The paradox of Artificial Intelligence adoption in talent management: do managerial dynamic capabilities matter?

Purpose This paper investigates how artificial intelligence (AI) adoption in talent management (TM) unfolds within the banking sector, with a focus on the role of managerial dynamic capabilities (MDCs), specifically managerial cognition, social capital and human capital, in enabling or constraining its strategic implementation. Design/methodology/approach Adopting a qualitative research design, the study draws on 30 in-depth semi-structured interviews with key stakeholders in the Qatari banking sector, a context characterized by rapid digital transformation and institutional complexity. Findings The study reveals a paradoxical landscape in which differences between early and laggard adopters are driven less by technical or financial constraints and more by variations in adoption readiness, which is rooted in leadership commitment, cultural openness and strategic framing. Second, MDCs are central to shaping AI adoption in TM and vary based on the level of AI adoption. Early adopters possess strong cognitive framing of AI as a strategic enabler, leverage relational networks for cross-functional collaboration and invest in continuous learning and digital fluency. In contrast, laggard adopters exhibit reactive mindsets, siloed operations and limited AI literacy. Third, the study empirically demonstrates how two key, context-sensitive mechanisms, called configuration and orchestration, operate in practice to translate MDCs from latent resources into actionable strategies. Practical implications The findings compel a fundamental reorientation of priorities for organizations. Adoption readiness and the systematic development of managerial capabilities warrant equal, if not greater, emphasis than technical investment in AI adoption for TM. Organizations are therefore urged to cultivate managerial cognition through the strategic repositioning of AI as an enabler of transformation, to entrench digital fluency and learning orientation through disciplined and sustained development programs, and to reinforce social capital by forging robust cross-functional partnerships that enable coordinated action across organizational boundaries. Originality/value Among the few studies examining AI adoption in TM, this research extends current understanding by applying the MDC framework to the Gulf Cooperation Council (GCC) context, pointing out how managerial cognition, social capital and human capital drive AI adoption in TM. It also articulates and provides empirical grounding for configuration and orchestration as context-sensitive mechanisms through which MDCs translate into strategic action. This offers a more nuanced, agency-oriented and contextually embedded understanding of AI adoption in TM.