AI‑enabled management information systems for economic resilience and organizational performance: Analytics, governance, cyber risk and decision automation

Management information systems (MIS) are evolving from passive reporting platforms to AI‑driven engines that support strategic, tactical and operational decision making. This title‑driven scoping review analyses spanning national security, workforce forecasting, energy governance, fraud detection, healthcare diagnostics, digital twins and plant disease recognition to understand how AI enables economic resilience and organizational performance. We infer, from titles alone, a conceptual framework where MIS decision loops integrate data acquisition, predictive analytics, anomaly detection, optimisation, decision recommendations, actions, monitoring and audit trails. The review maps AI capabilities—forecasting, anomaly detection, optimisation, natural language processing, and explainable decision intelligence—and identifies deployment architectures across cloud, edge, hybrid and federated settings. Governance and risk controls inferred include audit logs, access controls, bias and transparency mechanisms, cybersecurity and deepfake detection, and privacy‑preserving learning. Sectoral synthesis reveals MIS adoption for critical infrastructure governance, smart energy grids, digital finance and workforce analytics. Tables summarise the decision‑loop frameworks, resilience KPIs, cyber/fraud risk controls and an MIS assurance checklist. The review concludes with a research agenda on measuring MIS impact, standardising governance metrics, integrating privacy‑preserving learning, robust decision intelligence under distribution shift, and human trust calibration.