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Choose the platform that can turn your own event data into a decision and then support the action that follows. For AI-driven operations, that means validating source-system access, data preparation, analytical depth, monitoring, security, scale and the path from process insight to automation or agent governance. Treat advertised AI as a hypothesis to test on your process, not as proof of fit.
What process mining contributes to AI-driven operations
Process mining reconstructs how work actually happens from timestamped records in systems such as ERP, CRM and workflow applications. Each event normally identifies an activity, a time and a case; useful models may also include the person, team, location, value or other attributes. The result is an evidence-based view of paths, delays, rework, exceptions and performance rather than a process map based only on interviews.
That evidence matters before an organization deploys an AI assistant, agent or automation. It can show which process is stable enough to automate, where exceptions require human judgment, which variants create risk and whether an intervention changed cycle time or compliance. Gartner describes process-intelligence platforms as unifying mining, modeling and monitoring so enterprises can visualize, analyze and automate processes (5 May 2026). The description is a category definition, not a guarantee that every product has the same functions.
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A first project may only need a trustworthy process map and bottleneck analysis. A larger operating model may require continuous monitoring, opportunity ranking, orchestration, execution controls and evidence that an AI-enabled change produced the intended result.
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1. Start with the decision, not the feature list
Write down the operational decision the project must improve before requesting demonstrations. A precise decision gives every vendor the same test and prevents an impressive but irrelevant AI demonstration from determining the purchase.
Examples of decisions to define
- Find the source of order-to-cash or accounts-receivable delays.
- Compare execution between regions, business units, products or suppliers.
- Detect rework, policy deviations or approval loops.
- Select a process that is a realistic candidate for automation or an AI agent.
- Provide an agent with current operational context and constraints.
- Measure whether a new workflow, model or agent changed outcomes.
State the process boundary, the population of cases, the desired outcome, the person who will act on findings and the time horizon for action. “Improve customer operations with AI” is too broad. “Identify the top two causes of invoice-payment delay in the European order-to-cash process and select one automation candidate” is testable.
2. Prove that your data can support the analysis
Data access and preparation are procurement requirements, not technical details to postpone. For every process step, identify the system of record, the event name, the timestamp, the case identifier and the owner of the data. Then test how records from different applications will be joined into one case history.
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- Case key: Can every event be assigned to the same business object, such as an order, invoice, claim or service ticket?
- Activity: Are events named consistently enough to distinguish creation, approval, rework, cancellation and completion?
- Time: Are timestamps complete, correctly time-zoned and precise enough to measure the delays that matter?
- Ordering: Can simultaneous or late-arriving events be handled without creating false paths?
- Attributes: Are the dimensions needed for comparison—region, product, channel, value or risk—available and governed?
- Refresh: How often can data be extracted, transformed and made available for monitoring?
Map cloud, on-premises and legacy sources separately. A connector label does not prove that the resulting data model will represent your process. Ask the vendor to show authentication, extraction, transformations, joins, error handling and refresh behavior with representative records. SAP Signavio’s selection guidance emphasizes connector quality and data preparation because analyst effort in these steps can determine whether a project reaches value.
Include data ownership, access controls, retention, residency and compliance review in the initial design. A cross-system process may require a data warehouse or other transformation layer; include that work and its operating cost in the business case rather than assuming it is included in a license.
Process mining versus task mining
Process mining analyzes system event logs at the process level. Task mining observes desktop interactions to show how people perform a task, including clicks, screens, repeated work and common mistakes. It answers a different question.
Evaluate task mining when material work occurs in spreadsheets, email, remote desktops or applications that do not produce usable event logs. Microsoft Learn documents task mining as a way to reveal desktop actions, mistakes and automation candidates. Because recording employee activity can affect privacy and workforce relations, involve legal, security, works-council and HR stakeholders before collecting it. Do not use task-mining data as a substitute for a complete process event log when both are needed.
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3. Compare analytical depth with the use case
Core analytical functions are discovery, conformance checking and enhancement. Discovery reconstructs a process model from event data. Conformance checking compares actual paths with an intended reference model. Enhancement adds performance measures or other context to a model.
Capabilities to test
- Discovery: Can users see the dominant flow, meaningful variants and their frequencies without the visualization becoming unusable?
- Root-cause analysis: Can the tool connect delays, rework or exceptions to relevant attributes and preceding activities?
- Conformance: Can it compare execution with a reference model, quantify deviations and open the underlying cases?
- Performance: Can users measure throughput time, waiting time, service-level performance and rework with clear definitions?
- Monitoring: Can the platform refresh data at the interval required by the decision and alert the responsible team?
- Prediction and simulation: Are forecasts, what-if scenarios or recommendations required, and are they available in the proposed edition?
- Action: Can findings connect to workflow, orchestration or automation systems when the project intends to act on them?
- Reporting: Can operational and executive users share governed views without exporting uncontrolled copies of sensitive data?
Use a vendor-neutral capability frame covering data management, discovery, conformance, operational support, advanced enhancement, monitoring and reporting, security and compliance. A published comparison methodology from 2020 is useful for structuring questions, but its product tests were conducted in spring 2020 and are not current performance evidence.
