The delegation and use of artificial intelligence in the context of internal auditing
Keywords:
Artificial Intelligence; Decision Making; Technological Dominance Theory; Delegation; Internal Auditing.
Abstract
Objective: The aim of this research was to understand the factors that lead to the delegation or not of tasks
to artificial intelligence in the context of internal auditing, in the light of the theory of technological dominance.
Method: Semi-structured interviews were conducted with 13 internal audit professionals. Content analysis
was used for data analysis.
Originality/Relevance: There is still a gap in understanding how decision-makers can adapt to the effective
use of AI techniques and how they have incorporated these techniques into their analysis. There is a call for
qualitative studies that explain the reasons that lead to the decision to automate tasks, seeking to understand a specific context.
Results: Tasks that require professional judgment are preferable not to be delegated, especially when they
involve fraud detection and prevention. Models need to be parameterized in order to differentiate fraud from
an unintentional error. The need for new skills for audit professionals, identifying the lack of training in
programming and the use of artificial intelligence.
Theoretical/methodological/practical contributions: As theoretical contributions, this study complements
the literature on task delegation by identifying factors that can contribute to its adoption. It advances the
theoretical field by highlighting that age may have a moderating characteristic on experience. Another
theoretical contribution is the proposition of a delegation framework that can be explored in future research
using the Design Science Research (DSR) method. As a practical contribution, By delegating repetitive and
routine tasks to AI, internal auditors can focus on more complex and strategic activities, thereby increasing
the efficiency of the internal audit department.
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References
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Baird, A., & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1), 315–341. https://doi.org/10.25300/MISQ/2021/15882
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Bertomeu, J. (2020). Machine learning improves accounting: Discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135–1155. https://doi.org/10.1007/s11142-020-09554-9
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
Betti, N., & Sarens, G. (2021). Understanding the internal audit function in a digitalised business environment. Journal of Accounting & Organizational Change, 17(2), 197–216. https://doi.org/10.1108/JAOC-11-2019-0114
Bierstaker, J., Janvrin, D., & Lowe, D. J. (2014). What factors influence auditors' use of computer-assisted audit techniques?. Advances in Accounting, 30(1), 67-74. https://doi.org/10.1016/j.adiac.2013.12.005.
Borges, W. G., Leroy, R. S. D., Carvalho, L. F., Lima, N. C., & Oliveira, J. M. (2020). Implicações da Inteligência Artificial na Auditoria Interna no Brasil: Análise sob a Percepção de Profissionais. Sociedade, Contabilidade e Gestão, 15(1), 23–40. https://doi.org/10.21446/scg_ufrj.v0i0.25284
Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What Are You Saying? Using topic to Detect Financial Misreporting. Journal of Accounting Research, 58(1), 237–291. https://doi.org/10.1111/1475-679X.12294
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral Implications of Big Data’s Impact on Audit Judgment and Decision Making and Future Research Directions. Accounting Horizons, 29(2), 451–468. https://doi.org/10.2308/acch-51023
CFC (2016a). NBC TA 200 (R1) – Objetivos gerais do auditor independente e a condução da auditoria em conformidade com normas de auditoria. https://www1.cfc.org.br/sisweb/SRE/docs/NBCTA200(R1).pdf
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Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M. A. (2020). Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies, 25(3), 1098–1134. https://doi.org/10.1007/s11142-020-09546-9
Dyball, M. C., & Seethamraju, R. (2021). The impact of client use of blockchain technology on audit risk and audit approach—An exploratory study. International Journal of Auditing, 25(2), 602–615. https://doi.org/10.1111/ijau.12238
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Eilifsen, A., Kinserdal, F., Messier, W. F, & McKee, T. E. (2020). An Exploratory Study into the Use of Audit Data Analytics on Audit Engagements. Accounting Horizons, 34 (4): 75–103. https://doi.org/10.2308/HORIZONS-19-121
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research: NLP in Accounting, Auditing and Finance. Intelligent Systems in Accounting, Finance and Management, 23(3), 157–214. https://doi.org/10.1002/isaf.1386
Flick, U. (2012). Introdução à Metodologia de Pesquisa: um guia para iniciantes. Penso.
