AI-Enabled Orchestration of Event-Driven Business Processes in Workday ERP for Healthcare Enterprises

• URL: http://arxiv.org/abs/2511.15852v1
• Date: Wed, 19 Nov 2025 20:18:10 GMT
• Title: AI-Enabled Orchestration of Event-Driven Business Processes in Workday ERP for Healthcare Enterprises
• Authors: Monu Sharma,
• Abstract summary: This study proposes an AI-enabled event-driven orchestration framework within Workday ERP.<n>The framework intelligently synchronizes financial and supply-chain across distributed healthcare entities.<n>Results confirm that embedding AI capabilities into Workday's event-based architecture enhances operational resilience, governance, and scalability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era [74.42509044145417]
  MegaFlow is a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads.<n>In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization.
  arXiv  Detail & Related papers  (2026-01-12T13:25:33Z)
• AI Integration In ERP Evaluation Across Trends and Architectures [0.0]
  Review will investigate the latest trends, models of computing architecture, and analytical methods applied in assessing the performance of AI-integrated ERP services.<n>It identifies critical performance metrics and emphasizes the absence of any standard assessment frameworks or AI-aware systems.<n>We put forward a theoretical model that brings AI-enabled capabilities into alignment with metrics in performance assessment for ERPs.
  arXiv  Detail & Related papers  (2025-11-13T04:29:44Z)
• Workday's Approach to Secure and Compliant Cloud ERP Systems [0.0]
  Workday's compliance with global standards shows its ability to best protect critical financial, healthcare, and government data.<n>A comparative review demonstrates enhanced risk management, operational flexibility, and breach mitigation.<n>The paper also explores emerging trends, including the integration of AI, machine learning, and blockchain technologies.
  arXiv  Detail & Related papers  (2025-10-31T12:25:06Z)
• Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems [1.9751175705897066]
  Large Language Models (LLMs) are increasingly deployed within agentic systems-collections of interacting, LLM-powered agents that execute complex, adaptive using memory, tools, and dynamic planning.<n>Traditional software observability and operations practices fall short in addressing these challenges.<n>This paper introduces AgentOps: a comprehensive framework for observing, analyzing, optimizing, and automating operation of agentic AI systems.
  arXiv  Detail & Related papers  (2025-07-15T12:54:43Z)
• FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance [6.494553545846438]
  We present the first AI-native framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents.<n>The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation.<n>We show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance.
  arXiv  Detail & Related papers  (2025-06-02T08:22:28Z)
• E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing [1.5728609542259502]
  This paper presents an intelligent work automation approach in the context of contemporary digital transformation.<n>It integrates generative AI and Intelligent Document Processing technologies with an Automation Agent to realize End-to-End (E2E) automation of corporate financial expense processing tasks.
  arXiv  Detail & Related papers  (2025-05-27T05:21:08Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Flow: Modularized Agentic Workflow Automation [53.073598156915615]
  Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution.<n>However, the effective adjustment of agentic during execution has not been well studied.<n>In this paper, we define an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by agents.<n>Our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance.
  arXiv  Detail & Related papers  (2025-01-14T04:35:37Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• Online Learning Probabilistic Event Calculus Theories in Answer Set Programming [70.06301658267125]
  Event Recognition (CER) systems detect occurrences in streaming time-stamped datasets using predefined event patterns. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of rules weighted in the Event Calculus. Our results demonstrate the superiority of our novel approach, both terms efficiency and predictive.
  arXiv  Detail & Related papers  (2021-03-31T23:16:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.