AI-Augmented Decision Support Systems: Design, Implementation, And Evaluation

Decision Support Systems (DSS) have undergone a substantial transformation, evolving from static, rule-based expert systems into adaptive, data-driven, and learning-enabled platforms designed to support complex decision-making in dynamic and uncertain environments. This shift has been driven by advances in artificial intelligence (AI), particularly in machine learning, probabilistic reasoning, and human-in-the-loop (HITL) approaches, which emphasize collaboration between computational models and human expertise rather than full automation of decisions. Contemporary AI-augmented DSS combine predictive analytics, pattern recognition, optimization techniques, and simulation with domain knowledge and contextual awareness, enabling decision-makers to explore alternatives, assess trade-offs, and respond to changing conditions while maintaining responsibility and control. Beyond technical capability, these systems increasingly address human factors such as trust, transparency, usability, and workflow integration, recognizing that effective decision support depends as much on user interaction as on algorithmic performance. This article synthesizes prior research to present a unified perspective on the design, implementation, and evaluation of AI-augmented DSS, drawing on architectural models that integrate data, models, and interfaces; empirical findings from applied decision support deployments; and conceptual frameworks that describe varying degrees of automation and human involvement. By identifying recurring design patterns, socio-technical challenges, and evaluation methodologies, the paper provides researchers and practitioners with a structured foundation for developing trustworthy, effective, and human-centered AI-augmented decision support systems capable of delivering sustained value in real-world settings.