6 Process Mining Trends & 20 Stats to Watch for in 2025

updated on Aug 20, 2025

Process mining trends are summarized as integrations to other platforms, process mining as the automation enabler, additional capabilities offered by vendors, intelligent process mining and hybrid intelligence

Process mining has evolved into a mainstream approach to discover and improve business processes, and its market is projected to grow by 40-50% by passing $1 billion in 2022. ¹ It is being applied to numerous sectors and departments, ranging from healthcare to logistics. Process mining case studies have shown that retailers, telco, and finance companies were some of the top beneficiaries of process mining.

Explore experts’ opinions, and we leverage our own research to predict process mining trends in 2023, and how businesses can benefit from these trends.

1. Integration with other platforms

Wil van der Aalst, whose work laid the academic foundations of process mining, states that he observes a shift towards more integrated tools and capabilities in the process mining market.

Acquisitions contribute to the increase in the number of integrations:

Tech giants have started to acquire process mining solutions (e.g. IBM with myInvenio, SAP with Signavio)
Vendors merge process mining with different platforms, specifically automation platforms (e.g. IBM Cloud Pak for Business Automation).

2. Process mining as automation enabler

A typical problem in process automation efforts is the lack of understanding of the underlying business process. Process mining allows companies to analyze and visualize processes. By doing so, process mining helps discover areas to implement process automation.

In Gartner’s market guide in 2019, businesses were mostly interested in process mining as a way to improve business processes. However, by 2022, it is estimated that businesses will focus their use of process mining to drive digital transformation. (See Figure 1)

In some cases, process mining assists RPA projects by revealing insights about the ongoing automation processes. It is estimated that half of the RPA projects tend to fail or does not meet the measured ROI. ²

Process mining helps discover the processes involved in RPA projects. It ensures businesses identify the root causes of problems and deviations and predict the bottlenecks and failure points to improve in these projects.

For example, Piraeus Bank employed process mining to detect bottlenecks, and root causes of the issues in their automation project, which helped them to automate their loan application processes by shortening the time from 35 minutes to 5 minutes.

The image shows the use cases of process mining fro 2018 to 2022 according to the number of businesses.

3. Process mining is overtaking the BPM market

Since 2015, searches for process intelligence and related terms such as process mining and process discovery have been increasing. Google traffic for this year was twice as high as the traffic during 2016. The searches for business process management (BPM) show parallel trends, indicating that process intelligence and process mining have become central tools for managing and improving business processes.

Google trends show interest for process mining, process intelligence, process discovery and business process management.

Meanwhile, interest in process improvement techniques (methodologies), such as Kaizen, six sigma and total quality management, has slightly decreased, although they still hold significant traffic.

Google trends show interest for kaizen, six sigma and total quality management.

Similar trends are observed in academic studies. From 1995 to 2019, the number of books published on process intelligence exceeded that of books on process improvement techniques.

The number of books published on process intelligence and process improvement techniques.

Specifically, the number of process mining books has been accelerating while the books published on six sigma have decreased since 1995. In 2014, the number of books published on process mining passed the number of books on six sigma.

The number of books published on process mining and six sigma.

4. Additional capabilities

Process mining vendors offer additional capabilities to complement process mining, which are task mining, predictive analytics feature, and process simulation capabilities, such as Digital twins of organization technology (DTO). Figure 6 shows the interest in these tools on Google searches.

Although the overall traffic for task mining and process simulation remains lower than process mining, there has been some interest in the tools since 2015.

The google trends visual shows process mining increasing over time. Process simulation and task mining follow a flat trend while they both slightly increase over the last year.

DTO

DTO emerges as an application of digital twin technology combined with process mining which focuses on discovering, optimizing and simulating an organization’s processes. DTO enables companies to run numerous what if scenarios and make optimal decisions.

However, DTO is a topic of emerging research in process mining and we have not yet seen enterprise clients use such techniques frequently in production.

To discover how process mining improves DT, feel free to read our article.

Predictive analytics

Process mining users are asking for more than simple Directly Follows Graphs (DFGs) to discover processes, and are searching for ways to incorporate technologies that can offer conformance checking, prediction, and simulation, such as Business Process Model and Notation (BPMN).

For example, Gartner listed predictions among top 10 capabilities of process mining for 2024. ³ Enterprise users are expecting more predictive capabilities from process mining.

Predictive analytics and results of scenario analysis of DTO serve the same aim: Helping enterprises make optimal process related decisions.

Task mining

Task mining technology captures user interactions on websites and desktop and shows how employees/users perform their tasks. This solution can be useful to monitor user actions, assess employee performance and identify mistakes and issues in execution of such tasks. Businesses can improve their task efficiency, enable automation and ensure compliance, as result of task mining benefits.

Process mining software analysis and comparison we conducted show that several vendors offer two tools in one package or task mining as an additional capability. Leveraging these two solutions together provides more comprehensive data and ensures a successful process transformation.

Explore five major differences between process mining and task mining.

Multi-level process mining

Multi-level process mining (MLPM) deals with complex processes, such as procure to pay (P2P) and order to cash (O2C). Traditionally, process mining algorithms struggle to discover, model and map these complicated processes and oversimplified them.

Multi-level process mining can track the interactions between various process entities and ensures accurate and detailed analysis.

