Examining the Barriers to and Strategies for Artificial Intelligence Adoption in Audit Institutions: A Qualitative Grounded Theory Study

Maryam  Akbari; Khadijeh Khodabakhshi Parijan; Mojtaba  Rahimi; Fahimeh  Abbasabad Arabi

Authors

Maryam Akbari Ph.D.Student, Department of Accounting, WT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0002-2583-7221
Khadijeh Khodabakhshi Parijan Department of Accounting, WT.C., Islamic Azad University, Tehran, Iran https://orcid.org/0000-0001-6653-8653
Mojtaba Rahimi Assistant Professor, Department of Accounting, University of Science and Culture, Tehran, Iran. Author https://orcid.org/0009-0006-9062-8994
Fahimeh Abbasabad Arabi Ph.D.Student, Department of Accounting, WT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0008-9842-7497

Keywords:

Artificial intelligence, auditing, technology, organization, environment model, institutional theory, grounded theory

Abstract

This study aimed to investigate the process of artificial intelligence adoption in public-sector audit institutions using the qualitative grounded theory method. The required data were collected through in-depth semi-structured interviews with senior managers, auditors, and information technology specialists and were analyzed using the three-stage process of open, axial, and selective coding. The findings indicated that the central phenomenon underlying this process was the “transition from traditional auditing to intelligent auditing.” This phenomenon is shaped by causal conditions, including increasing pressure for efficiency and transparency, as well as intervening conditions, such as role conflict and the absence of adequate infrastructure. In response to this phenomenon, the principal actors employ a strategy of “stepwise implementation and proof of concept,” which ultimately leads to a transition toward an analytical–supervisory role and enhanced organizational legitimacy. The final model developed in this study, which emerged from the grounded theory analysis, can serve as a roadmap for policymakers and managers of audit institutions seeking to achieve the successful adoption of artificial intelligence.

References

[1] G. Vial, "Understanding Digital Transformation: A Review and a Research Agenda," (in English), The Journal of Strategic Information Systems, vol. 28, no. 2, pp. 118-144, 2019, doi: 10.1016/j.jsis.2019.01.003.

[2] B. W. Wirtz, J. C. Weyerer, and C. Geyer, "Artificial Intelligence and the Public Sector: Applications and Challenges," (in English), International Journal of Public Administration, vol. 42, no. 7, pp. 596-615, 2019, doi: 10.1080/01900692.2018.1498103.

[3] C. Dener, H. Nii-Aponsah, L. E. Ghunney, and K. D. Johns, GovTech Maturity Index: The State of Public Sector Digital Transformation. World Bank Publications (in English), 2021.

[4] D. Appelbaum, A. Kogan, and M. A. Vasarhelyi, "Big Data and Analytics in the Modern Audit Engagement: Research Needs," (in English), Auditing, vol. 36, no. 1, pp. 1-27, 2017, doi: 10.2308/ajpt-51684.

[5] D. Appelbaum, A. Kogan, M. A. Vasarhelyi, and Z. Yan, "Impact of Business Analytics and Enterprise Systems on Managerial Accounting," (in English), International Journal of Accounting Information Systems, vol. 28, pp. 59-76, 2018.

[6] A. B. Nassif, M. A. Talib, Q. Nasir, and F. M. Dakalbab, "Machine Learning for Anomaly Detection: A Systematic Review," (in English), IEEE Access, vol. 9, pp. 78658-78700, 2021, doi: 10.1109/ACCESS.2021.3083060.

[7] L. Del Caprio, "Artificial Intelligence in Auditing: Transforming Risk Assessment and Fraud Detection," Thesis, 2025. [Online]. Available: https://www.theseus.fi/bitstream/handle/10024/888303/Del%20Caprio_Laura.pdf?sequence=2

[8] A. Farras, A. Ali, and M. Audi, "Advancing Audit Practices through Technology: A Comprehensive Review of Continuous Auditing," (in English), Journal of Social Signs Review, vol. 3, no. 2, pp. 506-539, 2025.

