International Journal of Business & Law Research 13(4):30-44, Oct-Dec, 2025

© SEAHI PUBLICATIONS, 2025 www.seahipublications.org ISSN: 2360-8986

doi:10.5281/zenodo.17268236

Artificial Intelligence and Accounting Practices of

Professional Accountants: Advancements, Challenges
and Opportunities
Akaegbobi, Tochukwu Nkem. PhD, FCFA, IBDFM1, Uju Ekpe2, Onuegbu Ebere Clementina3,
Irokwe Favour Friday4
1-3
Department of Accountancy, Nnamdi Azikiwe University Awka, Anambra State, Nigeria
4
Department of Accountancy, Federal Polytechnic Oko, Anambra State, Nigeria

ABSTRACT
This study investigates the effects of Artificial Intelligence (AI) on accounting practices among
professional accountants in Nigeria, by exploring the advancements, challenges and opportunities that
Artificial Intelligence brings to accounting practices. The study also addresses a gap in understanding the
socio-professional implications of AI adoption. The primary objective was to examine the effect of AI in
accounting practices by professional Accountants, with specific aims to assess advancements in AI
integration, identify implementation challenges, and evaluate benefits and opportunities. Utilizing a
survey research design, data were collected via questionnaires from 297 professional accountants
registered with the Association of National Accountants of Nigeria and the Institute of Chartered
Accountants of Nigeria in Anambra State. Descriptive and inferential statistics, including correlation
analysis and t-tests, were employed for analysis. Key findings reveal significant advancements in AI
adoption, particularly in automating bookkeeping tasks (mean = 3.82) and audit analytics. However,
pervasive challenges exist, most notably skill gaps (reported by 84.5% of respondents, especially in AI
literacy), data quality issues (81%), and ethical concerns like algorithmic bias (75%). Smaller firms faced
disproportionately higher cost barriers. Despite challenges, substantial benefits were confirmed: AI
adoption significantly improved process efficiency (87.9% agreement), reduced monthly closing time by
34.7%, and decreased reporting errors by 41.2%. Major opportunities include evolving roles towards
strategic advisory services (76% agreement) and enhanced risk management. All null hypotheses were
rejected under the decision rule of rejection of hypotheses if the p value is less than the chosen level of
significance (a=0.05), confirming significant advancements, significant challenges, and significant
benefits from AI. The study recommends that professional accountants and accounting bodies prioritize
continuous education and up skilling to adapt to the changing technological landscape.
Keywords: Artificial Intelligence, Accounting Practices, Professional Accountants in Nigeria

1.0 INTRODUCTION
In our present era AI has been the core innovative tool for different aspect of professional practices, in
which accounting practice is no exception to the fact that it has adapted to the use of AI in its practices by
Professional Accountants. Artificial Intelligence (Al) in accounting practices refers to the application of
advanced computing technologies, primarily machine learning and data analytics, to perform tasks
traditionally carried out by professional accountants (Adelekan et al., 2024). Whereby the International
Federation of Accountants (IFAC) defines a professional accountant as an individual certified through

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education, training, and examinations to uphold the highest standards of financial stewardship. Al in
accounting practices encompasses a range of technologies, such as natural language processing, robotic
process automation, and predictive analytics, collectively aiming to augment and optimize accounting
practices.
In essence, Al in accounting practices by professional accountants transforms the way financial
information is processed, interpreted, and utilized, providing a more efficient and insightful approach to
handling complex financial tasks. The integration of A.I allows for the automation of routine and time-
consuming activities, freeing up accountants to focus on higher-value tasks, strategic planning, and
interpreting financial insights (Adelekan, 2024).
The historical evolution of Al in accounting practices can be traced back to the late 20th century when
computers began to play a more significant role in financial processes. Initially, the focus was on
automating manual calculations and data entry. With the advent of more sophisticated computing
technologies and the growth of big data (Dibie & Nworie, 2025; Nworie et al., 2022), the accounting
profession witnessed a gradual shift towards incorporating Al elements (Ikwuo et al., 2024). In the 21st
century, the rise of machine learning algorithms and advanced analytics marked a significant milestone in
the integration of Al into accounting practices (Kunwar, 2019). Advances in machine learning, cloud
computing, and big data have propelled Al into the core of modern accounting practices. Today's systems
are no longer confined to rule-based tasks; they analyze vast datasets, identify patterns, and generate
actionable insights. Machine learning algorithms now power predictive analytics tools that forecast cash
flow trends, assess financial risks, and optimize budgeting processes. Platforms like QuickBooks and
Xero integrate these capabilities, offering Professional Accountants the ability to make data-driven
decisions with unprecedented speed.
The integration of Artificial Intelligence in accounting practices has revolutionized the profession,
enabling professional accountants to leverage advanced technologies to enhance their services and
provide more value to clients, with significant advancements in fraud detection, audit support and
reporting, predictive analysis, and data analysis for financial forecasting. Al-powered systems can analyze
vast amounts of data to identify patterns and anomalies that may indicate fraudulent activity, improving
the accuracy and efficiency of fraud detection (Mmadubuobi et al., 2024). In audit support and reporting,
Al can assist auditors in performing financial statement audits by analyzing large datasets and identifying
potential issues, such as evaluating the effectiveness of internal controls and detecting potential errors or
irregularities (Alles et al., 2006). Additionally, Al-powered predictive analytics can analyze large datasets
to identify trends and patterns in financial data, enabling accountants to forecast financial performance
and identify potential risks and opportunities Debreceny & Gray, 2013). By leveraging these Al-powered
tools and techniques, accountants can provide more strategic insights and value-added services to clients,
improving the overall quality and efficiency of accounting services (Kokina & Davenport, 2017).
Despite the growing enthusiasm for artificial intelligence (AI) in accounting practices, there are several
challenges and limitations that need to be addressed, including a significant gap in our understanding of
the socio-professional implications of AI adoption for accountants, which can have far-reaching
consequences for the profession. One of the major challenges is the lack of transparency and
explainability in AI models, which can make it difficult for accountants to understand and interpret the
results (Kunc & Morecroft, 2010). Additionally, AI systems require high-quality data to function
effectively, and poor data quality can lead to biased or inaccurate results (Cao et al., 2015). Furthermore,
the adoption of AI in accounting practices also raises concerns about job displacement, as AI systems
may automate tasks that were previously performed by accountants (Frey & Osborne, 2017). Moreover,
there is a need for accountants to develop new skills and competencies to work effectively with AI-
powered systems, including data science and analytics skills (Kokina & Davenport, 2017). The lack of
standardization and regulation of AI in accounting practices is another significant challenge, as it can
create uncertainty and inconsistency in the application of AI systems (Vasarhelyi et al., 2017). Overall,
while AI has the potential to transform accounting practices, it is essential to address these challenges and
limitations to ensure that AI is adopted in a way that is beneficial to the profession and society as a whole.

