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4-Step Guide For RPA Deployment with Process Mining in 2024

4-Step Guide For RPA Deployment with Process Mining in 20244-Step Guide For RPA Deployment with Process Mining in 2024

Businesses can significantly improve their efficiency by automating their manual processes. However, RPA implementation can be costly and slow. Companies may incur substantial costs if they try to deploy RPA bots without analyzing their processes or choosing the wrong automation areas. Thus, they should first see the full picture of their processes and identify the most effective processes to automate to maximize their RPA deployment’s impact. 

Process mining can play a critical role and helps businesses deploy their RPA solutions rapidly. When implementing RPA solutions, process mining technology can:

  • understand processes’ strengths/weaknesses 
  • identify automation opportunities before RPA implementation, 
  • track and improve automation journey,
  • catch up with process changes by continuous monitoring

1. Have visibility and insights into existing processes

This step is critical for all companies that plan to implement an RPA solution. Without understanding processes, businesses might not deliver the expected results and identify the causes of their failure. That can also cause higher costs for companies. According to the recent surveys by Deloitte and EY, almost half of RPA projects fail, and the successful ones mostly do not achieve the expected ROI.

Companies have fundamental insights into their actual processes with process mining tools by benefiting from real-time data and event logs. These tools provide information about process variations, duration, error rates, cause-and-effect relationships between events, and sub-tasks within the processes.

While these insights can help businesses make better decisions for process improvement, they can understand how their processes work in real life and identify the root causes of problems and deviations.

For example, Siemens has integrated1 process mining into many of its business processes, from purchasing and ordering logistics to production, order handling and customer delivery. As a result, the company could observe their actual processes in real-time and track down the manual procedures and inefficiencies. Siemens eliminated process deviations and identified automation opportunities in ordering channels by understanding their processes.

2. Identify the best processes to automate

One of the main reasons RPA initiatives fail is choosing the wrong processes for automation. PwC indicates that even an RPA proof of concept often takes 4-6 months instead of the expected 4-6 weeks. That mostly happens because the process chosen for automation is too complicated and in no way suitable for RPA implementation.

For example, a process mining vendor claims2 that Piraeus Bank tried to automate its processes without analyzing them. As a result, the company received complaints from both its customers and branches. They also couldn’t identify the problematic part of their automation project because they didn’t have full insights into the process. Later, the company implemented process mining software to identify bottlenecks, discrepancies, and their causes. In the end, it is claimed that the company accelerated its loan application process from 35 minutes to 5 minutes.

Thus, it is vital to identify the correct processes to automate with RPA so that businesses can avoid the time-consuming and costly restructuring of RPA. A misplaced RPA investment may not pay itself back. Processes that RPA can easily automate include:

  • Rule-based
  • With few exceptions
  • Company-specific
  • Mature
  • Not on the roadmap for new systems

You can find more detailed information to prepare and implement RPA successfully in our RPA implementation guide.

You can easily and accurately identify such processes and speed up your RPA implementation initiative with process mining.

3. Monitor and optimize RPA implementations during the project

The implementation of RPA consists of generating the rules for bots and programming the workflow execution. You can do with process mining to check if your RPA initiative is being implemented correctly. You can leverage process mining to visualize the entire workflow of your RPA project to monitor the RPA implementation process itself.

Also, you can generate or upload a reference model based on best RPA practice or your ideal project to test whether the actual project complies with the planned project. You can measure the RPA project performance by checking the KPIs. If RPA performance doesn’t give the expected results or deviates from the reference model, the process mining tool can detect its root causes by performing root-cause analysis.

4. Catch up with the changes by following up

The results of automation projects can be challenging to measure. After implementing RPA successfully, businesses can use process mining tools to monitor their automation increase due to RPA and estimate ROI. That provides a factual assessment of RPA results, helping guide future RPA investments.
Another issue is that processes change with time. When changes occur, RPA solutions may need to be updated as well. Business leaders can employ process mining to detect when these changes affect RPA performance. As explained in the first step above, understanding processes can provide faster RPA deployments and reduce RPA maintenance costs.

For more on RPA and process mining

To explore RPA use cases in different industries, feel free to read our articles:

And to explore process mining use cases, feel free to read:

If you believe your business will benefit from a process mining or RPA solution, feel free to check our data-driven hubs of automation solutions and process intelligence solutions.

You can assess different vendors with a transparent methodology yourself by downloading our checklist: 

Get Process Mining Vendor Selection Guide

You can also check the following lists:

If you still have questions about process mining or RPA, we would like to help:

Find the Right Vendors


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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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