# Process mining vs task mining vs AI discovery: which finds your AI use cases?

Comparison · By Nolli research, The Nolli team · Published 2026-10-17 · Updated 2026-10-17 · Prices and features checked 2026-10-14

Canonical page: https://withnolli.com/blog/process-mining-vs-task-mining-vs-ai-discovery

**Short answer.** Process mining reads system event logs to show how a process flows from start to finish. Task mining records desktop actions to show how one task is done. AI discovery studies the work itself, across screens, email and documents, to decide which steps AI should take. Match the method to your question: a system flow, repetitive screen work, or what AI should do.

## The verdict

- **Pick process mining: if the work lives in an ERP or CRM.** It counts every case, so you get exact numbers on loops and waits. Skip it for work that runs through email and spreadsheets. The logs never see that part.
- **Pick task mining: for one team’s repeat screen work.** You get the exact clicks before you hand the task to RPA or an agent. Skip it if you need to know why people choose what they choose. It records actions, not reasons.
- **Pick AI discovery: if you cannot yet name what to automate.** It reads across screens, mail and documents, or asks people, so it finds the steps nobody wrote down. Skip it if you need exact counts across every case.
- **Combine them: when a wrong call would be costly.** McKinsey’s two cases below show what a single method left out. The gaps were large.

## Key takeaways

1. Process mining sees system event logs, task mining sees desktop actions, and AI discovery sees the work itself across screens, email and documents.
2. Each one is blind somewhere: logs miss work outside systems, screen recordings miss the reasons, and observed work gives patterns rather than exact counts across every case.
3. Pick by the question, settle privacy and employee notice before anything is switched on, and combine methods when the decision is large.

Imagine one purchase order. A team lead asks for twelve office chairs. Before anyone clicks Approve, the order passes through an ERP system, a supplier email, a spreadsheet and a short chat with a manager. Three kinds of tool claim to show how work like this happens. Each looks at a different piece of it.

Process mining reads the records business systems keep. Task mining records what a person does on their computer. AI discovery is our term for working out where AI belongs from the work itself. We walk the same order through all three, then compare them. Nolli makes software in the third group, so we say where it fits and where it does not. Everything here comes from public vendor and analyst pages, checked on 2026-10-14. We tested no product. The wider picture, including workshops and consultants, is in our guide How to find where AI belongs.

## One purchase order, five steps

The order below is made up to show the idea. It is not a customer story and not Nolli data. The dimmed steps are the ones that leave no record in a business system.

**One imagined purchase order, and what each method can see**

1. **Requester raises the order**: In the ERP. The system logs a case ID, an activity name and a timestamp. Process mining sees it. Task mining sees the typing if a recorder runs on that computer.
2. **Buyer emails the supplier for a better price**: No event log. Process mining is blind here. Task mining sees an email opened. A tool that reads mail sees what was said.
3. **Analyst copies the quote into a spreadsheet**: The copying and checking happen outside the ERP. Task mining records every click and paste. Process mining sees nothing.
4. **Manager agrees to go over budget in a hallway chat**: No system and no screen. Logs and recorders both miss it. Only an interview can bring it back.
5. **Manager clicks Approve in the ERP**: Logged again. Process mining now sees a gap of several days between steps 1 and 5, and cannot say why.

An illustration. What each method can and cannot see comes from the sources listed at the end of the post.

Process mining sees steps 1 and 5 and a long wait between them. Task mining sees what the analyst’s hands did in step 3 but not what the manager decided in step 4. A method that reads mail or asks people can recover steps 2 and 4. It cannot tell you how many of the company’s orders waited that long.

## Process mining: reading the receipts

A shop’s till receipts tell you what sold, in what order and when. They do not tell you the cashier phoned the supplier first. Process mining reads a company’s systems the same way.

> **Process mining.** Rebuilding how a process runs from the event logs of business systems, one path per case. Appian describes it as using system event logs to discover, monitor and improve processes. IBM says it relies mainly on business metrics and event log data from systems such as ERP and CRM tools.

An event log is the record a system keeps of what happened to each case. Microsoft’s documentation says every log needs a case ID on every activity, an activity name and a timestamp. For our order, that means steps 1 and 5.

Run it over a year of orders and it draws every route orders took, how often each route ran and how long each step took. McKinsey adds the deviations and exceptions, and who owned each step. That is the strength: exact counts, for every case.

The blind spot is steps 2, 3 and 4. Microsoft’s documentation says plainly that not all events you care about are logged. McKinsey lists what stays hidden: change requests sent by email or phone, spreadsheet clean-up before data goes into an ERP, and decisions delayed because the people needed were busy.

Getting the logs is the hard part. IBM says finding, joining and formatting them is usually the most time-consuming step, and it needs the IT staff who know each system. The data is system data, but IBM says it should include who executed each action, so it still describes people.