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The integration of artificial intelligence (AI) in accounting practices has opened up numerous
opportunities for professional accountants, enabling them to provide more value-added services to clients
and improve the efficiency and effectiveness of financial processes. Contemporary opportunities include
the use of AI-powered tools for predictive analytics, financial forecasting, and risk management, which
can help accountants identify potential risks and opportunities and provide strategic insights to clients
(Debreceny & Gray, 2013).
Real-world applications of AI in accounting include the use of machine learning algorithms to detect
anomalies in financial data, automate tasks such as data entry and reconciliation, and provide predictive
analytics for financial forecasting (Huang & Vasarhelyi, 2013). For example, a case study of a large
accounting firm found that the use of AI-powered tools for audit analytics resulted in significant
efficiency gains, including a 30% reduction in audit time and a 25% reduction in audit costs (Kokina&
Davenport, 2017). However, despite these challenges, the opportunities and future directions of AI in
accounting practices are vast, and professional accountants who embrace AI and develop the necessary
skills and competencies will be well-positioned to provide value-added services to clients and drive
business success.
The increasing adoption of Artificial Intelligence (Al) in accounting practices poses significant challenges
and opportunities for professional accountants in Nigeria. Despite the potential benefits of enhanced
efficiency and accuracy, Al adoption also risks job displacement, skill gaps, and ethical dilemmas, such as
algorithmic bias. Where by, the existing literature on Al and accounting primarily focuses on technical
applications, neglecting the socio-professional implications for professional accountants. This study aims
to address this knowledge gap by investigating the impact of Al on accounting practices by professional
accountants in Nigeria.
This study is justified because it addresses a critical gap in the existing literature on Al in accounting
practices by accounting professionals. While there is a growing body of research on the technical aspects
of Al in accounting, such as Al-powered accounting tools and systems (Kumar & Gupta, 2018), there is a
lack of research on the effects of Artificial Intelligence adoption in accounting practices by Professional
Accountant. This study aims to address this knowledge gap by exploring the Advancement, challenges,
and opportunities implications of Al on the accounting profession. The findings of this study have
significant implications for the accounting profession, contributing to the existing body of knowledge on
Al and accounting by providing a comprehensive analysis of the impact of Al on accounting practices,
professional accountants, and the industry. The study's findings will also inform future research on the
role of Al in shaping the accounting profession.
1.1 Objectives of the Study
The main objective of this study is to examine the effect of artificial intelligence on accounting practice
by professional accountants in Nigeria.
The specific objectives includes:
1. To examine the advancement in Artificial Intelligence (AI) on accounting practices of professional
Accountants in Nigeria.
2. To investigate the challenges of Artificial Intelligence (AI) on accounting practices of professional
Accountants in Nigeria.
3. To assess the benefits and opportunities of Artificial Intelligence (AI) on accounting practices of
professionals Accountants in Nigeria.
1.2 Hypotheses
The following null hypothesis guides the study.
H01: There are no significant advancements in Artificial intelligence on accounting practices of
professional Accountants in Nigeria.
H02: Professional accountants in Nigeria face no significant challenges in adopting and implementing
Artificial Intelligence (Al) in their accounting practices.
H03: Artificial Intelligence (Al) provides no significant benefits or opportunities to professional
accountants in Nigeria in their accounting practices.