4. Turn “AI-driven” into testable requirements
AI can help in several different parts of the operating model. Separate them so a broad claim does not hide a missing control.
Prioritization
Ask whether the platform can rank candidate processes or opportunities using observed volume, delay, cost, risk, variability and automation feasibility. Require an explanation of the inputs, update frequency and human review step. A ranking that cannot be traced to operational evidence is a marketing view, not a decision aid.
Context for agents
If an agent will approve, route, investigate or communicate, test whether the platform can supply current process state, policy constraints, prior events and exception context. Establish which systems the agent can read or write, how permissions are inherited and what happens when the process enters an unrecognized variant.
Outcome monitoring and governance
Define the measures that will show whether an AI-enabled change worked: cycle time, first-pass yield, rework, service-level attainment, compliance exceptions, customer impact or another agreed outcome. Check whether the platform can monitor those measures after deployment, identify drift and preserve an audit trail of recommendations, approvals and actions.
Gartner’s 2025 capability framework includes generative AI, process discovery, data access and preparation, task mining, orchestration, execution and automation opportunities. Those are category capabilities, not evidence that a particular edition includes them. Confirm general availability, licensing, data dependencies, model access, security commitments and review controls in writing.
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5. Match the product scope to your operating model
Do not require every advanced capability for a bounded first project. A focused engagement may need one reliable process map, a few variants and a bottleneck analysis. A center of excellence managing many processes may need a common repository, continuous monitoring, opportunity prioritization, task mining, automation governance and role-based administration.
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| Operating situation | Capabilities to emphasize | Evidence to demand |
|---|---|---|
| Single process, exploratory project | Reliable ingestion, discovery, variant analysis and clear reporting | Time from raw data to a decision-ready map; analyst effort; data-quality findings |
| Multiple processes or business units | Reusable data models, governance, permissions, collaboration and comparative views | Administration model, tenant or workspace separation, shared definitions and reference customers |
| Continuous improvement program | Monitoring, alerts, conformance, root-cause analysis and action integration | Refresh latency, alert routing, auditability and closed-loop outcome measures |
| AI-agent operating model | Opportunity ranking, process context, exception handling, execution controls and agent governance | Demonstration on your process, permission boundaries, human approvals and post-deployment monitoring |
6. Build a comparable shortlist
There is no universal winner. The right choice depends on installed systems, process scope, data volume, deployment constraints and governance requirements. Gartner’s 2025 study examined 16 vendor platforms, 10 critical capabilities and five use cases; its 2026 category research presents a somewhat different market view. These figures show that the category is broad, not that one product is best for an unspecified buyer.
| Comparison axis | Buyer question |
|---|---|
| Process and source-system fit | Does the platform reliably connect to the systems that hold evidence for our end-to-end process? |
| Analytical depth | Does it provide the discovery, conformance, enhancement and monitoring functions this decision needs? |
| Task-level visibility | Is desktop task mining necessary because important work is missing from event logs? |
| AI and action path | Does it provide validated prioritization, operational context, governance or links to automation that we will actually use? |
| Scale and governance | Can it meet volume, access, compliance, security, deployment and audit requirements? |
| Time to value | How much data preparation, specialist work and elapsed time are needed to produce an actionable result? |
| Economics | What are the complete license, capacity, add-on, implementation and support costs for our scenario? |
7. Run a buyer-controlled proof of concept
A polished vendor demo cannot establish fit with your data. Use your own process whenever possible. If that is impossible, require a source system, process and volume genuinely comparable to your environment.
- Supply a defined sample. Provide representative cases, including normal paths, exceptions, late events and the attributes needed for segmentation. Agree what data may be retained and where it will run.
- Start from raw inputs. Have the vendor show extraction or upload, field mapping, transformations, case construction and data-quality errors. Do not accept a preloaded model as the main test.
- Measure preparation. Record elapsed time, specialist roles, manual steps, failed records, cross-system joins and changes required in the source systems.
- Test the intended question. Ask the team to identify a delay source, compare two populations, find a deviation or select an automation candidate using the agreed data.
- Inspect analytical results. Check paths, variants, bottlenecks, conformance findings, drill-down to cases and the definitions behind every metric.
- Test monitoring. Load a new batch or simulate the required refresh and record latency, alert behavior, ownership and handling of late or corrected events.
- Test AI and action features. Verify the data and model dependencies, recommendation explanations, human approvals, permissions, execution logs and outcome measures. Confirm which functions are included in the proposed edition.
- Test enterprise controls. Review roles, row-level access, encryption, audit logs, retention, residency, deployment options and integration with identity and security operations.
- Document commercial effort. Capture capacity assumptions, environments, user types, implementation services, support, add-ons and renewal terms at the tested scale.
- Obtain comparable references. Ask for a customer using a similar source system, process complexity and volume. Speak specifically about preparation effort, implementation duration and production operation.
The controlled-comparison principle used by Viner, Stierle and Matzner—common logs, reference models and follow-up questions to vendors—remains useful for designing a fair test, even though their 16-tool study dates from spring 2020.
