<p>Artificial intelligence (AI) holds immense promise for transforming healthcare through advanced diagnostics and operational efficiencies, yet its deployment is fraught with paradox. Algorithms trained on biased data can perpetuate inequities, and their opacity can erode clinical accountability and the moral ecology of care. This narrative review and argumentative synthesis presents a nursing-centred perspective in which nurses’ longitudinal assessment, relational knowledge, and situated advocacy can strengthen ethical and equitable AI use within broader interprofessional healthcare teams and governance structures. Building on the Data-Information-Knowledge-Wisdom (DIKW) model as one useful interpretive heuristic among several, we propose an AI-Knowledge-Wisdom (AIKW) framework in which algorithmic outputs are treated as inputs for human processes that generate situated knowledge and prudential judgment. Drawing on Carper’s patterns of knowing, this narrative review illustrates how nurses evaluate AI through empirical, aesthetic, personal, ethical, and emancipatory lenses. We detail critical domains of nursing influence, including data co-creation as stewards, mediating AI’s role in the therapeutic alliance, and acting as frontline equity auditors. The review concludes with a call to action, outlining necessary transformations in education, policy, research, and cross-disciplinary partnership to formally integrate nursing wisdom into the governance of AI applications in healthcare settings. Without this integration, AI risks remaining a sophisticated technology that generates information without fully supporting the collaborative judgment needed for just and compassionate care.</p>