AI-Powered ERP Process Mining and Optimization Techniques for Agile Enterprise Transformation

Abstract

ERP systems coordinate mission-critical cross-functional workflows, which are often opaque, brittle, and difficult to respond to change. The proposed paper suggests an AI-enhanced process mining and optimization framework that facilitates agile transformation of the enterprise, combining event-log-based process mining with task mining, predictive monitoring, and prescriptive automation. The methodology consumes ERP event streams by modules (e.g., finance, procurement, order-to-cash) so as to find as-is process maps, measure bottlenecks, and conduct conformance testing with target designs. Machine learning predictors of time-series, predictors of inter-module dependencies in graph neural networks, and predictors of anomalies/fraud detectors give early warnings on time drift in the cycle, rework, and segregation-of-duty violations. Causal inference separates correlation and the actual cause of delay, whereas explainable AI can generate human-readable reasons behind recommended actions. A reinforcement learning interface assesses interventions (policy adjustments, automatic approvals, workload optimization) in a simulation-based controlled rollout of interventions in a so-called digital twin of the company to optimize service levels, cost, and risk. Large Language Models are copilots, which transform insights into remediation playbooks, produce change-management artifacts, and initiate automations through ERP APIs. Within the framework, they are embedded governance (audit trails, SoD controls), privacy protection (federated learning, differential privacy), as well as safe and continuous delivery practices of DevSecOps. We show usability in SAP and Oracle Cloud and Microsoft Dynamics 365 environments and show cycle time, touchless-processing rate, first-pass yield, and working-capital improvements, which allow us to have a quantifiable, agile journey of discovery to long-term value realization