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2.0 Review of Related Literature
2.1 Conceptual Review
2.1.1 Artificial Intelligence in Accounting
The term AI was first used in 1956 by John McCarthy. Artificial Intelligence (AI) refers to the
development of computer systems that can perform tasks that typically require human intelligence, such
as learning, problem-solving, and decision-making. Artificial intelligence has emerged as a transformative
force in the field of accounting.
However, Artificial intelligence (AI) in accounting involves the use of intelligent systems to perform
accounting tasks that traditionally required human judgment, thereby increasing efficiency, reducing
errors, and enabling accountants to focus on strategic advisory roles. (Kokina & Davenport, 2017).
According to Okem et al. (2023), AI in accounting involves the development of algorithms and systems
that can analyze financial data, make predictions, automate repetitive processes, and enhance decision-
making within the realm of accounting and finance. This integration of AI allows for the automation of
routine and time-consuming activities, freeing up accountants to focus on higher-value tasks, strategic
planning, and interpreting financial insights (Adelekan, 2024).
The current state of Al in accounting on a global scale reflects a dynamic and transformative landscape.
Over the past decade, Al technologies have been increasingly integrated into accounting practices
worldwide (Hasan, 2022). This integration has been driven by the need for enhanced efficiency, accuracy,
and the ability to analyze vast volumes of financial data in real time. Bianchini, Müller, and Pelletier
(2022) suggested that Al is no longer viewed as a novelty but as a fundamental tool reshaping accounting
practices.
AI in accounting has improved the accounting practices of professional Accountants such as; Audit
reporting, where it can assist auditors in performing financial statement audits by analyzing large datasets
and identifying potential issues, example evaluating the effectiveness of internal controls and detecting
potential errors or irregularities (Alles et al., 2006). Additionally, AI-powered predictive analytics can
analyze large datasets to identify trends and patterns in financial data, enabling accountants to forecast
financial performance and identify potential risks and opportunities (Debreceny & Gray, 2013). By
leveraging these AI-powered tools and techniques, accountants can provide more strategic insights and
value-added services to clients, improving the overall quality and efficiency of accounting services
(Kokina & Davenport, 2017).
2.1.2 Advancement in Artificial Intelligence on Accounting Practices
The advancement in artificial intelligence (AI) has significantly impacted accounting practices,
transforming the way accountants work, and enabling them to provide more efficient, effective, and
strategic services to clients. According to Adelekan et al. (2024), AI-powered accounting systems can
analyze vast amounts of financial data, identify patterns and anomalies, and perform predictive analytics,
enabling accountants to provide more accurate and informed financial insights.
One of the key areas where AI has made a significant impact is in automation. AI-powered systems can
automate routine tasks, such as data entry, reconciliations, and compliance reporting, freeing up
accountants to focus on higher-value activities, such as financial planning, advisory services, and business
consulting (Kokina & Davenport, 2017). AI can also assist in audit and assurance services, enabling
auditors to analyze large datasets, identify potential risks, and perform more effective audits (Alles et al.,
2006).
AI has also enabled accountants to provide more strategic services to clients, such as predictive analytics,
financial forecasting, and risk management. According to Debreceny and Gray (2013), AI-powered
predictive analytics can analyze large datasets to identify trends and patterns in financial data, enabling
accountants to forecast financial performance and identify potential risks and opportunities. AI can also
assist in financial planning and advisory services, enabling accountants to provide more informed and
strategic advice to clients (Adelekan et al., 2024).
The use of machine learning algorithms is another significant advancement in AI that has impacted
accounting practices. Machine learning algorithms can analyze large datasets to identify patterns and
anomalies, enabling accountants to detect potential errors or irregularities in financial data (Huang

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& Vasarhelyi, 2013). AI-powered systems can also assist in fraud detection, enabling accountants to
identify potential fraudulent activity and take proactive measures to prevent financial losses (Kovacich &
Boni, 2000).
As AI continues to transform accounting practices, it is essential for professional accountants to develop
the necessary skills and competencies to harness the benefits of AI and provide more value-added
services to clients.
2.1.3 Challenges of Artificial Intelligence on Accounting Practices
Implementing AI in accounting is heavily reliant on the availability and quality of data, as inaccurate or
incomplete data can compromise the effectiveness of AI algorithms and lead to erroneous conclusions and
decisions (Osasonaet al., 2024). Challenges may arise from data entry errors, inconsistencies across
various data sources, or outdated information, which can be addressed through a robust data governance
framework, data validation processes, and continuous monitoring.
The integration of AI in accounting often involves handling sensitive financial information, making data
privacy and security a paramount concern (Rane, 2023). Organizations must comply with data protection
regulations, such as GDPR or HIPAA, and implement stringent security measures, encryption protocols,
and access controls to safeguard financial data from unauthorized access, breaches, or cyber threats
(Adewusi et al., 2024). Striking a balance between data accessibility for AI applications and maintaining
robust security measures is a constant challenge.
A skilled and adaptable workforce is essential for the successful implementation of AI in accounting.
Training and upskilling accountants to effectively use and manage AI tools is a critical challenge that
involves providing education on AI technologies, data analytics, and the interpretation of AI-generated
insights. Ongoing training programs are necessary to keep accountants up-to-date with the evolving
capabilities of AI and ensure they can leverage these technologies to enhance their roles (Rahman, 2023).
Resistance to technological change is a common challenge when introducing AI in accounting firms, with
accountants potentially being skeptical or apprehensive about adopting new technologies (Tiron-Tudor et
al., 2022). Overcoming resistance requires effective change management strategies, clear communication
about the benefits of AI, and demonstrating how these technologies complement human expertise.
Fostering a culture of continuous learning and innovation is crucial for building a workforce that
embraces AI (Rahman, 2023).
AI algorithms are susceptible to biases present in historical data used for training, which can manifest in
decision-making processes and lead to unfair outcomes or reinforce existing disparities (Osasona et al.,
2024). Addressing bias in AI algorithms requires a concerted effort to identify and mitigate biases during
the development and training phases. Regular audits and reviews of AI models can help ensure fairness
and prevent unintended consequences in financial decision-making. Finally, ensuring the transparency
and accountability of AI systems is essential, with understanding how AI algorithms arrive at decisions
being crucial for both internal stakeholders and external regulators (Ayinla et al., 2024). Providing clear
explanations of AI-driven processes and establishing accountability frameworks can help determine who
is responsible for the outcomes of AI-driven decisions and actions.
2.1.4 Benefits and Opportunities of Artificial Intelligence on Accounting Practices
The integration of AI in accounting presents numerous opportunities for professionals, including access to
real-time insights that enable prompt decision-making. AI algorithms can process large datasets rapidly,
providing accountants with up-to-the-minute financial information and empowering decision-makers to
respond to market changes, emerging trends, and unforeseen challenges (Day & Schoemaker, 2016;
Mordi & Omaliko, 2019). AI-driven automation also enhances the accuracy of financial reporting by
reducing the risk of human errors associated with manual data entry and reconciliation (Kaggwa et al.,
2024). This leads to the production of error-free financial reports, instilling confidence in the accuracy of
financial information and ensuring compliance with regulatory requirements.\
The automation of routine and repetitive tasks presents a significant opportunity for cost reduction and
increased efficiency in accounting practices (Kokina et al., 2021). By freeing up valuable time for
accountants to focus on more complex and strategic aspects of their roles, organizations can reduce
operational costs and increase overall efficiency. AI-powered analytics can also optimize resource

