Systematic Review: An AI-Driven Expert DSS for Intelligent Resource Optimization in Enterprise IT Environments
DOI:
https://doi.org/10.51903/00r45751Keywords:
Decision Support System, Enterprise IT, Expert System Adaptation, Machine Learning, Resource OptimizationAbstract
Contemporary enterprise IT environments face increasing complexity in managing distributed infrastructure and dynamic resource demands, challenging traditional decision-making mechanisms that often lack adaptability. While artificial intelligence (AI) offers potential for enhancing decision support, existing research frequently emphasizes isolated algorithmic performance over integrated, context-aware system design. This systematic review investigates the adaptation and application of existing, yet underutilized, AI-driven expert decision support systems (DSS) for intelligent resource optimization in enterprise IT environments. Following a mixed-methods applied research design, the study synthesizes peer-reviewed literature from 2020 to 2025 across major databases including Scopus, Web of Science, and IEEE Xplore. Quantitative evaluation employed simulated enterprise workloads to assess system performance using metrics such as Mean Absolute Error (MAE) for prediction accuracy and optimization gain for resource utilization efficiency. Qualitative insights were gathered from IT practitioners to evaluate decision relevance and interpretability. Findings demonstrate that the repurposed hybrid DSS, integrating machine learning with expert knowledge-based inference, achieved significant improvements, including an 18–23% increase in resource utilization efficiency and up to a 34% reduction in MAE compared to baseline heuristic and rule-based models. Expert evaluators further noted enhanced interpretability of system recommendations. The study concludes that strategically adapting existing AI-driven DSS frameworks offers a viable, cost-effective pathway for enhancing IT resource governance, bridging a critical gap between theoretical AI research and practical operational management. This research contributes a conceptual model for cross-domain system reapplication and provides empirical evidence supporting hybrid DSS architectures in enterprise settings.
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