From operational efficiency to strategic meta-capability: A bibliometric analysis of the relationship between artificial intelligence and firm performance

Research background: Artificial intelligence (AI) has become a central element of firms’ digital transformation agendas, yet empirical evidence on how AI affects firm performance remains fragmented across theories, sectors, and performance dimensions, obscuring the mechanisms through which AI shapes organizational outcomes. Purpose of the article: The article aims to systematically map the scientific literature that explicitly examines the relationship between AI use and firm performance in the digital transformation era, and to clarify the main thematic areas, theoretical perspectives, and research gaps that structure this field. It addresses seven research questions covering the evolution of scientific production; key contributing countries, institutions, authors, and journals; dominant themes and conceptual clusters; the intellectual structure of the field; and the conceptualization of AI as a driver of business model and organizational transformation. Methods: The study uses a quantitative bibliometric design, drawing on 422 journal articles indexed in the Web of Science (WoS) Core Collection from 2000 to 2025 that explicitly address AI and firm performance. The Bibliometrix R package integrates performance indicators with science-mapping techniques, including co-word analysis, thematic mapping, and source co-citation networks. Findings & value added: The results show rapid post-2018 growth in research on AI and firm performance, strong internationalization, and a dual intellectual core that combines applied, technology-oriented journals with strategic management and organizational outlets. Conceptual analyses present AI as a strategic organizational meta-capability that affects performance through mechanisms of efficiency, agility, innovation, governance, and sustainability, while also identifying the field as fragmented and heavily reliant on cross-sectional designs and coarse measures of AI adoption. The article adds value by integrating dispersed contributions into a coherent bibliometric map, highlighting underexplored areas related to business model transformation, governance, sustainability issues, small businesses, and human-AI complementarity, and by outlining a future research agenda and implications for AI investment and governance.