BEYOND FRAGMENTATION – A CONCEPTUAL FRAMEWORK FOR AI-DRIVEN MANAGEMENT SOLUTIONS TO BRIDGE GEOPOLITICAL DIVIDES AND FOSTER GLOBAL RESILIENCE

This paper introduces a conceptual framework for "AI-Driven Global Management" (AIGM) as a novel solution to the problem of geopolitical fragmentation. The international system faces a paradox: existential, non-zero-sum challenges (pandemics, climate change) demand unprecedented global cooperation, yet nations are retreating into zero-sum, nationalist competition. While other researchers have identified this fragmentation or proposed specific AI tools for isolated problems (e.g., AI for climate modeling), the gap this paper fills is the lack of a holistic framework for using AI as a neutral management layer to bypass these political divides. Our contribution is the AIGM framework, which reframes AI from a tool of competition into a verifiable, techno-diplomatic utility. Our methodology is a qualitative framework assessment. The AIGM framework is built on three pillars: 1) AI as a Neutral Risk Modeler, 2) AI as a Trusted Resource Allocator, and 3) AI as a Decentralized Trust Verifier (using XAI and Federated Learning). Our results, from applying this framework to case studies in global health, climate, and supply chains, indicate that it provides a pragmatic path to collaboration without requiring political trust. The primary limitation (what is not good) is that this framework does not solve the core political problem but moves it: from negotiating outcomes to negotiating the AI's objective functions. This creates an urgent need for a trusted "metagovernor" an "IAEA for Algorithms" to set these goals and audit the system.