Find Process Automation Opportunities with Process Mining

How to Find Process Automation Opportunities with Process Mining

Process automation is a decision about where to change work, not just which task to hand to a bot. Process mining can help you see how work actually flows, quantify candidate steps, and check whether automation is likely to fit. It can also show that a step should be redesigned or left alone.

The strongest candidates tend to happen often, follow a consistent path, and take enough time to matter. Frequency, standardisation, volume, and handling time give you a practical way to build a shortlist. Exception rate helps you judge how much of that work could follow the automated path.

The Automation Hype and the Reality Check

Search for “process mining and RPA” and you will find plenty of vendor pages presenting mining as the obvious first step to automation. The pitch is simple: map the process, find repetitive tasks, and hand them to bots.

That story leaves out an important point. Many obvious automation candidates are already known. A finance team may know it copies invoice data between systems. A support team may know it routes the same ticket types by hand. You do not need a full Process Mining project to confirm what the people doing the work already see.

Mining earns its place when the picture is less clear: when work crosses teams or systems, when exceptions are hard to predict, or when you need evidence to compare the process before and after a change. It can help you decide what to automate, but it should not turn every process problem into an automation project.

A Quick Primer: RPA, Agentic Automation, and Process Mining

RPA is one form of digital automation: it moves data between interfaces. Process automation tools that drive whole workflows are another, and both are easier to choose once you can see the process.

These terms describe different parts of the work.

For a fuller explanation, read what process mining is. If you are new to RPA, see our RPA primer.

Agentic automation adds another approach. An RPA bot follows steps someone has specified; an agent receives a goal and selects actions. That makes a clear process description more important, not less. Before you delegate work to an agent, you need to understand the process variants, exceptions, and what a correct outcome looks like. The same evidence helps you decide whether a step is suitable for automation at all.

Where Process Mining Genuinely Helps

Process mining can support business process automation when you need evidence that is difficult to get from a workshop or a single team’s view.

• Find repetition across teams and systems. The same data may be entered in multiple places, or a handoff may repeatedly trigger rework. End-to-end process data can reveal patterns that are easy to miss when each team sees only its own tasks.
• Quantify the work involved. Measure how often a step occurs and how long it takes. That gives you a more grounded estimate of the effort in scope than a general claim about automation potential.
• Understand exceptions. A task may look consistent until you compare its variants. The exception rate helps you see how much work follows the common path and how much needs a different treatment.
• Check what changed after deployment. Compare process performance before and after a change. You can see whether the flow changed as expected and whether new bottlenecks or deviations appeared.
• Identify work not to automate. Mining may show that a task involves too many exceptions, depends on judgment, or happens too rarely to justify the effort. That is a useful finding, even if it means you do not build a bot.

Frequency × standardisation × volume: the shortlist criteria

Start with four factors. Frequency, standardisation, and volume help you judge whether a task is a repeatable candidate. Handling time shows how much effort is involved. Treat them as a screening method, not a promise of savings.

Score each factor from 1 to 5, using definitions your team agrees on. You can multiply the scores to rank candidates, but do not treat the result as a business case. A high score in one factor cannot compensate for a poor fit elsewhere. A frequent task with many exceptions may need redesign; a standard task that happens rarely may not merit automation.

Use exception rate as a further check. It tells you how often a case leaves the path you are assessing. A high exception rate is a prompt to investigate why, not an automatic reason to reject the candidate.

Calculating automation potential

Estimate the volume and handling time that could follow the common path. One simple screening calculation is:

addressable hours = instances per month × handling time × (1 − exception rate)

For example, suppose a credit check occurs 4,200 times a month and takes six minutes per instance. That is 420 hours of total handling time. If 18% of cases take a different path, the common-path estimate is about 344 hours a month.

This is an estimate of work in scope, not a forecast of savings. It does not account for the time needed to handle exceptions, monitor the automation, or build and maintain it. Before making a business case, check the estimate with the process team and account for those costs and responsibilities.

If you want to see how the process data supports this analysis, explore ProcessMind’s process mining capabilities.

Be Honest: You Don’t Always Need Process Mining

If you only need a list of tasks to automate, process mining may be more than you need.

Mining requires event data, preparation, and people who can interpret the findings. For a simple, well-understood task, a conversation with the team doing the work may be enough to define the steps and exceptions. You can then decide whether to build a bot.

For example, if an accounts payable team already knows it spends time copying invoice data from emails into an ERP, you may not need event log analysis to confirm that task exists. You still need to check the rules, exceptions, and risks before automating it.

Process mining is more useful when the process spans multiple teams or systems, when the actual flow differs from the documented one, or when you need a baseline to assess a change. It should help answer a real question, not become a prerequisite for every automation project.

