Evaluation of Enterprise Internal Control Based on Artificial Intelligence

This study examines how artificial intelligence (AI) addresses structural challenges in traditional internal control systems, such as monitoring delays, execution inefficiencies, and risk assessment inaccuracies. By leveraging the “data-algorithm-computing power” framework, AI introduces a governance paradigm shift through “process self-optimization, decision self-evolution, and supervision self-adaptation” reducing information asymmetry and agency problems to enable proactive, intelligent internal controls. A case study of Enterprise A shows AI extends risk warning windows and shifts governance from “post-event remediation” to “pre-event prevention”. However, AI’s application in internal control remains exploratory, requiring deeper integration at foundational, operational, and supervisory levels to fully realize its potential. This study offers a theoretical framework and practical insights for modernizing internal controls in the digital economy.