Survey Researchers

Plan, develop, or conduct surveys. May analyze and interpret the meaning of survey data, determine survey objectives, or suggest or test question wording. Includes social scientists who primarily design questionnaires or supervise survey teams.

The automation risk for the occupation of Survey Researchers stands at 52.3%, closely aligned with its base risk of 53.1%. This moderate risk indicates that a significant portion of the tasks performed by survey researchers can be automated, largely due to advances in data processing, statistical software, and workflow automation. The primary drivers of this risk are routine, repetitive tasks that can be codified or conducted through algorithms and machine learning systems. The daily functions of survey researchers often rely on systematic procedures, making them susceptible to automation-based efficiencies. Nevertheless, the occupation retains a measure of complexity due to human-centered and creative elements involved in designing and interpreting surveys. The three most automatable tasks in this role are highly procedural in nature. First, the review, classification, and recording of survey data in preparation for computer analysis can readily be managed by sophisticated data entry and organization algorithms. Second, monitoring and evaluating survey progress—by using sample disposition reports and calculating response rates—is another function that can be accomplished through dashboard analytics and automated report generation. Finally, the production of documentation concerning questionnaire development, data collection methods, and statistical decisions is process-driven and increasingly supported by template-driven documentation software. The procedural and standardized nature of these tasks makes them prime candidates for automation, especially as AI and analytics platforms evolve. Conversely, survey researchers also perform tasks that are resistant to automation due to the necessity of human insight, judgment, and interpersonal skills. For example, hiring and training recruiters and data collectors requires nuanced assessments of candidates and adaptive teaching strategies that current AI cannot replicate effectively. Collaboration with other researchers in planning, implementation, and evaluation leverages collective expertise, critical thinking, and the ability to adapt methodologies in real time—qualities that are inherently human. Writing proposals to win new projects is also highly resistant to automation, as it depends on originality, persuasive communication, and an understanding of client needs. Resistance to automation in these areas is reflected in the moderately low bottleneck skill scores for originality (3.0% and 3.4%), demonstrating that creative and collaborative skills still pose significant challenges for automated systems.