Process Mining Market by Component (Services, Software), Deployment Mode (Cloud, On-Premises), Organization Size, Industry Vertical - Global Forecast 2025-2032
Description
The Process Mining Market was valued at USD 3.16 billion in 2024 and is projected to grow to USD 3.82 billion in 2025, with a CAGR of 21.69%, reaching USD 15.20 billion by 2032.
How process mining delivers executive-grade visibility and continuous improvement capability to accelerate operational transparency and strategic decision-making
Process mining has matured from a niche analytics discipline into an executive-grade capability that directly addresses persistent operational opacity and inconsistent execution across complex enterprises. By ingesting event logs from transactional systems and visualizing end-to-end flows, process mining exposes bottlenecks, rework loops, and compliance divergences that traditional BI and ERP dashboards often obscure. For leaders focused on outcome-based performance, this level of visibility becomes the foundation for governance, process improvement, and automation prioritization.
Beyond visualization, process mining functions as a strategic feedback mechanism that informs continuous improvement cycles. When integrated with robotic process automation, task orchestration, and human-centered redesign, process mining enables targeted interventions that reduce cycle times, improve first-pass quality, and free capacity for higher-value work. Executives who embed process intelligence into their operating model gain a repeatable means to measure the impact of policy changes, process redesigns, and platform migrations, thereby turning anecdotal decision-making into empirically grounded strategy.
Key technological, regulatory, and operational shifts reshaping process mining adoption and the integration of continuous improvement into enterprise operating models
The landscape for process mining is shifting rapidly as several converging forces redefine what is possible and expected. Advances in machine learning and explainable AI have enhanced the fidelity of automated process discovery, enabling more accurate mapping of variants and exceptions without heavy manual annotation. Simultaneously, tighter integrations with automation stacks and orchestration platforms mean discovered process insights can now trigger automated remediation or task routing, compressing improvement cycles from months to weeks.
Cloud-first deployment models and modular software architectures are accelerating adoption by lowering implementation friction and enabling scalable observability across distributed ecosystems. At the same time, heightened regulatory scrutiny and privacy requirements are prompting a recalibration of data governance and model validation practices, ensuring insights remain auditable and defensible. Lastly, the growing emphasis on sustainability and resilience is pushing organizations to apply process mining to supply chain continuity and energy-efficient process redesign, extending the value proposition beyond cost and speed to include corporate risk and environmental stewardship.
How evolving tariff policies and trade adjustments are influencing deployment economics, procurement choices, and vendor delivery patterns for process mining programs
Cumulative tariff adjustments implemented in recent policy cycles have created measurable friction across technology supply chains and service delivery economics, and process mining programs are not insulated from these dynamics. Increased import duties on hardware components and specialized appliances raise the total cost of on-premises infrastructure, prompting many organizations to reassess the economics of local deployments versus cloud-based service models. As procurement costs rise for edge and server hardware, decision-makers are weighing the trade-offs between capital expenditure, vendor lock-in, and the agility offered by SaaS and platform services.
At the same time, tariffs that affect software licensing and cross-border data transactions can influence vendor pricing strategies and contracting terms, leading to a greater emphasis on flexible licensing, consumption-based billing, and regionally hosted cloud options. For services-led engagements, higher imported labor and travel expenses nudge professional services toward remote delivery models and modular implementation packages. In aggregate, these pressures incentivize vendor consolidation around localized delivery centers, increased partnerships with regional cloud providers, and renewed attention to resilient supply chains and procurement hedging strategies that preserve program momentum while controlling cost exposure.
Distinct component, deployment, organizational, and vertical segmentations that determine solution priorities, contracting approaches, and implementation velocity for process mining
Different components of the process mining ecosystem demand distinct commercial and technical approaches, and understanding these segmentations is central to designing a successful program. When considering component choices, there is a clear distinction between services and software, where services encompass managed offerings and a spectrum of professional services including consulting and advisory, implementation and integration, as well as ongoing support and maintenance. Software solutions themselves differentiate by functional specialization, with capabilities such as conformance checking to verify process adherence, process discovery to map actual flows, and process enhancement tools that recommend or effect change.
