Integrating artificial intelligence into accounting systems: a qualitative study on user experiences and challenges

TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 23, No. 3, June 2025, pp. 664~672
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v23i3.26409  664
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Integrating artificial intelligence into accounting systems: a
qualitative study on user experiences and challenges
Andhika1
, Lawrence Adi Supriyono2
1
Department of Computer Science, Faculty of Computer, Cakrawala University, Jakarta, Indonesia
2
Department of Software Engineering, Faculty of Information Technology, University of Jakarta International, Jakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Jun 16, 2024
Revised Feb 19,2025
Accepted Mar 12, 2025
This research explores the integration of artificial intelligence (AI) in
accounting systems, focusing on user experiences and challenges faced by
accountants and financial professionals. Using qualitative methods, in-depth
interviews with diverse accounting professionals reveal key themes:
optimism mixed with skepticism about AI’s potential, concerns over
algorithm transparency, and trust issues due to the “black box” nature of AI
systems. Participants highlight inadequate training programs, which hinder
effective AI use and fuel resistance to adoption. The study also discusses the
impact of AI on job roles, emphasizing a shift towards strategic thinking and
advisory functions while routine tasks are automated. Implementation
challenges include system compatibility, data integration issues, and
significant resource investments, compounded by organizational resistance
and lack of executive support. The findings stress the need for transparent AI
algorithms, comprehensive training programs, and managed job role
transitions to maximize AI benefits. This research provides insights into
real-world user experiences, offering a roadmap for organizations to support
effective AI integration in accounting, leading to improved performance, job
satisfaction, and acceptance of AI technologies.
Keywords:
Algorithm transparency
Artificial intelligence
Challenges in accounting
systems
Job role transitions
User experiences
This is an open access article under the CC BY-SA license.
Corresponding Author:
Andhika
Department of Computer Science, Faculty of Computer, Cakrawala University
Cakrawala University, South Jakarta, Daerah Khusus Ibukota Jakarta 12510, Indonesia
Email: andhika@cakrawala.ac.id
1. INTRODUCTION
A more intelligent and independent instrumentation system based on clever techniques like artificial
neural networks, fuzzy logic, and genetic algorithms was brought about by the involvement of computer
technology [1]-[4]. In an age defined by rapid technological advancements and digital transformation,
artificial intelligence (AI) stands at the forefront of innovation, particularly within accounting. With its
promise to revolutionise traditional accounting practices, AI has captured the imagination of practitioners,
scholars, and industry leaders alike [5], [6]. However, amidst the excitement surrounding its potential, there
exists a rich tapestry of experiences and challenges encountered by accounting professionals as they navigate
the integration of AI into their daily workflows [7]. This research embarks on a journey beyond the surface-
level examination of AI’s technical capabilities within accounting systems [8]-[14]. It seeks to delve into the
intricate nuances of human interaction with AI technology, uncovering the personal stories and perspectives
often overlooked in discussions centered solely on functionality and performance metrics [15], [16]. Through
qualitative inquiry and deep engagement with practitioners, this study aims to illuminate the multifaceted
landscape of AI adoption in accounting, offering insights far beyond quantitative analysis.

TELKOMNIKA Telecommun Comput El Control 
Integrating artificial intelligence into accounting systems: a qualitative study on user … (Andika)
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At its core, this research is driven by a desire to understand not only the “what” of AI
implementation in accounting but also the “how” and “why” behind the experiences of those directly
involved in its use. By exploring the lived experiences, perceptions, and challenges of accounting
professionals, we hope to gain a deeper appreciation for the complexities inherent in the integration of AI
into professional practice [17]-[19]. Moreover, we seek to shed light on the human dimensions of AI
adoption, including issues of trust, transparency, and ethical considerations, which are often overshadowed
by discussions of technological prowess [20]-[26]. Through rigorous qualitative inquiry, this study aims to
capture the essence of AI adoption in accounting- the triumphs, the setbacks, and the transformative potential
that lies ahead. By amplifying the voices of practitioners and illuminating their stories, we endeavor to
contribute to a more holistic understanding of AI’s role in shaping the future of accounting [27]. Furthermore,
we hope to provide valuable insights for practitioners, policymakers, and researchers by contextualizing AI
adoption within the broader technological innovation landscape and organisational change [28].
