|---|---|
| Why does completion take hours? | Event history plus approval timestamps | Investigate the waiting interval and approval design |
| Where is human handling effort? | Validated task trace or sampled observation | Reduce repeated checks or console work where appropriate |
| Why does approval stall? | Team walkthrough and example cases | Repair intake requirements and exception routing |
| Is the new route safe and useful? | Approval evidence, reviewed outcomes and before/after measures | Keep controls and compare effort, quality and elapsed time |
Automating the 12 minutes may not solve the four-hour delay. Repairing intake may improve turnaround without changing fulfillment. Combining the evidence helps choose the intervention that fits the actual problem.
Reusable asset: a question-first decision tree
1. What decision do you need to make? Write one sentence, such as “Should we improve intake or automate fulfillment?” If the question is only “Where can we use AI?”, first select a business problem and a bounded workflow.
2. Is the uncertainty about purpose, policy or responsibility? Begin with the people doing and owning the work. Map the normal path and exceptions. Use real cases to challenge memory and assumptions.
3. Is the uncertainty about case paths, delays or rework visible in systems? Inspect event data. Check timestamps, identifiers, missing events, status definitions and whether the logs represent the part of the process you care about. Process mining may be useful if the data can support the question.
4. Is the uncertainty about repeated actions across screens or systems? Consider task mining, a supported recorder or a small observed walkthrough. Agree permitted applications, data handling and participant controls first. Do not collect an entire desktop history merely because a narrower sample would answer the question.
5. Do the sources disagree? Investigate definitions and sampling before selecting the story you prefer. A ticket can appear closed while someone finishes documentation. A visible app session can include waiting. An interview can describe the intended procedure rather than its exceptions.
6. Is there enough evidence to choose a change? If so, build a limited trial using the appropriate existing tools. If not, collect the smallest additional evidence that could change the decision. More data is not automatically more understanding.
Make the evidence fit for use
For task evidence, document the observed scope, exclusions, sampling period and inference limits. Screen activity should not be equated with individual productivity or correctness. Review classification with people who know the work.
For event evidence, check how cases or objects are connected across systems, whether clocks and time zones align and what each event represents. A clean-looking process map can still rest on incomplete records. Preserve uncertainty rather than presenting the available path as the entire process.
For manual mapping, include staff who handle exceptions, not only the process owner. Separate “what happens now” from “what should happen.” Reconcile competing accounts using examples and agree where the map remains uncertain.
The output of any method should support a decision: a smaller intake change, a revised rule, training, a template, an automation or no change yet. A process map is useful when it helps answer what to do next.
Privacy and participation are design choices
Collection methods vary. Some systems use screenshots or detailed recordings; others use different interaction signals. Do not infer one product's capture behavior from the category name.
Before collecting, specify the purpose, permitted scope, retained fields, access, storage, retention, exclusions and participant controls. Explain who can see outputs and how people can raise concerns. Manual workshops and exported logs can also contain sensitive information; they need proportionate handling too.
Choose the least intrusive evidence that can answer the question. Keep the exercise focused on improving work rather than ranking people, and do not describe redacted or grouped data as automatically anonymous.
Where HourBack fits
HourBack helps customers assemble and interpret evidence, involve the team, decide which changes are worthwhile and measure the result. That can complement existing task-mining, process-mining, AI and automation capabilities. There is no need to replace a tool that already does the job well.
Implementation can stay with your team or partner. HourBack can also advise or help implement under an agreed engagement. Explore sample assessment outputs and choose the relevant starting point for insurance, engineering or IT service desks.