Mapping AI-Driven Productivity Research in Kazakhstan’s Economy

Kansulu Utepkaliyeva, Elmira Adiyetova, Gulmira Andabayeva, Aigul Bakirbekova, Raikhan UTALIYEVA, Kulyan Nursultanova

Economies, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/economies14080350
Data confidence 70 medium
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Abstract

This study maps the development of research on AI-driven productivity in Kazakhstan’s economy through a bibliometric analysis of publications indexed in Scopus. The analysis covers the period 2008–2024 and includes 386 articles and review articles. Using Biblioshiny (version 4.1.4) and VOSviewer (version 1.6.20), this study examines annual scientific production, leading countries and institutions, co-authorship networks, keyword co-occurrence, and bibliographic coupling. The results show a steady increase in scholarly attention after 2015, with stronger growth after 2020. The field is organized around five major themes: artificial intelligence and digital economy, machine learning and data analytics, economic growth and efficiency, human capital and digital skills, and Industry 4.0. The findings reveal three structural features that have not previously been systematically documented: the institutional concentration of knowledge production, the fragmentation of collaboration networks, and a thematic transition from general digitalization toward sector-specific AI applications, human capital, and Industry 4.0. The study’s scientific contribution lies in integrating these findings into a Kazakhstan-specific analytical framework in which AI-driven productivity is understood as the combined outcome of technological capabilities, organizational readiness, human capital, and institutional support. This framework provides a basis for comparative research in other emerging and resource-dependent economies.