In the following sections, we will delve into the intricacies of AI adoption in accounting, exploring
key themes such as user experiences, challenges, and implications for professional practice [29]. Through a
comprehensive examination of qualitative data and rich narrative analysis, we aim to offer a nuanced
perspective on the human side of AI integration. This perspective extends beyond technical functionality to
encompass the broader socio-cultural and organisational dynamics [30]. By elucidating the lived experiences
of accounting professionals and contextualizing them within the wider discourse on technological innovation,
this research aims to contribute to a more nuanced understanding of AI’s impact on the accounting
profession. Ultimately, our goal is not only to inform academic debates but also to provide practical insights
that can inform organisational strategies, enhance professional practice, and shape the future trajectory of the
accounting profession in an increasingly AI-driven world.
2. METHOD
This study employed a structured methodological approach with a qualitative research design to
explore users’ experiences and challenges with AI integration in accounting systems [31]. The research
aimed to gain in-depth insights into user interactions with AI, capturing the complexities and nuances of their
experiences. A qualitative approach was deemed appropriate as it comprehensively explains participants’
perspectives and the contextual factors influencing their experiences. Figure 1 illustrates the comprehensive
research method framework consisting of eight sequential phases: initial preparation, participant recruitment,
data collection, data recording, data analysis, results interpretation, validation and reliability, and publication.
Each phase was designed to ensure methodological rigor and capture the qualitative data needed to address
the research objectives.
Figure 1. Method research
Initial Preparation
Participant Recruitment
Data Collection
Data Recording
Data Analysis
Results Interpretation
Validation and Reliability
Publication

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Integrating AI into accounting systems significantly shifts how financial data is processed and
analyzed. This research explores the intricate experiences and challenges users face during this integration.
The following methodology outlines the comprehensive steps undertaken to gather and analyze qualitative
data from industry professionals.
2.1. Initial preparation
The initial preparation phase is crucial for establishing a strong research foundation. By establishing
clear objectives, designing comprehensive interview guidelines, and securing ethical approval, the study
ensures a structured and ethical approach to data collection and analysis. This preparation phase guarantees
that the subsequent steps of the research are aligned with the overarching goals and moral standards,
providing a robust framework for exploring user experiences and challenges in integrating AI into accounting
systems [32].
− Setting research objectives: define the objectives the research aims to achieve clearly and precisely.
This includes understanding how AI is integrated into accounting systems and identifying users’
experiences and challenges.
− Designing interview guidelines: develop semi-structured interview guides with key questions and
prompts to comprehensively explore the research objectives.
− Obtaining ethical approval: secure approval from relevant ethics committees to ensure that the study
adheres to ethical standards, particularly regarding informed consent and confidentiality.
2.2. Participant recruitment
In participant recruitment, employing the right approach is crucial to ensure the involvement of
individuals with relevant experience in AI systems within the accounting domain. Utilizing purposive
sampling allows for selecting participants capable of providing in-depth insights into the research questions.
Predefined inclusion criteria, such as a minimum of 3 years of experience in accounting or finance and active
use of AI in their work context, serve as the basis for selecting suitable participants.
− Selecting participants purposively: use purposive sampling to select participants with relevant
experience with AI in accounting systems. This method ensures that the sample includes individuals
who can provide rich, detailed insights into the research questions.
− Based on predefined inclusion criteria: establish inclusion criteria such as a minimum of 3 years of
experience in accounting or finance and active use of AI in their work context.
2.3. Data collection
In the data collection phase, in-depth interviews will be conducted with selected participants to
delve into their experiences and challenges [33] thoroughly. These interviews will adhere to semi-structured
interview guides crafted during the initial preparation, ensuring consistency across interviews while
permitting response flexibility.
