Artificial Intelligence (AI) can think, learn, and adapt, and is utilized in a variety of business processes to automate the process of decision-making, tasks, and management of customer relationships (McCollum 2017, 2017; Hsu and others. 2022a). The survey of 2019 conducted by Gartner (a prominent research and advisory firm) found that business adoption of AI increased by 270% over the past four years. The global spending on AI during the year 2019 reached $37.5 billion and is predicted to reach $97.9 billion by 2023 (IDC, 2019).

Auditing and accounting are both clearly affected by the rapid character that is AI implementation. The Big Four accounting firms (Deloitte, EY, KPMG, and PwC) have just launched the company own AI systems, which can monitor changes in the environment, automatically detect the data and analyze it, create invoices and create financial reports, which will improve the effectiveness and efficiency of auditing processes that have been used for decades. These AI systems will likely replace accountants with basic skills and enable managers with limited accounting knowledge to make informed decisions by relying on basic accounting information (Muggleton, 2014; IIA 2017d).

Internal auditors are considered to be guardians to ensure the accuracy of data and protect the shareholders’ assets. Without a proper auditing or routine audit process, there is a greater chance of an auditor’s failure. To prevent this from happening, auditing and accounting firms have taken on AI technology, which has the advantages of boosting the efficiency of auditors as well as improving decision consensus and the capability to handle huge volumes of data and communicate with relationships efficiently. The reports also revealed that accountants and auditors outfitted with AI technology can spot issues and possible losses earlier, and the solutions are strengthened before any harm to the business is caused (PWC 2018, and Alina et al. 2018). The increasing use of AI in the auditing and accounting profession will likely transform accounting practices. It is therefore important to study the present development of AI applications to the auditing and accounting professions and to modify the conventional auditing procedures to adapt to the ever-changing requirements of business (Negnevitsky, 2005; Meng et al. 2021).

A McKinsey Global AI survey found that companies are slow in a variety of sectors and are slow to adopt AI acceptance due to the obstacles and fears of privacy breaches or bias that is not intentional, as well as other negative outcomes. It was reported that the Institute of Internal Auditors (IIA) published its Global Perspectives and Prospects Report (IIA 2017a), in which they discuss the elements that are essential to an AI Internal Audit profession. It expects internal auditors to have a knowledge of AI and have the necessary skills to help develop AI for enterprises. The agency has proposed a set of internal auditing frameworks that are AI-driven and include three key elements: AI strategies, AI governance, and human aspects (IIA 2017b, C).

The AI-driven internal audit frameworks that are released by IIA do not take into account the essentiality of all elements and fail to consider the causal and effect relationships between them. This could result in organizations slow in the execution of their AI implementation strategy. A well-designed external audit framework that is AI driven will assist enterprises in converting their strategy into action, and provide certain indicators of business performance, and address some of the questions below. (1) What’s the relationship between the aspects/criteria that will lead to a the successful AI-driven adoption of internal audit frameworks? (2) What are the criteria for determining the core of these criteria and dimensions? (3) What is the best way to evaluate the effectiveness of an internal audit based on an AI system? (4) What is the real extent of the effectiveness of an internal audit based on AI in the real world, and how can we improve it in the future?
The previous studies of AI applications within the audit process internally typically used interviews or observation as well as traditional statistical techniques to arrive at their conclusions (Baldwin and colleagues. 2006 O’Leary and Watkins 1995 and Omoteso 2012). Sutton and others. 2016; Alina et al. 2018). The conventional statistical approaches assume that the criteria and dimensions are linearly, independently and hierarchically arranged (Peng and Tzeng, 2019). But in the real workplace, the challenges that arise from an internal audit system driven by AI are typically marked by interdependent relationships between criteria or dimensions and could even exhibit feedback-like results. So, statistical techniques are insufficient to deal with the above-mentioned tasks.

This study therefore examines the AI aspects of AI applications within the internal audit process as well as their intricate inter-relationships (Liou 2011. Hirsch 2018, Nayak as well as Misra 2019,) and evaluates the data using expert surveys. The purpose is to pinpoint the most influential factors to increase the effectiveness and effectiveness of internal audit procedures in the age of massive data.