ASSESSING THE IMPACT OF AI ADOPTION IN ACCOUNTING INFORMATION SYSTEM AT WILMAR BENBY CORPORATION: BASIS FOR A PROPOSE EXTREME IMPLEMENTATION PLAN

Abstract

This study assesses the impact of artificial intelligence adoption on minimizing errors in the accounting information system at Wilmar Benby Corporation. It examines employee perceptions of AI in terms of perceived usefulness, perceived ease of use, and actual system utilization. The research further explores how demographic factors relate to AI adoption and identifies challenges encountered in error reduction. Findings from this assessment inform the development of an Extreme Implementation Plan to enhance AI integration in accounting operations. A descriptive‑correlational design is employed, with purposive sampling used to select thirty employees across finance, audit, and compliance departments. A structured questionnaire—validated by subject‑matter experts—measures perceptions of AI usefulness, ease of use, system usage frequency, and encountered obstacles. Data are analyzed using frequency distributions, percentages, weighted means, and ANOVA to test for differences in perceptions across demographic groups. AI adoption improved reporting accuracy and processing efficiency while reducing manual errors. Respondents rated perceived usefulness at a weighted mean of 3.75 and ease of use at 3.60 on a four‑point scale. Actual usage recorded a moderate mean of 2.90, reflecting constraints in familiarity and training. Key challenges included workflow disruptions, limited user training, and outdated system components. ANOVA results revealed no significant differences in AI perceptions by department, tenure, or role. AI demonstrated a clear potential to minimize errors within the accounting information system when supported by robust training, seamless integration, and ongoing user assistance. As a result, an Extreme Implementation Plan is proposed, comprising five phases: system needs assessment, technology selection, integration and customization, comprehensive user training, and continuous performance monitoring. Future studies are recommended to replicate this research in different industries and with larger samples to validate these findings and refine implementation strategies.