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The convergence of artificial intelligence (AI) and process mining — a technology that analyzes event logs within information systems to understand, monitor and improve business processes — is helping enterprises work better.
As organizations seek to become more data-driven and agile, the combination of the two is positioning itself as a foundational technology for digital transformation, improved automation and more robust operations.
Process Mining Today vs. Early Days
In March 2022, Microsoft acquired Minit, a Slovakia-based process mining company known for its advanced data ingestion, event log analysis and visualization capabilities.
The acquisition was a strategic move to integrate process mining into Microsoft's Power Platform and Dynamics 365 ecosystem, so users could identify inefficiencies, model improvements and automate corrective actions using AI, Microsoft stated.
It was inevitable that other major software vendors — including IBM, SAP and Salesforce — would also intensify their focus on process mining, either through acquisitions or internal product development. SAP, for instance, has expanded its capabilities within the SAP Signavio suite, while IBM has integrated process mining into its AI-powered automation portfolio.
Using AI and machine learning to improve traditional process mining techniques is at the core of this shift. These techniques include automated discovery of process variants, anomaly detection, root cause analysis, and predictive modeling.
Early process mining tools primarily provided retrospective insights based on event logs from systems such as enterprise resource management (ERP), customer relationship management (CRM) and business process management software. Today’s AI-based products offer real-time monitoring, continuous improvement and recommendations.
The AI Difference in Process Mining
As with many other technologies, adding AI does not change the tool's purpose, but it is expected to change the performance. In the case of process mining, adding AI means process mining can make predictions as well as being a diagnostic tool, explained Marcus Mossberger, future of work strategist at Infor.
Instead of just mapping what happened in the past, AI-enhanced process mining anticipates what is coming and identifies which tasks can be handled by technology, Mossberger said. It identifies patterns that humans might miss, spots anomalies before they become problems, and suggests optimizations based on process variant analysis.
The system also becomes increasingly intelligent over time, learning from each process execution to refine its recommendations.
“With unlimited AI capabilities woven throughout — from writing and analysis to translation and process optimization — organizations aren't just solving today's process challenges,” Mossberger said. “They're creating tomorrow's workplace where technology handles routine tasks while people focus on strategic, creative, and uniquely human contributions.”
"The common thread across industries is that organizations that embrace industry-specific process models and pre-built automation solutions aren't just fixing inefficiencies — they're fundamentally elevating human work by automating routine tasks,” Mossberger said.
Changes to the Process Mining Market
This is also affecting the process mining market. As the market becomes more competitive, companies such as Celonis, IBM and SAP are under increasing pressure to differentiate themselves, AnswerRocket CTO and co-founder Mike Finley told Reworked. According to Finley, the real power now lies with foundation model providers — companies that build large language models (LLMs).
Traditional expertise in process mining is becoming less of a competitive advantage, Finley explained, because LLMs can now generate custom code with minimal input, often surpassing the output of even experienced systems analysts.
However, the true battleground lies in implementation, Finley said. Process mining is not just about technology, he said — it requires change management, which involves people. Enterprises will not transition to fully AI-driven operations overnight. Instead, the shift will be gradual, with the workforce evolving from using tools to managing intelligent agents.
Further downstream, Finley identifies another emerging area of competition: the underlying ERP and CRM systems. “Most companies don’t use the full capabilities of platforms like SAP or Salesforce,” he said. Process mining may reveal how little these tools are actually used, which could open the door for generative AI solutions to replace legacy vendors by creating more streamlined and adaptive systems.
Industry-Specific Process Improvements
AI in process mining offers other advantages, ABBYY process AI leader Jon Knisley told Reworked. AI-powered process mining can establish a “digital twin” of business-critical workflows, enhancing visibility into every step of a process and also identifying inefficiencies, their root causes and better solutions.
Machine learning also tracks processes in real time and flags or even forecasts deviations, so leaders can proactively respond.
Ultimately, process mining makes it easier for businesses in any industry to use their own data for strategic, effective and efficient process improvements. It is already being used in a number of industries:
1. Financial Services
Process mining helps these institutions improve claims management, fraud detection, and compliance monitoring, as well as customer-facing experiences such as onboarding.
2. Healthcare
Process mining can reduce administrative costs, prevent delays and abandonment during referrals, and close gaps in patient scheduling.
3. Manufacturing, Transportation and Logistics
Process mining helps organizations track the shipment and receipt of various goods and materials, Knisley said. From the supply chain to store shelves, process mining keeps these processes on track with real-time monitoring and enhanced visibility.
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AI-driven process mining can also protect sensitive data by alerting process leaders of any unexpected deviations or anomalies that could lead to data breaches.
“As agentic AI continues to introduce more autonomy (and therefore more risk) to AI-driven systems, process mining and digital twin technology could emerge as potential guardrails against instances of intrusion or noncompliance,” Knisley said.
But while AI improves efficiency and supports critical workflows, guardrails are still necessary, Knisley warned. Process mining will become more important as organizations implement autonomous systems in additional areas of their business.
Integrating AI Automates Improvements
The next evolution is not about individual technologies, but about integration across the entire process improvement lifecycle, Mossberger said.
Today’s process mining solutions work in three synchronized stages: first diagnosing operational inefficiencies through process analysis, then automating improvements through robotic process automation and generative AI, and finally optimizing continuously through pre-built industry solutions.
Integration means process mining reveals hidden patterns and inefficiencies, while automation and AI use these discoveries to improve operations. What sets market leaders apart is their ability to deliver this complete cycle through industry-specific solutions that understand the unique requirements of each business sector.
The proof is in the numbers: 74% of C-suite executives confirm these advanced technologies create real business value, especially when implemented with industry benchmarking and conformance analysis, according to Deloitte. It is about turning generic process optimization into targeted performance enhancement.
Editor's Note: Read other takes on process mining below:
- How Process Mining Unearths Your Company's Digital Footprints — Process mining isn’t a new concept, but it is fast gaining traction, thanks to AI developments enabling faster, more efficient scaling.
- Why the Process Mining Market Is Heating Up — Microsoft's recent buy of Minit process mining underlines the importance of getting processes right — and why digital leaders are starting to pay attention.
- Market Trends: Predictions for 2025 — Legacy ERP systems will feel the heat, agentic AI's definition will be up for grabs and more predictions for the year ahead.