Optimizing enterprise software interfaces requires a synergistic integration of Artificial Intelligence (AI) and human-centered design to enhance usability, efficiency, and security. This paper presents a framework that leverages AI- driven techniques for intelligent interface optimization, informed by user-centric design principles. Drawing inspiration from machine learning applications in fraud detection, such as Logistic Regression, Random Forest, XGBoost, Decision Tree, and AdaBoost models applied to imbalanced datasets with SMOTE re-sampling, the proposed methodology ensures accurate and reliable system performance. Further, the study incorporates insights from geospatial AI, IoT, and cybersecurity domains, including climate resilience, next-generation drug delivery systems, and real-time environmental monitoring, demonstrating the applicability of AI across diverse enterprise contexts. By combining predictive analytics, secure data management, and intuitive design, the framework facilitates improved decision-making, enhances user engagement, and ensures robust cyber-secured operations. The proposed approach provides a foundation for future research in developing intelligent, human-centered, and secure enterprise systems adaptable to dynamic organizational needs.
Rifat Perween (Wed,) studied this question.