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.

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this way should help lay the foundations for future research.