Deployment preferences also vary by organizational risk appetite and control requirements, with options spanning cloud and on-premises models. Cloud choices typically present as platform-as-a-service and software-as-a-service alternatives that prioritize speed of adoption and operational scalability, while on-premises configurations often rely on traditional licensed models or subscription-based contracts that emphasize data residency and direct infrastructure control. Organization size further conditions vendor selection and program scope; large enterprises, including those in the Fortune 500, frequently require enterprise-grade scalability, integration depth, and custom SLAs, whereas small and medium enterprises, whether medium or small in scale, often seek rapid time-to-value, lower cost of entry, and packaged use cases.
Industry verticals impose another layer of specificity. Financial services demand rigorous audit trails and conformance checking across banking, capital markets, and insurance processes. Healthcare and life sciences organizations have distinct needs around patient privacy and regulatory validation across biotech, providers, and pharmaceutical operations. Information technology and telecommunications buyers prioritize integration with IT service management and software provider ecosystems. Manufacturing customers, whether discrete or process-oriented, focus on throughput, quality, and supply chain synchronization. Retail and consumer goods companies look to enhance omnichannel fulfillment and inventory turn across consumer goods and retail subsegments. Each segmentation axis drives different capability priorities, commercial constructs, and vendor service models.
How regional regulatory frameworks, cloud maturity, and service ecosystems influence adoption models, governance practices, and vendor strategies across global geographies
Regional dynamics materially influence adoption patterns, regulatory constraints, and the economic calculus of deployment. In the Americas, a strong appetite for cloud-native architectures and a mature professional services market create fertile conditions for large-scale process mining programs that emphasize automation integration and measurable operational ROI. Data privacy and sector-specific regulations in certain jurisdictions shape how event data is ingested and anonymized, requiring robust governance frameworks even as organizations pursue aggressive efficiency targets.
Across Europe, the Middle East and Africa, regulatory frameworks and data residency considerations are often more prescriptive, encouraging hybrid architectures that balance local control with centralized analytics. Organizations in this region frequently prioritize auditability and compliance-driven conformance checking while also exploring cross-border service partnerships to access specialized implementation expertise. The Asia-Pacific landscape is marked by heterogeneous maturity and rapid digital transformation investments; some markets leapfrog to cloud-first deployments and advanced process discovery at scale, while others prioritize localization, in-country data handling, and partnerships with regional cloud and systems integrators. These geographic nuances affect vendor go-to-market strategies, partnership models, and the design of service-level commitments.
Vendor and service provider strategies that combine integrated platforms, best-of-breed modules, and managed delivery to accelerate enterprise process intelligence adoption
Leading vendors and service providers are adopting differentiated approaches to capture the expanding opportunity in process intelligence. Some firms emphasize end-to-end suites that combine discovery, conformance, and enhancement capabilities within a single integrated platform to reduce vendor sprawl and simplify governance. Other players focus on best-of-breed modules optimized for specific use cases such as compliance control frameworks or root-cause analysis, integrating through open APIs and partner ecosystems to deliver broader functionality.
Service-centric providers increasingly offer managed process mining as an operational capability, running continuous monitoring programs that surface anomalies and maintenance priorities on behalf of clients. Professional services practices are packaging rapid assessment and modular implementation engagements to accelerate time-to-insight while reducing upfront advisory spend. Strategic partnerships between software vendors, cloud hyperscalers, and systems integrators are becoming a common route to scale deployments across global footprints. Finally, product roadmaps reflect a persistent emphasis on explainability, role-based dashboards for operational owners, and pre-built content for vertical-specific process archetypes that shorten the path from discovery to measurable impact.
Practical executive actions and procurement, governance, and capability-building priorities to convert process intelligence into repeatable operational improvements and risk mitigation
Leaders seeking to maximize the value of process mining should align investments with measurable business outcomes, beginning with a clear articulation of prioritized process objectives tied to revenue protection, cost reduction, or regulatory compliance. Executives should mandate data governance practices that ensure event logs are complete, timestamp-consistent, and auditable, while also ensuring privacy-preserving transformations are applied where required. Procurement strategies ought to favor flexible commercial models that allow phased adoption, such as consumption-based billing or scoped subscription bundles that align payment with realized value.