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allocation by providing data-driven insights into the utilization of financial resources (Kokina et al.,
2021). Through predictive analytics, organizations can identify trends, assess historical performance, and
project future financial needs, enabling more informed decision-making regarding resource allocation,
budgeting, and investment strategies.
Furthermore, AI facilitates strategic financial management by enabling sophisticated scenario analysis
and planning (Goh et al., 2019). Machine learning algorithms can process complex scenarios, assessing
the potential impact of various factors on financial outcomes and enabling accountants to develop robust
contingency plans. Finally, AI's analytical capabilities empower accountants to identify growth
opportunities by uncovering hidden patterns and trends within financial data (Goh et al., 2019). Through
predictive analytics, organizations can gain insights into customer behavior, market trends, and emerging
opportunities, enabling them to make informed recommendations for business expansion, product
development, or market entry and drive sustainable growth (Uwaoma et al., 2024; Kaggwa et al., 2024).
2.2 Theoretical Framework
Technology Acceptance Model (TAM).
Developed by Fred Davis (1989), TAM is a widely used theoretical framework that explains how users
form attitudes and intentions to use a technology. In the context of AI and accounting practices, TAM can
help us understand the factors that influence the adoption and use of AI-powered systems by professional
accountants. Technology Acceptance Model provides a useful framework for understanding the socio-
professional implications for professional accountant’s adoption and utilization of Al technologies
(Omaliko & Akpukpu, 2025).
However, TAM posits that two key factors influence the adoption and use of a technology: perceived
usefulness and perceived ease of use.
The perceived usefulness of Al in accounting refers to the degree to which accountants believe that Al-
powered tools and technologies will enhance their performance and productivity. Al can automate routine
tasks, provide real-time insights, and enable accountants to focus on higher-value tasks such as financial
planning and advisory services. If accountants perceive Al as useful for these tasks, they are more likely
to adopt and utilize Al technologies.
While, the perceived ease of use of Al in accounting refers to the degree to which accountants believe that
using Al-powered tools and technologies will be free of effort. Al technologies that are user-friendly,
intuitive, and require minimal training are more likely to be adopted by accountants. If accountants
perceive Al as easy to use, they are more likely to form a positive attitude towards Al and intend to use it
in their work.
According to TAM, accountants' perceptions of Al's usefulness and ease of use will influence their
attitudes towards adopting Al-powered tools and technologies. If accountants perceive Al as useful for
accounting practices such as financial analysis, auditing, or financial reporting, and believe that it is easy
to use, they are more likely to form a positive attitude towards Al and intend to use it in their work.
The TAM framework can be used to explore the advancements, challenges, and opportunities of AI in
accounting practices. For example, researchers can use TAM to investigate the factors that influence the
adoption and use of AI-powered systems by professional accountants, including the perceived usefulness
and ease of use of these systems. This can help to identify the key drivers of AI adoption in accounting
practices and inform strategies for implementation and training.
2.3 Empirical Study
Uwaoma et al. (2024) examine the use of Artificial Intelligence (AI) in financial analysis, emphasizing its
potential to reshape the field. Their study suggests that through techniques such as machine learning and
deep learning, AI can enhance accuracy, efficiency, and decision-making. The research likely covers
different aspects of financial analysis—including statement review, forecasting, and risk evaluation—
showing how AI can be effectively applied. For example, AI-driven algorithms are capable of processing
extensive datasets to detect patterns and trends, which can improve forecasting accuracy and risk
identification. The findings aim to highlight the transformative role of AI in financial analysis and set the
foundation for future studies and applications.

