Primary focus area: AI for business processes
Secondary focus areas: safeguards and trust in AI; AI for e-governance
Abstract
This project develops methods for real-time, adaptive optimization of business processes using structured and unstructured data. By detecting performance degradations, diagnosing their causes, and recommending data-driven interventions, the approach shifts from static process redesign to dynamic, operational-level improvement. Simulation methods quantify intervention impacts, supporting decision-making in both public and private sectors.
Research Gap
Process mining has produced powerful methods for analyzing structured logs of business process executions to suggest optimizations that enhance the efficiency and quality of these processes. However, current approaches are limited by: (1) a fixed set of predefined interventions, (2) per-case rather than system-wide recommendations, and (3) exclusion of unstructured data such as emails or policy documents. Early efforts using LLMs focus narrowly on process discovery, as opposed to dynamic adaptation or real-time intervention. This RBO fills the gap by creating adaptive, explainable optimization techniques that use both structured and unstructured data, and that consider system-wide interactions and second-order effects.
Objective
To develop AI-driven techniques that monitor business processes in real time, diagnose performance drops, propose corrective interventions, and evaluate their effects using short-horizon simulations. The approach is novel in addressing unforeseen changes, modeling second-order effects, and integrating structured and unstructured data sources.
Approach
We will combine process mining, causal inference, reinforcement learning, and LLMs to:
- Detect causes of process degradation from business process execution logs
- Use LLMs to extract context and confirm causal links from unstructured data
- Recommend interventions, guided by business process redesign knowledge
- Simulate each intervention’s impact using online simulation from the current process state
- Present recommendations through counterfactual explanations and tailored visualizations
Validation will include computational experiments with public datasets and 2–3 in-depth case studies with business and public sector partners. Feedback from these studies will inform iterative method development.
Impact
By combining adaptive analytics, LLMs, and simulation, this project will improve how organizations respond to changing process conditions. Use cases include smarter loan approval, tailored customer interactions, and faster response to public sector requests.