Similar language, different starting points
Process mining, task mining and process intelligence are often presented as interchangeable. They are not. Each starts from different evidence and answers a different class of question.
The practical decision is not which label is most advanced. It is which method can produce a trustworthy answer from the data, maturity and operating context you actually have.
The AI Process Intelligence method is intentionally broader than log analysis. It connects process evidence with risks, decisions, ownership and project context when the work cannot be understood from system events alone.
Process mining
Process mining reconstructs flows from timestamped event logs. It is strongest when work is executed through stable systems that record a case identifier, activity and time consistently.
It is a good fit when you need to discover variants, measure waiting, compare actual flow with a defined model or identify rework across a high-volume process.
Its limitation is also clear: it can only see what the systems record. An event log may show that an approval waited nine days without explaining whether the cause was missing evidence, unclear authority or a deliberate technical review.
Task mining
Task mining observes repeated user activity, often at desktop level. It can expose copy-and-paste work, repeated navigation and manual handoffs that enterprise systems do not represent.
It is useful for stable, repetitive tasks with clear privacy boundaries. It is weaker for collaborative decisions, engineering exploration and low-frequency work where variation carries meaning.
Broader process intelligence
Process intelligence uses available system evidence but adds the context required to support a decision. That can include criteria, risks, open assumptions, ownership, quality conditions and source documents.
The broader model is useful when the question is not simply where time is spent, but why a project state is unreliable, why teams disagree about readiness or which automation opportunity is safe to pursue.
The TRACE decision framework
Use five checks before selecting an approach.
Traces
Do reliable event logs exist? If yes, process mining can provide a powerful factual baseline. If not, begin by defining the evidence model instead of buying a discovery engine with nothing trustworthy to discover.
Repetition
Is the work repeated frequently enough for statistical variants to matter? High-volume transaction flows suit mining. Low-volume technical decisions often require qualitative context alongside data.
Ambiguity
Are the main problems visible in timestamps, or do they live in criteria, ownership and interpretation? Ambiguity is a signal that a broader process-intelligence model is needed.
Consequence
What decision will the analysis change? Reducing wait time, controlling project risk and selecting an automation candidate require different outputs.
Ethics
Can the evidence be collected with proportionate privacy, consent and governance? A method that produces insight by undermining trust is not operationally sustainable.
Example: design review delays
A hardware organization sees long lead time between design submission and approval. Process mining can measure queues if the PLM system records each transition. That is valuable, but incomplete.
Interviews and review evidence reveal that submissions are reopened because electrical, mechanical and sourcing teams use different readiness criteria. The queue is not the root problem. The decision contract is.
The combined intervention becomes:
- use event data to quantify waiting and rework;
- define shared evidence required for each review type;
- assign decision rights and escalation conditions;
- measure first-pass acceptance;
- automate reminders or routing only after the criteria stabilize.
Mining provides the trace. Process intelligence turns it into a governance decision.
A compact selection guide
- Choose process mining when reliable event data and repeated flow already exist.
- Choose task mining when the bottleneck is repetitive desktop activity and privacy conditions are clear.
- Choose broader process intelligence when evidence is fragmented and the real constraint involves risk, criteria or ownership.
- Combine methods when log data describes the flow but not the reason behind it.
Start with readiness, not tooling
Before evaluating platforms, test whether the process has a clear scope, owner, evidence source and measurable outcome. The Process Readiness assessment helps expose those prerequisites.
For a cross-functional process where different stakeholders still disagree about the problem, use a scoped advisory sprint to define the decision and evidence model first.