Data Science Integration as Practice: Micro-Level Dynamics from Intelligence Agencies

Public organizations face growing pressure to integrate data science and AI into their work, yet empirical insight into how such integration unfolds in practice remains limited. This article examines data science integration through a multiple embedded case study of intelligence agencies in the Netherlands and the United States, drawing on 28 semi-structured interviews with personnel in managerial, analytic, and data science roles. Using a strategy-as-practice perspective and a micro-level Ability-Motivation-Opportunity (AMO) lens, the study analyzes how public servants encounter and respond to organizational, technical, and legal challenges in everyday work. Interviewees linked integration to leadership literacy, data culture and expertise, opportunities for experimentation, technical and acquisition conditions, and legal and accountability requirements. The analysis identifies different patterns of integration, including fragmented and ad hoc use, incorporation into specific workflows, and more routinized use. The article contributes to public management research on digital governance by conceptualizing integration as a practice-based organizational process in which data-driven tools have to become useful and justifiable within everyday work. It also shows how the AMO framework can help explain the micro-level conditions under which personnel engage with such tools and identifies implications for strategic prioritization, validation, cross-role collaboration, and sustained integration.