Process mining and RPA work best in sequence: process mining reads event logs to show how work really runs, and RPA then automates the repetitive steps it exposes. The main condition is choosing processes from evidence, not assumption.
This guide follows that sequence from log to bot, and ends with a checklist for picking the first process. For wider context, see our overview of AI and automation tools for software teams.
What process mining shows
Process mining uses recorded operational data to rebuild how work moves through a business. IgniteSAP’s overview of SAP process mining and RPA describes it as taking the place of the preparatory modelling work, and notes that it reveals bottlenecks that can develop over time. A workshop-based process diagram shows how people believe work happens. A mined model shows what the systems recorded.
The raw material is the event log. The same overview quotes a Celonis co-founder explaining that every user action within the business was being recorded in IT systems in the form of event logs. A project therefore rarely needs new data collection; it needs access to logs that already exist. Before extracting, check that each record tells you what happened, to which piece of work, and when. Without those three anchors a log cannot be turned into a process map.
Traditional event logs have limits. A Process Excellence Network report on process mining and RPA notes that object-centric process mining techniques address those limitations. If your first extract looks incomplete or confusing, that is a reason to look at object-centric approaches before concluding the process is too messy to map.
How process mining feeds RPA
RPA relies on programmable software robots that copy human actions and perform certain tasks, as ITRex’s RPA vendor comparison explains. A bot can only copy what someone has already defined, so the quality of the definition decides the quality of the automation. Process mining supplies that definition from observed behaviour.
The IgniteSAP overview quotes a Celonis chief scientist: with process mining you can detect repetitive work and then provide the information needed to automate it. It also says that analysed data sources and business processes become the foundation for event-triggered robotic process automation. In practice, the mined model tells the developer which path the bot should follow and which events should trigger it.
The same overview argues that process mining has to be combined with workflow management and RPA to deliver its full benefit. That is the case for treating this as a workflow automation effort rather than a bot project. Teams that want to build the surrounding workflow without heavy coding can read Low-Code, No-Code Automation Tools: Build Workflows Faster in 2026 (A Practical Guide) – Interestingdevelopment.
Evidence on outcomes is encouraging but secondary. The Process Excellence Network report states that mapping and optimising workflows before automating them has been shown to increase automation success rates. Treat that as a reason to map first, not a guarantee for your processes. Separately, UiPath’s blog on RPA and process mining after the recession cites a Gartner forecast that organizations would lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes. The emphasis falls on redesign, not on bots alone.
Rule-based bots do not adapt on their own. For steps that need judgement rather than a fixed path, Custom AI Agents: How To Build, Deploy, and Benefit From Them In 2026 – Interestingdevelopment covers the alternative.
A roadmap from event log to bot
The stages below turn a log extract into a running bot. Each stage produces something the next one needs.
- Scope the systems. Pick the application or applications where the work happens and the period of history to analyse. A narrow scope gets you to a readable map sooner.
- Extract and check the log. Confirm that actions, work items and times are recorded consistently. Gaps found here are cheaper to fix than gaps found after a bot misbehaves.
- Discover the process. Run the mining tool and compare the real flow with the documented one. Look for loops, waiting time and the paths that most cases follow.
- Select candidates. Apply the checklist in the next section and keep only the processes that pass.
- Build the bot on the dominant path. Define triggers and steps from the mined model, and decide where unusual cases go to a person.
- Pilot, then re-mine. Run the bot on live work and mine the new event data to see what changed.
- Extend deliberately. Automate more steps only when the pilot’s measurements justify it.
UiPath’s blog gives an illustrative scenario for the measurement stage. An invoice process showed a bottleneck, and after the pilot the lead time had already been reduced from nine days to seven days, with the Process Invoice activity 24% automated. After full automation, the lead time dropped to 34 minutes. It is a vendor scenario, not a benchmark, but it shows the pattern: the same mined view that found the bottleneck is also the scoreboard for the bot.