8. Evaluate enterprise readiness and security
Assess the platform as an operating system for process evidence, not only as an analyst’s visualization tool.
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- Connectivity: Determine whether native connections cover required systems and whether third-party ETL is mandatory.
- Deployment: Compare software-as-a-service, private, on-premises or hybrid options against architecture and residency rules.
- Identity and access: Check single sign-on, role granularity, segregation of duties and access to sensitive case attributes.
- Governance: Require versioned data definitions, model ownership, approval workflows and change history.
- Auditability: Verify logs for data access, model changes, recommendations, approvals and automated actions.
- Scale: Test representative case counts, event volumes, concurrent users, refresh intervals and the largest expected process.
- Operations: Ask about monitoring, backup, incident response, service levels, support geography and escalation.
- Implementation ecosystem: Verify the vendor’s experience with your source systems and whether skilled internal analysts can operate the platform after go-live.
9. Compare total cost, not a headline license
Request an itemized proposal based on your actual scenario: users and roles, process and event volume, refresh rate, environments, deployment model, included capacity, add-ons, implementation, training, support and renewal increases. Include the cost of data engineering, security review, governance and ongoing process ownership.
Product-specific figures illustrate why generic price comparisons are unsafe. Microsoft documents a 90-day Power Automate trial and a 100 MB process-mining capacity limit per process, along with specific premium and add-on capacity allocations. Those limits and allocations apply to that product and can change; verify the current Microsoft licensing page before procurement. They are not market-wide benchmarks and should not be projected onto other platforms.
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| Cost component | Questions to put in the proposal |
|---|---|
| Subscription or license | Is pricing based on users, processes, cases, events, capacity, environments or a combination? |
| Capacity and refresh | What happens when volume, retention or refresh frequency increases? |
| Data preparation | Which mapping, cleansing, warehouse and connector work is included? |
| AI and automation | Are models, orchestration, execution calls or agent features separate entitlements? |
| Implementation | What services, milestones, assumptions and customer resources are required? |
| Operations | What do support, training, upgrades, monitoring and governance cost each year? |
10. Score the proof of concept transparently
Use a weighted scorecard agreed by business, process, data, security and procurement stakeholders before results are known. A simple 0-to-5 scale works if each score has a definition: 0 means absent, 3 means usable with documented workarounds and 5 means demonstrated at representative scale.
- Data connectivity and case construction: weight according to the number and criticality of source systems.
- Ability to answer the defined operational question.
- Discovery, variants, bottleneck and conformance quality.
- Monitoring latency and reporting usability.
- AI prioritization, context, governance and action integration.
- Security, access, audit and deployment fit.
- Performance and capacity at expected volume.
- Preparation effort, implementation risk and internal skills required.
- Total cost over the period relevant to the business case.
- Quality of references and support model.
Set “must-pass” criteria separately from weighted preferences. For example, failure to construct a trustworthy case history, meet residency requirements or provide an auditable action trail should disqualify a platform regardless of its visualization score.
11. Avoid common selection failures
Choosing from a generic demo
Symptom: The demonstration uses clean, preloaded data and produces an attractive map in minutes.
Fix: Require raw-to-result work on your source system or a comparable one, including joins, errors and refresh.
Buying an AI label instead of an operating capability
Symptom: The proposal promises intelligent recommendations but does not explain inputs, permissions, review or outcomes.
Fix: Convert each claim into a test: what evidence is used, who approves the result, what action follows and how impact is monitored.
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Symptom: Desktop recordings are expected to explain an end-to-end process, or event logs are expected to reveal work performed outside core systems.
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Fix: Decide whether each question concerns system-level flow, desktop execution or both, then apply the appropriate privacy and governance controls.
Underestimating preparation
Symptom: License costs are approved before case keys, timestamps, joins and ownership are understood.
Fix: Measure preparation in the proof of concept and include data engineering and governance in the business case.
Overbuying for a first project
Symptom: A team purchases a broad automation and AI suite before proving one process decision.
Fix: Stage the rollout. Make the first gate a reliable map and an adopted action; add advanced monitoring, task mining or agent governance when the operating need is demonstrated.
Treating old comparisons as current rankings
Symptom: A 2020 test or dated methodology is presented as a current performance league table.
Fix: Use older studies for evaluation dimensions and experimental design only. Base the purchase decision on current, buyer-controlled testing.
12. A practical rollout sequence
- Frame one decision: Select a process with a named owner, measurable outcome and accessible data.
- Establish data feasibility: Confirm case keys, timestamps, joins, permissions and refresh before comparing advanced features.
- Run a bounded proof: Demonstrate raw-data preparation, analysis and an action with representative cases.
- Operationalize measures: Define the baseline, target, monitoring cadence, alert owner and escalation path.
- Expand deliberately: Add processes, task mining, automation or agent capabilities only after governance and support responsibilities are clear.
- Review value: Compare actual outcomes and preparation effort with the business case, then renew, expand or change course.
This sequence keeps the purchase tied to an observable operational result while leaving room for a broader process-intelligence program.
Quick Recap
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