Process Mining vs. Discovery Workshops: When to Use Each and Both

Use business discovery workshops with observation and sampled records for a bounded process decision. Add process mining when the decision requires quantifying paths, rework or delays across many cases and suitable event history is available. Use both when business context and transaction-level evidence are needed to understand the problem and agree a change.

The choice depends on the question, data coverage and consequences of uncertainty. There is no universal transaction count or organization size that makes a mining platform necessary. This guide provides a practical selection framework, a workshop plan and a combined discovery sequence.

What is the difference between workshops, process mining and task mining?

A process discovery workshop brings practitioners, process owners and relevant specialists together to explain current work, inspect examples and agree scope, rules and unresolved questions. It should include evidence, not rely entirely on recollection.

Process mining uses application event data to reconstruct and analyze recorded process behavior. It can help examine paths, repeated activities and performance patterns. Microsoft describes its process mining capability as extracting event data from systems of record to visualize processes and monitor performance. Microsoft: Process mining overview.

Task mining examines how selected tasks are performed through desktop interactions. It can complement the system-level view when important manual work happens between application events. Its evidence and privacy considerations differ from an ERP event-history analysis. UiPath: Combining process mining and task mining.

A workshop, event analysis and task observation answer related but different questions. None automatically determines the right automation technology.

How do the discovery approaches compare?

Approach	Strongest use	Evidence needed	Main limitation
Workshop plus case walkthroughs	Clarify scope, rules, handoffs, ownership and undocumented work	Practitioners, source records and representative examples	Small samples and recollection may miss variation
Process mining	Quantify recorded paths, repetition and delay across a defined population	Usable event history and agreed event meaning	Work outside the logs may be invisible
Task observation or task mining	Examine manual desktop work within a selected task	Approved observation or capture and a clear question	Captured interaction time does not by itself explain business intent
Combined approach	Connect patterns across cases to causes and workable changes	Business participation plus relevant system and task evidence	More coordination; scope must remain focused

A spreadsheet analysis or existing report may resolve the question without a dedicated mining platform. Ask what additional decision a tool would enable before selecting it.

When is a business discovery workshop sufficient?

A workshop-led assessment can be sufficient for the next discovery decision when the scope is narrow, the relevant practitioners are available, the main variants can be inspected and sampled records support the proposed baseline. It is not a substitute for technical validation and business acceptance before production.

For example, a team may need to understand how a standard report is compiled from two approved sources. Walking through ordinary and failure cases, checking volumes and documenting the output may provide enough evidence to decide whether to test an integration.

Use this exit checklist:

• The trigger, completed outcome and exclusions are agreed.
• The people performing and receiving the work have validated the map.
• Normal, exception and significant variant cases have been examined.
• Volume and effort estimates have sources and stated uncertainty.
• Rules, decision authority and missing-input ownership are clear enough for the proposed scope.
• Remaining unknowns have owners and are unlikely to reverse the immediate decision without being checked first.

If a critical unknown remains, commission the smallest additional investigation that resolves it. That could be another walkthrough, a targeted data query or a mining assessment.

When should you assess process mining tools?

Consider process mining when a decision depends on questions such as: which sites follow different paths, how often cases return to an earlier step, or which variants contribute most to long completion times? It is particularly useful to assess when competing accounts of a process need to be compared against a broad recorded population.

Before proceeding, confirm that the system records relevant historical activities rather than only the current status. A conventional case-based analysis requires a way to associate events with cases, identify activities and place them in time; Microsoft specifies the fields required by its implementation. Microsoft: Prepare processes and data.

Pause tool selection if access is unapproved, event meanings are unclear or the important work happens outside the available records. A polished process map built from incomplete or incorrectly joined data can support the wrong conclusion.

The decision should also account for extraction effort, stakeholder availability and the time available to act on the findings. If the analysis cannot inform a decision within the required window, a narrower evidence-gathering approach may be more useful initially.

When should you use workshops and process mining together?

Use both when the question spans multiple teams or variants and the logs need business interpretation. Workshops establish what events mean, what counts as a completed case and which distinctions matter. Mining tests how frequently patterns occur. Follow-up sessions investigate the causes and decide what can change.

A repeat activity could mean avoidable rework, a legitimate approval cycle or a batch update. A long gap could mean waiting for a customer, an unrecorded manual task or a delayed system update. Validate the explanation before assigning a saving to the gap.

1. FrameAgree the question and boundary
2. CheckValidate records and event meaning
3. AnalyzeCompare paths and performance
4. ExplainReview cases with practitioners
5. DecideChoose a change and a measure

How do you prepare and run a discovery workshop?

Assemble the right participants

Include the process owner, practitioners who know normal and exception work, a downstream recipient and a facilitator or analyst. Invite an application or data owner for system questions. Bring finance, security, support or other decision owners where their input is relevant to the scope.

A sponsor can explain the objective, but should not be the only voice describing daily work. Make room for differences between sites, shifts and customer types.

Prepare evidence before the meeting

Request an existing process description, volume reports, examples of completed and failed cases, relevant rules and an initial system list. Use approved or de-identified material. Ask participants to identify which claims are measured, estimated or disputed.

State the decision the meeting supports. “Assess retrieval of invoice dispute evidence for one case type” is a workable boundary. “Discover all AI opportunities in finance” needs a broader program of sessions.

