Auditing in the age of artificial intelligence: potential, challenges and sustainable implementation

Abstract

Artificial intelligence (AI) is becoming a key tool in auditing, with the ability to support and enhance efficiency at almost every stage. This includes planning (risk analysis, identifying materiality), performing the audit (analyzing entire datasets, detecting anomalies, continuous testing) and reporting (automatically summarizing evidence, drafting, and standardizing reports). Many platforms and solutions like MindBridge, KPMG Clara, PwC Halo, Deloitte Omnia and Caseware AiDA have demonstrated the potential to increase both productivity and accuracy in auditing. However, the application of AI still faces many challenges: unstandardized data, an incomplete governance and legal framework and issues related to ethics and model bias. This article focuses on analyzing the potential and challenges of AI in the auditing field, while also proposing an appropriate implementation strategy to ensure successful deployment. According to the article, simply adopting technology and platforms is not enough; organizations need to build a comprehensive strategy that includes data governance, model oversight and human resource development. This approach allows them to sustainably maximize the value of AI in auditing.