Generative AI-Enhanced Business Process Modeling in Governmental Organizations: Opportunities and Challenges
Document Type
Conference Proceeding
Source of Publication
2026 17th Student Research Conference on Applied Computing Src 2026
Publication Date
4-22-2026
Abstract
Governmental organizations seek to enhance their process efficiency and service quality by relying increasingly on Business Process Modeling (BPM). However, traditional BPM remains slow, resource-intensive, and mostly driven by manual analysis. Meanwhile, generative artificial intelligence (GenAI) has emerged as a promising technology with a broad range of capabilities, including automating content creation, analyzing complex information, and supporting process optimization. Despite its rapid growth in several private sectors, the potential of GenAI to support BPM applications in the public sector remains little known. This study examines how GenAI can enhance BPM across the process modeling lifecycle-from process discovery to process redesign-while identifying the technical, organizational, ethical, and data-related risks that accompany its adoption. By analyzing current BPM practices in the public sector, GenAI capabilities, and existing AI applications in public administration, this research highlights opportunities to accelerate workflows, improve decision-making, and enhance compliance. In parallel, it acknowledges challenges including data governance, system interoperability, transparency, bias, accountability, and workforce readiness. To address these issues, the study examines the current literature on critical factors influencing the responsible and trustworthy integration of GenAI into governmental BPM, grounded in risk management, human-in-The-loop oversight, explainability, and governance controls. The findings contribute to academic discourse on intelligent process automation and provide policymakers with evidence-based guidance to support innovation while protecting public values.
DOI Link
ISBN
[9798319510167]
Publisher
IEEE
Disciplines
Business | Computer Sciences
Keywords
Business Process Modeling, Digital Transformation, Generative AI, Large Language Models, Process Automation, Public Sector Applications, Risk Management
Scopus ID
Recommended Citation
Al-Mussabi, Hanan Saleh; Al-Shamsi, Muzoun Helal; and Salman, Sinan, "Generative AI-Enhanced Business Process Modeling in Governmental Organizations: Opportunities and Challenges" (2026). All Works. 8222.
https://zuscholars.zu.ac.ae/works/8222
Indexed in Scopus
yes
Open Access
no