− Conducting one-on-one in-depth interviews: perform detailed, one-on-one interviews with selected
participants. These interviews should be conducted to allow participants to share their experiences and
challenges in depth.
− Using semi-structured interview guides: utilize the semi-structured interview guides developed in the
initial preparation phase to ensure interview consistency while allowing for response flexibility.
2.4. Data recording
In the data recording phase, all interviews will be meticulously recorded to facilitate accurate
transcription and analysis, with prior consent from participants obtained. Transcribing the interviews
verbatim will ensure the preservation of data integrity. Additionally, field notes will be taken during and after
interviews to capture non-verbal cues and contextual details that may not be evident in the transcripts.
− Recording interviews: record all interviews to ensure the data can be accurately transcribed and
analyzed. Ensure participants’ consent is obtained for recording.
− Ensuring transcription accuracy: transcribe the recorded interviews verbatim to maintain data integrity.
− Supplementing with field notes: take field notes during and after interviews to capture non-verbal cues
and contextual information that might not be evident in the transcripts.
2.5. Data analysis
During the data analysis phase, the thematic analysis will discern and scrutinize patterns, themes,
and sub-themes within the transcribed data. This process entails coding the data and organising these codes

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into broader thematic categories. Through iterative refinement and categorization of codes, the objective is to
unearth key themes that directly address the research objectives.
− Thematic analysis of transcripts: use thematic analysis to identify and analyze patterns, themes, and
sub-themes within the transcribed data. This involves coding the data and grouping codes into broader
themes.
− Identifying patterns, themes, and sub-themes: continuously refine and categorize codes to uncover key
themes that address the research objectives.
2.6. Results interpretation
In interpreting the findings, the identified themes will be thoroughly examined to grasp their broader
implications, linking them to the research objectives and existing literature. This process aims to provide
insight into the significance of the data. Subsequently, a comprehensive research report will be compiled,
presenting the research findings, including results from thematic analysis, participant quotations, and
interpretations, to provide a cohesive understanding of the study’s outcomes.
− Interpreting findings: interpret the identified themes to understand the broader implications of the data.
This involves linking findings to the research objectives and existing literature.
− Compiling the research report: compile a comprehensive report that presents the research findings,
including thematic analysis results, participant quotes, and interpretations.
2.7. Validation and reliability
To ensure the validity and reliability of the findings, data triangulation will be employed, comparing
interview data with field notes and existing literature to validate the conclusions drawn. Additionally,
reliability will be upheld through meticulous analysis, maintaining a comprehensive audit trail of the research
process, including coding decisions and developing thematic elements [34]. This approach aims to enhance
the credibility and trustworthiness of the study’s results.
− Validating findings through triangulation: data triangulation compares interview data with field notes
and existing literature to validate findings.
− Ensuring reliability through meticulous analysis: ensure reliability by maintaining a detailed audit trail
of the research process, including coding decisions and thematic development.
2.8. Publication
In the publication phase, the research report will be meticulously drafted with a structured format
encompassing an introduction, methodology, findings, discussion, and conclusion. Subsequently, the report
will be submitted to pertinent academic journals for publication, adhering to the target journals’ standards
and guidelines. This process aims to disseminate the research findings to the broader educational community
and contribute to advancing knowledge in the field.
− Drafting the research report: draft the report with a clear structure, including an introduction,
methodology, findings, discussion, and conclusion.
− Publishing the research report: submit the report to relevant academic journals for publication, ensuring
that it meets the standards and guidelines of the target journals.
This structured approach ensures a comprehensive exploration of the integration of AI in accounting
systems, providing valuable insights into user experiences and challenges [35]. The methodology adheres to
scientific rigor, enhancing the validity and reliability of the study’s findings.
3. RESULTS AND DISCUSSION
The following results and discussion will present findings from a qualitative study on user
experiences and challenges in integrating AI into accounting systems. Data from in-depth interviews with
accounting professionals reveal various perspectives and barriers to adopting AI technology in daily practice.
