Process mining expands to enable guided decisions with AI agents
From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining
Artificial IntelligenceComputational Engineering, Finance, and Science
Summary
Process mining usually studies event logs to understand how work gets done and spot problems. This paper suggests moving beyond just looking back at events to helping AI agents decide what actions to take next based on rules, risks, and privacy. The authors propose new ways to organize event data into useful parts like action evidence and governance rules. This idea aims to make process mining help with making accountable choices in real time, not just explaining the past. They see this as possible because several related technologies are progressing at the same time.
process miningevent logsagentic AIbusiness process managementgovernance contractsprivacy-preserving learningcausal process monitoringobject-centric event dataruntime decisionsorganizational behavior
Authors
Yiyuan Yang, Zheshun Wu, Yong Chu, Zhenghua Chen, Zenglin Xu, Qingsong Wen
Abstract
Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime predictions. Agentic AI changes the target. Process-aware agents will not only ask what happened. They will ask whether a proposed action should be taken, given the available evidence, privacy budget, organizational authority, and downstream risk. This BlueSky paper proposes event-to-action process mining: a process-mining agenda for transforming heterogeneous operational event data into governed action. The goal is not another dashboard, a generic enterprise simulator, or a language interface over logs. We argue that the community needs four mineable artifacts: event-object representations, action evidence packages, governance contracts, and benchmarks where act, defer, ask, and refuse are all valid outputs. This agenda is timely because agentic business process management (BPM), LLM-assisted process mining, object-centric event standards, causal process monitoring, and privacy-preserving learning are maturing separately. Bringing them together defines a data-mining target inside process mining: mining logged organizational behavior for accountable action, not only retrospective insight.