• Abstract

    Nowadays, artificial intelligence (AI) has experienced a remarkable resurgence of interest in the business world due to its ability to perform tasks traditionally carried out by humans, replicating cognitive capacities and professional judgment through machines and computers. This technological advancement, now in its golden age, enables organizations to respond to rapidly evolving market demands, achieve competitive advantages, and ensure long-term sustainability. Consequently, managers are increasingly adopting AI tools to integrate them into their organizational processes and decision-making activities. Internal auditing, as a key function within organizations, has been profoundly affected by this technological revolution, experiencing significant transformations through the automation of audit processes, expansion of its scope, reduction of processing times, and, ultimately, improvement in audit quality. The present article aims to theoretically explore how AI techniques contribute to enhancing the quality of internal auditing, relying on the Resource-Based View (RBV) framework as a guiding theoretical framework. Specifically, it identifies five explanatory dimensions through which AI supports auditing practices: task automation, document processing and analysis, risk and fraud detection, communication of results, and the reduction of human errors. Each dimension represents a critical pathway through which AI can increase audit efficiency, accuracy, and overall effectiveness. Furthermore, the competence of internal auditors is introduced as a moderating variable, as it conditions the extent to which these technological contributions can be effectively utilized and translated into meaningful improvements in audit performance. By highlighting the interaction between technological resources and human expertise, this study emphasizes the strategic value of integrating AI within internal auditing practices.

  • References

    1. Accenture. (2017). Technology vision 2017: Technology for people, AmplifYou. https://www.accenture.com/us-en/insight-disruptive-technology-trends-2017
    2. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
    3. Ahuja, G., Coff, R., & Lee, P. M. (2005). Managerial foresight and attempted rent appropriation: Insider trading on knowledge of imminent breakthroughs. Strategic Management Journal, 26(9), 791–808. https://doi.org/10.1002/smj.474
    4. Ait Mouzoun, M., & El Mezouari, S. (2019). Revue de la littérature académique et professionnelle relative aux facteurs d’efficacité des fonctions d’audit interne. Revue du Contrôle, de la Comptabilité et de l’Audit, 3(4), 568–605. https://doi.org/10.5281/zenodo.3596347
    5. Ajayi, F. A., & Akinrinola, O. (2023). Artificial intelligence and internal audit quality of commercial banks in Nigeria. International Journal of Management and Economics Invention, 9(4), 2897–2907. https://doi.org/10.47191/ijmei/v9i4.05
    6. Ameen, N., Sharma, A., & Tarba, S. Y. (2024). Coupling artificial intelligence capability and strategic agility for enhanced product and service creativity. British Journal of Management, 35(4), 1916–1934. https://doi.org/10.1111/1467-8551.12797
    7. Ameen, N., Sharma, G. D., & Tarba, S. (2023). Artificial intelligence and creativity in marketing: A proposed typology and new directions for academia–industry collaborations. In M. Pagani & R. Champion (Eds.), Artificial intelligence for business creativity (pp. 82–98). Routledge. https://doi.org/10.4324/9781003287582-8
    8. Ammanath, B., Hupfer, S., & Jarvis, D. (2020). Thriving in the era of pervasive AI: Deloitte’s state of AI in the enterprise (3rd ed.). Deloitte. https://deloitte.wsj.com/cio/thriving-in-the-era-of-pervasive-ai-01595358164
    9. Argyres, N. S., & Zenger, T. R. (2012). Capabilities, heterogeneity, and intermediate outcomes: Internal and external rent generation. Strategic Management Journal, 33(6), 893–909. https://doi.org/10.1002/smj.1973
    10. Argyres, N. S., & Zenger, T. R. (2012). Capabilities, transaction costs, and firm boundaries: A dynamic perspective and integration. Organization Science, 23(6), 1643–1657. https://doi.org/10.1287/orsc.1110.0736
    11. Baharom, Z. (2025). The transformative role of artificial intelligence in internal auditing: A critical review. International Journal of Research and Innovation in Social Science, 9(6), Article 217. https://doi.org/10.47772/IJRISS.2025.906000217
    12. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
    13. Barraud, B. (2019). L’intelligence de l’intelligence artificielle. L’Harmattan. https://hal.science/hal-02327501
    14. Bizarro, P. A., & Dorian, M. (2017). Artificial intelligence: The future of auditing. Internal Auditing, 32(5), 21–26.
