Artificial intelligence adoption and audit quality in Nigerian audit firms

The audit profession in Nigeria stands at a critical inflection point. As artificial intelligence technologies mature and become commercially viable, audit firms face mounting pressure to integrate these tools into their workflows—yet the relationship between AI adoption and audit quality in the Nigerian context remains poorly understood. This study investigates the effect of artificial intelligence adoption on audit quality among Nigerian audit firms, controlling for firm size, audit experience, technological infrastructure, auditor expertise, regulatory compliance, client industry, and data quality. Drawing on the Technology-Organization-Environment (TOE) framework and the Technology Acceptance Model (TAM), we develop and test a cross-sectional regression model using data from 1,090 audit firms registered with the Institute of Chartered Accountants of Nigeria (ICAN) and the Financial Reporting Council of Nigeria (FRCN). The findings reveal a statistically significant positive relationship between AI adoption and audit quality (β = 0.347, p < 0.01), with auditor expertise and data quality emerging as the most influential control variables. Technological infrastructure and regulatory compliance also demonstrate significant moderating effects, while client industry shows sector-specific variations. The model explains approximately 61.8% of the variation in audit quality (adjusted R² = 0.618). Post-estimation diagnostics confirm the robustness of the results, with no evidence of multicollinearity, heteroscedasticity, or model misspecification. These findings carry substantial implications for audit firms, regulators, and policymakers in Nigeria. The study contributes to the scarce empirical literature on AI-driven auditing in developing economies and offers a validated framework for understanding how technology adoption translates into measurable quality improvements in audit engagements. Practically, the results suggest that Nigerian audit firms cannot simply acquire AI tools and expect quality improvements—they must simultaneously invest in human capital development, data governance, and infrastructure readiness.