2023B02 – Understanding Auditors’ Reliance on Emerging Audit Technologies

Audit firms are rapidly integrating Generative AI (GenAI) into their workflows. While these tools can enhance efficiency and support complex judgments, the key challenge is not whether AI provides useful input, but whether auditors use it appropriately. The literature shows that auditors’ reliance on AI is shaped more by behavioral responses, system design, and organizational context than by the underlying technology. Three insights emerge. First, auditors face a calibration problem. They may under-rely on AI due to algorithm aversion, discounting AI-based evidence, relative to human experts, even when it is equally reliable. At the same time, they may over-rely on AI when outputs appear authoritative, fluent, or easy to use. Both problems impair audit quality: under-reliance biases judgments toward management, while over-reliance reduces professional skepticism. Second, reliance depends critically on how AI is designed and embedded in the audit process. Features such as perceived control (e.g., the ability to provide input), adaptability of algorithms, and task–technology-fit influence whether auditors trust and use AI outputs. AI is more effective when it aligns with task uncertainty and complexity, and when auditors can meaningfully engage with the system. Poorly designed or poorly communicated tools risk being ignored or misused. Third, AI affects not only decisions but also how auditors think about decisions. GenAI can improve understanding of complex evidence and help auditors better identify when to raise issues, particularly in remote settings. However, AI can also inflate confidence while reducing self-monitoring, making auditors less aware of when they may be wrong. This creates a risk of overconfidence and inappropriate reliance. Overall, the literature highlights that successful AI adoption is also a behavioral and organizational challenge, not just a technological one. To realize the benefits of AI, audit firms should consider three key levers. First, governance: providing clear guidance on when and how AI should be used and evaluated. Second, design and communication: ensuring that tools align with task demands and enable auditors to meaningfully engage with the system. Third, training and oversight: developing auditors’ ability to critically assess AI outputs and appropriately calibrate their reliance.