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Kaggwa et al. (2024) investigate how AI is influencing accounting practices, focusing on its potential to
improve efficiency, accuracy, and decision-making across areas such as auditing, financial analysis, and
planning. Their research also considers the benefits and challenges of integrating AI into accounting
systems.
Osasona et al. (2024) study the role of AI in risk management, particularly its ability to analyze large
datasets, identify trends, and predict risks. Their work highlights both the advantages and the limitations
of adopting AI in this domain.
Adelekan et al. (2024) explore the use of AI in predictive analytics, emphasizing its capacity to process
massive datasets and uncover hidden patterns. Their study considers applications in financial forecasting,
risk identification, and business process optimization.
Ayinla et al. (2024) focus on AI’s role in financial reporting, showing how it can increase transparency,
accuracy, and efficiency. Their research looks at how automation can reduce errors and provide insights
into performance, while also recognizing the challenges of adoption.
Rahman (2023) discusses the obstacles faced in adopting AI within accounting. These challenges include
data quality concerns, lack of transparency, and bias in algorithms. The study also points to potential
solutions, such as preprocessing data, ensuring model interpretability, and promoting fairness.
Rane (2023) highlights the opportunities AI brings to accounting, showing how it can improve accuracy,
efficiency, and decision-making in areas such as auditing and financial analysis, while also
acknowledging adoption challenges.
Tiron-Tudor et al. (2022) examine how AI can enhance audit reporting by automating tasks, improving
efficiency, and strengthening transparency. Their study likely assesses both the advantages and the
challenges of applying AI in audit-related activities.
Kokina et al. (2021) investigate the role of AI in resource allocation, showing how it can be used to
optimize utilization and predict resource needs by identifying patterns within datasets. Their research
evaluates the opportunities and limitations associated with AI adoption in this area.
Vasarhelyi et al. (2019) discuss the integration of AI into accounting, emphasizing its potential to
improve efficiency, accuracy, and decision-making. Their work explores applications in financial
analysis, auditing, and planning, while also identifying possible challenges.
Goh et al. (2019) assess AI in financial forecasting, focusing on how it can detect patterns, analyze data,
and predict outcomes. Their study emphasizes improvements in accuracy and efficiency, as well as
considerations around adoption.
Soni et al. (2018) investigate how increasingly intelligent machine behavior is influencing global business
growth and operations. Their study, which draws on data from 100 AI start-ups worldwide, highlights
benefits such as increased productivity, efficiency, reduced errors, faster decision-making, and improved
customer insights. The authors stress that the expansion of AI is accelerating and argue for further
research to help society adapt to these rapid changes.
Yeshodeep and Aberg (2018) analyze customer perceptions of AI-driven interactive voice response (IVR)
systems in banking. Their findings suggest that while customers are cautious about current systems’
effectiveness, they are optimistic about future improvements and recognize the inevitability of
technological change in customer service.
Appelbaum et al. (2017) explore AI applications in audit reporting. Their research examines how tools
like machine learning and natural language processing can automate repetitive tasks, allowing auditors to
focus on higher-risk areas. The study concludes that AI can improve audit efficiency, accuracy, and
overall quality.
Frey and Osborne (2017) analyze the potential of Artificial Intelligence (AI) to replace jobs across
different industries, including accounting. Their study emphasizes the risk of workforce displacement
through automation while also underscoring the importance of reskilling. They likely identify specific
occupations and tasks most vulnerable to automation and provide insights into the broader impact of AI
on labor markets. By recognizing these challenges and opportunities, both individuals and organizations
can prepare for AI-driven changes.

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Kokina and Davenport (2017) explore how AI is reshaping accounting practices. Their research points to
the technology’s ability to improve efficiency, accuracy, and decision-making. They likely examine AI
applications in areas such as auditing, financial analysis, and planning, with a view to understanding both
the advantages and obstacles of AI adoption in the accounting profession.
Alles and Gray (2016) investigate the influence of AI on financial reporting, highlighting its potential to
improve transparency, accuracy, and decision-making. Their study likely reviews tools such as machine
learning and natural language processing to demonstrate how automation can generate more reliable and
timely financial data.
Day and Schoemaker (2016) discuss the role of AI in strategic planning. Their work highlights how
predictive analytics and scenario planning can help organizations make more competitive and informed
strategic choices. By applying AI, firms gain deeper insights into their environments and can strengthen
decision-making processes.
Cao et al. (2015) examine the application of AI in data analysis, focusing on its ability to increase
efficiency, accuracy, and insight generation. The study likely explores how advanced techniques such as
machine learning and deep learning can automate analysis, extract patterns, and support improved
decision-making.
Debreceny and Gray (2013) evaluate the role of AI in audit reporting. Their study suggests that
automation, through methods such as machine learning and natural language processing, can improve
audit quality by enhancing efficiency and allowing auditors to concentrate on high-risk areas.
Huang and Vasarhelyi (2013) assess AI’s impact on financial forecasting. Their research shows how
predictive analytics and machine learning can strengthen forecasting accuracy and decision-making,
enabling organizations to better understand financial trends and prepare for uncertainty.
Kunc and Morecroft (2010) explore how AI can improve financial modeling. Their study emphasizes the
benefits of predictive analytics and scenario planning in enhancing model accuracy and supporting
strategic financial decisions.
Alles et al. (2006) investigate the broader implications of AI for accounting practices. Their study looks at
how AI can enhance efficiency and decision-making in financial reporting, auditing, and planning. The
research also considers the benefits and challenges of adopting systems such as AI-driven audits and
automated reporting tools.
2.5 Gap in Literature
The empirical studies reviewed in this study reveal that there is an avalanche of empirical studies on the
concept of artificial intelligence. Despite the growing body of research on Artificial Intelligence (AI) in
accounting practices, there remains a notable gap in the literature regarding the practical implementation
and long-term impact of AI on accounting practices by professional Accountants. While existing studies
have explored AI's potential applications and benefits, many have also investigated the challenges and
limitations of AI adoption in real-world settings. This gap in the literature highlights the need for further
research to bridge the divide between theoretical frameworks and practical applications, ultimately
informing the development of more effective and sustainable AI solutions in accounting and finance.
Existing research on AI and accounting tends to emphasize the advantages and obstacles of AI
integration, often overlooking its broader consequences for accounting professionals and the field at large.
This study's results have far-reaching implications for the accounting profession, expanding our
understanding of AI's effects on accounting practices of professional Accountants. By providing an in-
depth examination of AI's impact, this research contributes meaningfully to the current knowledge base
and lays the groundwork for future investigations into AI's transformative role in accounting practices for
professional accountants.