Let’s delve into the relevant findings and discussions about each research stage. In our analysis of user
experiences and challenges in AI integration, we identified four key themes that emerged from the qualitative
data. Table 1 summarizes these themes along with supporting real data and explanations. The table highlights
two primary aspects: user experience in AI integration (focusing on efficiency and acceptance) and
challenges in AI implementation (focusing on integration and data security). Each theme is supported by
concrete examples from the participants’ experiences.

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Table 1. Themes in user experiences and challenges in AI integration
Aspect Theme Real data Explanation
User experience
in ai integration:
Efficiency An accounting firm reported a 30%
increase in efficiency in data analysis
processes after implementing AI
systems.
The implementation of AI technology has helped
improve efficiency in various tasks, including data
analysis, where an accounting firm reported a 30%
increase in efficiency after adopting the technology.
Acceptance An early AI user in the finance
department initially expressed
skepticism but now sees AI as a
valuable tool in enhancing productivity.
Although some early users may have been skeptical of
AI, direct experience with the technology often helps
change their views to a more positive one when they
realize the significant benefits it provides.
Challenges in
AI
implementation:
Integration A financial company reported
significant difficulties in integrating AI
systems with existing IT infrastructure.
The process of integrating AI with existing
infrastructure is often a major challenge, especially
when the infrastructure is complex and well-
established.
Data
security
More than half of the participants
expressed concerns about potential data
breaches and security issues associated
with the use of AI in accounting systems.
Concerns about data security and privacy breaches
often serve as major barriers to the adoption of AI
technology, especially in sensitive contexts such as
accounting systems.
3.1. User experience in AI integration
In exploring the user experience regarding AI integration, our study sheds light on two crucial
aspects: efficiency and acceptance. Firstly, we observe a notable enhancement in efficiency within
accounting firms upon adopting AI systems for data analysis, reflecting a positive shift in operational
effectiveness. Secondly, through a detailed examination of a finance department’s early AI adoption case, we
uncover a transformation from initial scepticism to enthusiastic acceptance, underlining the pivotal role of
firsthand experience in reshaping attitudes towards AI utilization [36], [37].
− Efficiency: this data reflects the positive experience of an accounting firm after adopting AI systems in
data analysis processes, indicating a significant increase in efficiency.
− Acceptance: the case of an early AI user in the finance department highlights a change in perspective
from scepticism to positive AI usage after experiencing its benefits firsthand.
3.2. Challenges in AI implementation
In addressing the challenges of AI implementation, our study highlights two critical hurdles:
integration and data security [38]. Firstly, we examine a real-world scenario where a financial company
grapples with the complexities of integrating AI into its established IT infrastructure, revealing common
practical obstacles organisations face in this process [39]. Secondly, the significant number of participants
expressing apprehensions regarding data security emphasizes the imperative of prioritizing security
considerations throughout the AI implementation journey, particularly in sensitive realms like accounting
systems.
− Integration: the example of a financial company facing difficulties integrating AI with existing IT
infrastructure illustrates practical challenges often encountered by organisations.
− Data security: the percentage of participants expressing concerns about data security underscores the
importance of considering security aspects in AI implementation, especially in sensitive contexts such
as accounting systems.
By including accurate data and explanations, the discussion on user experience and challenges in AI
implementation becomes more concrete and detailed, strengthening the validity of the findings generated
from this research.
3.3. Challenges in AI implementation: additional information: survey results on AI perception
To provide a deeper understanding of how accounting professionals’ perception of AI has changed,
we surveyed before and after AI technologies’ implementation [40]. The study aimed to capture shifts in
attitudes and experiences as professionals interacted with AI systems daily. Table 2 presents the percentage
distribution of positive, neutral, and negative perceptions at these two critical time points. The data reveals a
significant shift toward positive perceptions following hands-on experience with AI technologies in
accounting systems.
