This chapter examines the way in which AI is shaping the audit landscape and the need to balance innovation and accountability. The chapter details how AI-powered tools, including machine learning algorithms and natural language processing, allow auditors to analyze entire populations of data, find fraud and anomalies in near real-time, and enhance efficiency well beyond sample-based techniques. The chapter also identifies regulatory and ethical challenges: data privacy (GDPR), bias in outputs of algorithms, and a strong call for transparency and explainability. Drawing from real-life experiences such as artificial intelligence-enabled fraud detection in JPMorgan and large-scale implementations in the Big Four (Big-4), the chapter illustrates the point where solid governance frameworks, human-in-the-loop controls and comprehensive risk management can help ensure that such pitfalls are avoided. In addition, it describes the distinctive impediments faced by small- and medium-sized enterprises and developing economies-and hence the demand for context-specific strategies and capacity-building. This chapter illustrates how auditor firms and regulators are expressing best practices, from model validation to continuous auditing, in promoting the responsible adoption of AI, thereby ensuring additional confidence in financial reporting while respecting the core ethical and professional standards.