Organizationally, it is critical to embed process mining capabilities within cross-functional teams that combine process owners, data engineers, and operations managers so that discovered insights translate into actionable process changes. Start with high-impact pilot programs that validate integration pathways with automation and orchestration platforms, then codify success criteria and escalation paths to scale those pilots into programmatic deployments. Invest in capability building through role-based training, and prioritize tooling that offers both conformance checking for risk-sensitive environments and process discovery for improvement initiatives. Lastly, adopt a governance model that balances centralized standards with local execution autonomy, enabling consistent measurement while preserving speed of adoption.
A rigorous, practitioner-validated research approach combining interviews, vendor briefings, and architecture review to produce evidence-based insights for decision-makers
The research underpinning these insights combined qualitative executive interviews, technology vendor briefings, and structured analysis of publicly available product documentation and implementation case studies. Primary interviews were conducted with practitioners spanning operations, process excellence, IT architecture, and procurement to capture diverse perspectives on deployment challenges, governance practices, and measurable outcomes. Vendor briefings provided transparency into roadmap priorities, integration patterns, and commercial models, which were then validated against practitioner experience to triangulate practical implications.
Analysts also reviewed anonymized implementation artifacts and architecture patterns to surface common implementation constraints, data integration complexity, and success enablers. The synthesis emphasized reproducible evidence over anecdote, applying a cross-check methodology that reconciles vendor claims with practitioner-reported results and observable architectural patterns. Ethical research practices were maintained throughout, with interview participants providing informed consent and sensitive information de-identified to preserve confidentiality while enabling actionable findings.
A strategic synthesis on embedding process intelligence into enterprise operating models to achieve measurable operational resiliency and continuous improvement
Process mining has evolved into an indispensable tool for leaders who demand empirical clarity about how work actually flows through their organizations. By pairing process discovery with conformance checking and targeted enhancement, organizations can close the loop between insight and action, unlocking efficiency gains, improving compliance, and accelerating automation outcomes. Success requires a thoughtful combination of technology selection, data governance, organizational alignment, and flexible commercial models that together reduce adoption friction and amplify impact.
As organizations navigate changing trade dynamics, evolving regulation, and shifting technology paradigms, process mining offers a resilient foundation for continuous improvement. Executives who prioritize the capability today position their organizations to respond faster to disruption, make evidence-based decisions, and sustain measurable performance improvements across operations and customer-facing processes. The path forward is pragmatic: start with focused use cases, build governance that supports scale, and iterate rapidly to translate technical insights into operational outcomes.
Note: PDF & Excel + Online Access - 1 Year
How process mining delivers executive-grade visibility and continuous improvement capability to accelerate operational transparency and strategic decision-making
Process mining has matured from a niche analytics discipline into an executive-grade capability that directly addresses persistent operational opacity and inconsistent execution across complex enterprises. By ingesting event logs from transactional systems and visualizing end-to-end flows, process mining exposes bottlenecks, rework loops, and compliance divergences that traditional BI and ERP dashboards often obscure. For leaders focused on outcome-based performance, this level of visibility becomes the foundation for governance, process improvement, and automation prioritization.
Beyond visualization, process mining functions as a strategic feedback mechanism that informs continuous improvement cycles. When integrated with robotic process automation, task orchestration, and human-centered redesign, process mining enables targeted interventions that reduce cycle times, improve first-pass quality, and free capacity for higher-value work. Executives who embed process intelligence into their operating model gain a repeatable means to measure the impact of policy changes, process redesigns, and platform migrations, thereby turning anecdotal decision-making into empirically grounded strategy.
Key technological, regulatory, and operational shifts reshaping process mining adoption and the integration of continuous improvement into enterprise operating models
The landscape for process mining is shifting rapidly as several converging forces redefine what is possible and expected. Advances in machine learning and explainable AI have enhanced the fidelity of automated process discovery, enabling more accurate mapping of variants and exceptions without heavy manual annotation. Simultaneously, tighter integrations with automation stacks and orchestration platforms mean discovered process insights can now trigger automated remediation or task routing, compressing improvement cycles from months to weeks.