Choosing which processes to automate first
The IgniteSAP overview is direct on this point: the correct processes for automation must be selected, to reduce errors and save workforce time. The Process Excellence Network report adds that process mining targets the most valuable and repeatable processes before they are automated. Use the checklist below as a gate for each candidate.
- Repetitive in the data. The mined log should show the same work being done again and again. Repetition seen in event data is better evidence than a team’s impression.
- Valuable. Choose processes where a bottleneck or manual effort has a visible cost, so a pilot can show a real change in time or errors.
- Repeatable path. The bot should follow the dominant path, with a defined route for variants it cannot handle.
- Measurable baseline. Record the current lead time from the mined model before building, as the UiPath scenario does, so the result can be compared.
- Systems it can reach. The Process Excellence Network report finds that scalability is often limited by legacy system incompatibility. Check integration before committing.
- A place in a wider strategy. The same report names a lack of automation strategy as a limit on scale, so confirm the candidate fits a plan beyond a single bot.
A candidate that fails two or more of these is usually better left for later. It will not give the early, measurable result that earns support for further work.
Process mining and RPA tools compared
Tools overlap, and some RPA platforms now include discovery. The table compares the vendors named in the supported material on the points that matter when planning.
| Tool | Role | Notable points | Planning use |
|---|---|---|---|
| UiPath | RPA platform | Default programming language VB.NET, with C# introduced in 2020. Offers process mining and automation assessment tools as add-ons. | Suits teams that want discovery and bots on one platform; confirm your developers are comfortable with its languages. |
| Celonis | Process mining | Signed its first reseller agreement with SAP in 2015. | Relevant where SAP data is the main source of event logs. |
| Automation Anywhere | RPA platform | Process Discovery uses AI and process mining to discover processes without manual data collection. Listed drawbacks: expensive, and challenging to integrate with major ERP platforms such as Salesforce and SAP. | Test ERP integration early before choosing it for ERP-heavy processes. |
Figures and capabilities here come from ITRex’s RPA tool comparison and IgniteSAP’s process mining overview.
The practical choice is whether discovery and execution come from one vendor or two. A single platform simplifies handover from mined model to bot. A dedicated mining tool may suit organizations whose logs sit mostly in one enterprise system. Whichever you pick, run the same pilot process through discovery first, because integration problems appear there.
Pitfalls that stall automation
The most common failure is stopping after the first bot. The Process Excellence Network report found that approximately 60 percent of organizations in its study encountered significant barriers in extending RPA beyond isolated use cases. Its conclusion is that RPA is not a plug-and-play solution for digital transformation but a strategic enabler.
- Treating a bot as finished. ITRex notes that first-generation programmable bots have a fixed set of capabilities and cannot learn or evolve without altering their code. When the process changes, the bot needs maintenance, and re-mining after go-live is how you notice the change.
- Ignoring legacy systems. As noted above, legacy incompatibility limits scale. Raise it during selection, not after the pilot.
- Automating without strategy. Isolated bots chosen opportunistically are the pattern the study links to stalled scaling.
- Trusting a thin log. A map built from incomplete event logs can mislead the selection of processes. Validate the extract before ranking candidates.
- Skipping redesign. The Gartner forecast cited by UiPath ties cost reduction to redesigned processes as well as technology, so use the mined map to remove waste before encoding it in a bot.
Frequently asked questions
What is the difference between process mining and RPA?
Process mining analyses recorded event data to show how work actually runs and where bottlenecks are. RPA uses programmable software robots to copy human actions and carry out tasks. One diagnoses; the other acts.
What savings have organizations reported from automation?
ITRex’s comparison reports that HDI Versicherung saved 600 one-person days over a year and a half, and that automation spared Newcastle Hospitals 7,000 working hours a year. These are reported results from individual organizations, not averages to expect.
How can a small productivity gain be framed as return on investment?
UiPath’s blog gives an illustrative example that assumes operating profit is 10% of employee costs. On that assumption, a 5% productivity gain yields a 50% rise in operating profit. Replace the assumed margin with your own figures before using the approach in a business case.
