Process mining: finding automation opportunities in your operations

Process mining reconstructs how processes actually run from event logs already recorded by ERP, CRM and service systems. For operations leaders, its value is evidence: it shows where activity volumes, process variants, rework and waiting time concentrate, while resource or execution data can help identify where manual effort is involved. This gives automation decisions a measurable basis rather than relying only on assumptions made in workshops.

What is process mining and how does it reveal automation opportunities?

Process mining is a data-driven method for reconstructing and analysing how a business process actually runs by using event data from operational systems.

Instead of asking employees to describe every path through procure-to-pay, order-to-cash or customer service, process mining reads the events those systems already record.

A typical case might show:

Create purchase order → Approve purchase order → Change price → Re-approve → Receive goods → Receive invoice → Pay invoice

When thousands of cases are analysed together, the organization can see which paths dominate, how many variants exist, where work repeats and where cases wait.

This is the fundamental difference from a workshop.

A workshop often starts with the intended process.

Process mining starts with observed execution.

People remain essential because an event log cannot explain every business reason behind a deviation. But the discussion becomes much more useful when teams are looking at evidence instead of debating which anecdote is representative.

Process mining is commonly described through three related activities:

• process discovery reconstructs process flows from recorded events;
• conformance checking compares actual execution with an expected process or policy;
• process enhancement adds information such as time, cost or organizational context to improve the model.

For automation, each answers a different question.

Discovery shows where the work is.

Conformance shows where exceptions and workarounds occur.

Enhancement helps estimate what those deviations cost.

That makes process mining different from conventional BI. A dashboard might tell you that average invoice cycle time is nine days. Process mining can show which sequence of activities created those nine days and which variants account for most of the delay.

Process mining vs task mining

Process mining follows business cases across systems. Task mining observes how users perform work on their desktops, for example application switches, clicks, copy-paste activity or repeated manual steps.The two answer different questions.

An ERP event log may show that an invoice was received at 09:00 and entered at 09:18.

Process mining sees the eighteen-minute interval.

Task mining can potentially show that the employee opened the email, downloaded the attachment, copied several fields and switched repeatedly between applications during that interval.

Task mining can therefore expose work that enterprise systems do not log. Task mining is also more privacy-sensitive because it can capture much finer-grained information about employee activity. For EU-facing deployments, that means applying data minimization, clear purpose limitation and transparent employee communication, alongside appropriate access and retention controls. Where monitoring could create a high risk to employees’ rights and freedoms, organizations should also assess whether a DPIA is required before collection begins.

Seven signals of an automation opportunity in process data

In this guide, we use seven practical signals to identify potential automation opportunities in process data: high-volume manual work, short repetitive handling, rework loops, long waiting periods, frequent manual exceptions, low touchless rates and excessive process variants. 

The distinction between signals is important because not every signal means “build a bot.”

1. High-volume manual activity

Start with activities performed repeatedly by people.

Volume alone does not make something a strong candidate, but large volumes make even small reductions in handling time meaningful.

2. Short, repetitive handling

Repetitive actions lasting a few minutes are frequently missed in transformation programs because each individual action looks insignificant.

Across tens of thousands of cases, they become material.

The relevant measure is total annual effort rather than time per transaction.

3. Rework loops

Repeated approvals, corrections or data changes often indicate a process problem rather than an automation problem.

For example:

Create PO → Approve → Change price → Re-approve

The best intervention may be better contract data or purchasing controls, not faster re-approval.

4. Waiting time between hand-offs

When the underlying event log contains sufficiently detailed timing data, process mining can distinguish active handling time from waiting or elapsed time.

A case may require only ten minutes of actual work while taking four days to complete because it waits in queues between departments. That pattern can point to workflow automation, redesigned approval thresholds or automated routing.

5. Manual exceptions

If most manual work happens only in exceptions, automating the standard path further may produce little value.

The opportunity may instead be to understand why cases become exceptions.

6. Low automation or touchless rate

Measure how many cases complete without manual intervention.

A low touchless rate can reveal where supposedly automated processes still depend heavily on people.

7. Variant explosion

A process with hundreds of variants is not automatically an attractive AI use case.

It may mean the organization has inconsistent policies, master data or local practices.

Standardization can create more value than automating every variant independently.

A useful rule is that signals such as high volume, repetitive handling and low automation rates often point directly toward automation. Rework, exceptions and variant explosion usually deserve root-cause analysis first.

From finding to fix: not every process problem needs automation

Process mining should determine what intervention a process needs, not merely identify places where software could be added.

This distinction prevents one of the most common automation mistakes: making a bad process execute faster.

If employees manually copy the same order information from CRM to ERP thousands of times, the process data has identified an automation opportunity.

But the next question should be architectural.

Does the ERP expose an API?

If it does, direct integration is generally more robust than automating the screen. The principles in our API-first development guide explain why stable software interfaces create a better foundation for reusable automation.

RPA still makes sense when legacy software leaves no reasonable integration alternative.

Unstructured documents are different again. If invoice exceptions result from employees reading PDFs, intelligent document processing may solve the bottleneck without changing the rest of the workflow.

And when people spend most of their effort investigating context and deciding which path to take, AI agents may become relevant.

The important point is that process mining diagnoses the pattern.