Cloud-first deployment models and modular software architectures are accelerating adoption by lowering implementation friction and enabling scalable observability across distributed ecosystems. At the same time, heightened regulatory scrutiny and privacy requirements are prompting a recalibration of data governance and model validation practices, ensuring insights remain auditable and defensible. Lastly, the growing emphasis on sustainability and resilience is pushing organizations to apply process mining to supply chain continuity and energy-efficient process redesign, extending the value proposition beyond cost and speed to include corporate risk and environmental stewardship.
How evolving tariff policies and trade adjustments are influencing deployment economics, procurement choices, and vendor delivery patterns for process mining programs
Cumulative tariff adjustments implemented in recent policy cycles have created measurable friction across technology supply chains and service delivery economics, and process mining programs are not insulated from these dynamics. Increased import duties on hardware components and specialized appliances raise the total cost of on-premises infrastructure, prompting many organizations to reassess the economics of local deployments versus cloud-based service models. As procurement costs rise for edge and server hardware, decision-makers are weighing the trade-offs between capital expenditure, vendor lock-in, and the agility offered by SaaS and platform services.
At the same time, tariffs that affect software licensing and cross-border data transactions can influence vendor pricing strategies and contracting terms, leading to a greater emphasis on flexible licensing, consumption-based billing, and regionally hosted cloud options. For services-led engagements, higher imported labor and travel expenses nudge professional services toward remote delivery models and modular implementation packages. In aggregate, these pressures incentivize vendor consolidation around localized delivery centers, increased partnerships with regional cloud providers, and renewed attention to resilient supply chains and procurement hedging strategies that preserve program momentum while controlling cost exposure.
Distinct component, deployment, organizational, and vertical segmentations that determine solution priorities, contracting approaches, and implementation velocity for process mining
Different components of the process mining ecosystem demand distinct commercial and technical approaches, and understanding these segmentations is central to designing a successful program. When considering component choices, there is a clear distinction between services and software, where services encompass managed offerings and a spectrum of professional services including consulting and advisory, implementation and integration, as well as ongoing support and maintenance. Software solutions themselves differentiate by functional specialization, with capabilities such as conformance checking to verify process adherence, process discovery to map actual flows, and process enhancement tools that recommend or effect change.
Deployment preferences also vary by organizational risk appetite and control requirements, with options spanning cloud and on-premises models. Cloud choices typically present as platform-as-a-service and software-as-a-service alternatives that prioritize speed of adoption and operational scalability, while on-premises configurations often rely on traditional licensed models or subscription-based contracts that emphasize data residency and direct infrastructure control. Organization size further conditions vendor selection and program scope; large enterprises, including those in the Fortune 500, frequently require enterprise-grade scalability, integration depth, and custom SLAs, whereas small and medium enterprises, whether medium or small in scale, often seek rapid time-to-value, lower cost of entry, and packaged use cases.
Industry verticals impose another layer of specificity. Financial services demand rigorous audit trails and conformance checking across banking, capital markets, and insurance processes. Healthcare and life sciences organizations have distinct needs around patient privacy and regulatory validation across biotech, providers, and pharmaceutical operations. Information technology and telecommunications buyers prioritize integration with IT service management and software provider ecosystems. Manufacturing customers, whether discrete or process-oriented, focus on throughput, quality, and supply chain synchronization. Retail and consumer goods companies look to enhance omnichannel fulfillment and inventory turn across consumer goods and retail subsegments. Each segmentation axis drives different capability priorities, commercial constructs, and vendor service models.
How regional regulatory frameworks, cloud maturity, and service ecosystems influence adoption models, governance practices, and vendor strategies across global geographies
Regional dynamics materially influence adoption patterns, regulatory constraints, and the economic calculus of deployment. In the Americas, a strong appetite for cloud-native architectures and a mature professional services market create fertile conditions for large-scale process mining programs that emphasize automation integration and measurable operational ROI. Data privacy and sector-specific regulations in certain jurisdictions shape how event data is ingested and anonymized, requiring robust governance frameworks even as organizations pursue aggressive efficiency targets.
