ARTIFICIAL INTELLIGENCE AND AUDITING PERFORMANCE OF MANUFACTURING FIRMS IN NIGERIA

Auditing in Nigeria has faced increasing challenges arising from low technological maturity, inadequate auditor training, weak regulatory enforcement, and infrastructural deficiencies. These issues have constrained the effective integration of Artificial Intelligence (AI) into audit processes, despite its potential to transform the quality, efficiency, and timeliness of audits. Against this backdrop, this study investigates the impact of AI on the auditing performance of listed manufacturing firms in Nigeria between 2018 and 2024, with audit report lag employed as a proxy for performance. The study adopted an ex post facto research design and relied on secondary data obtained from annual reports of sampled firms. Using 84 firm-year observations, the data were analyzed through descriptive statistics, correlation analysis, and pooled Ordinary Least Squares (OLS) regression. The study examined three distinct dimensions of AI adoption— AI-Driven Auditing, AI Integration, and AI-Based Auditing—to provide a nuanced understanding of how AI tools affect audit outcomes. Descriptive analysis indicated increasing adoption of AI practices across firms, although the extent of usage varied considerably. Correlation analysis revealed weak associations between the AI variables and auditing performance. The regression model itself was statistically significant (F = 9.753, p = 0.024) and explained 12.7% of the variation in auditing performance (R² = 0.127). However, the coefficients for AI-Driven Auditing [coef. = 13.239, p = 0.237], AI Integration [coef. =–1.612, p = 0.867], and AI-Based Auditing [coef. =–11.233, p = 0.254] were all statistically insignificant. These findings suggest that although AI adoption is growing among Nigerian manufacturing firms, its current application does not meaningfully improve audit timeliness or overall effectiveness. The insignificant results are attributed to contextual challenges such as shallow implementation of AI systems, shortage of skilled audit professionals, lack of standardized regulatory guidelines, and infrastructural limitations that hinder full realization of AI’s benefits. The study concludes that the presence of AI tools alone is insufficient to enhance audit outcomes unless accompanied by deep integration, organizational readiness, and regulatory alignment. It therefore recommends continuous professional training for auditors on AI applications, substantial investment in technological infrastructure, and the development of clear regulatory frameworks to guide AI implementation in the Nigerian auditing environment