Navigating AI disruption: Systems theory framework for the European Social Survey

The article examines how AI-driven technological disruption may reshape expectations on how surveys are best implemented and what is the character of survey data. It applies systems theory to the paradigmatic case of the European Social Survey, analysing how the European Social Survey has increased its internal complexity to meet demands for data quality, relevance, and usability, thereby consolidating its status as a flagship within the traditional survey model. Drawing on the theory of disruptive innovation, the study then theorizes the European Social Survey’s response to AI disruption, shaped by its dual commitment to innovation and scientific rigour. Based on a heuristic typology of three data-collection models, it argues that ex ante validation by the science system is a necessary condition for the European Social Survey to consider coupling with silicon data alternatives. At the same time, the European Social Survey’s history of incremental innovation positions it to engage with lower-risk forms of AI augmentation. The study complements technical research on AI-enabled survey methods by contributing a systems-theoretical perspective to strategic debates on risks and benefits of integrating traditional survey models with AI advancements.