Process mining vs task mining vs AI interviews: what each one actually sees
Process mining reads system event logs, task mining records desktops, AI interviews ask the people. Each sees a different slice of how work really happens.
Process mining, task mining and AI interviews answer three different questions. Process mining reconstructs a flow from the event logs your systems already keep, so it sees what touched a system and when. Task mining records what happens on a desktop, so it sees clicks, screens and applications. AI interviews ask the people who do the work, so they see intent, workarounds and the parts of the process that never touched a system at all.
Process mining sees what the system recorded
The Process Mining Manifesto defines the idea as discovering, monitoring and improving "real processes (i.e., not assumed processes) by extracting knowledge from event logs" (IEEE Task Force on Process Mining, 2012). Every technique in the field assumes each recorded event refers to an activity and is related to a particular case.
Microsoft's process mining needs three fields: a case ID present for all activities in the log, an activity name, and a timestamp (Microsoft Learn, 2026). Give it an order ID and the timestamped steps in the ERP and it will draw every variant of order-to-cash across a year of history.
The blind spot is in the definition, and the same Microsoft page notes that not all events you are interested in are logged. A shipment sat blocked for three days and then moved. The log shows a three-day gap and a status change. The phone call that unblocked it is nowhere. The email chain where the real approval happened, before anyone clicked approve, is nowhere either.
Task mining sees the desktop
Microsoft describes task mining as capturing detailed steps for tasks performed on users' desktops, with a report showing which applications teams spend the most time in (Microsoft Learn, 2025). A recorder runs while a person performs the task. Recorded actions are grouped into activities, and Microsoft's guidance is to remove any sensitive information from a recording before analysis (Microsoft Learn, 2023).
This catches the seventeen clicks to reconcile one invoice. It also stops at the edge of the screen. The private spreadsheet appears as an Excel window, not as the inventory system it actually is. The hallway conversation where two managers agreed a discount does not appear.
And it is monitoring. The UK Information Commissioner's Office counts screen captures and keystroke monitoring as device activity monitoring, says it "is likely to capture excessive amounts of workers' personal information", classes keystroke monitoring as behavioural biometric data, and requires a data protection impact assessment before any processing likely to cause high risk (ICO, 2023). Vendors ship controls for this, and people who know they are being recorded still behave accordingly.
AI interviews see what people meant
An interview asks the person. It is the only one of the three that sees intent: why the planner keeps her own sheet, who actually approves the discount (whoever answers first in the deal channel). It sees contradictions, because two people describing the same handoff differently is a finding rather than noise. And it sees what never touched a system: the phone call, the corridor, the one person a whole team quietly depends on.
Memory rounds off frequencies, though, and no interview gives you cycle times across 40,000 cases. Candour has to be arranged. If people believe their manager will read what they said about the workaround, they describe the official process. Privacy from management has to be enforced in the system rather than promised in an email.
The three compared
The table is our framing, illustrative rather than measured.
| Process mining | Task mining | AI interviews | |
|---|---|---|---|
| Observes | System events: case, activity, timestamp | Desktop actions: clicks, keystrokes, screens | What people say: intent, workarounds, contradictions |
| Misses | Calls, email approvals, what the private sheet means | Calls, hallway decisions, the reason behind the click | Exact frequencies; what people cannot see or will not say |
| Data needed | Event log with case ID, activity, timestamp | Recorder on each desktop, informed participants | Five to ten minutes a day from each person |
| Time to first result | Weeks to months to extract and clean logs | Days to record, then grouping | Depends on how fast people answer; everyone in parallel |
| Privacy exposure | Low; it is system data | High; impact assessment usually needed | Low if privacy from management is enforced |
| Best for | High-volume flows inside one or two systems | Repetitive screen work in one team | Cross-functional, judgment-heavy, undocumented work |
Our view
Which is best is the wrong argument. The three instruments look at different objects, and the honest question is which object your problem lives in. If the problem is a high-volume flow inside one ERP, mine the logs. If a claims team performs the same forty clicks a thousand times a day, record it.
The projects that fail are rarely those. They fail on cross-functional work where the real process is a chain of emails and a spreadsheet nobody admits is the system, and there the logs are the least informative source. We built Transcript for that third category: everyone heard at once and privately, each person approving what was learned from their words, contradictions kept rather than averaged away. Send the miner in afterwards to measure what the conversations found.
Questions people ask next
Can process mining and interviews be combined?
Yes, and that is the sensible order: interviews first to find where the real process lives, mining afterwards to measure the flows that do run through systems. The interviews also tell you which event logs are worth extracting.
Do we need a case ID to do any of this?
Only for process mining, where the case ID has to be present on every activity in the log. Task mining needs a recorder and a person performing the task; interviews need nothing but people's time.
What do the privacy rules say about task mining?
It can be done lawfully, with a documented purpose, a lawful basis, an impact assessment where the risk is high, and workers informed. The ICO treats keystroke monitoring as behavioural biometric data. Involve employee representatives before the recorder is installed.
How do interviews avoid people telling us the official story?
By making it impossible for management to read an individual's answers. In Transcript that is enforced with row-level security in the database: managers see the world model of the whole organisation and never a message, quote or summary of one person. Remove the guarantee and you get the SOP folder read back to you.
Lightbloom AI starts discovery with the people, because the phone call that unblocked the shipment and the email chain that was the real approval are in none of the logs. For the longer comparison, read What is AI process discovery?.
References
- IEEE Task Force on Process Mining, Process Mining Manifesto, in BPM 2011 Workshops, LNBIP 99, Springer (2012), https://www.tf-pm.org/upload/1580737614108.pdf
- Microsoft Learn, Prepare processes and data, process mining in Power Automate (2026), https://learn.microsoft.com/en-us/power-automate/process-mining-processes-and-data
- Microsoft Learn, Overview of task mining, Power Automate (2025), https://learn.microsoft.com/en-us/power-automate/task-mining-overview
- Microsoft Learn, Prepare processes and recordings, Power Automate (2023), https://learn.microsoft.com/en-us/power-automate/process-advisor-processes
- Information Commissioner's Office, Employment practices and data protection: monitoring workers, specific data protection considerations for different ways or methods of monitoring workers (2023), https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/employment/monitoring-workers/specific-data-protection-considerations-for-different-ways-or-methods-of-monitoring-workers/
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