Artificial Intelligence and Audit Practice of Public Audit Firms in Nigeria: Does Audit Experience and Expertise Matter?

This study investigates the moderating role of auditors’ expertise and experience on the relationship between artificial intelligence (AI) adoption and audit quality in audit firms in Nigeria. Grounded on Task-Technology Fit (TTF) theory, a quantitative method was employed using data collected from a structured questionnaire administered to 237 respondents after validity and reliability tests. The structural equation modelling (SEM) shows a significantly positive relationship between expert systems of AI, neutral network of AI, machine learning of AI, fuzzy logic of AI, on audit quality of public audit firms in Nigeria, and a significantly positive relationship between auditors’ expertise and experience on audit quality of public audit firms in Nigeria. The study also revealed that auditors’ expertise and experience moderate a significantly positive relationship between artificial intelligence (expert systems, neutral network, machine learning and fuzzy logic) on audit practice (audit quality) on audit quality of public audit firms in Nigeria. Based on the findings, the paper concludes that artificial intelligence is a significant determinant of audit quality in Nigeria. The findings suggest that auditors’ expertise and experience significantly moderate the association between artificial intelligence as a significant determinant of audit quality of audit firms in Nigeria. The study highlights the consequences of human capital in the digital transformation of audit practices and demands better investment in auditor training, digital literacy, and continuous professional development. This valuable knowledge gained suggests very useful implications for audit policymakers, regulatory bodies, and institutions, pointing to the improvement of audit quality through AI integration.