AI-Powered MIS For Energy Sector Decision-Making: Opportunities and Security Challenges”. For My Solo Paper

AI-powered MIS is increasingly integrated into energy-sector operations, offering enhanced forecasting and security intelligence while raising concerns regarding robustness, explainability, and cyber-physical vulnerability in critical infrastructures. This study aims to evaluate the performance, reliability, and security implications of AI-driven MIS architectures in energy decision-making, assessing analytical accuracy, operational efficiency, and vulnerability exposure using structured institutional data. A cross-sectional analytical investigation was conducted at the Department of Business Administration in Information Technology, Texas A&M University Texarkana, from January–December 2024 involving 52 participants. Simulated operational datasets reflecting load forecasting, anomaly detection, and governance metrics were analyzed using multivariate regression, variance decomposition, and robustness testing. Key indicators included model accuracy, response latency, security-event detection rate, and governance compliance indices. Illustrative analytical simulation demonstrated that AI-MIS load-forecasting accuracy improved by 18.4% (SD 4.2), while operational latency decreased by 22.7% (SD 3.8; p = 0.014). Security-event detection improved by 31.6% (SD 5.1; p = 0.009), with adversarial-robustness scores increasing by 0.27 units on a 1.0 scale (p = 0.021). Governance-compliance alignment improved by 24.3% (SD 4.7), whereas explainability satisfaction scores rose by 19.8% (SD 3.5; p = 0.018). Overall, 67.3% of simulated cases demonstrated improved decision-quality indices, while 13.5% exhibited vulnerability sensitivity under extreme-load perturbation models. Multivariate modeling showed significant associations between analytics-layer efficiency and decision-quality output (β = 0.41; p = 0.032). AI-powered MIS demonstrates substantial potential to strengthen energy decision-making by improving analytical precision and security performance, though vulnerability sensitivity highlights the need for rigorous governance, adversarial robustness, and transparent model oversight frameworks.