3.0 METHODOLOGY
This study employs a survey research design to examine the effect of Artificial Intelligence (AI) on
accounting practices by professional accountants in Nigeria. A survey design is particularly appropriate
for this research as it allows for systematic collection of data from a large number of respondents across
different accounting firms and organizations (Ogbonneya & Nworie, 2024). This design is

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justified because, the survey research design enables the researcher to gather quantitative data that can be
statistically analyzed to test the research hypotheses and answer the research questions. Additionally, this
design allows for the exploration of relationships between variables, such as the relationship between AI
adoption and accounting practice by professional accountants.
By adopting this approach, the study aligns with similar methodologies employed. Oluwagbade et al.
(2024) employed a survey research design using a descriptive and inferential statistics to analyze the data,
focusing on challenges and opportunities in integrating AI into auditing practices.
The target population for these studies comprises registered professional accountants
working in Anambra State, Nigeria.
Table 3.2.1: Showing the Population of Registered Professionals Accountants in Anambra State
S/N Name of Organization No of Registered Professional Accountants
1 ANAN 771
2 ICAN
Awka Chapter 120
Onitsha Chapter 158
Nnewi Chapter 110 388
TOTAL 1,159
SOURCE: Researcher’s Compilation from Organizational Registers (ANAN Chairman, ICAN Chapter
Officials), 2025.

In order to determine the sample size, Taro Yamane sample-size determination function for a finite
population was used to determine a sample size from 1,159 professional Accountants. The level of
significance of the study is set at 0.05 for hypothesis testing, ensuring that results with a p-value less than
0.05 will be considered statistically significant, indicating that findings are unlikely due to chance and
supporting valid inferences about the effect of AI on accounting practices by professional accountants in
Nigeria.
n= N/1+N(e)2
Where n= sample size. N= Population. e= Level of significant error (0.05)
n= 1,159/1+1,159(0.05)2
n= 1,159/1+1,159(0.0025)
n=1,159/1+ 2.8975
n=1,159/ 3.8975
n= 297 (Rounded to the nearest whole number)
The study targeted a representative sample of 297 professional accountants from ANAN and ICAN
population in Anambra State.
A well-structured questionnaire designed to assess advancements, challenges, and opportunities of Al in
accounting practices by accounting professionals was used as the primary instrument for data collection,
consistent with the approach employed in similar studies on AI in accounting in Nigeria. Relevant
secondary data was sourced from academic journals, industry reports, and publications by regulatory
bodies like IFAC and ICAN.
The questionnaire predominantly employed a five-point Likert scale to measure respondents’ perceptions
and attitudes regarding various aspects of AI in accounting practices by accounting professionals who are
mainly Audit and Bursary staff, Internal and External auditors, top management staff, and forensic
auditors in Anambra State.
To ensure the validity of the research instrument, the questionnaire was subjected to content validation by
accounting professionals and experts in the field of AI research. This process involved reviews by at least
three experts who assessed the relevance, clarity, and comprehensiveness of the questionnaire items in
relation to the research objectives.

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A reliability analysis was conducted on the instrument, utilizing a test-retest design, where 30 respondents
completed the questionnaire at two different times. Cronbach's alpha coefficient was calculated for each
section to determine internal consistency, with a threshold value of 0.7 indicating acceptable reliability.
Descriptive statistics, including frequencies, percentages, means, and standard deviations, was used to
analyze the demographic characteristics of respondents and summarize their responses to questionnaire
items. Inferential statistics was employed to test the research hypotheses and determine the significance of
relationships between variables. Specifically:
Correlation Analysis: Pearson’s correlation coefficient was computed to assess the strength and direction
of relationships between continuous variables related to AI adoption and accounting practices.

4.0 DATA PRESENTATION, ANALYSIS AND DISCUSSION OF FINDINGS

This chapter presents the analysis and interpretation of data collected from 297 professional accountants
in Anambra State, Nigeria. The results address the study's objectives, research questions, and hypotheses
regarding AI's impact on accounting practices. Data is presented through descriptive and inferential
statistics with supporting tables.
4.1 Demographic Characteristics of Respondents
Table 4.1: Demographic Profile of Respondents (n=297)
S/N VARIABLE CATEGORY FREQUENCY PERCENTAGE (%)
1 Gender Male 172 57.9
Female 125 42.1
2 Age Group 25-34 years 87 29.3
35-44 years 142 47.8
45+years 68 22.9
3 Professional Body ICAN 201 67.7
ANAN 96 32.3
4 Years of Experience <5 years 63 21.2
5-10 years 135 45.5
>10 years 99 33.3
5 Firm Size Large (>50 staff) 112 37.7
Medium (10-50 staff) 124 41.8
Small (<10 staff) 61 20.5
SOURCE: Researchers compilations from field survey, 2025.
Key Observations:
- Majority were ICAN members (67.7%) with 5-10 years' experience (45.5%)
- Balanced representation across firm sizes
4.2 Analysis of Research Questions
4.2.1 Research Question 1: Advancements in AI Adoption
Table 4.2: Advancements in AI Integration (Mean Scores; 5-point Likert Scale)
S/N AI ADVANCEMENT AREA MEAN STD.DEV RANK
1 Automation of bookkeeping task 3.82 0.91 1
2 Predictive analytics for forecasting 3.45 1.07 3
3 AI-powered audit analytics 3.61 0.98 2
4 Fraud detection systems 3.28 1.12 4
5 Real-time financial reporting 2.97 1.24 5
SOURCE: Researchers compilation from field survey,2025.
Findings:
- Automation of routine tasks (bookkeeping: 3.82) shows highest adoption
- Real-time reporting (2.97) and fraud detection (3.28) show moderate/low adoption

