Artificial Intelligence - Analysis from an Accounting
Purpose: This study aims to analyze the integration of Artificial Intelligence (AI) within accounting systems, identifying key strengths, weaknesses, opportunities, and threats through a SWOT analysis. The paper seeks to answer critical research questions regarding the benefits, barriers, and recommendations for AI adoption in accounting, addressing both technological and organizational dimensions. Design/Methodology/Approach The research employs a qualitative methodology combining a literature review, case studies, and a SWOT analysis. Data is gathered from academic publications, industry reports, and documented AI implementations in accounting. A deductive approach is applied to synthesize findings and highlight emerging trends. Findings: AI offers significant advantages in accounting, including automation of repetitive tasks, real-time financial data analysis, enhanced fraud detection, and improved reporting accuracy. However, challenges such as high implementation costs, dependency on skilled personnel, algorithmic errors, and employee resistance remain substantial barriers. The study highlights the importance of data quality and organizational readiness in successful AI integration. Practical Implications: The findings provide actionable insights for accounting professionals, business leaders, and policymakers. Recommendations include investing in employee training, fostering organizational digital readiness, addressing algorithmic biases, and establishing clear legal and regulatory frameworks for AI use in accounting. Originality/Value: This paper contributes to the growing body of knowledge by offering an interdisciplinary perspective on AI adoption in accounting, integrating technological, and organizational viewpoints. It also emphasizes practical solutions to overcome implementation barriers, making it valuable for both academia and industry practitioners.