The expansion of AI workloads intensifies the energy demand of data centers, resulting in rising operational costs and environmental impacts. Although various approaches aim to enhance energy efficiency, existing solutions remain fragmented and as a consequence, organizations lack integrated transparency and actionable guidance to manage the performance–energy trade-offs of AI operations holistically. This study introduces an Information Systems perspective to enable cost-effective and energy-efficient AI operations in data centers through decision support systems. Employing a Design Science Research (DSR) methodology, a first design cycle is conducted that includes the development of a prototype consolidating energy-related information while providing decision support for cost-effective and sustainable AI inferencing. The prototype artifact is evaluated through practicing experts, indicating its usefulness in increasing transparency and supporting energy-aware operational decisions. The findings inform a set of design principles for decision support systems that foster sustainable AI operations in data centers.