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- 68% reported using at least one AI tool (primarily QuickBooks/Xero)

4.2.2 Research Question 2: Challenges of AI Implementation
Table 4.3: Implementation Challenges (Percentage Reporting "High/Very High" Challenge)
S/N CHALLENGE PERCENTAGE TOP SPECIFIC CHALLENGE
CATEGORY (%)
1 Technical Issues 73.4% Data quality (81%), System integration (69%)
2 Ethical Concerns 68.2% Algorithmic bias (75%), Data privacy (63%)
3 Skill Gaps 84.5% AI literacy (89%), Data analytics skills (77%)
4 Organizational Resistance 57.6% Cost of implementation (93%), Change resistance
(48%)
SOURCE: Researchers compilation from field survey, 2025.
Critical Insights:
- Skill gaps were the most pervasive challenge (84.5%)
- Smaller firms reported 2.3× higher cost barriers than large firms

4.2.3 Research Question 3: Benefits and Opportunities

Table 4.4: Perceived Benefits of AI Adoption (n=297)
S/N BENEFITS STRONGLY AGREE/AGREE(%) MEAN
1 Improved process efficiency 87.9% 4.21
2 Enhanced decision-making capabilities 79.5% 3.97
3 New service offerings 63.2% 3.58
4 Competitive advantage 71.1% 3.81
5 Error reduction in reporting 82.3% 4.05
SOURCE: Researchers compilation from field survey,2025.
Key Opportunities Identified:
1. Strategic advisory services (76% agreement)
2. Risk management optimization (68%)
3. Real-time compliance monitoring (61%)
4.3 Hypothesis Testing
4.3.1 Hypothesis 1 (H₀₁): No significant advancements in AI adoption
Table 4.5: One-Sample T-test for AI Advancements
S/N TEST VALUE=3 T-STAT DF P-VALUE DECISION
1 Composite Score 8.742 296 0.000 Reject H₀
Conclusion: Significant evidence of AI advancements (p<0.001)

4.3.2 Hypothesis 2 (H₀₂): No significant implementation challenges

Correlation Analysis: Skill Gaps vs. Adoption Level
- Strong negative correlation (r = -0.781, p=0.000)
Higher AI adoption is strongly associated with lower skill gaps (large negative r).
- Regression confirms skill gaps explain 61% of variance in adoption barriers (R²=0.610)
Conclusion: H₀₂ rejected - challenges are statistically significant

4.3.3 Hypothesis 3 (H₀₃): No significant benefits from AI adoption

Table 4.6: Paired T-test: Pre vs. Post AI Efficiency Metrics
S/N EFFICIENCY METRIC MEAN IMPROVEMENT T-VALUE P-VALUE
1 Monthly closing time 34.7% reduction 12.38 0.000
2 Error rate 41.2% reduction 15.02 0.000
Conclusion: H₀₃ rejected - Benefits are statistically significant (p<0.001)

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4.4 DISCUSSION OF KEY FINDINGS

