Enabling research to improve audit quality

Audit firms across jurisdictions face a persistent and increasingly acute challenge in attracting and retaining early-career audit talent. A commonly cited explanation for this trend is that today’s students and junior professionals are less willing to accept the demanding working conditions traditionally associated with auditing. While workload and work-life balance undoubtedly play an important role, this explanation implicitly assumes that students possess an accurate understanding of what early-career audit work actually entails.

We argue that this assumption is questionable. Drawing on evidence from our recent Accounting Horizons study (Dierynck, Marangoni, Peters, and Weijers 2025), we suggest that an important, but underappreciated, driver of the audit talent shortage is an expectations-reality gap: a systematic mismatch between what students believe the junior auditor role involves and what junior auditors actually experience in practice. Understanding this gap is critical for audit practice. If students base their career decisions on inaccurate or overly pessimistic beliefs about audit work, firms may lose potential entrants before recruitment efforts can meaningfully engage them. Moreover, misaligned expectations at entry may contribute to early dissatisfaction and turnover, further weakening the talent pipeline.

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.

This masterclass explores recent research on the introduction of artificial intelligence in the audit profession and the role of management control practices in enabling its effective use. Based on findings from the Unlocking the Potential of Artificial Intelligence in Auditing project, the session examines how AI tools are being adopted in audit firms, how auditors work with these technologies, and which organizational challenges emerge.

The masterclass is delivered by the research team and provides empirical insights into how AI can be integrated into audit practice in a responsible and effective way.

This masterclass discusses recent research on expanded risk assessment and its implications for audit practice. Drawing on findings from the Auditors at the Crossroads project, the session explores how auditors perceive and apply the expanded risk assessment requirements, and where challenges and opportunities arise in practice.

The masterclass is delivered by the research team behind the project and combines empirical insights with reflection on professional judgment and audit decision-making.

This literature note provides insights into how auditors assess and respond to fraud risks. Overall, the evidence shows that fraud audit effectiveness depends not only on standards and technical guidance, but also on judgment structures, cognitive framing, team dynamics, and tone at the top.

First, auditors often recognize heightened fraud risk but can fail to translate that recognition into targeted, effective audit responses. Instead, they frequently rely on increases in sample sizes rather than designing substantive procedures focused specifically on the fraud area. Encouragingly, identifying more fraud-focused risk factors may enable auditors to more effectively modify standard audit programs in response to fraud risks.

Second, how fraud risk assessment is structured and framed significantly influences auditor judgments. Expanding the traditional fraud triangle to incorporate management capability leads to higher fraud risk assessments. Presenting fraud information in frequency formats rather than probabilities improves judgments when fraud base rates are low. Compared to using a holistic fraud assessment approach, decomposing the likelihood and magnitude assessments makes the low likelihood of fraud more salient and leads to lower fraud risk assessments. These findings suggest that judgment aids with respect to fraud must be carefully designed and evaluated.

Third, audit team dynamics and leadership emphasis on professional skepticism play a critical role. Audit teams’ high-quality fraud brainstorming strengthens the relations between risk factors, risk assessments, and audit responses. Moreover, partner emphasis on professional skepticism improves both the effectiveness and efficiency of auditors’ fraud judgments.

Collectively, the literature highlights that improving fraud risk assessments and responses requires a multifaceted effort. Audit firms should focus on strengthening auditor fraud cue recognition, promoting targeted responses, refining fraud risk assessment aids, improving brainstorming practices, and reinforcing a tone at the top that prioritizes professional skepticism and audit effectiveness.

Auditors often seek both formal and informal advice from a variety of sources, including
specialists (e.g., IT, valuation, tax, and/or forensic experts), national offices, and engagement
team members. This literature note reviews six academic studies on auditors’ use of advice
and its impact on their professional judgment and audit quality.

Specialists provide critical expertise, yet prior research finds that auditors often limit expert
involvement due to concerns about budgets, deadlines, and client relationships. Furthermore,
misaligned perceptions and communication gaps between auditors and specialists reduce
effective knowledge sharing and integration of expertise. The need to balance between
professionalism and client service further adds to the complexity of the issue.

The quality of auditor-specialist relationships and the strength of shared team identity can
influence auditors’ reliance on specialist advice. Positive relationships facilitate integration
and mutual understanding, while strained relationships can hinder audit effectiveness.
However, a strong identity or social bond may create trust heuristics (i.e., auditors overly rely
on the advice regardless of its quality). Excessive dependence on seeking explicit
knowledge – knowledge that can be accessed more efficiently elsewhere – from colleagues
can harm auditor reputation and performance.