Use a focused agenda

This is an illustrative two-hour agenda for a bounded initial session, not a promise that discovery finishes in two hours. Prework and follow-up are separate.

Time	Activity	Output
0–15 minutes	Confirm outcome, boundary and exclusions	Agreed discovery question
15–35 minutes	Map suppliers, inputs, main steps and recipients	Initial SIPOC
35–65 minutes	Walk through normal and exception cases	Detailed handoffs and evidence gaps
65–85 minutes	Review volume, effort and waiting	Baseline assumptions with sources
85–105 minutes	Compare improvement options and unknowns	Candidate changes and dependencies
105–120 minutes	Assign next decisions and owners	Action log and evidence plan

Use the SIPOC workshop companion for roles, mapping prompts and a reusable process worksheet. ASQ describes SIPOC+CM as a high-level process view including constraints and measures; detailed decisions need additional mapping. ASQ: SIPOC+CM.

Ask questions that reveal the actual work

Ask “show the last case that came back,” “where did this field come from,” “who can approve this difference,” and “what happens when this system is unavailable?” Follow an example through its handoffs instead of accepting a diagram that omits exceptions.

Record disagreements as questions to investigate. Do not average conflicting handling-time estimates until you know whether people are describing different variants or scopes.

How do you add mining to the workshop findings?

1. Translate the question into measures. Define case completion, rework, elapsed time and relevant segments. Separate business waiting from active handling.
2. Choose the case and events. Agree whether the case is an invoice, invoice line, dispute or another object. Document relationships that could duplicate records.
3. Validate a small extract. Trace selected cases back to source records before loading a wider population. Check identifiers, timestamps, event definitions and missing history.
4. Analyze a representative period. Include the variants and seasonal conditions relevant to the decision. Identify open cases and exclusions explicitly.
5. Review findings with practitioners. Select examples behind the pattern and test competing explanations. A visual correlation is not proof of cause.
6. Create improvement hypotheses. Link each proposed change to a process step, evidence, owner and expected measure.
7. Test and review. Agree a bounded trial and acceptance criteria before a wider implementation.

The step-by-step process mining assessment guide expands the data model, quality checks, sample event log and analysis sequence.

Worked example: manufacturing invoice disputes and chargebacks

Imagine a manufacturer whose AR team reports slow dispute resolution across several business units. This is a teaching example; it does not report client results or a promised recovery amount.

The first workshop maps a case from registered deduction to an approved recorded outcome. Practitioners explain that gathering delivery evidence is difficult. A few sampled cases establish possible causes but cannot establish how widespread each cause is across business units.

The event-data assessment checks whether case creation, evidence requests, review and closure are recorded consistently. If the data supports it, analysis compares elapsed time and repeat requests by dispute category and unit. The team avoids treating a delivery record’s upload date as the date delivery occurred.

The validation workshop inspects cases behind the findings. One category may involve incomplete shipment identifiers; another may wait for a contractual clarification. Those categories need different interventions.

The proposed changes could be earlier capture of shipment identifiers, approved retrieval of delivery records and a clearer exception queue. AI may assist with classifying a notice or preparing an evidence-linked summary if a trial supports it. Authorized staff retain settlement decisions.

The benefit review measures packet preparation effort, completeness at first review and reopened cases. Elapsed dispute duration and recoveries are reviewed separately because external responses and dispute merits also affect them.

If the event history proves inadequate, the team can still proceed with a narrower, explicitly sampled assessment. It should not present that assessment as a population-wide mining result.

Which tools and deliverables should you expect?

For a workshop, use an approved whiteboard or diagram tool, source examples, an evidence register and an action log. For data exploration, a spreadsheet or SQL query may be sufficient before a platform trial.

Power Automate Process Mining and UiPath Process Mining are examples to assess against data coverage, organizational access, analysis needs, maintainability and the ability to act on findings. Their inclusion here is not a preferred-vendor designation. Check current capabilities and terms for the proposed environment. Microsoft overview, UiPath documentation.

Expect an agreed process boundary, evidence sources, a validated map, baseline definitions, identified gaps, prioritized improvement hypotheses and named next actions. For mining work, also expect the event definitions, extraction assumptions, data-quality findings and reproducible analysis filters. A dashboard alone is an incomplete handover.

Download the discovery approach decision worksheet (CSV). Use it to document why workshop evidence, process mining, task observation or a combined approach fits your question.

Common questions about workshops and process mining

Can process mining replace business workshops?

It can reduce some manual work involved in finding recorded patterns. It does not replace agreement on business meaning, missing context, decision authority or the change the organization will adopt. The amount of workshop time should fit those needs.

Do we need process mining before RPA implementation?

No. A bounded, well-evidenced task may be assessed through workshops, observation and records. Mining becomes useful when broader recorded variation matters to the investment or design decision.

Is task mining the same as process mining?

No. Task mining focuses on captured desktop activity; process mining analyzes recorded process events. Choose based on the evidence gap, with suitable access and privacy arrangements for either approach.

What if management has a savings target and a short deadline?

Work backwards from the benefit definition and required realization date. Select discovery work that can resolve important uncertainty in time to support delivery and adoption. Use the savings target and deadline guide to assess whether the proposed portfolio can plausibly meet the target.