Preparedness and institutional drivers of AI adoption in records management: perspectives of archives and records management professionals in Nigeria

Abstract

Purpose This study investigates the preparedness of archives and records management (ARM) professionals in Nigeria for the adoption of artificial intelligence (AI) technologies. It also examines the influence of institutional drivers on AI integration efforts. The research addresses a critical gap in localized understanding of how individual and organizational readiness intersect to shape AI adoption in the Nigerian records management sector. Design/methodology/approach The study employed a cross-sectional survey design targeting ARM professionals registered with the Society of Nigerian Archivists. A structured questionnaire was administered via an online platform. Data from 217 respondents were analysed using descriptive statistics, t-tests and Pearson correlation to assess levels of awareness, preparedness, institutional support and perceived barriers. Findings The findings reveal individual preparedness, positive perceptions of AI benefits and strong willingness to adopt AI if trained. However, institutional support was weak, particularly in leadership engagement, strategic planning and funding. A significant positive correlation was found between institutional drivers and AI preparedness, while no gender-based differences in readiness were observed. Research limitations/implications The study emphasizes the importance of institutional reform in driving AI readiness. It suggests that national and organizational policies should prioritize AI training, leadership commitment and infrastructural investment. The results offer a basis for evidence-driven policy, professional development and curriculum design to support digital transformation in records management. Originality/value This research is among the first empirical studies to assess AI adoption readiness specifically within Nigeria’s records management sector. It contributes a localized understanding of how individual competencies and institutional enablers jointly affect AI integration. The findings provide a foundation for comparative research across similar contexts in developing nations.

The authors' abstract, as published at the source. Performance Measurement and Metrics, 2026 · DOI ↗