About This Research
This study proposes an enterprise operating platform that brings requests, tasks, files, approvals, knowledge, and performance indicators into one environment, then adds an AI layer for retrieval, summarization, and early detection of operational risk. It begins from the premise that digital transformation is not achieved by multiplying systems, but by connecting data with the context of work and decision-making. The study adopts Design Science Research and draws on knowledge-management and information-systems literature, retrieval-augmented generation, the Saudi Digital Government Strategy 2023-2030, and national data and AI governance principles. It develops a seven-layer architecture covering channels, workflow, data, knowledge, AI, analytics, security, and governance. A generic advertising-agency operating system is used as the applied case. The evaluation frame work includes request cycle time, reporting accuracy, knowledge-retrieval speed, source-supported decisions, overdue work, permission compliance, and data protection. The proposed platform is a field-testable design model rather than a completed empirical result.
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