Best question for it: which system-based process loops, waits or needs rework, and how often? That is a solid start in finance, procurement or order handling. It is a weak start for work that happens mostly in email and documents.

## Task mining: a camera over one shoulder

Now imagine a camera over the analyst’s shoulder in step 3. It does not care about the order as a whole. It records what her hands do.

> **Task mining.** Using user interaction data, also called desktop data, to assess how efficiently one task runs inside a larger process. IBM says that data includes keystrokes, mouse clicks and data entries, and can include recordings and screenshots taken at intervals.

Celonis lists the kind of action it records: checking a spreadsheet, copying and pasting data, opening an email, searching in a web browser. For step 3 that is the whole job, move by move. Celonis says task mining captures the parts of a process that do not occur in transactional systems. IBM notes the detail helps when you plan to automate with RPA, which is software that repeats fixed screen steps.

The camera has limits. Appian says task mining is limited to human work and weak at recommending changes for a whole process. IBM lists missing context and concept drift, which is what happens when a task changes and the old recordings go stale. Lightbloom, which sells a rival method, adds that screen data stops at the screen edge. Calls and hallway decisions do not appear. Step 4 stays dark.

Setup means installing a recorder on each participating computer, recording for a period, then grouping the actions into tasks. Privacy is the heavy part. IBM names data privacy as a challenge and says users should approve the tools before activation, with personal data anonymized. Vendors differ in how narrow the recording is. IBM’s chapter on process mining describes recorders set to store only events tied to the process and ignore everything else. UiPath’s documentation says its recorder captures screenshots, keyboard actions and clicks, needs continuous recording, and lets users pause it and see which apps are recorded.

Best question for it: which repetitive screen tasks could a bot or an agent take over? It tells you the clicks. It does not tell you why a person made a choice, so add a second source before you design an agent for judgment-heavy work.

## AI discovery: the steps nobody logged

> **AI discovery.** The work of finding where AI belongs in a company: which workflows, which steps inside them, and what each change is worth. It starts from how the work actually happens, not from what people remember in a workshop.

This is our term, and there is no standard definition. Vendors build it in two ways. Some ask people. Lightbloom’s “AI interviews” talk to the people who do the work, and its page says they surface intent, workarounds and steps that never touched a system. Others, including Nolli, learn from the work itself. Our guide counts both as AI discovery when they combine people, systems and documents.

Back to the order. An interview tool would ask the manager and the analyst how it really went, which is the only way to recover step 4. A tool that learns from the work, as Nolli does, reads the supplier email in step 2 and sees the spreadsheet in step 3. Either way, the output differs from the other two methods. It is a ranked list of AI opportunities: the steps nobody wrote down, who holds the know-how, and which steps suit an agent, plain code or a person. It is not a map of delays.

It has its own blind spot. Lightbloom says memory rounds off frequencies, and no interview gives cycle times across 40,000 cases. Observed work has a different limit: it covers only the sources and devices it is connected to. If you need exact counts, use logs.

Privacy and effort depend on the sources. Observing work on devices or reading mail and calendar raises the same notice and policy questions as task mining. Interviews take people’s time and need a way to keep answers private from management. Nolli describes little system integration.

Research points the same way. A 2025 systematic review in ACM Transactions on Management Information Systems analyzed 24 studies on unstructured data in process mining. It found the research mostly handles text and mostly aims to extract event logs for process discovery.

## What each method can see

The same comparison as a grid, then the short version in words.

**What each method can see** (none, partly or fully)

| | Process mining | Task mining | AI discovery |
| --- | --- | --- | --- |
| Exact counts of every case in a system’s event log | fully | none | none |
| Desktop actions and clicks | none | fully | partly |
| Work in email and calendar | none | partly | fully |
| Documents and what experts know | none | none | fully |
| Work that leaves no digital trace | none | none | partly |

Our reading of the sources listed at the end, not a measurement. The AI discovery column describes the approach Nolli takes: device and mail capture plus interviews. Interview-only tools would score lower on email. Per Lightbloom, only interviews reach work that never touched a system, so that cell is a partial ball. Task mining gets a partial ball on email because Celonis lists opening an email as a recorded action, but a recording of a click is not the content of the message.