Technology selection comes afterwards.

Examples across common processes

The same process can contain several interventions at once.

Process mining is useful precisely because it prevents an organization from treating the entire process as one automation use case.

How to run your first process mining project

A first process mining project should focus on one high-volume process, one measurable business question and a limited data scope, with the output being a prioritized automation backlog rather than a perfect digital twin.

The duration of a first process-mining assessment depends mainly on data accessibility, event-log quality, privacy review requirements and source-system complexity.

Start with the business question.

Examples are much stronger than “let’s implement process mining”:

Why do 28% of invoices require manual intervention?

Where does order fulfillment spend most of its elapsed time?

Which service activities create enough repetitive manual effort to justify automation?

The core team can remain small.

You typically need:

• a business process owner who understands the operating outcome;
• an analyst who can interpret process data;
• an IT or source-system expert who understands the events.

Additional privacy, security or automation specialists can join where the scope requires them.

Avoid analysis paralysis.

A process-mining project can always generate another visualization, filter or variant analysis.

The point is to decide what to change.

Choosing process mining technology

Choose tools based on data access, analytical requirements and operating model rather than a generic ranking.

Options include dedicated process-mining platforms, process-mining modules inside wider automation or workflow platforms, and open-source frameworks for analytical or proof-of-concept work.

Examples across these categories include Celonis, SAP Signavio, Microsoft Power Automate Process Mining, ServiceNow Process Mining, UiPath Process Mining, ABBYY Timeline, iGrafx, PM4Py and ProM.

Relevant selection criteria include:

• connectors to your ERP, CRM and service systems;
• ability to import custom event logs;
• export access to your own data;
• data residency and security model;
• conformance and variant analysis;
• licensing model;
• integration with BI and automation;
• ability to operationalize findings rather than only visualize them.

The tool should not become the process architecture.

Your event definitions, KPIs and automation backlog should remain usable even if the platform changes.

What process mining cannot tell you, and why AI makes it more useful

Process mining only sees recorded events and shows patterns rather than automatically explaining their business causes, so it should be combined with people, task-level evidence and domain knowledge.

Its main limitations are straightforward. If work happens in email, spreadsheets, phone calls or offline conversations, the event log may not see it. If the source data is poor, the resulting process model will be poor. If a case identifier cannot connect events reliably, reconstruction becomes difficult.

If a process is highly variable knowledge work, a conventional process map may simplify reality too aggressively. And finding a correlation does not establish the root cause. A re-approval loop might be caused by poor master data, changing customer requests, a policy issue or employee error. Process mining shows where to investigate.

This is also why AI agents do not make process mining obsolete. They increase the need to understand processes.

Before giving an agent access to multiple enterprise systems, organizations need to know which activities exist, how exceptions move, where approvals belong and what performance looks like before automation.

After deployment, the same process metrics can show whether automation actually changed:

• cycle time;
• touchless rate;
• rework;
• exception volume;
• SLA performance.

AI can automate more variable work, but that makes evidence about the surrounding process more valuable, not less.

Webellian’s guide to AI agents in business explains why production agents need governed data, controlled tools and integration with the enterprise systems where work already happens.

Talk to Webellian about using process and operational data to identify and prioritize automation opportunities.

FAQ

What is process mining in simple terms?

Process mining uses timestamps and activity records from business systems to reconstruct how processes actually run. It can show which paths cases follow, where they wait, where activities repeat and which process variants create most of the manual work.

What are the three types of process mining?

The three commonly described types are process discovery, conformance checking and process enhancement. Discovery reconstructs the actual process, conformance compares actual execution with an expected model, and enhancement adds information such as timing or performance to support deeper analysis.

What data do you need for process mining?

For traditional case-centric process mining, the minimum event data usually includes a case identifier, an activity and a timestamp. Useful additional attributes can include the person or system performing the activity, financial values, supplier, customer, department or other information used to segment the process.

Object-centric process mining uses a different model: events can relate to multiple business objects rather than being assigned to one single case, which makes it better suited to processes involving interconnected objects such as orders, items, deliveries and invoices.

What is the difference between process mining and task mining?

Process mining reconstructs end-to-end business processes from system event logs. Task mining observes user-level activity such as clicks and application switching. Task mining can expose manual work between systems, but it also creates greater employee-privacy considerations.

How does process mining identify automation opportunities?

Look for high-volume manual activities, repetitive handling, rework, waiting time, manual exceptions, low touchless rates and excessive process variants. Then determine whether each finding needs automation, integration, process redesign, data improvement or another intervention.

How long does a process mining project take?

There is no universal duration. For a focused first assessment with accessible data, six to ten weeks can be a useful planning range. Complex source systems, privacy reviews and difficult event-log reconstruction can make the project longer.

Sources

https://learn.microsoft.com/en-us/power-automate/process-mining-processes-and-data

https://learn.microsoft.com/en-us/power-automate/process-mining-normalized-import-authoring-guide

https://www.edpb.europa.eu/sme-data-protection-guide/faq-frequently-asked-questions_en

https://www.edpb.europa.eu/our-work-tools/documents/public-consultations/2026/edpb-dpia-template_en

https://www.gesetze-im-internet.de/englisch_betrvg/englisch_betrvg.html