AI Implementation Management System in the Fuel and Energy Complex of the Russian Federation

Abstract

The article examines the management system for implementing artificial intelligence in the fuel and energy complex of the Russian Federation. The management problem lies in the fact that fuel and energy companies already use AI in production, processing, transportation, power generation, equipment maintenance, and corporate analytics. However, implementation often develops through separate pilot projects, fragmented centers of responsibility, and incomparable performance indicators. The article substantiates the need to move from project-based AI implementation to an integrated management system that includes digital maturity diagnostics, selection of application scenarios, effect calculation, responsibility allocation, risk monitoring, and industrial scaling. A five-phase model of AI implementation management is proposed: diagnostics, KPI definition, cost–benefit assessment, econometric verification, adjustment, and replication. Special attention is paid to the corporate and industry levels of management, the role of digital competence centers, the indicators CBI, AI-Opex Impact, and Safety-Δ, as well as the need for a unified case database for the Russian fuel and energy complex.