Although condition monitoring systems (CMS) provide detailed information about the health of critical wind turbine components, their practical application in wind farm operations and maintenance (O&M) remains limited, significantly diminishing their value. The reasons for this situation are not merely technical issues, rather, a complex interplay of various factors besides the condition monitoring algorithms themselves. The purpose of the paper is to investigate these reasons and propose a potential solution. Through in-depth analysis, two gaps are identified. The first gap is the Data-Model Gap, in which operational wind turbine data do not adequately satisfy the assumptions and quality requirements of condition monitoring algorithms. The second is the Model-Decision Gap, where the CMS output fails to provide sufficient information to support maintenance decisions. Based on this, a Data-Model-Decision Gap (DMDG) framework is further developed to explain how maintenance requirements can be used to determine the CMS outputs and, subsequently, the wind turbine condition monitoring data that are needed to produce these CMS outputs. Such a CMS design logic reverses the usual data-to-model development sequence by starting from understanding what kind of CMS outputs can actually support decision-making in practical wind farm O&M. This new CMS design approach emphasizes the alignment of data, models, and decision processes through an iterative and integrated design strategy. Research findings indicate that merely improving algorithm accuracy is insufficient to enhance the practical value of CMS. Bridging the aforementioned gaps is the key to transforming the CMS design from “monitoring-oriented” to “decision-oriented” and in the end, providing “prescriptive maintenance solutions”. This change is critical for unlocking the full potential of CMS in wind farm O&M.