[9] F. Harman, "Artificial Intelligence (AI) and Public Sector Auditing: A New Era of Accuracy and Accountability," (in English), SAI Malaysia, 2025. [Online]. Available: https://asosaijournal.org/artificial-intelligence-ai-and-public-sector-auditing-a-new-era-of-accuracy-and-accountability-sai-malaysia.

[10] S. T. Kau and F. Fitriana, "The Role and Impact of Artificial Intelligence on Public Sector Audit Transformation in the Digital Era," (in English), E-Jurnal Akuntansi, vol. 35, no. 4, pp. 1217-1245, 2025, doi: 10.24843/EJA.2025.v35.i04.p02.

[11] J. Kokina, S. Blanchette, T. H. Davenport, and D. Pachamanova, "Challenges and Opportunities for Artificial Intelligence in Auditing: Evidence from the Field," (in English), International Journal of Accounting Information Systems, vol. 56, p. 100734, 2025, doi: 10.1016/j.accinf.2025.100734.

[12] S. Anomah, B. Ayeboafo, A. Owusu, and M. Aduamoah, "Adapting to AI: Exploring the Implications of AI Integration in Shaping the Accounting and Auditing Profession for Developing Economies," (in English), EDPACS, vol. 69, no. 11, pp. 28-52, 2024, doi: 10.1080/07366981.2024.2388412.

[13] S. Anomah, "Assessing the Institutional Readiness and Capacity for AI Adoption in Public Audit Institutions in Developing Countries: Evidence from Ghana," (in English), Telematics and Informatics Reports, vol. 20, p. 100260, 2025, doi: 10.1016/j.teler.2025.100260.

[14] J. Baker and M. Davi, "Organizational Readiness for Big Data Adoption: A Technology-Organization-Environment Framework," (in English), Business Horizons, vol. 62, no. 2, pp. 269-278, 2019.

[15] M. A. Khan, "Contextual Considerations of Audit Technology Acceptance, Accounting Regulations, Administrative Influences, and Organizational Efficiency: A Novel I-TOE Adoption Formulation," (in English), Pakistan Journal of Life and Social Sciences, vol. 23, no. 1, pp. 4563-4582, 2025, doi: 10.57239/PJLSS-2025-23.1.00361.

[16] C. F. Goh and G. Richards, "A Hybrid Model for Digital Transformation in Public Administration: Integrating TOE and Institutional Theory," (in English), Government Information Quarterly, vol. 37, no. 4, p. 101503, 2020.

[17] W. R. Scott, Institutions and Organizations: Ideas, Interests, and Identities, 4th ed. SAGE Publications (in English), 2014.

[18] P. J. DiMaggio and W. W. Powell, "The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields," (in English), American Sociological Review, vol. 48, no. 2, pp. 147-160, 1983, doi: 10.2307/2095101.

[19] F. Zhao, S. Trimi, and Z. He, "Innovation and Entrepreneurship in the Era of Digital Transformation: The Role of Institutional Support," (in English), Journal of Business Research, vol. 145, pp. 726-736, 2022.

[20] F. Morandín-Ahuerma, "Ten UNESCO Recommendations on the Ethics of Artificial Intelligence," 2023.

[21] D. K. Njiru, D. M. Mugo, and F. M. Musyoka, "Ethical Considerations in AI-Based User Profiling for Knowledge Management: A Critical Review," (in English), Telematics and Informatics Reports, p. 100205, 2025, doi: 10.1016/j.teler.2025.100205.

[22] W. D. Eggers, D. Schatsky, and P. Viechnicki, "AI-Augmented Government: Using Cognitive Technologies to Redesign Public Sector Work," Deloitte Insights, 2020. [Online]. Available: https://www.readkong.com/page/ai-augmented-government-using-cognitive-technologies-to-7248338

[23] B. Glaser and A. Strauss, The Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge (in English), 2017.