Garven, S., & Scarlata, A. (2020). An examination of factors associated with investment in internal auditing technology. Managerial Auditing Journal, 35(7), 955–978. https://doi.org/10.1108/MAJ-06-2019-2321
Gray, G. L., Chiu, V., Liu, Q., & Li, P. (2014). The expert systems life cycle in AIS research: What does it mean for future AIS research? International Journal of Accounting Information Systems, 15(4), 423–451. https://doi.org/10.1016/j.accinf.2014.06.001
Hampton, C. (2005). Determinants of reliance: An empirical test of the theory of technology dominance. International Journal of Accounting Information Systems, 6(4), 217–240. https://doi.org/10.1016/j.accinf.2005.10.001
Hass, S., Abdolmohammadi, M. J., & Burnaby, P. (2006). The Americas literature review on internal auditing. Managerial Auditing Journal, 21(8), 835–844. https://doi.org/10.1108/02686900610703778
Huang, F., & Vasarhelyi, M. A. (2019). Applying robotic process automation (RPA) in auditing: A framework. International Journal of Accounting Information Systems, 35, 100433. https://doi.org/10.1016/j.accinf.2019.100433
International Auditing and Assurance Standards Board [IAASB] (2009). International Standard on Auditing 200: Overall objectives of the independent auditor and the conduct of an audit in accordance with international standards on auditing. https://www.ifac.org/system/files/publications/files/A009%202012%20IAASB%20Handbook%20ISA%20200.pdf
Institute of Internal Auditors [IIA]. (2021). The IIA's Internal Audit Competency Framework. https://www.theiia.org/en/content/guidance/mandatory/standards/ia-competency-framework/
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Jones, K. K., Baskerville, R. L., Sriram, R. S., & Ramesh, B. (2017). The impact of legislation on the internal audit function. Journal of Accounting & Organizational Change, 13(4), 450–470. https://doi.org/10.1108/JAOC-02-2015-0019
Kenno, S. A., McCracken, S. A., & Salterio, S. E. (2017). Financial Reporting Interview-Based Research: A Field Research Primer with an Illustrative Example. Behavioral Research in Accounting, 29(1), 77–102. https://doi.org/10.2308/bria-51648
Kokina, J., & Blanchette, S. (2019). Early evidence of digital labor in accounting: Innovation with Robotic Process Automation. International Journal of Accounting Information Systems, 35, 100431. https://doi.org/10.1016/j.accinf.2019.100431
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
Koreff, J (2022). Are Auditors' Reliance on Conclusions from Data Analytics Impacted by Different Data Analytic Inputs?. Journal of Information Systems, 36 (1), 19–37. https://doi.org/10.2308/ISYS-19-051
Korhonen, T., Selos, E., Laine, T., & Suomala, P. (2020). Exploring the programmability of management accounting work for increasing automation: An interventionist case study. Accounting, Auditing & Accountability Journal, 34(2), 253–280. https://doi.org/10.1108/AAAJ-12-2016-2809
Krieger, F., Drews, P., & Velte, P. (2021). Explaining the (non-) adoption of advanced data analytics in auditing: A process theory. International Journal of Accounting Information Systems, 41, 100511. https://doi.org/10.1016/j.accinf.2021.100511
Lei n. 13.709, de 14 de agosto de 2018 (2018). Lei Geral de Proteção de Dados Pessoais (LGPD). https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm
Lamboglia, R., Lavorato, D., Scornavacca, E., & Za, S. (2021). Exploring the relationship between audit and technology. A bibliometric analysis. Meditari Accountancy Research, 29(5), 1233–1260. https://doi.org/10.1108/MEDAR-03-2020-0836
Losbichler, H., & Lehner, O. M. (2021). Limits of artificial intelligence in controlling and the ways forward: A call for future accounting research. Journal of Applied Accounting Research, 22(2), 365–382. https://doi.org/10.1108/JAAR-10-2020-0207
Mala, R., & Chand, P. (2015). Judgment and Decision-Making Research in Auditing and Accounting: Future Research Implications of Person, Task, and Environment Perspective. Accounting Perspectives, 14(1), 1–50. https://doi.org/10.1111/1911-3838.12040
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Published
2026-01-02
How to Cite
Marques Correa, W. V., Bonato Marcolin, C., & da Silva Momo, F. (2026). The delegation and use of artificial intelligence in the context of internal auditing. Enfoque: Reflexão Contábil, 45(1), 1-21. https://doi.org/10.4025/enfoque.v45i1.70818
Section
Original Articles
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