From the pages listed under Sources, checked 2026-10-14. The AI discovery column describes the approach we take at Nolli, not a standard definition.

|  | Process mining | Task mining | AI discovery |
| --- | --- | --- | --- |
| Reads | Event logs from systems such as an ERP or a CRM. | Clicks, keystrokes and data entry, and sometimes screenshots. | Screens, mail and calendar, documents and AI agent logs. Some vendors interview people instead. |
| You must set up | Find, join and format the logs. IBM calls this usually the most time-consuming part. | A recorder on each participating computer, then grouping actions into tasks. | Devices and sources connected, or interviews run. Little system integration, by Nolli’s own description. |
| Privacy load | Lower. System data, though IBM says it should include who executed each action. | Highest of the three. Records screens or keystrokes, so tell people and get agreement first. | Depends on the sources. Capture on devices, mail and calendar raises the same notice questions as task mining. |
| The AI question it answers | Which system-based process has the most loops, waits and rework? | Which repetitive screen tasks could RPA or an agent take over? | Where should AI work, which steps should it take, and what is each change worth? |

## Which to pick

- **Pick process mining if** your question is about a flow inside one or two systems, such as order-to-cash, procure-to-pay or a ticket queue, and IT can give you event logs with case IDs. Skip it if the work mostly happens in email and spreadsheets.
- **Pick task mining if** a defined team repeats the same screen steps all day and you want the exact actions so you can automate them with RPA or an agent. Skip it if you need the reasons behind the steps.
- **Pick AI discovery if** the work crosses teams, apps, email and documents, you cannot name the workflows yet, and you must decide what an agent should do before you build one. Skip it if you need exact counts across every case.
- **Combine them if** the decision is large, or the first method leaves a gap you cannot explain.

**Effort to start against how much of the work each method sees** (x: Effort to start, 0 to 100; y: Kinds of work it can see, 0 to 100; corners: top-left Easy start, wide view, top-right Hard start, wide view, bottom-left Easy start, narrow view, bottom-right Hard start, narrow view)

- Process mining: x 78, y 20
- Task mining: x 50, y 30
- AI discovery: x 40, y 60
- All three together: x 92, y 90

Positions are our reading of the sources, not measurements. Vertical: how many points each method scores on the five-row grid above, counting a full ball as 2 and a half ball as 1 (process mining 2 of 10, task mining 3, AI discovery 6, all three together 9). Horizontal: setup effort as described on the pages in Sources. IBM calls getting logs usually the most time-consuming part of process mining, task mining needs a recorder on each computer, and AI discovery needs devices and sources connected.

## Combining them

McKinsey argues for combining process and task mining, and gives two cases. Both are single companies that McKinsey does not name, so read them as examples, not averages.

- **About 25%**: of the $200 million to $500 million in savings that process mining flagged at one large international retailer had improvement initiatives identified by the company’s teams. McKinsey says delays outside the ERP, such as email, phone and spreadsheet work, were not captured. (source: McKinsey, March 18, 2024, https://www.mckinsey.com/capabilities/operations/our-insights/better-together-process-and-task-mining-a-powerful-ai-combo)
- **65%**: of orders needed manual updates by sales staff, found by process mining 1.5 million transactions across three systems at one global industrial distributor. Task mining of about 100 sales employees had shown they spent about a third of computer time on order entry. (source: McKinsey, March 18, 2024, https://www.mckinsey.com/capabilities/operations/our-insights/better-together-process-and-task-mining-a-powerful-ai-combo)

McKinsey describes two orders. Run process mining first to find the pain points, then task mining there. Or start with task mining to find the tasks that absorb time, then use process mining to find what drives the delays. Lightbloom recommends a third: interviews first, then mining to measure the flows that run through systems. That is a vendor view. Our own sequence below leans the same way, because for work that crosses teams you need to know the workflows exist before you can measure them.

1. **Write the question first.** Name the decision. For example: which three workflows in finance should an agent take over, and what is each worth? Then pick the method whose data can answer it. Do this today: Do this today: write one sentence that starts “We need to decide…”.
2. **Map the real work with AI discovery.** Find which workflows exist, where the unwritten steps and workarounds are, and who holds the know-how. This step finds the work outside your systems. Do this today: Do this today: list the five workflows your team complains about most.
3. **Measure the system-based candidates with process mining.** For workflows that run through an ERP, CRM or ticketing tool, pull the event logs and count the paths, loops and waits. Counts replace estimates. Do this today: Do this today: ask IT which systems log a case ID for your top workflow.
4. **Record narrow, repetitive tasks with task mining.** Where one team repeats a task all day, record it for a short period to get the exact steps before you automate. Do this today: Do this today: pick one task that takes a person over an hour a day.
5. **Prove the change on real work.** Before you build, run the agent beside the people in shadow mode and compare it with how they do the work now. Do this today: Do this today: define what a good result looks like for the first workflow.

## Privacy and employee notice

> **Answer these before you switch anything on** All three methods describe how people work, and task mining and AI discovery can touch screens, mail and documents. Sources differ on how intrusive this is. McKinsey calls these tools less intrusive than traditional manual mapping. The UK Information Commissioner’s Office (ICO) says device activity monitoring is likely to capture excessive amounts of workers’ personal information. Both can be true, because they compare against different alternatives. This is not legal advice. Ask your legal and privacy advisers, and your employee representatives where required.