Across Europe, the Middle East and Africa, regulatory frameworks and data residency considerations are often more prescriptive, encouraging hybrid architectures that balance local control with centralized analytics. Organizations in this region frequently prioritize auditability and compliance-driven conformance checking while also exploring cross-border service partnerships to access specialized implementation expertise. The Asia-Pacific landscape is marked by heterogeneous maturity and rapid digital transformation investments; some markets leapfrog to cloud-first deployments and advanced process discovery at scale, while others prioritize localization, in-country data handling, and partnerships with regional cloud and systems integrators. These geographic nuances affect vendor go-to-market strategies, partnership models, and the design of service-level commitments.
Vendor and service provider strategies that combine integrated platforms, best-of-breed modules, and managed delivery to accelerate enterprise process intelligence adoption
Leading vendors and service providers are adopting differentiated approaches to capture the expanding opportunity in process intelligence. Some firms emphasize end-to-end suites that combine discovery, conformance, and enhancement capabilities within a single integrated platform to reduce vendor sprawl and simplify governance. Other players focus on best-of-breed modules optimized for specific use cases such as compliance control frameworks or root-cause analysis, integrating through open APIs and partner ecosystems to deliver broader functionality.
Service-centric providers increasingly offer managed process mining as an operational capability, running continuous monitoring programs that surface anomalies and maintenance priorities on behalf of clients. Professional services practices are packaging rapid assessment and modular implementation engagements to accelerate time-to-insight while reducing upfront advisory spend. Strategic partnerships between software vendors, cloud hyperscalers, and systems integrators are becoming a common route to scale deployments across global footprints. Finally, product roadmaps reflect a persistent emphasis on explainability, role-based dashboards for operational owners, and pre-built content for vertical-specific process archetypes that shorten the path from discovery to measurable impact.
Practical executive actions and procurement, governance, and capability-building priorities to convert process intelligence into repeatable operational improvements and risk mitigation
Leaders seeking to maximize the value of process mining should align investments with measurable business outcomes, beginning with a clear articulation of prioritized process objectives tied to revenue protection, cost reduction, or regulatory compliance. Executives should mandate data governance practices that ensure event logs are complete, timestamp-consistent, and auditable, while also ensuring privacy-preserving transformations are applied where required. Procurement strategies ought to favor flexible commercial models that allow phased adoption, such as consumption-based billing or scoped subscription bundles that align payment with realized value.
Organizationally, it is critical to embed process mining capabilities within cross-functional teams that combine process owners, data engineers, and operations managers so that discovered insights translate into actionable process changes. Start with high-impact pilot programs that validate integration pathways with automation and orchestration platforms, then codify success criteria and escalation paths to scale those pilots into programmatic deployments. Invest in capability building through role-based training, and prioritize tooling that offers both conformance checking for risk-sensitive environments and process discovery for improvement initiatives. Lastly, adopt a governance model that balances centralized standards with local execution autonomy, enabling consistent measurement while preserving speed of adoption.
A rigorous, practitioner-validated research approach combining interviews, vendor briefings, and architecture review to produce evidence-based insights for decision-makers
The research underpinning these insights combined qualitative executive interviews, technology vendor briefings, and structured analysis of publicly available product documentation and implementation case studies. Primary interviews were conducted with practitioners spanning operations, process excellence, IT architecture, and procurement to capture diverse perspectives on deployment challenges, governance practices, and measurable outcomes. Vendor briefings provided transparency into roadmap priorities, integration patterns, and commercial models, which were then validated against practitioner experience to triangulate practical implications.
Analysts also reviewed anonymized implementation artifacts and architecture patterns to surface common implementation constraints, data integration complexity, and success enablers. The synthesis emphasized reproducible evidence over anecdote, applying a cross-check methodology that reconciles vendor claims with practitioner-reported results and observable architectural patterns. Ethical research practices were maintained throughout, with interview participants providing informed consent and sensitive information de-identified to preserve confidentiality while enabling actionable findings.
A strategic synthesis on embedding process intelligence into enterprise operating models to achieve measurable operational resiliency and continuous improvement
Process mining has evolved into an indispensable tool for leaders who demand empirical clarity about how work actually flows through their organizations. By pairing process discovery with conformance checking and targeted enhancement, organizations can close the loop between insight and action, unlocking efficiency gains, improving compliance, and accelerating automation outcomes. Success requires a thoughtful combination of technology selection, data governance, organizational alignment, and flexible commercial models that together reduce adoption friction and amplify impact.
