3 Ways Intelligent Process Mining Enables Automation in 2024

Updated on Jan 3

3 min read

Table of contents

Process Mining as an automation enabler Intelligent Process mining Other Intelligent Process Mining features supporting automation Further reading

Enterprises and their consultants have been spending decades on process improvement using a variety of approaches such as 6 sigma, Lean etc. However, automation technologies are evolving daily and new opportunities of process improvement via automation arise.

Process mining is one of the process intelligence technologies that enables analysts to have full visibility of their processes and identify activities that require further modifications or automation, in a data-driven manner. Yet, the process mining market offers various capabilities and integrations, such as intelligent process mining that might confuse decision-makers.

In this article, we’ll explain intelligent process mining features that allow business leaders to accelerate their process automation journey.

Process Mining as an automation enabler

As PwC indicates, RPA projects take 4-6 months instead of 4-6 weeks because automation teams fail at choosing the correct processes to deploy RPA. Process intelligence tools such as process mining can overcome such issues. This explains why 26% of process mining use cases targeted process automation last year.

Process mining discovers, visualizes and monitors business processes by extracting and analyzing real-time event logs data recorded in IT systems (e.g, CRM & SAP). It allows business leaders and relevant teams to understand their processes and identify the activities and tasks that need automation. Thus, process mining allows users to constantly monitor their RPA implementation and predict failures to improve.

For example, a manufacturer that benefited from process mining to discover and optimize their procure-to-pay operations automated the purchase order to invoicing process by ~75%.

Read our research to have a more comprehensive understanding of how process mining can facilitate RPA.

Intelligent Process mining

Intelligent Process Mining, also known as AI or machine learning-enabled Process Mining, refers to process mining software that leverages artificial intelligence. Intelligent process mining does not only automate the core features such as process discovery but also offers several additional capabilities that can reduce the time and resources allocated for automation projects (e.g. RPA projects). This allows enterprises to quickly take advantage of automation opportunities.

For example, intelligent process mining enables users to create a digital twin of an organization (DTO) to simulate as-is and what-if processes, calculate the ROI, and monitor operations after the change is implemented.

A bank that employed created a DTO simulated their back office operations by assuming that they deployed RPA for 90% of their activities. The bank achieved the expected ROI and saved costs by 85%.

Other Intelligent Process Mining features supporting automation

Automatic Bot Generation

Once a business automation team identifies tasks to automate and measures the potential value that automation could bring to your business, they are then expected to allocate some time for the automation phase.Intelligent process mining can shorten the transition time between process analysis and automation stages with automatic bot generation.

Automatic bot generation is a capability that translates the user behavior recordings into an RPA script. As a result, users can generate RPA bots that replicate these manual and repetitive tasks.

For example, when an automation team identifies a repetitive, automatable task during their analysis with process mining, they can

record automatable actions during the process by utilizing task mining.
replicate the user’s activities with a bot that can do the same repetitive tasks. Automatic bot generation feature automates this step by automatically creating bots that complete these repetitive tasks.

Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.