arXiv:2512.17629v4 Announce Type: replace Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KPIs). In realistic settings, interventions are rarely isolated: organizations need to align sequences of interventions to jointly steer the outcome of a case. Existing PresPM approaches only partially address this challenge. Many focus on a single intervention decision, while others treat multiple interventions independently, ignoring how they interact over time. Methods that do address these dependencie
The increasing complexity of business processes and the maturation of AI research in causal inference are driving the need for sophisticated prescriptive tools.
This development allows for more effective automated optimization of critical business processes, directly impacting efficiency and performance indicators at scale.
Approaches to process monitoring shift from reactive or single-intervention prescriptions to proactive, sequence-aware causal optimization of multiple interventions.
- · Businesses with complex operational processes
- · AI/ML intervention platform providers
- · Consulting firms specializing in process optimization
- · Traditional, static process monitoring solutions
- · Inefficient manual intervention management
Automated business processes become significantly more efficient due to optimized sequential interventions.
This leads to increased productivity and cost savings across various industries, enhancing competitive advantage.
The broader adoption of such AI systems could accelerate the collapse of certain white-collar workflows, as agentic systems manage complex operational decisions autonomously.
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Read at arXiv cs.LG