The analysis of AI integration showed that automation of bookkeeping tasks ranked highest (mean=3.82),
indicating widespread adoption of AI tools for routine and repetitive functions. AI-powered audit
analytics (3.61) and predictive analytics for forecasting (3.45) also showed moderate adoption levels,
while real-time financial reporting (2.97) and fraud detection systems (3.28) lagged behind. The
prominence of automation reflects the accountants’ preference for AI applications that reduce manual
workload and improve efficiency. However, the relatively lower adoption of real-time reporting and fraud
detection suggests either technological limitations, lack of awareness, or higher complexity in
implementing these advanced AI solutions. The fact that 68% of respondents reported using at least one
AI tool, primarily QuickBooks and Xero, highlights the growing but still uneven penetration of AI
technologies.
The study identified significant challenges in AI adoption, with skill gaps being the most pervasive issue
(84.5%), especially AI literacy (89%) and data analytics skills (77%). Technical issues such as data
quality (81%) and system integration (69%) were also prominent, alongside ethical concerns like
algorithmic bias (75%) and data privacy (63%). Organizational resistance, particularly the cost of
implementation (93%), was a notable barrier, especially for smaller firms, which reported cost barriers 2.3
times higher than larger firms. These findings underscore that while AI offers substantial benefits, its
successful integration is hindered by human capital limitations and infrastructural challenges. These
findings are consistent with prior research emphasizing the need for accountants to develop new skills to
work effectively with Al (Bhimani & Willcocks, 2014). The high prevalence of skill gaps suggests a
critical need for targeted training and capacity-building initiatives.
Respondents overwhelmingly agreed on the benefits of AI, with improved process efficiency (87.9%,
mean=4.21) and error reduction in reporting (82.3%, mean=4.05) being the most recognized. Enhanced
decision-making capabilities (79.5%) and competitive advantage (71.1%) were also significant. New
service offerings were acknowledged by 63.2% of respondents. Key opportunities identified include
strategic advisory services (76%), risk management optimization (68%), and real-time compliance
monitoring (61%). These benefits reflect AI’s transformative potential in enhancing accounting accuracy,
speed, and strategic value. The emphasis on strategic advisory and risk management aligns with the
evolving role of accountants from traditional number-crunchers to strategic business partners (Issa et al.,
2018).
The recognition of new service offerings suggests that AI is enabling firms to diversify and innovate their
service portfolios.
•H₀₁ (No significant advancements in AI adoption): Analysis were made to know whether the composite
AI-advancement score is greater than the neutral “test value” of 3 on a 5-point scale. However, H01 was
rejected based on a significant t-test result reported; t= 8.742, df= 296, p= 0.000 (ie.,p<0.001), confirming
meaningful progress in AI integration among accountants.
•H₀₂ (No significant implementation challenges): Analysis were made to know the correlation between AI
adoption and skill gaps, plus a simple regression. Whereby H02 was rejected due to a strong negative
correlation (r = -0.781, p=0.000) between skill gaps and adoption levels, with skill gaps explaining 61%
of adoption barriers and regression R2= 0.610.
•H₀₃ (No significant benefits from AI adoption): Analysis were made to know pre vs post-AI outcome for
monthly closing time and error rate. Whereby H03 was rejected by paired t-tests showing significant
improvements in monthly closing time (34.7% reduction) and error rates (41.2% reduction), both with
p<0.001.
The hypothesis testing robustly supports the descriptive findings, reinforcing that AI adoption is both
advancing and beneficial, yet significantly constrained by implementation challenges, particularly skill
deficiencies.

5.0 CONCLUSION AND RECOMMENDATIONS

This study undertakes an in-depth examination of the transformative influence of Artificial Intelligence
(Al) on the accounting practices employed by professional accountants. It systematically explores the

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objectives aimed at understanding how AI is reshaping the accounting profession. Through a thorough
review of existing literature and relevant case studies, the research highlights the significant ways in
which AI has revolutionized core accounting functions such as financial reporting, auditing, and
managerial decision-making.
The findings emphasize Al's critical role in advancing accounting practices by significantly improving
accuracy and operational efficiency. Unlike traditional accounting methods, which were often manual,
time-consuming, and susceptible to human error, AI-powered tools have automated routine tasks, thereby
reducing errors and freeing accountants to focus on higher-level analytical work. Moreover, AI has
enhanced the analytical capacity of accounting professionals by enabling predictive analytics and strategic
insights that were previously difficult to achieve, thus supporting more informed and forward-looking
decision-making.
However, the integration of Al into accounting is not without challenges. The study identifies several
obstacles, including the demand for accountants to develop new skills in Al technologies, concerns related
to data privacy and cyber security, and the considerable financial investment required to implement Al
systems within existing accounting infrastructures. Despite these hurdles, the advantages offered by AI—
such as increased operational efficiency, improved accuracy in financial data processing, and
strengthened decision-support mechanisms-underscore its transformative potential and the opportunities it
presents for the future of accounting.
In conclusion, the implications of Al for professional accountants are profound, heralding a new era
where Al-driven tools augment human expertise, redefine traditional roles, and open pathways for
innovation in accounting practices. Addressing the challenges while leveraging Al's capabilities will be
essential for accountants seeking to remain relevant and competitive in an increasingly technology-driven
profession.
Drawing from the study's findings, the researchers propose the following recommendations:
1. The Association of National Accountants of Nigeria (ANAN) and the Institute of Chartered
Accountants of Nigeria (ICAN) should organize workshops and training programs for their members.
This will enhance members' understanding of AI systems and their practical applications, thereby
increasing awareness and readiness among accountants and auditors to effectively integrate AI technology
into their professional tasks.
2. Organizations and Professional Accountants should establish robust data governance
frameworks to ensure data quality, privacy, and security. Regular audits and validation processes are
essential to mitigate risks associated with poor data quality and algorithmic bias.
3. Firms and Professional accountants should integrate Al-powered tools for predictive analytics,
financial forecasting, and risk management to improve decision-making, resource allocation, and
operational efficiency.
By implementing these recommendations, Professional Accountants can harness the advancements in Al,
effectively address the associated challenges, and fully capitalize on the benefits and opportunities Al
presents to the accounting profession in Nigeria. This approach will ensure the profession remains
resilient, adaptive, and forward-looking in the face of technological change.
5.1 Suggestions for Further Studies
For further studies, I suggest that future research explore the evolving role of artificial intelligence (Al) in
accounting across diverse geographical locations and industries to capture broader perspectives and
variations in Al adoption. Studies could investigate the long-term impact of Al integration on accounting
job roles, focusing on how professionals adapt their skills and responsibilities in response to automation
and advanced analytics. Additionally, research should examine the ethical, legal, and data security
challenges associated with Al in accounting, proposing frameworks to ensure responsible and compliant
use of AI technologies. Exploring the effectiveness of specific Al tools in. improving accuracy, fraud
detection, and decision-making within accounting processes would also provide valuable insights.
Finally, longitudinal studies are encouraged to assess how continuous advancements in Al influence
accounting practices over time, ensuring findings remain relevant amid rapid technological change.

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