Aug 2026· International Journal of Public Sector Management· pp. 1-19· 0 citations· 26 references
TL;DR
An early-stage adoption pattern in which auditors perceive strong AI benefits – particularly in automation, data analysis, and text processing – yet willingness to implement AI remains moderate is suggested, consistent with an audit context where adoption requires not only expected gains but also confidence in explainability and evidential traceability.
Abstract
This study aims to examine auditors' readiness and attitudinal profiles toward for implementing artificial intelligence (AI) in public sector external auditing, comparing professionals working in institutions affiliated with the European Organisation of Supreme Audit Institutions (EUROSAI) and the European Organisation of Regional Audit Institutions (EURORAI).
Using survey data from 557 external public auditors, the study combines comparative tests, k-means clustering to identify readiness profiles, and multi-group partial least squares structural equation modeling to assess whether the determinants of willingness to implement AI differ across the two institutional settings.
The results suggest an early-stage adoption pattern in which auditors perceive strong AI benefits – particularly in automation, data analysis, and text processing – yet willingness to implement AI remains moderate. This gap is consistent with an audit context where adoption requires not only expected gains but also confidence in explainability and evidential traceability, alongside sufficient organisational support. The comparison between EUROSAI and EURORAI indicates selective rather than pervasive differences: willingness is more closely linked to enabling conditions in EURORAI, while effort-related perceptions play a more visible role in EUROSAI. Additionally, three readiness profiles (sceptical, moderate and enthusiastic) highlight substantial internal heterogeneity.
The study brings public audit institutions into the public management debate on AI-enabled digital transformation by integrating auditor heterogeneity with a comparative perspective. It shows that AI-acceptance mechanisms are selectively conditioned by institutional setting, underscoring the importance of considering both individual perceptions and contextual conditions when designing AI implementation strategies in public auditing.
The relevance of external auditors’ recommendations is crucial for promoting transparency, accountability and effective governance in public sector entities. However, limited implementation of audit recommendations globally and in Africa, including Tanzania, raises concerns about their practical usefulness and decision-making value. This study investigates the effect of external auditors’ perceived independence on the relevance of audit recommendations in Tanzanian Local Government Authorities (LGAs). Guided by Agency Theory, the study adopts a positivist research philosophy, employing a quantitative approach and Structural Equation Modeling (SEM) to analyse survey data from 345 audit and governance stakeholders across 126 sampled LGAs. Descriptive analysis indicated that respondents recognised auditors’ perceived independence particularly freedom from managerial interference, operational autonomy, access to information and adequate logistical support as critical for relevant recommendations. Regression results revealed a positive and statistically significant relationship between auditors’ perceived independence and relevance of audit recommendations (P<.001, F=34.860, t=5.904, β=.304 and Adjusted R-square =9%) and confirms that perceived independence has the modest explanatory power on the relevance of audit recommendations. The findings show the importance of institutional and regulatory frameworks that safeguard auditors perceived independence, while highlighting that perceived independence alone is insufficient without management responsiveness and effective follow-up mechanisms. By focusing on auditor’s perceived independence as a determinant of relevance of audit recommendations, this study fills both theoretical and empirical gaps in public sector auditing literature, providing actionable insights for improving audit relevance and governance in Tanzanian LGAs.
Lenatusi L. Munyangabi, Henry Chalu, Sarah Ngomuo· Journal of Science, Innovati...· 0 citations
The authors conclude that governance and transparency issues, regional disparities in implementation, and algorithmic complexity contribute to the difficulties in auditor practices in adopting AI tools.
John Joshua C. Rañeses, Dr. Ain Bemisal Alavi, Rafia et al.· Journal of Business Insight...· 0 citations
Artificial intelligence (AI) is fundamentally reshaping the
auditing profession, challenging traditional competency
frameworks and redefining the scope of the auditor’s role.
This study is based on the premise that, beyond traditional
financial audit tasks, the contemporary auditor is
increasingly expected to contribute to audit committee
governance, sustainability (ESG) assurance, as well as
the direct application of AI-based tools in audit
engagements. Despite the growing academic and
professional interest in AI adoption, a comprehensive and
integrated framework capturing the full spectrum of AI-
related competencies required across all auditor roles
remains insufficiently developed in the literature. This
paper addresses this gap through a Structured Literature
Review (SLR) that examines 22 peer-reviewed articles
indexed in Web of Science and published between 2019
and 2025, identifying and synthesizing evidence on how
AI is reshaping auditor competencies across four
interconnected roles: financial auditor, audit committee
member, ESG assurance provider, and user of AI tools.
Based on the synthesized evidence, the authors propose
an integrated competency framework for the auditor
prepared for the AI era, structured around six competency
dimensions and four professional roles, with direct
implications for professional bodies, Continuing
Professional Development (CPD) programmes, and
certification requirements.
Elena Claudia BADEA (FLOREA), A. Olteanu (Burca), M. Bunea et al.· Audit Financiar· 0 citations
The rapid integration of generative artificial intelligence (GenAI) and emerging agentic AI systems is reshaping the audit profession, with significant implications for workforce development, audit methodologies, and human–AI collaboration. Although public narratives often depict AI as a threat to early-career roles, evidence from industry reports and field studies reveals a more nuanced transformation. Firms are reallocating routine, task-based audit work to AI tools, potentially contributing to reduced early-career hiring but simultaneously elevating the cognitive demands placed on auditors. Audit methodologies are also evolving, with AI enhancing risk assessment, journal entry testing, anomaly detection, documentation quality, and emerging assurance domains. These changes support fuller population analysis, greater analytical depth, and richer insights. Across these developments, human–AI collaboration is becoming a defining feature of modern auditing, requiring auditors to supervise, validate, and contextualize AI-generated outputs. This paper synthesizes current research and industry reports to examine how GenAI and agentic AI are transforming audit practice and to identify strategies for preserving talent pipelines while enhancing professional judgment in an AI-enabled audit environment.
Cory Ng, Amanda Acevedo· The Journal of Business and...· 0 citations
This study examines how artificial intelligence (AI)-assisted tender evaluation can be supported by governance conditions associated with legal defensibility in UAE public construction procurement, with emphasis on evidence required for oversight, supplier grievances, and audit. A comparative design covers federal, Abu Dhabi, and Dubai procurement regimes. Procurement defensibility is modelled as an organisational outcome produced by three governance mechanisms – explainability quality, bias-audit rigour, and audit-trail completeness (ATC) – with internal audit involvement (IAI) specified as an assurance enabler. Survey data from 330 procurement, internal audit, and legal/contract professionals are analysed using partial least squares structural equation modelling, with measurement invariance of composite models and multi-group analysis. All three mechanisms have positive, significant effects on defensibility, with ATC the strongest driver. IAI positively but modestly strengthens the audit-trail -> defensibility relationship, indicating that assurance routines increase contestability-by-design. The model explains substantial variance in defensibility (R2 = 0.62) and links defensibility to higher confidence in value-for-money and compliance (R2 = 0.30). Cross-regime results show portability of the framework, but different emphases: Abu Dhabi places greater weight on criteria-aligned explanations, whereas federal and Dubai contexts depend more on reconstruction-ready audit trails. The study offers a practical defensibility-readiness roadmap for AI-enabled tender evaluation.
Amer Morshed· Proceedings of the Instituti...· 0 citations
Big data analytics is the most widely adopted technology among Lebanese auditors and adoption of artificial intelligence, blockchain, robotic process automation, and metaverse tools remains limited, and no significant differences were found between their users and non-users.
Walaa Khoder Kattar, Mehmet Nuri Salur· Audit Financiar· 0 citations
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