Intelligent Process Automation in Government-Enterprise Collaboration: The Role and Application of AI-OA

Abstract

The integration of Intelligent Process Automation (IPA) within government-enterprise collaboration scenarios presents a transformative opportunity to enhance public service delivery and regulatory efficiency. This paper introduces and evaluates the novel paradigm of AI-OA (AI-Operated Architecture), a structured framework designed to orchestrate intelligent workflows in complex, multi-stakeholder environments. We investigate the application of this architecture in automating critical cross-boundary processes, including joint permit approvals, compliance verification, and data-driven policy feedback loops. The core of our research lies in the development of a multi-agent system that synergizes Robotic Process Automation (RPA) for rule-based task execution with machine learning models for predictive analytics and exception handling, all managed within a unified AI-OA platform. A case study of a municipal-industrial project approval pipeline demonstrates the efficacy of this approach, revealing a 40% reduction in processing time and a significant decrease in administrative errors. However, the study also identifies key implementation challenges, such as data siloing between administrative and corporate systems, algorithmic accountability requirements, and the need for new digital governance models. The findings indicate that AI-OA is not merely a tool for efficiency but a catalyst for institutional innovation, fostering more transparent, responsive, and collaborative governance mechanisms. This work provides a foundational framework and practical insights for deploying human-centered AI to redesign bureaucratic interactions.