Explainable AI for Intelligent and Sustainable Decision-Making

Organizations increasingly use artificial intelligence (AI) to improve strategic planning, operational efficiency, and sustainability. However, conventional machine learning models often lack transparency, limiting managerial trust, accountability, and the practical adoption of AI-driven decisions. This study proposes an explainable AI (XAI) framework that integrates predictive analytics, interpretable machine learning, and sustainability indicators to support transparent, reliable, and evidence-based decision-making across technological, organizational, and environmental dimensions. The proposed framework combines Random Forest, XGBoost, and Explainable Boosting Machines with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Evaluation uses a benchmark dataset containing operational performance, resource consumption, organizational efficiency, and environmental indicators. Comparative experiments assess predictive accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), explanation consistency, decision latency, computational cost, and sustainability performance. Baseline models include Logistic Regression, conventional Random Forest, and XGBoost without explanation mechanisms. Ablation studies examine the individual contributions of interpretability and sustainability-aware optimization. The experimental evaluation is designed to determine whether the proposed framework improves predictive reliability, explanation quality, and sustainability-aware decisions compared with conventional approaches. Quantitative reporting will include percentage improvements in predictive performance, reductions in decision latency and resource consumption, and explanation stability across repeated experiments. Qualitative analysis will assess managerial understandability, perceived trustworthiness, fairness, and practical implementation challenges. Numerical outcomes will be reported only after experiments are completed, avoiding unsupported performance claims. The proposed framework establishes a comparative approach for integrating explainability, predictive intelligence, and sustainability into organizational decision-making. By connecting model predictions with transparent explanations and measurable sustainability indicators, the framework aims to strengthen accountability, operational effectiveness, and responsible technology adoption. Future validation across multiple industries will establish its generalizability and practical value. Keywords: Predictive Analytics; Explainable Artificial Intelligence; Sustainable Decision-Making; Interpretable Machine Learning; Organizational Intelligence.