**Questions to settle first**

- [ ] What is the purpose, and could a less intrusive method answer the same question? The ICO says that if you can reach your aim a less intrusive way, you must.
- [ ] What exactly is captured: event data, screens, keystrokes, email content, calendar entries, or only summaries?
- [ ] Who sees the results: individuals, or only grouped patterns? The ICO suggests aggregated reports that do not identify individual workers.
- [ ] How and when are workers told, and have employee representatives been involved where the rules require it?
- [ ] Which tools and apps are excluded, such as personal use, password managers and payment or health pages?
- [ ] Who is the controller and who is the processor, what does the written contract say, and how long is the data kept?
- [ ] Which legal check applies where you work, such as a data protection impact assessment? The ICO asks for one before processing likely to cause high risk. That guidance is UK guidance, and the ICO notes it is under review.

## Where Nolli fits, and where it does not

Nolli is AI discovery. It learns how your company works from your people’s work on company devices, your email and calendar, and the logs of the AI agents they use. Then it shows where AI belongs, what it is worth, and how it performs on your own work. Nolli is not an ERP event-log suite. It does not replace process mining’s exact case counts, variants and conformance checks across a system. Capturing work on devices is also close to what task mining does, so the same notice questions apply, and your company sets the policy and tells people. See Why Nolli and Trust and governance. No public price. Request a demo.

## Questions people ask

**What is the difference between process mining and task mining?**

Process mining rebuilds a process from the event logs of business systems, so it shows the end-to-end flow of every case. Task mining records what people do on their computers, so it shows how individual tasks are done. IBM and Appian both describe this split.

**What is the difference between task mining and AI discovery?**

Task mining analyzes recorded desktop actions to find inefficiencies in a task. AI discovery uses several sources of the work, such as screens, email, documents and agent logs, or interviews, to decide which workflows and steps AI should take and what each is worth.

**Can process mining find AI use cases on its own?**

It can find loops, waits and rework in system data, which is a good start. It cannot see work outside the logged systems. McKinsey reports a retailer whose process mining flagged $200 million to $500 million of savings, but whose teams identified initiatives for only about a quarter of it.

**Is task mining the same as employee monitoring?**

It records user activity, and the UK ICO lists screen captures and keystroke monitoring under device activity monitoring. Whether a given setup counts as monitoring, and what you must do, is a legal question for your advisers in your country.

**Do I need all three methods?**

No. Pick the one whose data matches your question, then add another if the first leaves gaps. McKinsey recommends combining them, and Celonis and IBM both say task mining should be used alongside process mining.

## Sources

- Kar-Woon Choy, Rodrigo Chaparro Gazzo, Rohit Panikkar and Rohit Sood. "Better together: Process and task mining, a powerful AI combo." McKinsey & Company, March 18, 2024. https://www.mckinsey.com/capabilities/operations/our-insights/better-together-process-and-task-mining-a-powerful-ai-combo
- "What Is Task Mining?." IBM Think, accessed October 14, 2026. https://www.ibm.com/think/topics/task-mining
- "The Types of Process Mining (The Art of Automation, Chapter 11)." IBM Think, accessed October 14, 2026. https://www.ibm.com/think/topics/process-mining-types
- "Process discovery methods." Celonis, accessed October 14, 2026. https://www.celonis.com/blog/process-discovery-methods
- Rachel Nizinski. "Process Mining vs. Task Mining: 3 Key Differences." Appian, March 17, 2023. https://appian.com/blog/acp/process-mining/process-mining-vs-task-mining
- "Process mining vs task mining vs AI interviews: what each one actually sees." Lightbloom AI, September 4, 2026. https://lightbloom.ai/process-mining-vs-task-mining-vs-ai-interviews
- "Prepare processes and data (process mining in Power Automate)." Microsoft Learn, accessed October 14, 2026. https://learn.microsoft.com/en-us/power-automate/process-mining-processes-and-data
- "Protection of personal data and privacy of the users (Task Mining)." UiPath Documentation, accessed October 14, 2026. https://docs.uipath.com/task-mining/automation-cloud/latest/user-guide/protection-of-personal-data-and-privacy-of-the-users
- "Specific data protection considerations for different ways or methods of monitoring workers." UK Information Commissioner’s Office (ICO), accessed October 14, 2026. 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/
- Fabian König, Andreas Egger and Wolfgang Kratsch. "Unstructured Data in Process Mining: A Systematic Literature Review." ACM Transactions on Management Information Systems, Volume 16, Issue 3, May 15, 2025. https://dl.acm.org/doi/10.1145/3727148