    15. Blohm, I., Antretter, T., Sirén, C., Grichnik, D., & Wincent, J. (2022). It’s a people’s game, isn’t it?! A comparison between the investment returns of business angels and machine learning algorithms. Entrepreneurship Theory and Practice, 46(4), 1054–1091. https://doi.org/10.1177/1042258720945206
    16. Brown-Liburd, H., & Vasarhelyi, M. A. (2015). Big data and audit evidence. Journal of Emerging Technologies in Accounting, 12(1), 1–16. https://doi.org/10.2308/jeta-10468
    17. Chamorro-Premuzic, T., Polli, F., & Dattner, B. (2019). Building ethical AI for talent management. Harvard Business Review. https://hbr.org/2019/11/building-ethical-ai-for-talent-management
    18. Chartered Institute of Internal Auditors. (2019). What is internal audit? https://charterediia.org/about-us/what-is-internal-audit/
    19. Chen, F.-H., Hsu, M.-F., & Huang, K.-H. (2022). Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model. Information Technology & Management, 23, 213–231. https://doi.org/10.1007/s10799-021-00342-8
    20. Chukwuani, V. N., & Egiyi, M. A. (2020). The transformational impact of automation and artificial intelligence on the accounting profession. International Journal of Accounting and Financial Risk Management, 5(1), 1–8. https://doi.org/10.5281/zenodo.14546797
    21. Cohen, A., & Sayag, G. (2010). The effectiveness of internal auditing: An empirical examination of its determinants in Israeli organisations. Australian Accounting Review, 20(3), 296–307. https://doi.org/10.1111/j.1835-2561.2010.00092.x
    22. Davenport, T. H., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. HarperBusiness.
    23. DeAngelo, L. E. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3(3), 183–199. https://doi.org/10.1016/0165-4101(81)90002-1
    24. Dong, X., & McIntyre, S. H. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. Quantitative Finance, 14(11), 1895–1896. https://doi.org/10.1080/14697688.2014.946440
    25. El Gharbaoui, B., Benrrezzouq, R., & Chraibi, A. (2021). L’impact de la qualité d’audit interne sur la performance financière des sociétés cotées à la BVC. Alternatives Managériales Économiques, 3(3), 118–138. https://doi.org/10.48374/IMIST.PRSM/ame-v3i3.27427
    26. Erragragui, S., & Aoufir, M. (2023). Comprendre l’approche de la transformation digitale : Les déterminants de la TD, opportunités et défis, proposition d’un modèle théorique. International Journal of Accounting, Finance, Auditing, Management and Economics, 4(3-1), 384–411. https://doi.org/10.5281/zenodo.8055764
    27. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
    28. Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x
    29. Ghanoum, S., & Alaba, F. M. (2020). Integration of artificial intelligence in auditing: The effect on auditing process (Master’s thesis). Kristianstad University, Faculty of Business.
    30. Haddoud, M. Y., Jones, P., & Newbery, R. (2018). SMEs’ export performance in Algeria: A configuration approach. In D. Higgins, P. Jones, & P. McGowan (Eds.), Creating entrepreneurial space (pp. 91–115). Emerald Publishing. https://doi.org/10.1108/S2040-72462018000009A006
    31. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
    32. Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850. https://doi.org/10.1002/smj.2247
    33. Institute of Internal Auditors. (2024). AI adoption in internal audit functions: A 2023–2024 comparison. https://www.theiia.org/en/content/articles/executive-knowledge-brief/2024/july/ai-adoption-in-internal-audit-functions-a-2023-2024-comparison/
    34. International Auditing and Assurance Standards Board. (2014). A framework for audit quality: Key elements that create an environment for audit quality. https://www.ifac.org/system/files/publications/files/A-Framework-for-Audit-Quality-Key-Elements-that-Create-an-Environment-for-Audit-Quality-2.pdf
    35. INTOSAI Journal. (2025). The use of artificial intelligence (AI) in the execution of audits. INTOSAI Journal. https://www.intosaijournal.org/journal-entry/the-use-of-artificial-intelligence-ai-in-the-execution-of-audits/
    36. ISACA. (2019). The intelligent audit. ISACA Journal. https://www.isaca.org/resources/isaca-journal/issues/2019/volume-6/the-intelligent-audit