Process Mining Does More Than Find Automation Targets

The “process mining for RPA” framing can make mining sound like a scouting tool. It can also help you understand and improve the wider process.

• Monitor process performance. Track cycle times, deviations, and changes in the flow. See how continuous process monitoring works.
• Check conformance. Compare actual process paths with the expected process. This can support compliance and governance work, but it does not fix a process that is not working well.
• Investigate root causes. If orders are delayed, compare the cases by supplier, region, team, or process variant to see where the delays occur.
• Redesign before automating. Process analysis can show where to remove steps, reduce handoffs, or simplify approvals. A simpler process may be a better starting point than automating the current one.
• Model and simulate changes. With ProcessMind, you can model process changes and simulate their potential impact before implementation.

Automation is one option. First understand the problem, then decide whether to redesign, automate, or leave the process as it is.

A Practical Approach, Step by Step

Step-by-step flow from event data to an automation shortlist

Use this sequence to connect process analysis with automation decisions:

1. Understand the process. Review its variants, bottlenecks, rework, and exceptions.
2. Improve the process first. Ask whether you can remove unnecessary steps or handoffs before building automation.
3. Shortlist suitable tasks. Compare frequency, standardisation, volume, handling time, and exception rate.
4. Assess the effort and risks. Check the systems involved, the rules the task follows, and how exceptions will be handled.
5. Monitor the result. Compare the process after the change with your baseline and investigate unexpected effects.
6. Reassess as work changes. New policies, systems, and process variants can change whether automation still fits.

For the monitoring step, see our guide to continuous process monitoring. To document a target process before you automate it, explore BPMN modeling.

Build a Shortlist You Can Review

Use this template to compare candidate steps. Fill in the evidence before you rank the options. A blank or uncertain field is a question to investigate, not a reason to assume the candidate is suitable.

The examples illustrate how to structure a review; they are not a forecast of savings. Use your own data and confirm the assumptions with the people who handle the work.

Three practices make the shortlist more useful:

• Fill in every column before ranking. Frequency alone can make a task look more attractive than it is.
• Record the number of systems involved. Each system boundary can affect implementation and support effort.
• Keep “redesign first” and “do not automate” as valid outcomes. The best decision may be to remove the task or change how it is done.

Vendors: All-in-One or Best-of-Breed?

Some vendors offer both RPA and process mining on one platform. UiPath started in RPA and added process mining later. Celonis comes from process mining and partners with automation vendors. These approaches have different trade-offs.

For a closer look at one vendor comparison, read our UiPath vs. ProcessMind analysis. The right choice depends on your existing systems, team, and requirements. Compare the process mining and automation capabilities separately, and check whether the platform supports the decisions you need to make.

Where ProcessMind Fits

ProcessMind brings process mining, process modeling, and simulation together. You can use it to:

• Discover how processes run using event data.
• Analyze variants, bottlenecks, and deviations.
• Document processes with BPMN modeling.
• Simulate proposed changes before implementation.
• Monitor process performance over time.

ProcessMind helps you understand the process and assess improvement options. It does not decide what you should automate or execute automation for you. You can use the findings to make a case for automation, redesign the process, or decide not to automate.

If you are weighing the broader business case for mining, see how to build a process mining business case. For guidance on the data you need, read how to create a process mining event log.

Busting the Myths

Myth 1: “Process mining will output an automation plan”

Process mining shows how a process runs in the data. It can reveal inefficiencies, bottlenecks, and deviations. Your team still needs to interpret those findings and decide what to automate, redesign, or leave alone. Treat any claim of a one-click automation plan with caution.

Myth 2: “Process mining is mainly for analyzing RPA bots”

Mining can help you assess the wider process around a bot, but it does not explain every detail of the bot’s actions. If a bot follows a defined script, review the script and its logs to understand its behavior. Use process mining when you need to see how the automated step fits into the end-to-end process.

Myth 3: “You need process mining before every automation project”

You do not. If the task is simple, well understood, and its exceptions are clear, you may be able to proceed without mining. Process mining is more useful when the process is complex or the full picture is not visible to any one team.

Myth 4: “Automation and process mining need the same data access”

They do not. RPA works through user interfaces and may need access to screens and applications. Process mining needs structured event data from business systems. The data, access, and skills involved can differ.

Planning for both at once means coordinating across these boundaries: different questions (“What can we script?” and “What is happening across the process?”), different owners, and different data access.

Start with Evidence, Not an Automation Target

Process mining can help you rank automation candidates, estimate the work in scope, and check how the wider process changes. It can also tell you that automation is not the right next step. That is a useful result: the goal is to make a sound decision, not to automate more tasks.

Learn how to assess process health before and after a change.

Design the target state before automating it so you can review the process you intend to build.