This research develops a strategic management framework examining artificial intelligence integration with next-generation smart grids to enhance energy efficiency in university settings. As energy infrastructures become increasingly decentralized with higher renewable energy adoption and fluctuating demand patterns, academic institutions need sophisticated solutions to optimize consumption, minimize operational expenses, and achieve sustainability targets. The framework employs machine learning forecasting algorithms, multi-agent coordination systems, and reinforcement-learning optimization techniques to improve energy distribution, predict consumption patterns, and strengthen financial planning across campus operations. Additionally, it establishes energy governance metrics, enabling institutions to formulate transparent, evidence-based sustainability policies. Through combining organizational assessment, economic analysis, and AI-driven decision-making processes, this model demonstrates substantial capacity to decrease peak demand, enhance demand-response program participation, and boost system resilience. This interdisciplinary work bridges smart grid technology, artificial intelligence applications, and business management, providing a scalable methodology for universities, energy suppliers, and governmental organizations pursuing energy transition goals. The results underscore how intelligent management platforms facilitate more efficient, adaptable, and environmentally sustainable energy ecosystems. The proposed approach offers practical implementation pathways for institutions seeking to modernize their energy infrastructure while balancing economic viability with environmental responsibility, operational excellence, and strategic positioning in evolving energy landscapes characterized by technological and regulatory transformation.