ARTIFICIAL INTELLIGENCE IN INTERNAL AUDITING-PROSPECTS AND CHALLENGES OF LARGE CONGLOMERATE COMPANIES. A STUDY OF DANGOTE PLC.
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ARTIFICIAL INTELLIGENCE IN INTERNAL AUDITING-PROSPECTS AND CHALLENGES OF LARGE CONGLOMERATE COMPANIES. A STUDY OF DANGOTE PLC.
Abstract
The rapid advancement of artificial intelligence (AI) has transformed business operations globally, presenting significant opportunities and challenges for internal auditing practices, particularly in large conglomerate companies. This study investigates the prospects and challenges of implementing AI in internal auditing within Dangote Plc, Nigeria’s largest industrial conglomerate. Using a mixed-methods approach, data were collected through questionnaires administered to internal audit staff and management, complemented by interviews with key stakeholders. The study examines how AI adoption can enhance audit efficiency, accuracy, fraud detection, and decision-making processes, while also addressing barriers such as high implementation costs, technical complexity, and staff resistance. Findings indicate that AI integration significantly improves audit quality, risk assessment, and real-time monitoring of financial and operational processes, yet challenges in infrastructure, data management, and human expertise persist. The study concludes that strategic investment in AI technologies, coupled with staff training and robust governance frameworks, can optimize internal auditing functions in large conglomerates. The research contributes to the growing body of knowledge on AI in auditing and provides practical insights for corporations seeking to leverage emerging technologies for effective internal control and corporate governance.
Keywords: Artificial Intelligence, Internal Auditing, Large Conglomerates, Dangote Plc, Audit Efficiency, Challenges, Prospects.
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Artificial Intelligence (AI) has emerged as a transformative technology in modern business operations, influencing areas such as decision-making, risk management, and audit processes (Brynjolfsson & McAfee, 2017). In particular, internal auditing—a critical function that evaluates risk management, control systems, and governance practices—has increasingly incorporated AI technologies to enhance efficiency and effectiveness (Kokina & Davenport, 2017). AI applications in internal auditing include automated risk assessment, anomaly detection, predictive analytics, and real-time monitoring of financial transactions (Alles, 2015).
Large conglomerates, such as Dangote PLC, face complex operational structures spanning multiple industries, including cement, sugar, salt, and flour production. This complexity necessitates robust internal audit functions capable of ensuring compliance, detecting fraud, and optimizing operational efficiency (Dangote Annual Report, 2022). Traditional auditing techniques, often manual and time-consuming, may not adequately meet these demands. Consequently, the integration of AI into internal auditing presents both significant prospects and challenges for large conglomerates seeking to strengthen governance and improve performance (Yoon, Hoogduin & Zhang, 2015).
While AI promises enhanced efficiency, predictive capabilities, and data-driven decision-making, it also introduces challenges such as high implementation costs, the need for skilled personnel, ethical concerns, and data privacy issues (Cao, Chychyla & Stewart, 2015). Understanding these prospects and challenges is crucial for firms like Dangote PLC, where effective internal auditing supports strategic goals, regulatory compliance, and stakeholder confidence (IFAC, 2019).
1.2 Statement of the Problem
Despite the potential of AI to revolutionize internal auditing, its adoption in large conglomerates remains inconsistent. Many internal audit departments still rely on manual processes, resulting in delays, inefficiencies, and potential errors (Kokina & Davenport, 2017). Additionally, large firms face challenges in integrating AI due to the complexity of operations, lack of technical expertise, cost implications, and resistance to change (Alles, 2015; Cao et al., 2015).
In the context of Dangote PLC, there is limited empirical evidence on how AI adoption affects internal auditing performance, risk management effectiveness, and overall corporate governance. Furthermore, the challenges associated with implementation, including technical, ethical, and operational issues, remain underexplored. This gap hampers the ability of management and policymakers to make informed decisions regarding AI investment in internal audit functions (IFAC, 2019).
Thus, it becomes necessary to investigate the prospects and challenges of AI in internal auditing within large conglomerate companies like Dangote PLC to inform best practices and strategic adoption.
1.3 Objectives of the Study
The main objective of this study is to examine the prospects and challenges of integrating AI into internal auditing within large conglomerate companies, using Dangote PLC as a case study.
The specific objectives include:
To identify the prospects of AI integration in internal auditing functions of Dangote PLC.
To examine the challenges associated with AI adoption in internal auditing within Dangote PLC.
To assess the impact of AI on the efficiency and effectiveness of internal auditing in large conglomerate companies.
To provide recommendations for enhancing AI adoption in internal auditing processes.
1.4 Research Questions
The study seeks to answer the following research questions:
What are the key prospects of using AI in internal auditing within Dangote PLC?
What are the main challenges hindering the adoption of AI in internal auditing in Dangote PLC?
How does AI influence the efficiency and effectiveness of internal auditing in large conglomerate companies?
What strategies can be employed to enhance the adoption of AI in internal auditing functions?
1.5 Research Hypotheses
The study will test the following hypotheses:
H1: AI integration in internal auditing significantly improves auditing efficiency in large conglomerate companies.
H2: The challenges associated with AI adoption significantly hinder its effective use in internal auditing.
1.6 Significance of the Study
This study is significant for several stakeholders:
For Management: It provides insights into the strategic benefits of AI adoption in internal auditing, including efficiency, accuracy, and risk mitigation (Alles, 2015).
For Internal Auditors: It highlights emerging trends, tools, and skills required to leverage AI in audit practices (Kokina & Davenport, 2017).
For Policymakers and Regulators: The study offers guidance on developing policies, frameworks, and standards for AI adoption in corporate governance (IFAC, 2019).
For Academics and Researchers: It contributes to the literature on technology adoption in auditing, particularly in the context of large conglomerates in emerging economies.
1.7 Scope of the Study
The study focuses on Dangote PLC, one of Nigeria’s largest conglomerates, to examine AI adoption in internal auditing. Geographically, the study is limited to the company’s head office and key operational sites where internal audit functions are actively performed. The study covers AI applications in auditing processes including risk assessment, anomaly detection, financial reporting, and internal control monitoring. The research period focuses on recent developments over the last five years when AI integration in auditing became more pronounced.
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