The Role of AI-Assisted Accounting Information Systems in Enhancing Audit Effectiveness
Keywords:
artificial intelligence, accounting information systems, audit effectiveness, digital auditing, task-technology fitAbstract
This study examines the relationship between AI-assisted accounting information systems (AIS) and perceived audit effectiveness using Task-Technology Fit (TTF) theory as the principal theoretical lens. Against the rapid diffusion of artificial intelligence and data analytics in audit work, empirical evidence remains limited on whether AI capabilities embedded in AIS translate into more effective audit procedures, particularly in developing-economy settings. Using a quantitative explanatory design and purposive sampling, data were collected during 2026 from Indonesian professionals with experience in digital accounting systems and audit-related activities. AI-assisted AIS was operationalized through automation capability, anomaly and risk detection, analytical accuracy and speed, real-time monitoring and reporting, and decision-support capability; audit effectiveness was reflected by accuracy, procedural efficiency, timeliness, fraud/error detection, and reliability of audit evidence. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model shows a positive and significant relationship between AI-assisted AIS and audit effectiveness (β = 0.796, t = 15.170, p < 0.001), with R² = 0.634, f² = 1.730, and Q² = 0.615. The findings support the TTF argument that technology is more likely to improve performance when its capabilities correspond to audit-task requirements. However, the benefits of AI remain contingent on data quality, user competence, system governance, explainability, and continued professional judgment. The study contributes to digital auditing research by linking intelligent AIS capabilities to perceived audit effectiveness and by identifying conditions that may strengthen or constrain these benefits.
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