    37. Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511
    38. Jackson, P. (1999). Introduction to expert systems (3rd ed.). Addison-Wesley.
    39. Jiang, F., et al. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
    40. Jones, K. S. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21. https://doi.org/10.1108/eb026526
    41. Knechel, W. R., Krishnan, G. V., Pevzner, M., Shefchik, L. B., & Velury, U. K. (2013). Audit quality: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 32(Supplement 1), 385–421. https://doi.org/10.2139/ssrn.2040754
    42. Kobiyh, M., & El Amri, A. (2024). Rational individual and managerial decision model: A critical review of the standard rationality hypothesis. Business Ethics and Leadership, 8(3), 120–132. https://doi.org/10.61093/bel.8(3).120-132.2024
    43. Kobiyh, M., El Amri, A., Sahib Eddine, A., & Oulfarsi, S. (2024). Literature review and theoretical generalizations of the ethics role in business and management: Family business as a case study. Business Ethics and Leadership, 8(1), 93–106. https://doi.org/10.61093/bel.8(1).93-106.2024
    44. Kobiyh, M., El Amri, A., Sahib Eddine, A., & Oulfarsi, S. (2025). Relational perspective of coopetition, cooperative efforts and effects on firm performance. Business Ethics and Leadership, 9(3), 130–144. https://doi.org/10.61093/bel.9(3).130-144.2025
    45. Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
    46. KPMG. (2023). The impact of artificial intelligence on the audit profession. https://kpmg.com/nl/en/home/topics/future-of-audit/ai-audit/impact-artificial-intelligence-audit-profession.html
    47. Krichene, A., & Baklouti, E. (2021). Internal audit quality: Perceptions of Tunisian internal auditors—An exploratory research. Journal of Financial Reporting and Accounting, 19(1), 28–54. https://doi.org/10.1108/JFRA-01-2020-0010
    48. Liburd, H. F., & Vasarhelyi, M. A. (2015). Big data and audit evidence. Journal of Emerging Technologies in Accounting, 12(1), 1–16. https://doi.org/10.2308/jeta-10468
    49. Mach Evalee. (2021). How artificial intelligence can help internal auditing. Aviana Global. https://avianaglobal.com/how-artificial-intelligence-can-help-internal-auditing
    50. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. International Journal of Information Management, 58, 101424. https://doi.org/10.1016/j.im.2021.103434
    51. Mikalef, P., Islam, N., Parida, V., Singh, H., & Altwaijry, N. (2023). Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective. Journal of Business Research, 164, 113998. https://doi.org/10.1016/j.jbusres.2023.113998
    52. Mohammad, S. J., et al. (2020). How artificial intelligence changes the future of the accounting industry. International Journal of Economics and Business Administration, 8(3), 478–488. https://doi.org/10.35808/ijeba/538
    53. Mohammed, M. A., & Anbar, S. G. (2016). Quality audit for the adoption of artificial intelligence: Applied research in a sample of regulatory bodies working in the Federal Financial Supervisory Office. Journal of Accounting and Financial Studies, 11(4), 28–76. https://doi.org/10.13140/RG.2.2.27487.82080
    54. Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
    55. Nilsson, N. J. (1982). Principles of artificial intelligence. Springer-Verlag.
    56. Pizzi, S., Venturelli, A., Variale, M., & Macario, G. P. (2021). Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis. Technology in Society, 67, 101738. https://doi.org/10.1016/j.techsoc.2021.101738
    57. Puthukulam, G., Ravikumar, A., Sharma, R. V. K., & Meesaala, K. M. (2021). Auditors' perception of the impact of artificial intelligence on professional skepticism and judgment in Oman. Universal Journal of Accounting and Finance, 9(5), 1184–1190. https://doi.org/10.13189/ujaf.2021.090527
    58. Radford, A., et al. (2019). Language models are unsupervised multitask learners. OpenAI Blog. https://openai.com/research/language-unsupervised
    59. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
    60. Renard, J. (2017). Théorie et pratique de l’audit interne. Eyrolles.
    61. Rizvan Hasan, A. (2022). Artificial intelligence in accounting and auditing: A literature review. Open Journal of Business and Management, 10(1), 440–465. https://doi.org/10.4236/ojbm.2022.101026
    62. Roder, S. (2019). Guide pratique de l’intelligence artificielle dans l’entreprise. Eyrolles.
    63. Samagaio, A., & Felício, T. (2023). Determinants of internal audit quality. European Journal of Management and Business Economics, 32(4), 417–435. https://doi.org/10.1108/EJMBE-06-2022-0193
    64. Shawaqfeh, G. N., Nasr, S. Y., & Shehab, S. T. (2024). The impact of artificial intelligence on internal auditing. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4917446
    65. Stahl, B. C., Obach, M., Yaghmaei, E., Ikonen, V., Chatfield, K., & Brem, A. (2017). The responsible research and innovation (RRI) maturity model: Linking theory and practice. Sustainability, 9(6), 1036. https://doi.org/10.3390/su9061036
    66. Supriadi, T. (2019). Influence of auditor competency in using information technology on the success of e-audit system. EURASIA Journal of Mathematics, Science and Technology Education, 15(10), em1769. https://doi.org/10.29333/ejmste/109529
    67. Tolun, M. R., Sahin, S., & Oztoprak, K. (2016). Expert systems. In Kirk-Othmer encyclopedia of chemical technology (pp. 1–12). John Wiley & Sons, Inc. https://doi.org/10.1002/0471238961.0524160518011305.a01.pub2
    68. Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12523
    69. Zhang, C., Mohammed Shah, S., Lau, Y. W., & Ngalim, S. M. (2024). The mediating effect of information technology usage on the relationship of internal audit processes and internal auditor competence toward internal audit effectiveness: Evidence from the Chinese financial sector. International Journal of Business and Management, 19(6), 139–154. https://doi.org/10.5539/ijbm.v19n6p139
    70. Zouhri, A. (2019). Big data, intelligence artificielle et la performance des entreprises de demain. Revue du contrôle, de la comptabilité et de l’audit, 4(3), 916–931. https://doi.org/10.5281/zenodo.3605684

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Rejjaoui, R., El Amri, A., Eddine, A. S., & Marzougui, Y. (2026). The role of artificial intelligence in enhancing internal audit quality: A resource-based view approach. Multidisciplinary Reviews, 9(9), 2026406. https://doi.org/10.31893/multirev.2026406
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