Read our multi-level process mining article to learn how it works in detail, what are the examples, and how to verify it.

5. AI-powered process mining

Though researchers and vendors are using AI increasingly in process mining, the practical use of these tools is still limited according to Wil van der Aalst because of

Constantly changing state of processes limit data availability for ML based approaches
ML based approaches require significant volumes of steady state data for high performance.

Machine learning and AI help automate process mining activities and bring ease to modeling and prediction features. For example, there are machine learning algorithms which allow users to model and discover business processes using Python, R, Java and other languages. Discover top machine learning process mining applications.

AI technologies speed process discovery via computer vision and automation, identifying human interactions and workflows. This reduces implementation time, surpassing manual methods. Vendors and academics often term this AI-driven approach “automated process discovery.”

There are RPA, analytics and process mining companies providing automated process discovery tools. To learn more on the process discovery tools, read our comprehensive article on process discovery tools.

Machine learning is going to play a bigger part in process mining once organizations have cleaner process data and once process mining tools pull data from most enterprise systems thanks to integrations. Therefore, traditional process mining techniques like manual process discovery and conformance checking will continue be applied in 2022.

6. Hybrid intelligence (HI)

AI enhances process mining, yet human intelligence is vital. It ensures accurate results, particularly in object-centric and action-oriented approaches. AI serves as a tool, not a replacement for human input.

OCPM strives to overcome the single object assumption of process mining tools. Process mining tools rely on the belief that there is a single case identifier. Yet, in reality, one event can refer to multiple objects (e.g., a customer, multiple items, and a location).

AOPM helps turn observed events into management actions to guide the operational system. AOPM automates correcting of activities that are diagnosed by process mining. In AOPM, human intervention is critical to clarify the unprecedented situations.

Top 20 Process Mining Stats

Market Size and Forecasts

In 2020, Gartner’s estimate for new product license and maintenance revenue in the process mining market was ~$550 million, which indicates ~70% growth in the market size in a year. (Gartner)
In 2022, Gartner expects that the process mining market will pass ~$1 billion, by growing 40% to 50%. (Gartner)
The global process analytics market size is expected to grow from $185 million in 2018 to $1.42 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 50% during the forecast period. (Market and Markets)
According to Gartner’s report, Celonis led the market with more than 1000 clients, and 200 partners as of 2021 (Gartner). In a blog post from 2020, the company claimed to hold over 60% share of the process mining market with almost 400% year-over-year growth.

Business Adoption

Adoption of basic process mining types as follows: process discovery (38%), enhancement (28%) and conformance (34%). Also, there is a significant trend towards an increased focus on using process mining for process enhancement. It is estimated that process enhancement applications will reach to 42% and exceed process discovery in 2022. (Gartner report in 2021)
83% of business decision makers plan to increase the adoption of process optimization in customer journey mapping and 57% of them are planning to increase it significantly. Process mining is a major tool in the process optimization toolkit. (Forrester)
93% of all questionnaire respondents stated they wanted to apply process mining within their organizations, 79% indicated never having used this technique. (PwC)

Benefits

Early adopters of process mining tools have achieved significant benefits including cost reduction, improved customer experience and compliance while applying process mining in various use cases.

Digital transformation initiatives are delayed by misunderstood processes. Manual routing and process gaps further complicate the picture and 37% of business and technology decision-makers report that their organizations experience these problems. (Forrester)
78% of people who automate say process mining is key to enabling their RPA efforts. (2020 Process Mining Sector Scan)
By using process mining during RPA implementation, businesses can increase the business value by 40% while reducing RPA implementation time by 50% and RPA project risk by 60% (QPR)
61% of respondents state that provision of factual process data which can be used for further diagnosis is the most prominent benefit of process mining. (Jan Claes)
Procurement process optimization (22%) and audit and control-related activities (19%) are the most common answers to where executives see most opportunities. (QPR)

Case studies are one of the most effective methods to learn about the benefits of new technology. You can check out process mining case studies.

Challenges

Top challenges preventing the adoption of process mining are: unavailability of process mining tooling or expertise (52%), limited focus due to a missing process function (33%), and a complex IT landscape (30%) (PwC)
80% of the efforts and time are spent on locating, selecting, extracting, and transforming the process data. The time needed to apply process mining is short when the data are there. (Gartner)

Funding of process mining vendors

ARIS process mining is provided by Software AG which is a public company. Software AG has acquired 23 organizations. Their most recent acquisition was Built.io on Sep 28, 2018. (Crunchbase)
Celonis has raised ~$1.4B in four funding rounds. Their latest funding of ~$1B was raised in June 2, 2021. (Crunchbase)
Lana Labs has raised a funding round in 2018 without revealing the amount raised. They were acquired by Appian in August 2021. (Crunchbase)
Minit has raised ~€10.3M in two rounds. Their latest funding was raised in October 2019 and it was worth €7M. Minit has been acquired by Microsoft on March 2022. (Crunchbase)
QPR which is one of the oldest players in this field went public in 2002 and has a market cap of >€20m as of September 2020. (M2)
UiPath which also focuses on the RPA market, acquired ProcessGold and has raised a total of $2B in funding over 11 rounds. Their latest funding of $750m was raised on February 3, 2021. (Crunchbase)