Top 7 Machine Learning Process Mining Use Cases with GenAI

updated on Oct 2, 2025

For more than a decade, machine learning process mining has been used to enhance traditional methods.¹ Today, vendors promote process mining AI with features such as predictive analytics and recent generative AI integrations, but many business leaders still struggle to see how these capabilities translate into practical benefits.

Explore how process mining can leverage ML, and which applications can benefit from AI-enabled process mining.

Does process mining use AI?

Modern process mining tools integrate artificial intelligence features such as machine learning algorithms (also known as process mining algorithms) and deep learning to automate collection, discovery, visualization and monitoring of process data in IT systems.

AI-enabled process mining, also known as process mining AI or intelligent process mining, can enhance the features of different applications such as digital twins and predictive analytics which rely on regular process mining capabilities.

Digital twins and simulations

ML can be leveraged in process mining applications such as digital twin of an organization where businesses simulate products and existing business processes in order to estimate success and error rates.

Descriptive process mining

Cluster similar cases under one group.
Detect outliers.
Evaluate issues and learn to find similar errors based on training data.

Descriptive processes are used to understand business operations data. One example is lead time data in manufacturing sector.

Automated process discovery

Process discovery attribute enables users to analyze and visualize their process data. Today, some vendors offer process discovery tools that leverage AI technologies and algorithms to automate discovering workflows and generating a process model, known as automated process discovery. Automated process discovery can also help with identification of human interaction by employing computer vision.

Explore our detailed articles on automated process discovery tools and leading vendors.

Diagnostic process mining

Diagnostic process mining refers to process diagnostics method to:

Analyze issues to find the root causes.
Classify problems into categories.
Capture the trend in process change over time.

Diagnostic processes are applied to find a problem in a process. For example, in delivery systems, diagnostic process mining detects conforming and non-conforming cases with the root of the problem in the relevant regions.

Automated root-cause analysis

Process mining vendors leverage machine learning algorithms, such as anomaly detection, to offer automated root-cause analysis. These algorithms typically calculate correlations and splits the data accordingly to provide user-friendly diagrams.

Predictive process mining

Predictive process mining forecasts case outcomes by analyzing key performance indicators (process KPIs).

Predictive processes are useful as they predict future issues by relying on the current information. In delivery services, processes mining predicts for On-Time Delivery (OTD). It utilizes existing customer order list to estimate expected delivery times and possible delays.

Prescriptive process mining

Sends notifications and alerts to users.
Initiates RPA, workload automation, and different business workflows.
Updates enterprise resource planning (ERP and SAP) systems.

After predicting potential issues, prescriptive process mining offers recommendations to address and capitalize on them. To continue with the delivery example, the OTD can be hyperlinked to the email system to send an email to customers in cases of delays.

Companies typically implement process mining with all these categories since they want to understand processes, detect problems, predict future issues, and prevent them in time. However, prescriptive process mining is still in progress compared other three types as process mining trends indicate.

Context awareness

Another application of ML in process mining is context awareness, which is a system component to collect information about entity’s environment. Context awareness recognize the context and uses this information.

Event logs may store extra data such as timestamps, order size, related persons, etc. These additional information are called context-related information. In some sectors (e.g., logistics and manufacturing), the frequency of a process or the entire cycle is included in event logs as context information.

This additional information can be extracted from event logs while grouping similar events or estimating frequency. However, they can be crucial to determine errors in a process. Therefore, storing such additional information needs to be taken into account to ensure quality of processes.

Machine learning is a context-aware system. It detects similarities between items in large data sets and categorizes them according to the respected context. As a result, it provides convenient help or information concerning a user’s task. Applying machine learning and process mining together provides critical context awareness for unstructured event data which can improve the results of process mining.

Generative AI for Process Mining

Generative AI can expand process mining beyond analysis into interaction, explanation, and design. It helps businesses not only detect process inefficiencies but also simulate and co-create improvements. Here are some generative AI applications in process mining:

1. GenAI process intelligence copilot

Generative AI enables conversational interaction with process mining tools instead of static dashboards. Users can ask questions like “Why are my invoices delayed this month?” and receive instant log-based explanations.

For example, an operations manager might be notified that delays are due to supplier bottlenecks, while a compliance officer receives insights into approval steps. This can make process mining more accessible for non-technical staff and more efficient for analysts.

2. Narrative intelligence and auto-reporting

Instead of showing data, generative AI produces business-friendly narratives that explain root causes and link them to outcomes. For example, when bottlenecks appear in an order-to-cash process, GenAI integrated PM tools can explain the deviation in plain language and generates an executive-ready report. This helps reduce manual analysis and bridge the gap between technical findings and decision-making.

3. Synthetic log generation for scenario simulation

Generative AI can create synthetic event logs to test “what-if” scenarios before real data is available. For example, a logistics company can ask “What happens if demand triples on Black Friday?” and GenAI simulates warehouse congestion within hours, helping managers prepare staff in advance. This can shift process mining from reactive monitoring to proactive planning.

4. Process design automation

Generative AI also supports process design by suggesting optimized workflows, generating BPMN diagrams, and simulating alternatives. For example, in a bank’s loan approval process, GenAI proposes bypassing manual review for low-risk cases and simulates the impact, reducing approval times from seven days to one. This can turn process mining from a diagnostic tool into a design partner.

Recommendation to business

According to process mining trends, AI applications in process mining remains limited despite growing interest. This is because businesses still struggle with data integration problems, limiting them from leveraging AI capabilities that require extensive quality data.

Companies can tackle the integration problem by putting their data on cloud or a data warehouse. By leveraging warehouse automation tools, companies can ease their process to consolidate their data and start applying process mining for process improvement.

Besides, business leaders need to determine whether they need for intelligent process mining software. One way to understand it is to evaluate by deploying open source process mining tools or free trials from top vendors.

Identify the best open source process mining tools and vendors offering free trials with Top 11 Free Trial & Open Source Process Mining Tools
Feel free to scroll down our data-driven list of process mining software.
Check out our celonis process mining capabilities, pros and cons.