Table 2. Survey result on AI perception
Perception change Before AI implementation (%) After AI implementation (%)
Positive 25 70
Neutral 40 20
Negative 35 10

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3.3.1. Before AI implementation
Only a quarter of the participants viewed AI positively before its implementation. These participants
were typically more forward-thinking and optimistic about the potential of AI to enhance their work
processes and productivity. They likely had some prior knowledge or exposure to AI technologies,
influencing their favourable opinion. The largest group, comprising 40% of the participants, was neutral
towards AI. These individuals were unsure what to expect and had not formed a strong opinion. Their
neutrality could be attributed to a lack of direct experience with AI or insufficient information about how AI
would impact their specific roles.
A significant portion, 35%, viewed AI negatively before implementation. This group expressed
concerns about the potential disruptions AI might cause, including job displacement, increased workflow
complexity, and a lack of understanding of AI technologies. Scepticism and fear of the unknown were shared
among these participants.
3.3.2. After AI implementation
Following the implementation of AI, the proportion of participants with a positive view of AI
increased dramatically to 70%. This shift highlights the substantial impact that firsthand experience with AI
can have. Participants who initially had positive or neutral perceptions often found that AI exceeded their
expectations, enhancing their efficiency and allowing them to focus on more strategic tasks. The percentage
of participants with a neutral view decreased to 20% after implementation. This reduction suggests that direct
interaction with AI technologies provided enough information for many participants to move from neutrality
to a more definitive positive or negative stance.
The negative perception dropped significantly to 10%. This decline indicates that many initial
concerns and fears were alleviated once participants experienced the benefits of AI firsthand. However, the
remaining 10% still held opposing views, possibly due to persistent challenges such as integration issues or
data security concerns that were not fully addressed during implementation. This data underscores the
importance of practical experience in shaping perceptions of AI and highlights the potential for overcoming
initial scepticism through effective implementation and support.
3.3.3. Interpretation and implications
The survey results indicate a substantial attitude shift towards AI among accounting professionals
after its implementation [41]. The increase in positive perception suggests that direct experience with AI
technologies can significantly change initial scepticism and fear into appreciation and acceptance. This
transformation can be attributed to several factors:
− Improved efficiency: many participants likely experienced firsthand how AI could streamline and
expedite routine tasks, allowing them to allocate more time to higher-value activities.
− Enhanced understanding: interaction with AI systems gave participants a better understanding of the
technology, demystifying its operation and potential benefits.
− Training and support: effective training programs and support during the implementation phase may
have played a crucial role in easing the transition and addressing concerns.
− Visible benefits: participants were able to see tangible improvements in their workflows and
productivity, reinforcing the positive aspects of AI integration.
4. CONCLUSION
The study demonstrates that implementing AI technologies in accounting significantly transforms
professionals’ perceptions of AI. Initially, many accounting professionals exhibited scepticism and
apprehension towards AI, driven by fears of job displacement, increased complexity, and a lack of
understanding of the technology. However, post-implementation, there was a remarkable shift towards a
more positive outlook. This transformation can be attributed to several key factors: improved efficiency,
which allows accountants to handle data analysis and routine tasks more swiftly and accurately, enabling
them to focus on more strategic activities; enhanced understanding, as hands-on experience with AI systems
demystifies the technology and makes its benefits more apparent; practical training, which equips
professionals with the necessary skills and knowledge to leverage AI tools, thereby reducing resistance and
fostering a positive attitude; and visible benefits in daily workflows, such as faster data processing, reduced
error rates, and more insightful data analysis, which reinforce the perceived value of AI. These findings
underscore the critical importance of practical experience and robust support systems in overcoming initial
resistance to AI adoption. As organisations continue integrating AI into their accounting functions, exploring
the long-term impacts of this technological shift is imperative. Future research should focus on several areas:
investigating the sustained effects of AI on accounting practices over extended periods, identifying and

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developing best practices for AI integration, exploring how AI affects various accounting functions such as
auditing, tax preparation, and financial forecasting, and studying how AI integration influences decision-
making processes within accounting. By delving deeper into these areas, future studies can provide more
comprehensive insights into the transformative potential of AI in accounting, guiding organisations towards
more effective and beneficial AI adoption strategies and ultimately enhancing the productivity and
satisfaction of accounting professionals.
