One of the typical roles of industrial–organizational (I-O) psychologists working as practitioners is administering employee surveys measuring job satisfaction/engagement. Traditionally, this work has involved developing (or choosing) the items for the survey, administering the items to employees, analyzing the data, and providing stakeholders with summary results (e.g., percentages of positive responses, item means). In recent years, I-O psychologists moved into uncharted territory via the use of survey key driver analysis (SKDA), which aims to identify the most critical items in a survey for action planning purposes. Typically, this analysis involves correlating (or regressing) a self-report criterion item (e.g., “considering everything, how satisfied are you with your job”) with (or on) each of the remaining survey items in an attempt to identify which items are “driving” job satisfaction/engagement. It is also possible to use an index score (i.e., a scale score formed from several items) as the criterion instead of a single item. That the criterion measure (regardless of being a single item or an index) is internal to the survey from which predictors are drawn distinguishes this practice from linkage research. This methodology is not widely covered in survey methodology coursework, and there are few peer-reviewed articles on it. Yet, a number of practitioners are marketing this service to their clients. In this focal article, a group of practitioners with extensive applied survey research experience uncovers several methodological issues with SKDA. Data from a large multiorganizational survey are used to back up claims about these issues. One issue is that SKDA ignores the psychometric reality that item standard deviations impact which items are chosen as drivers. Another issue is that the analysis ignores the factor structure of survey item responses. Furthermore, conducting this analysis each time a survey is administered conflicts with the lack of situational and temporal specificity. Additionally, it is problematic to imply causal relationships from the correlational data seen in most surveys. Most surprisingly, randomly choosing items out of a hat yields validities similar to those from conducting the analysis. Thus, we recommend that survey providers stop conducting SKDA until they can produce science that backs up this practice. These issues, in concert with the lack of literature examining the practice, make rigorous evaluations of SKDA a timely inquiry.