Four insights from the literature on the effects of AI on audit practice

1. AI is improving audit quality and efficiency

The literature consistently shows that, when properly implemented, AI strengthens audit effectiveness. AI helps auditors to identify fraud risks, detect misstatements, analyze entire populations of transactions rather than samples, and reduce human error. What is more, it automates repetitive and time-consuming tasks, allowing audits to be completed more efficiently.

Key message: Overall, AI is enhancing both the effectiveness and efficiency of audit engagements, supporting the profession’s ability to provide timely and reliable assurance.

2. AI changes auditors’ work experience in both positive and negative ways

AI introduces new demands on individual auditors by requiring additional skills and creating uncertainty about future professional roles. These developments may increase mental load and psychological strain. At the same time, AI reduces auditors’ workload and involvement in repetitive tasks, enabling them to focus on more meaningful and value-adding work. The result is a dual effect, where AI simultaneously increases stress while also improving job experience and perceived meaningfulness of auditors’ work.

Key message: Understanding how AI affects auditors’ well-being, engagement, motivation, and job satisfaction is highly relevant.

3. Professional judgment becomes the central challenge

An important concern emerging from the literature is the effect of AI on auditors’ professional judgment and skills. As AI increasingly handles data collection, processing, and basic analytical work, auditors have fewer opportunities to engage in experiential learning, which is essential for building professional judgment and expertise. Reduced exposure to traditional audit tasks may hinder the development of professional expertise and skepticism, particularly among junior auditors. This risk is amplified by the fact that AI can sometimes produce inaccurate outputs that require careful critical evaluation. This creates a challenge: AI imperfections increase the need for critical judgment while potentially weakening some of the mechanisms through which that judgment is developed.

Key message: Future success with AI in auditing will depend not only on technological capabilities but also on maintaining and strengthening auditors’ professional skepticism, independent judgment, and ability to challenge AI-generated outputs.

4. AI should be viewed as an interconnected system of benefits and risks

The literature review demonstrates that AI simultaneously creates resources and demands for audit practice. Improvements in accuracy and efficiency may coexist with deskilling, increased cognitive demands, and challenges to professional judgment. These effects do not operate independently. They influence and potentially offset one another.

Key message: The ultimate impact of AI on auditing will depend not on any single effect, but on how its benefits and risks combine and evolve over time.

On June 17-18 the tenth FAR Annual Conference took place. Under the conference theme ‘The Organization of Innovation’, researchers and practitioners explored these questions from a wide range of perspectives.

Join us for the FAR Conference 2026 on 17–18 June

This two-day event brings together leading academics, practitioners, and regulators to explore the latest research and developments in auditing and assurance. With keynote speakers, panel discussions, and interactive sessions, the FAR Conference offers a unique opportunity to engage with cutting-edge insights and connect with peers from around the world.

The program brings together rigorous research and practical perspectives on key developments in auditing. You can visit the program page for more information.

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Auditors are selected and paid for by the organizations they audit. Policymakers are concerned that this structure influences auditor independence which, in turn, impairs quality. Accordingly, policymakers consider whether audit quality is enhanced if the auditor is appointed by an external (independent) party.

The authors study the working of such a model in a natural setting for local subsidiary audits conducted by the big-4 as part of group audits, where the local auditor is either assigned to the subsidiary through parent firm management or self-selected by the subsidiary.

The authors find that audit partners assigned to subsidiaries receive less information from the auditee, issue fewer going concern opinions, identify fewer control deficiencies, identify and correct fewer misstatements, and are less likely to constrain earnings management compared to self-selected auditors.

Some further preliminary evidence the authors collect suggests that assigned auditors produce lower audit quality through effort reduction.

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.

How do financial auditors incorporate ESG information into their client risk assessments in financial audits? Why do some ESG issues receive more attention than others, and how do auditors respond when the information is difficult to evaluate or integrate? When do they seek support from ESG specialists?

Join us for an interactive masterclass with Andreea-Ioana Stǎnescu and Yasemin Zengin-Karaibrahimoglu from the University of Groningen, who will discuss initial findings from the FAR study Expanding the Risk Lens in Financial Audits: Auditor Perceptions of ESG Integration, conducted together with Paolo Quattrone from Alliance Manchester Business School and the Stockholm School of Economics, and Vlad-Andrei Porumb from Alliance Manchester Business School.

Drawing on the findings of interviews with financial auditors and ESG specialists from Big Four and medium-sized audit firms in the Netherlands, the masterclass opens an engaged discussion on the following topics:

• Expanding risk assessment and recognizing ESG-related financial risks: how auditors determine which environmental, social, and governance issues are relevant to the financial audit.
• Connecting ESG to the financial statements: how audit teams translate qualitative and forward-looking information into risks affecting accounts, estimates, disclosures, and assertions.
• Unequal attention to ESG risks: how measurability, familiarity, and financial relevance shape auditors’ judgments.
• Working with ESG specialists: when specialist involvement strengthens audit judgments and when it may create challenges relating to coordination, knowledge sharing, and responsibility.
• Preparing the profession: what audit methodologies, guidance, training, and organizational support are needed to integrate ESG information more effectively.

This masterclass combines recent research with practical discussion, giving participants the opportunity to consider what the findings mean for their own work. Join us to explore different perspectives, share experiences, and discuss how ESG developments are reshaping financial audit practice.

The masterclass will begin on 9 September 2026 at 15:00. After the session, participants are warmly invited to stay for drinks and snacks from 16:30 to 17:30.

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.