Auditor professional skepticism is a key aspect in determining audit quality and audit process efficiency. This study aims to analyze the role of auditor professional skepticism in improving audit quality and the challenges that arise in its implementation. Using a qualitative approach through the Systematic Literature Review (SLR) method, this study reviewed 36 relevant articles from various national and international journals. The results show that 25 studies support professional skepticism as a major factor in improving audit quality. Skeptical auditors are able to detect material errors and indications of fraud more accurately, increase the transparency of financial statements, and strengthen corporate accountability. However, another 11 studies highlighted that excessive professional skepticism can hinder audit efficiency by extending audit duration, increasing costs, and creating tension with clients. In addition, time budget pressures and efficiency demands may reduce the application of auditor skepticism in some circumstances. The success of auditors' professional skepticism depends not only on high applicability, but also balance with audit efficiency. Factors such as auditor experience, external pressures, and applicable regulations are key elements in determining the effectiveness of professional skepticism in audits. Therefore, the professional skepticism implementation strategy should be tailored to the specific needs and characteristics of the audit to ensure a quality audit without hampering efficiency.

Artificial Intelligence (AI) is increasingly used in financial accounting to improve decision-making effectiveness. This research analyzes the role of AI in supporting data-driven decision making and identifies challenges in its implementation. Using a qualitative approach with the Systematic Literature Review (SLR) method, this study reviewed 41 relevant articles from national and international journals. The results showed that 28 studies supported the effectiveness of AI in improving financial decision-making by automating transaction recording, enabling algorithm-based predictive analysis, and detecting financial anomalies. AI enables companies to respond faster to market changes, increase transparency of financial reports, and reduce human errors in accounting processes.However, 13 studies highlighted challenges such as technological complexity, limited transparency in decision-making, algorithmic bias, and organizational readiness. In addition, evolving regulations are an obstacle to ensuring optimal use of AI while minimizing ethical and legal risks. The success of AI in financial decision-making depends on infrastructure readiness, regulatory support, and human resource competencies. Without a well-planned strategy, AI may pose new challenges that hinder its effectiveness. Therefore, this study provides insights into the optimal AI implementation strategy to ensure that this technology improves the accuracy and transparency of decision making while maintaining financial accounting accountability.