As organizations navigate changing trade dynamics, evolving regulation, and shifting technology paradigms, process mining offers a resilient foundation for continuous improvement. Executives who prioritize the capability today position their organizations to respond faster to disruption, make evidence-based decisions, and sustain measurable performance improvements across operations and customer-facing processes. The path forward is pragmatic: start with focused use cases, build governance that supports scale, and iterate rapidly to translate technical insights into operational outcomes.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
193 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of process mining with robotic process automation for end-to-end workflow insights
- 5.2. Adoption of AI-driven predictive process mining models to forecast operational bottlenecks
- 5.3. Deployment of cloud-native process mining platforms to enable scalable real-time analytics
- 5.4. Utilization of process mining for supply chain sustainability and carbon footprint optimization
- 5.5. Implementation of privacy-preserving process mining techniques to comply with data protection regulations
- 5.6. Leveraging process mining for dynamic customer journey mapping and personalized experience design
- 5.7. Emergence of low-code process mining solutions to accelerate enterprise digital transformation
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Process Mining Market, by Component
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.1.2.1. Consulting And Advisory
- 8.1.2.2. Implementation And Integration
- 8.1.2.3. Support And Maintenance
- 8.2. Software
- 8.2.1. Conformance Checking
- 8.2.2. Process Discovery
- 8.2.3. Process Enhancement
- 9. Process Mining Market, by Deployment Mode
- 9.1. Cloud
- 9.1.1. PaaS
- 9.1.2. SaaS
- 9.2. On-Premises
- 9.2.1. Licensed
- 9.2.2. Subscription
- 10. Process Mining Market, by Organization Size
- 10.1. Large Enterprise
- 10.2. Small And Medium Enterprise
- 10.2.1. Medium Enterprise
- 10.2.2. Small Enterprise
- 11. Process Mining Market, by Industry Vertical
- 11.1. Banking Financial Services And Insurance
- 11.1.1. Banking
- 11.1.2. Capital Markets
- 11.1.3. Insurance
- 11.2. Healthcare And Life Sciences
- 11.2.1. Biotechnology
- 11.2.2. Healthcare Providers
- 11.2.3. Pharmaceuticals
- 11.3. Information Technology And Telecommunications
- 11.3.1. IT Services
- 11.3.2. Software Providers
- 11.3.3. Telecommunications
- 11.4. Manufacturing
- 11.4.1. Discrete Manufacturing
- 11.4.2. Process Manufacturing
- 11.5. Retail And Consumer Goods
- 11.5.1. Consumer Goods
- 11.5.2. Retail
- 12. Process Mining Market, by Region
- 12.1. Americas
- 12.1.1. North America
- 12.1.2. Latin America
- 12.2. Europe, Middle East & Africa
- 12.2.1. Europe
- 12.2.2. Middle East
- 12.2.3. Africa
- 12.3. Asia-Pacific
- 13. Process Mining Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
- 14. Process Mining Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
- 15. Competitive Landscape
- 15.1. Market Share Analysis, 2024
- 15.2. FPNV Positioning Matrix, 2024
- 15.3. Competitive Analysis
- 15.3.1. ABBYY USA Software House, Inc.
- 15.3.2. Appian Corporation
- 15.3.3. Apromore Pty Ltd
- 15.3.4. Celonis SE
- 15.3.5. Cyclone Robotics
- 15.3.6. International Business Machines Corporation
- 15.3.7. Kofax, Inc.
- 15.3.8. MEHRWERK GmbH
- 15.3.9. Microsoft Corporation
- 15.3.10. mindzie, inc.
- 15.3.11. Minit s.r.o.
- 15.3.12. Pegasystems Inc.
- 15.3.13. QPR Software Oy
- 15.3.14. SAP SE
- 15.3.15. Shanghai EntropyHub Technology Co., Ltd. (Proxverse)
- 15.3.16. Skan, Inc.
- 15.3.17. Smart Process S.A. (UpFlux Process Mining)
- 15.3.18. Software AG
- 15.3.19. StereoLOGIC Ltd.
- 15.3.20. UiPath Inc.
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