Levina, NataliaGkeredakis, EmmanouilFayard, Anne-Laure2026-01-142026-01-142026-051476-1270PURE: 148272859PURE UUID: 4ed37443-aa3d-49a8-9660-6bb739558482crossref: 10.1177/14761270251410676Scopus: 105029354090ORCID: /0000-0001-5274-3760/work/214347276http://hdl.handle.net/10362/199023Publisher copyright: © The Author(s) 2025.Modern organizations face growing institutional and competitive pressures to adopt AI for predictive data science and to generate knowledge from vast digital datasets. While AI adoption promises new insights, it also engenders hidden capability traps, risking the conflation of reality with algorithmic representations and the neglect of non-digital or analogue dimensions of organizational life. This paper introduces the concept of epistemic stance—the underlying approach and orientation to generating knowledge in organizations—to critically examine the organizational implications of predictive data science. It unpacks the components and promises of a data science epistemic stance, highlights its epistemic risks, and explains its appeal to modern organizations. The paper argues that organizations can strengthen their knowledge capabilities by combining multiple epistemic stances through carefully designed sociotechnical systems.17817464engArtificial intelligenceData scienceBig dataEpistemic stancePhilosophy of sciencejournal article10.1177/14761270251410676https://journals.sagepub.com/doi/10.1177/14761270251410676