Bridging the Gap: Enhancing Traditional Auditing Techniques Using AI for Improved Audit Quality in the digital era

Abstract

Bridging the Gap: Enhancing Traditional Auditing Techniques Using AI for Improved Audit Quality in the digital era

Extended Abstract

In recent years, every facet of the business environment is experiencing a rapid technological mind shift, shedding the old ways of operations and embracing the new normal offered by technology (Popkova & Sergi, 2021). Artificial Intelligence (AI) has come to stay, as it has now been infused into the operations of several businesses to optimize profit, increase revenue, and reduce costs (Almufadda & Almezeini, 2021). The reporting aspect of business is not left out as AI is revolutionizing the traditional accounting process. Natural language processing, machine learning, big data, predictive analysis, and advanced accounting software has transformed the way financial information is processed (Mwachikoka, 2024). This is done by automating data collection, analysis, and report preparation, thereby enhancing the accuracy, timeliness, and reliability of financial information while reducing human intervention.  

In today’s fast changing business environment, auditing remains essential in providing assurance and instilling trust in the financial statement (Vlaović-Begović & Tomašević, 2016; Fotoh & Lorentzon, 2023), as it tells how true and fair the financial statements are, as presented by the directors (IAASB 2024). For much of its history, auditing has relied on traditional methods such as sampling, substantive testing, analytical procedures, and professional judgment — all of which rely heavily on the auditor’s skill, expertise and intuition. These techniques have proven sufficient in traditional business environments. However, the world auditors operate in today has changed dramatically with technology. Organizations now generate and store vast amount of data across digital systems, and financial transactions occur in real time through complex, interconnected systems. This new digital reality exposes a fundamental tension between the constraints of traditional audit methods and the opportunities offered by emerging technologies (Usul & Alpay, 2024).

This article describes this issue as the audit technology assurance gap (ATAG). There is a growing disconnect between the demands of the digital business environment and the capacity of traditional auditing techniques to meet these demands (Usul & Alpay, 2024). This gap emerges in several forms. There is a gap of scale, as manual audit procedures can no longer keep up with the sheer volume and velocity of digital transactions being processed across global systems (Celestin & Vanitha, 2019; Al-Ateeq et al., 2022). There is also a gap of perception, where even the most skilled human auditors may overlook complex misstatements or subtle anomalies hidden within massive datasets (Celestin & Vanitha, 2019; Rahman et al., 2021). In the same vein, there is a gap of time, as traditional audits provide assurance based on past events — a snapshot of financial health at a particular moment, while stakeholders nowadays seek real-time insights into financial performance, emerging risks and financial integrity.

As financial reporting has therefore had to be more timely and transparent, therefore audit and assurance services are expected to adapt and likewise, evolve (Sewpersadh, 2025). However, the traditional audit model, when used in its basic form, struggles to meet the evolving expectations of these stakeholders (Usul & Alpay, 2024). Consequently, the auditing profession is at a critical point, requiring a modification of its existing methodologies to remain in tune with the digital transformation influencing other areas of business (Sewpersadh, 2025).

Although there is a growing body of literature exploring the relationship between AI and auditing, most of it remains conceptual, focusing on the technological advantages or potential of AI as it relates to auditing. That is, these articles focus on bridging the audit expectation gap by incorporating AI and other digital technologies in the audit process (Almufadda & Almezeini, 2021; Fotoh & Lorentzon, 2023; Mwachikoka, 2024). These studies focussed on how AI can automate audit tasks, detect anomalies, or enhance analytical precision. The practical aspect of examining how these technologies can be integrated to modify the traditional auditing procedures remains underexplored. The gap, which this study seeks to address is the ATAG, which is understanding how AI can complement traditional auditing techniques in the current real-world settings. This gap is significant because discussions around AI-assisted audit has to shift from abstract theorization to applied understanding. This article seeks to move the debate from “what AI can do” to “how AI can improve” the established audit framework to improve audit quality.

The aim of this article is to demonstrate how AI-assisted audit techniques could assist in bridging the audit technology assurance gap. The article will focus on practical terms, how transitioning from traditional auditing techniques to AI-assisted audit techniques can enhance audit quality by increasing efficiency, accuracy, and depth of analysis across the audit process. This discussion begins with a review of relevant theories to establish a strong theoretical background for understanding the subject matter. This study has a theoretical foundation on the Technology Acceptance Model (TAM), Sociotechnical Systems Theory (SST), and the Adaptive Structuration Theory (AST). A review of these theories should guide our understanding of how AI could be integrated to traditional audit techniques for improved audit quality in the digital era. It will then explore the audit process from a practical point of view to identify how it can be enhanced by AI integration. Subsequently, the article will examine the challenges of AI adoption in auditing. Thereafter, the article will conclude and propose recommendations towards having improved audit quality through AI assisted audit techniques.