Cybernetic Model of AI Implementation in Industrial Enterprise Management: a Five-Node Approach Based on Uzbekistan Case

Integrating artificial intelligence (AI) into industrial management remains challenging for transitional economies due to misalignment between technological advancement and institutional-market readiness. Existing studies rarely examine these dimensions as an interconnected system. Objective. This study develops a cybernetic five-node model to diagnose and optimize AI implementation in industrial enterprises, using Uzbekistan’s Digital Uzbekistan – 2030 strategy as a case. Methods. We apply systems cybernetics and mixed methods: policy document analysis (2020–2026), quantitative assessment of inter-node feedback coefficients (α ij ), and causal loop diagramming (CLD). Data are drawn from presidential decrees, World Bank/OECD reports, and national IT statistics. Results. The analysis reveals structural asymmetry: technological and political nodes advance rapidly (IT exports grew 6× to ~$1B; 2,800+ IT Park residents), while market and social nodes lag. Three critical feedback gaps hinder progress: (1) regulatory lag (α 21 = 0.3–0.4), where innovation outpaces legal adaptation by 12–18 months; (2) absorption paradox (α 32 = 0.2–0.3), marked by low private-sector AI adoption despite high infrastructure readiness; (3) human capital leakage risk (α 43 = 0.3–0.4), where training 1.2M specialists fails to generate domestic industrial demand. Conclusion. Uzbekistan’s holistic strategy effectively covers all five system nodes but must shift focus from resource allocation to accelerating inter-node feedback loops. The proposed cybernetic framework offers a replicable diagnostic tool for optimizing AI-driven industrial transformation in transitional economies.

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