Gartner has published its 2025 Magic Quadrant for Process Mining Platforms. This year’s report paints a detailed picture of the current process mining market – from key trends and mandatory features to adoption drivers and market evolution.
It also groups 16 process mining vendors into four categories – challengers, leaders, niche players and visionaries – with vendors placed on two axes: the ability to execute and completeness of vision.
Here are our five takeaways from the 2025 Gartner Magic Quadrant for Process Mining Platforms.
1. Process mining market growth
Worldwide process mining software spending grew by more than 30 percent in 2024, continuing on its high-growth trajectory from 2023 with a 39.5 percent growth rate, Gartner states. With US$871.6 million in revenue in 2023, the market spending is forecast to cross the $2 billion mark by 2028 at a five-year compound annual growth rate of about 18 percent from 2023 to 2028.
2. Mandatory features of process mining
Gartner lists four mandatory features of process mining. These span event log extraction, process models and analysis, comparative process mining and process model enhancement.
- Event log extraction: This involves connecting with and extracting event logs from source systems on which business processes run. The platform should offer robust extraction, transformation and loading (ETL) capabilities to ensure that data is efficiently processed and transformed into a format suitable for analysis.
- Process models and analysis: This involves automated discovery of process models, exceptions and process instances, client interactions and employee interactions, together with basic frequencies and statistics from event logs.
- Comparative process mining: This incorporates capabilities to check conformance and compliance, not only graphically through overlays, but also through data analysis and performing gap analysis.
- Process model enhancement: This involves intelligent support for enhancing or extending existing or a priori process models by using additional data from recorded logs and events.
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3. Common process mining features
The common features for the process mining market include:
- Process orchestration: This extends process mining capabilities across different processes with advanced analytics capabilities and decision management capabilities.
- Generative artificial intelligence (AI) capabilities: Generative AI-powered capabilities improve data preprocessing and discovery, support adaptive process modifications through real-time monitoring and refined recommendations and enhance explainability and understandability, broadening process mining accessibility to a wider audience.
- Execution support: Involves execution capabilities that turn insights into action, ranging from simply updating source applications to preparing or creating scripts that support the execution of tasks or processes.
- Data access and preparation: Data preparation, data quality and data integration, supporting big data as well as different ways to handle data.
- Business activity monitoring and management: Real-time dashboards with support for key performance indicators (KPIs) that are continuously monitored and enable decision support.
- Advanced process analysis: Predictive analysis, prescriptive analysis, scenario testing, simulation and advanced process analytics capabilities that use contextual data.
- Task mining: Involves inferring useful information from low-level event data available in UI logs that describe the single steps within a task done by a user, such as using a workstation based on keystrokes, mouse clicks and data entries.
4. Process mining adoption drivers
Gartner recognizes five main drivers for the adoption of process mining – process analysis, optimization and automation, digital transformation, AI and generative AI, sustainability and operational resilience/autonomous operations.
- Process analysis, optimization and automation: Enterprises are increasingly turning to process optimization and automation to boost efficiency and reduce costs, Gartner states. However, without understanding the context of processes and interdependencies, scaling optimization and automation efforts becomes challenging. Consequently, there is a strong focus on leveraging process mining as a critical tool for dissecting and understanding current workflows.
- Digital transformation: Digital transformation needs an overhaul in the collaboration between IT, business operations and executive leadership. Organizations are increasingly leveraging process mining to enhance business users’ understanding of their processes within the broader enterprise framework.
- AI and generative AI: The adoption of generative AI needs a sound understanding of underlying processes, where process mining plays a key role, Gartner says. Process mining aids in mapping and analyzing workflows, providing insights into process efficiency and bottlenecks. It also addresses challenges in generative AI adoption by ensuring transparency, compliance and enablement of continuous process improvement through real-time monitoring and adaptive process modifications.
- Sustainability: Process mining enhances sustainability objectives by revealing and optimizing operational inefficiencies. It also minimizes resource waste, reducing an organization’s environmental impact through improved process transparency and optimization.
- Operational resilience and autonomous business operations: Advanced process mining algorithms provide an accurate model of an organization’s way of working in a format that anyone in the organization can understand. This ensures that everybody can be engaged in the change initiative. Also, it enables continuous adaptation and improvement because, after the change, the new operational data will give insights into the new way of working.
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5. Market direction and evolution
The process mining market is becoming highly competitive, with successful incumbents expanding their capabilities, a host of new entrants offering niche domain-related process mining solutions and enterprise software vendors entering the space through acquisitions.
Gartner sees five directions in which the process mining platform market will evolve spanning the infusion of AI, generative AI and machine learning, market consolidation and acquisitions, object-centric process mining (OCPM), business operations intelligence or operational intelligence and specialized/focused solutions.
- Infusion of AI, generative AI and machine learning: Gartner anticipates more generative AI-driven innovations in the process mining offerings of vendors, with innovators and leaders focusing on a mix of AI, machine learning and generative AI capabilities that generate real business value.
- Market consolidation: Further market consolidation will see smaller vendors, unable to scale operations, become prime acquisition targets, according to Gartner. The rapid market growth and strong commitment from enterprises to invest in process mining have significantly heightened interest from private equity investors and major enterprise software vendors.
- OCPM: One of the major trends in process mining will be OCPM, which shifts the focus from single-case analysis to a multi-object perspective. This enables enterprises to track various entities like customers, products or services and their interactions within processes providing a richer view of operations and facilitating deeper insights into complex relationships and dependencies.
- Business operations intelligence or operational intelligence: An extended version of the pure-play process mining market will gradually evolve into a platform for business operations intelligence. This will provide a dynamic model of any organization that relies on operational or other data.
- Specialized/Focused Solutions: Gartner expects a continued flow of new entrants, especially for specialized or niche vendors. These vendors will concentrate on vertical solutions, horizontal solutions or specific market segments. Vertical solutions might combine specialized quality processes or external auditing applications. Horizontal solutions could target financial processes or subprocesses within supply chain and logistics. Additionally, specific market segments might focus on small and midsize businesses or offer stand-alone process mining tools.
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