ACKNOWLEDGEMENTS
The authors would like to express their deepest gratitude to all participants who have contributed
their time and insights to this research. Special thanks go to Cakrawala University and University of Jakarta
International for their invaluable support and funding, without which this research would not have been
possible. Additional thanks go to the government, here is the Ministry of Education and Science and
Technology, for their assistance in data collection and analysis.
FUNDING INFORMATION
This research was jointly funded by Cakrawala University (grant number: CU/LPPM/2024/057) and
University of Jakarta International. The funding provided support for data collection, participant recruitment,
and research analysis. The funders had no role in the study design, data analysis, decision to publish, or
preparation of the manuscript.
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author
contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author C M So Va Fo I R D O E Vi Su P Fu
Andhika ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Lawrence Adi
Supriyono
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
C : Conceptualization
M : Methodology
So : Software
Va : Validation
Fo : Formal analysis
I : Investigation
R : Resources
D : Data Curation
O : Writing - Original Draft
E : Writing - Review & Editing
Vi : Visualization
Su : Supervision
P : Project administration
Fu : Funding acquisition
CONFLICT OF INTEREST STATEMENT
Authors state no conflict of interest.
INFORMED CONSENT
We have obtained informed consent from all individuals included in this study. All participants were
informed about the purpose of the research, how their data would be used, and their right to withdraw at any
time. Documentation of this consent has been stored securely in accordance with institutional policies.
ETHICAL APPROVAL
When The research related to human use has been complied with all relevant national regulations
and institutional policies in accordance with the tenets of the Helsinki Declaration and has been approved by
the authors’ institutional review boards. This study received ethical approval from the Research Ethics
Committee of Cakrawala University (approval number: CU/LPPM/2024/057) and the Ethics Review Board
of the University of Jakarta International.

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DATA AVAILABILITY
The data that support the findings of this study are available on request from the corresponding
author, A. The data contain information that could compromise the privacy of research participants and are
therefore not publicly available. However, anonymized derived data supporting the findings can be made
available upon reasonable request and with appropriate confidentiality agreements in place.
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BIOGRAPHIES OF AUTHORS
Andhika received his Master’s degree in Informatics Engineering from Putra
Indonesia University YPTK, Padang, Indonesia, in 2016. He is currently an Assistant
Professor in the Department of Computer Science at Cakrawala University. Renowned for his
innovative teaching methods, his research focuses on artificial intelligence, educational
technology, e-learning, and software engineering. He has published numerous papers in
prestigious journals and is a sought-after speaker at international conferences. Passionate
about mentoring, he is dedicated to fostering curiosity and innovation in the next generation of
engineers. Outside of academia, he enjoys exploring new tech gadgets, coding challenges, and
spending time with his family. For collaborations or inquiries. He can be contacted at email:
andhika@cakrawala.ac.id.
Lawrence Adi Supriyono his completed his Master’s degree in Electronics and
Robotics Engineering from Sultan Agung Islamic University (UNISSULA) Semarang in 2022.
Currently, he serves as a Lecturer in the Software Engineering Department at University of
Jakarta International. He is renowned as an innovative educator, with a primary focus on
teaching software programming, web UI/UX design, and AI and Machine Learning robotics.
He has led several prominent projects, including the development of advanced E-SIM testing
systems, hospital management information systems (SIMRS), and various innovative projects
such as high-tech baby incubators, weather detectors, computer vison, hydroponic and
aquaculture technology, and color recognition learning devices for kindergarten children.
Additionally, his extensive academic research has been published in both national and
international journals, reinforcing his position as a leading researcher in his field. He can be
contacted at email: lawrence.supriyono@uniji.ac.id.

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