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Title:
DIGITAL TRANSFORMATION ADOPTION ON THE PRODUCTIVITY OF SELECTED MANUFACTURING FIRM, ABUJA (A STUDY OF NIGERIAN BOTTLING COMPANY, IDU ABUJA)

Authors:
Dorcas Ufedo-Ojo Achimugu, Ohumeje Emmanuel Owoicho and Aduku Hope Livingstone, Nigeria

Abstract:
Background: Digital transformation is important to manufacturing firms seeking operational efficiency and AI. Evidence on specific digital transformation components influencing AI adoption in Nigerian manufacturing, particularly in Abuja, remains limited. This study examined the relationship between digital transformation and AI adoption at the Nigerian Bottling Company (NBC), Idu, Abuja. Method: A cross-sectional survey design was employed. The population comprised 92 management and technical staff in production, information technology, quality control and digital operations. Stratified enumeration was used, and 83 questionnaires were distributed; 78 responses were analysed, giving a 94% response rate. Data were collected using a questionnaire and analysed with descriptive statistics, simple and multiple regression in SPSS version 26 at a 0.05 significance level. Results: Digital infrastructure w as moderate (mean = 3.29), while data integration and enterprise resource planning (ERP) systems showed moderate effectiveness (mean = 3.27). AI adoption was low (mean = 2.73), although respondents indicated plans to expand AI deployment (mean = 3.69). Digital infrastructure significantly and positively influenced AI adoption (R = 0.462, R² = 0.213, ? = 0.462, p < 0.001). Data integration and ERP systems significantly and positively influenced AI adoption (R = 0.571, R² = 0.326, ? = 0.571, p < 0.001). Conclusion: Digital transformation significantly supports AI adoption at NBC, Idu, Abuja. Nevertheless, unreliable power supply, weak network connectivity, fragmented data flows, limited data standardisation, manual data entry and inadequate data quality constrain AI deployment. Strengthening infrastructure reliability, data integration, ERP maturity and digital skills is recommended to improve AI-enabled manufacturing productivity.

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