ARTIFICIAL INTELLIGENCE ADOPTION AND OPERATIONAL EFFICIENCY IN THE NIGERIA CUSTOMS SERVICE: ASSESSING DIGITAL READINESS, EFFICIENCY GAINS, AND DATA INFRASTRUCTURE
Abstract
This study examined the adoption of artificial intelligence (AI)-driven technologies and their implications for operational efficiency, transparency, and data-management systems within the Nigeria Customs Service (NCS). Specifically, it assessed the level of AI adoption, determined the extent to which AI influences customs efficiency and cargo clearance processes, and evaluated the adequacy of existing data-management infrastructure supporting AI deployment. The study adopted a descriptive quantitative research design, drawing data from a structured questionnaire administered to 500–600 customs officers selected through stratified random sampling across major customs formations in Nigeria. Data were analysed using descriptive statistics and inferential techniques, including regression analysis and chi-square tests. Findings revealed that AI adoption within the NCS is at a moderate but developing stage, characterised by partial integration of digital systems and uneven deployment across operational units. Results further showed that AI-driven technologies moderately enhance operational efficiency, improve cargo clearance speed, and strengthen transparency, although these gains remain constrained by institutional and infrastructural limitations. The study also found that existing data-management systems are only moderately adequate, with significant weaknesses in data integration, staff capacity, and system interoperability, despite relatively stronger performance in data security. The study concludes that while AI adoption is yielding measurable improvements in customs operations, its full potential remains limited by weak data infrastructure and incomplete institutional readiness. Strengthening data governance, enhancing digital capacity, and expanding AI integration are therefore critical for achieving full customs modernisation in Nigeria.
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