Leveraging Generative AI in ERP Systems: Use Cases for Higher Education and Public Sector Operations
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I6P106Keywords:
Generative AI, Large Language Models, ERP Systems, Higher Education, Public Sector, Intelligent Automation, Predictive Analytics, Digital Governance, Workflow Optimization, LLM-ERP IntegrationAbstract
Generative Artificial Intelligence (GenAI) has emerged as a transformative technology capable of reshaping enterprise resource planning (ERP) ecosystems across industries. Within higher education and the public sector domains characterized by complex administrative workflows, large data volumes, and stringent regulatory requirements GenAI offers unprecedented opportunities to enhance decision-making, streamline operations, and improve stakeholder experience. This paper investigates the integration of GenAI into ERP platforms, presenting architectural frameworks, operational methodologies, and applied use cases. The study provides a detailed exploration of GenAI–ERP synergies, including intelligent process automation, smart procurement, predictive budgeting, automated student services, workforce optimization, and regulatory compliance management. Using qualitative analyses, workflow modeling, and simulated performance assessments, the research highlights improved operational efficiency, reduced manual effort, and enhanced data-driven governance. The proposed methodology includes a multi-layer GenAI-ERP integration model encompassing data pipelines, LLM-based knowledge layers, prompt engineering structures, and adaptive automation workflows. Results from simulated use-case implementations demonstrate measurable improvements: up to 46% reduction in processing time for administrative tasks, 32% increase in forecasting accuracy, and 58% reduction in redundant manual data entry. The paper concludes that GenAI-enhanced ERP ecosystems can deliver strategic value to higher-education institutions and public agencies by enabling personalized services, real-time insights, and operational agility. Future research directions include federated LLM architectures, cross-institutional knowledge graphs, and secure on-prem GenAI deployment models for government-grade environments
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