{"ID":2839723,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15852","arxiv_id":"2511.15852","title":"AI-Enabled Orchestration of Event-Driven Business Processes in Workday ERP for Healthcare Enterprises","abstract":"The adoption of cloud-based Enterprise Resource Planning (ERP) platforms such as Workday has transformed healthcare operations by integrating financial, supply-chain, and workforce processes into a unified ecosystem. However, traditional workflow logic in ERP systems often lacks the adaptability required to manage event-driven and data-intensive healthcare environments. This study proposes an AI-enabled event-driven orchestration framework within Workday ERP that intelligently synchronizes financial and supply-chain workflows across distributed healthcare entities. The framework employs machine-learning triggers, anomaly detection, and process mining analytics to anticipate and automate responses to operational events such as inventory depletion, payment delays, or patient demand fluctuations. A multi-organization case analysis demonstrates measurable gains in process efficiency, cost visibility, and decision accuracy. Results confirm that embedding AI capabilities into Workday's event-based architecture enhances operational resilience, governance, and scalability. The proposed model contributes to the broader understanding of intelligent ERP integration and establishes a reference for next-generation automation strategies in healthcare enterprises.","short_abstract":"The adoption of cloud-based Enterprise Resource Planning (ERP) platforms such as Workday has transformed healthcare operations by integrating financial, supply-chain, and workforce processes into a unified ecosystem. However, traditional workflow logic in ERP systems often lacks the adaptability required to manage even...","url_abs":"https://arxiv.org/abs/2511.15852","url_pdf":"https://arxiv.org/pdf/2511.15852v1","authors":"[\"Monu Sharma\"]","published":"2025-11-19T20:18:10Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}