The implementation of modern developments in the field of accounting is a natural practice since advanced digital tools are utilized worldwide to simplify calculations and optimize all procedures. One of the forms of such innovation is artificial intelligence (AI), which has many functions and allows minimizing human participation in financial procedures. However, attitudes towards AI adoption can differ among various stakeholders. This literature review is aimed at revealing the peculiarities of the perception of AI in accounting by interested parties and highlighting specific views on this process. As a rationale, relevant academic resources will be involved, and the review will be a convenient instrument to assess the current findings of credible authors to draw appropriate conclusions regarding the proposed topic.
While analyzing the findings from the current resources, one can note that the simplicity and convenience of a specific digital algorithm may be the criteria influencing the perception of AI in accounting. According to Damerji and Salimi (2021), the technological preparedness of personnel depends on how accessible and understandable the associated software is. The authors conducted a quantitative study among accounting students and argued that the adoption of a particular technology directly correlates with its simplicity (Damerji & Salimi, 2021). In other words, if the interface of a digital program is intuitive, there is a high chance of a positive attitude of stakeholders towards it.
At the same time, even the convenience of utilizing AI in accounting cannot be considered a significant benefit if the implementation of the technology is accompanied by ethical issues. Munoko et al. (2020) address these issues and remark that users may assess AI negatively due to a number of factors. Among them, the authors list a lack of transparency, risks of privacy threats, potential insecurity from a malware defense perspective, and several other problems (Munoko et al., 2020).
AI in accounting can be perceived positively only if users can receive comprehensive information at any time without limited access to data. In this regard, the aforementioned accessibility factor proposed by Damerji and Salimi (2021) confirms its relevance. Consequently, one of the aspects that determine the attitude of interested parties to AI in this area is the transparency of the technology, which manifests itself in constant opportunities to check and adjust its operation flow.
The safety factor is no less important criterion than transparency. As Huttunen et al. (2019) state, the attitude towards AI in accounting largely depends on whether users feel that the system is sufficiently protected from external threats and data leakage. Many programs are young, and their security protocols are not comprehensively verified, which carries risks for financial assets (Huttunen et al., 2019). The researchers also point to distinctive benefits for different users as a controversial aspect of AI in this area (Huttunen et al., 2019).
The use of innovative technologies in large companies can be justified by the possible savings and acceleration of operations. In small firms, the costs of purchasing and maintaining the appropriate software may not be comparable in value with the benefits of such algorithms. As a result, the perception of users is determined by the criteria of safety and financial benefits.
Despite the advantages of AI in accounting, one of the problems associated with the perception and attitudes towards this technology is employees’ concern regarding the risk of losing their jobs. Ologe (2020) cites the results of a Taiwanese study and notes that, according to the survey, 32% of respondents are worried about the threat of losing their positions due to the replacement of their jobs (p. 20). However, the overwhelming majority of accountants in other countries are convinced that such innovation can improve their performance and help avoid mistakes in the workflow (Ologe, 2020). These results suggest that the existing labor market principles and job allocation are important criteria to take into account when evaluating attitudes towards AI in accounting.
When comparing perceptions regarding the implementation of AI in accounting, stakeholder views vary by job title. According to Punnala (2021), administrative staff perceives this innovation more positively, while some frontline employees express concerns about the innovation. At the same time, as the author states, despite favorable assessments, managers often do not understand all the capabilities and functions of AI, which may indicate a biased and insufficiently competent attitude (Punnala, 2021).
The lack of knowledge regarding the features of such a technology is fraught with gaps in maintaining a robust workflow and, therefore, potential security issues. Afroze and Aulad (2020) also consider this bias and argue that, for employees, objective and sustainable implementation model is perceived more favorably than innovative but untested optimization algorithms. As a result, one of the aspects to consider when evaluating the perception of AI in accounting is the stability of the technology and a convenient technical background for its implementation.
In addition to the aforementioned risks, some authors view AI in accounting as an underperforming innovation. Stancheva-Todorova (2018) notes that, according to some employees, due to the complexity and multitasking in the accounting field, AI programs do not allow performing a sufficient set of functions. Human participation in control over the work of such an algorithm is mandatory, and complete computerization is impossible. Thus, even despite optimization, such innovation cannot cover the entire spectrum of accounting objectives comprehensively.
The types of AI systems are essential to consider when analyzing the attitudes towards these programs. Albawwat and Frijat (2021) highlight different types of such digital algorithms and argue that accountants’ perception of this technology varies across functionality. The researchers analyze this topic and note that assisted systems are rated higher than augmented ones (Albawwat & Frijat, 2021).
This finding confirms Stancheva-Todorova’s (2018) assertion that AI systems can be used as auxiliary tools, but trusting them with full control over the audit is risky. These types of programs differ in the aforementioned criteria of ease of use and usability, which also determine the choice of stakeholders. Therefore, when considering the perception of AI algorithms in accounting, a wide range of nuances should be assessed. This helps reveal interested parties’ common attitudes and draw a conclusion about the value of such a technology in the industry under consideration.
Analyzing the perception of AI implementation in accounting by assessing relevant findings from academic sources provides comprehensive data on stakeholder expectations. Various criteria need to be considered, including the ease of use, the functionality of these programs, their value in specific organizations, the financial rationale for the transition to automated control, and some other aspects. Stakeholders’ views on this technology vary depending on these aspects, and different opinions should be taken into account when implementing AI systems in accounting.
References
Afroze, D., & Aulad, A. (2020). Perception of professional accountants about the application of artificial intelligence (AI) in auditing industry of Bangladesh. Journal of Social Economics Research, 7(2), 51-61. Web.
Albawwat, I., & Frijat, Y. (2021). An analysis of auditors’ perceptions towards artificial intelligence and its contribution to audit quality. Accounting, 7(4), 755-762. Web.
Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107-130. Web.
Huttunen, J., Jauhiainen, J., Lehti, L., Nylund, A., Martikainen, M., & Lehner, O. M. (2019). Big data, cloud computing and data science applications in finance and accounting. ACRN Oxford Journal of Finance and Risk Perspectives, 8, 16-30.
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. Web.
Ologe, S. O. (2020). Perceptions on the use of artificial intelligence in accounting: An empirical study among accounting professionals in Nigeria [Unpublished doctoral dissertation]. Griffith College.
Punnala, S. (2021). A case study: Perceptions and experiences on artificial intelligence in financial administration [Unpublished master’s thesis]. Jyväskylä University.
Stancheva-Todorova, E. P. (2018). How artificial intelligence is challenging accounting profession. Journal of International Scientific Publications, 12, 126-141.